%TI KEYNOTE: Observations from Studying Cognitive Systems in Context
%AU David Woods
%SC Saturday, August 13, 6:00 p.m.
%TI PLENARY: Identifying the Modules of the Mind with fMRI: Imaging the Biological Stages in Visual and Language Processing
%AU Walter Schneider
%AU Steven Small (discussant)
%SC Sunday, August 14, 9-10:30 a.m.
%TI PLENARY: A Picture is Worth a Thousand Words -- but that's the Problem
%AU Lila Gleitman
%AU Paul Smolensky (discussant)
%SC Monday, August 15, 9-10:30 a.m.
%TI PLENARY: The Role of Existing Knowledge in Generalization
%AU Michael J. Pazzani
%AU Mark Keane (discussant)
%SC Tuesday, August 16, 9-10:30 a.m.
%TI PLENARY PANEL: Cognitive Science 2004: The Last 10 Years
%AU Tony Simon (moderator)
%AU Joseph Bates
%AU Dedre Gentner
%AU Jim Greeno
%AU Gil Harman
%AU Michael Pazzani
%AU Walter Schneider
%SC Tuesday, August 16, 2-3:30 p.m.
%TI WORKSHOP: Education in Cognitive Science: Planning for the 21st Century
%AU N. Nersessian (chair)
%AU J.L. Kolodner (chair)
%SC Wednesday, August 17, 9-5:30 p.m.
%TI TALK SESSION: Categorization
%SC Sunday, August 14, 11-12:30
%AB Carbrera, "Functional and Conditional Equivalence: Conceptual Contributions From Behavior Analysis"
Pevtzow & Goldstone, "Categorization and the Parsing of Objects"
Kurbat, Smith, & Medin, "Categorization, Typicality, and Shape Similarity"
Kruschke & Erickson, "Learning of Rules That Have High-Frequency Exceptions: New Empirical Data and a Hybrid Connectionist Model"
Miller, "Modeling Inter-Category Typicality within a Symbolic Search Framework"
%TI TALK SESSION: Reasoning
%SC Sunday, August 14, 11-12:30
%AB Byrne & Tasso, "Counterfactual Reasoning: Inferences From Hypothetical Conditionals"
Melis, "How Mathematicians Prove Theorems"
Bush, Johnson, & Siefret, "The Implications of Corrections: Then Why Did You Mention It?"
Ohlsson & Robin, "The Power of Negative Thinking: The Central Role of Modus Tollens in Human Cognition"
Tabachneck, Koedinger, & Nathan, "Toward a Theoretical Account of Strategy Use and Sense-Making in Mathematics Problem Solving"
%TI TALK SESSION: Collaborative Problem Solving
%SC Sunday, August 14, 2-3:30
%AB Derry & Tookey, "Effects of Collaborative Interaction and Computer Tool Use"
Engle & Greeno, "Managing Disagreement in Intellectual Conversations: Coordinating Interpersonal and Conceptual Concerns in the Collaborative Construction of Mathematical Explanations"
Coulson & Flor, "Rational Choice and Framing Devices: Argumentation and Computer Programming"
Liu & Sycara, "Distributed Meeting Scheduling"
Turner & Eaton, "Handling Unanticipated Events During Collaboration"
%TI TALK SESSION: Representation in Connectionist Networks
%SC Sunday, August 14, 2-3:30
%AB Dennis, "The Null List Strength Effect in Recognition Memory: Environmental Statistics and Connectionist Accounts"
Phillips, "Strong Systematicity Within Connectionism: The Tensor-Recurrent Network"
Niklasson & van Gelder, "Can Connectionist Models Exhibit Non-Classical Structure Sensitivity?"
French, "Dynamically Constraining Connectionist Networks to Produce Distributed, Orthogonal Representations to Reduce Catastrophic Interference"
Tesar & Smolensky, "Synchronous Firing Variable Binding is a Tensor Product Representation With Temporal Role Vectors"
%TI TALK SESSION: Situated Natural Language
%SC Sunday, August 14, 4-5:30
%AB Carpenter & Alterman, "A Taxonomy for Planned Reading"
Moorman & Ram, "Integrating Creativity and Reading: A Functional Approach"
Peterson, Mahesh, Goel & Eiselt, "KA: Situating Natural Language Understanding in Design Problem Solving"
Cassell, Stone, Douville, Prevost, Achorn, Steedman, Badler, & Pelachaud, "Modeling the Interaction Between Speech and Gesture"
Nelson, Lehman, & John, "Integrating Cognitive Capabilities in a Real-Time Task"
%TI TALK SESSION: Foundations
%SC Sunday, August 14, 4-5:30
%AB Tash, "Formal Rationality and Limited Agents"
Byrne, "Integrating, Not Debating, Situated Action and Computational Models: Taking the Environment Seriously"
van Gelder & Niklasson, "Classicalism and Cognitive Architecture"
Slezak, "Situated Cognition: Empirical Issue, `Paradigm Shift' or Conceptual Confusion?"
%TI TALK SESSION: Analogical Reasoning
%SC Monday, August 15, 11-12:30
%AB Clausner, "Commonsense Knowledge and Conceptual Structure in Container Metaphors"
Burstein, "Case Age: Selecting the Best Exemplars for Plausible Reasoning Using Distance in Time or Space"
Faries & Shlossberg, "The Effect of Similarity on Memory for Prior Problems"
Gentner & Bowdle, "The Coherence Imbalance Hypothesis: A Functional Approach to Asymmetry in Comparison"
Forbus, Ferguson, & Gentner, "Incremental Structure-Mapping"
%TI TALK SESSION: Sentence Processing
%SC Monday, August 15, 11-12:30
%AB Ferstl, "The Construction-Integration Model: A Framework for Studying Context Effects in Sentence Processing"
Stevenson, "A Unified Model of Preference and Recovery Mechanisms in Human Parsing"
Mahesh & Eiselt, "Uniform Representations for Syntax-Semantics Arbitration"
Mayberry III & Miikkulainen, "Lexical Disambiguation Based on Distributed Representations of Context Frequency"
Burgess & Lund, "Multiple Constraints in Syntactic Ambiguity Resolution: A Connectionist Account of Psycholinguistic Data"
%TI TALK SESSION: Problem Solving
%SC Monday, August 15, 2-3:30
%AB Ahn, Bailenson, & Gordon, "Causal Attribution as Mechanism-Based Story Construction: An Explanation of the Conjunction Fallacy and the Discounting Principle"
Recker, Govindaraj, & Vasandani, "Troubleshooting Strategies in a Complex, Dynamical Domain"
Catrambone, "The Effects of Labels in Examples on Problem Solving Transfer"
Vollmeyer, Holyoak, & Burns, "Goal Specificity in Hypothesis Testing and Problem Solving"
Blessing & Ross, "Problem Content Affects the Categorization and Solutions of Problems"
%TI TALK SESSION: Brain Modeling
%SC Monday, August 15, 2-3:30
%AB Grunewald & Grossberg, "Binding of Object Representations by Synchronous Cortical Dynamics Explains Temporal Order and Spatial Pooling Data"
Wan, Touretzky, & Redish, "Computing Goal Locations From Place Codes"
Bullinaria, "Connectionist Modelling of Spelling"
Braisby, Franks & Hampton, "On the Psychological Basis for Rigid Designation"
%TI TALK SESSION: Visual Perception
%SC Monday, August 15, 4-5:30
%AB Fischer, "Attention Allocation During Movement Preparation"
Francis & Grossberg, "How Do Representations of Visual Form Organize Our Percepts of Visual Motion?"
Isaak & Just, "The Curtate Cycloid Illusion: Cognitive Constraints on the Processing of Rolling Motion"
Polk & Farah, "A Simple Co-Occurrence Explanation for the Development of Abstract Letter Identities"
Jin, "Computational Simulation of Depth Perception in the Mammalian Visual System"
%TI TALK SESSION: Mental Models
%SC Monday, August 15, 4-5:30
%AB Glasgow, "Array Representations for Model-Based Spatial Reasoning"
Bara, Bucciarelli, Johnson-Laird, & Lombardo, "Mental Models in Propositional Reasoning"
Samarapungavan & Wiers, "Do Children Have Epistemic Constructs About Explanatory Frameworks: Examples From Naive Ideas About the Origin of Species"
Johnson-Laird & Barres, "When `Or' Means `And': A Study in Mental Models"
Moore & Schwartz, "Mental Models for Proportional Reasoning"
%TI TALK SESSION: Learning
%SC Tuesday, August 16, 11-12:30
%AB Doane, Sohn, Adams, & McNamara,"Learning from Instruction: A Comprehension-Based Approach"
Bielaczyc, Pirolli, & Brown, "Collaborative Explanations and Metacognition: Identifying Successful Learning Activities in the Acquisition of Cognitive Skills"
Leake, "Towards a Computer Model of Memory Search Strategy Learning"
Cox, "Machines That Forget: Learning From Retrieval Failure of Mis-Indexed Explanations"
Sekaran & Sen, "Learning With Friends and Foes"
%TI TALK SESSION: Belief Modeling
%SC Tuesday, August 16, 11-12:30
%AB Elio & Pelletier, "The Effect of Syntactic Form on Simple Belief Revisions and Updates"
Barnden, Helmreich, Iverson, & Stein, "Combining Simulative and Metaphor-Based Reasoning About Beliefs"
Hewson, "Empirical Evidence Regarding the Folk Psychological Concept of Belief"
Veale & Keane, "Belief Modelling, Intentionality and Perlocution in Metaphor Comprehension"
Chalupsky & Shapiro, "SL: A Subjective, Intensional Logic of Belief"
%TI POSTER+DISCUSSANT SESSION: Speech
%SC Sunday, August 14, 11-12:30
%AB
Roelofs, "On-Line Versus Off-Line Priming of Word-Form Encoding in Spoken Word Production"
Meijer, "Towards a New Model of Phonological Encoding"
Markey, "Acoustic-Based Syllabic Representation and Articulatory Gesture Detection: Prerequisites for Early Childhood Phonetic and Articulatory Development"
Content & Sternon, "Modelling Retroactive Context Effects in Spoken Word Recognition With a Simple Recurrent Network"
Harm, Altmann, & Seidenberg, "Using Connectionist Networks to Examine the Role of Prior Constraints in Human Learning"
Abu-Bakar & Chater, "Distribution and Frequency: Modelling the Effects of Speaking Rate on Category Boundaries Using a Recurrent Neural Network"
Gaskell & Marslen-Wilson, "Inference Processes in Speech Perception"
%TI POSTER+DISCUSSANT SESSION: Analogy
%SC Sunday, August 14, 2-3:30
%AB M. Burstein (discussant)
Keane, "Adaptation as a Selection Constraint on Analogical Mapping"
Ohnishi, Suzuki, & Shigemasu, "Similarity by Feature Creation: Reexamination of the Asymmetry of Similarity"
Hummel, Melz, Thompson, & Holyoak, "Mapping Hierarchical Structures With Synchrony for Binding: Preliminary Investigations"
Wharton & Lange, "Analogical Transfer Through Comprehension and Priming"
Burns & Holyoak, "Competing Models of Analogy: ACME Versus Copycat"
Law, Forbus, and Gentner, "Simulating Similarity-Based Retrieval: A Comparison of ARCS and MAC/FAC"
Ferguson, "MAGI: Analogy-Based Encoding Using Regularity and Symmetry"
%TI POSTER+DISCUSSANT SESSION: Visual Reasoning
%SC Sunday, August 14, 4-5:30
%AB J. Glasgow (discussant)
Cheng, "An Empirical Investigation of Law Encoding Diagrams for Instruction"
Cox, Stenning, & Oberlander, "Graphical Effects in Learning Logic: Reasoning, Representation and Individual Differences"
Lindsay, "Understanding Diagrammatic Demonstrations"
Merrill & Reiser, "Scaffolding Effective Problem Solving Strategies in Interactive Learning Environments"
Gattis & Holyoak, "How Graphs Mediate Analog and Symbolic Representation"
Tabachneck, Leonardo, & Simon, "How Does and Expert Use a Graph? A Model of Visual and Verbal Inferencing in Economics"
Naraynan, Suwa, & Motoda, "A Study of Diagrammatic Reasoning From Verbal and Gestural Data"
Clement, "Imagistic Simulation and Physical Intuition in Expert Problem Solving"
%TI POSTER+DISCUSSANT SESSION: Perception
%SC Monday, August 15, 11-12:30
%AB H. Narayanan (discussant)
Thorisson, "Simulated Perceptual Grouping: An Application to Human-Computer Interaction"
Gilbert & Richards, "Using Trajectory Mapping to Analyze Musical Intervals"
Tennenbaum, "Functional Parts"
McGraw, Rehling, & Goldstone, "Letter Perception: Toward a Conceptual Approach"
Schyns & Bulthoff, "Viewpoint Dependence and Face Recognition"
Olds, "A Connectionist Account of Global Precedence: Theory and Data"
McAuley, "Time as Phase: A Dynamic Model of Time Perception"
Large, "Models of Metrical Structure in Music"
%TI POSTER+DISCUSSANT SESSION: Learning
%SC Monday, August 15, 2-3:30
%AB G. Collins (discussant)
Oehlmann, Edwards, & Sleeman, "Changing the Viewpoint: Re-Indexing by Introspective Questioning"
Suwa & Motoda, "PCLEARN: A Model for Learning Perceptual-Chunks"
Seger, "Multiple Learning Mechanisms Within Implicit Learning"
Jimenez & Cleeremans, "Direct and Indirect Measures of Implicit Learning"
Cox & Ram, "Failure-Driven Learning as Input Bias"
Fox & Leake, "Using Introspective Reasoning to Guide Index Refinement in Case-Based Reasoning"
Van Dyne & Tsatsoulis, "An Experiment to Determine Improvements in Automated Problem Solving in a Complex Problem Domain"
Hiraki, "Abstraction of Sensory-Motor Features"
%TI POSTER+DISCUSSANT SESSION: Language Acquisition
%SC Monday, August 15, 4-5:30
%AB L. Gleitman (discussant)
Taraban & Taraban, "A Lexical Model of Learning to Read Single Words Aloud"
Ling, "Predicting Irregular Past Tenses: Comparing Symbolic and Connectionist Models Against Native English Speakers"
Peterson & Billman, "Correspondences Between Syntactic Form and Meaning: From Anarchy to Hierarchy"
Batali, "Artificial Evolution of Syntactic Aptitude"
Finch & Chater, "Distributional Bootstrapping: From Word Class to Proto-Sentence"
Gillis, Daelemans, & Durieux, "Are Children `Lazy Learners'? A Comparison of Natural and Machine Learning of Stress"
Hastings & Lytinen, "Objects, Actions, Nouns, and Verbs"
Lampinen & Faries, "Levels of Semantic Constraint and Learning Novel Words"
Cartwright & Brent, "Segmenting Speech Without a Lexicon: Evidence for a Bootstrapping Model of Lexical Acquisition"
Westermann & Miikkulainen, "Verb Inflections in German Child Language: A Connectionist Account"
%TI POSTER+DISCUSSANT SESSION: Syntactic Processing
%SC Tuesday, August 16, 11-12:30
%AB J. Holbrook (discussant)
Ferstl, "Context Effects in Syntactic Ambiguity Resolution: The Location of Prepositional Phrase Attachment"
Schutze, "A Connectionist Model of Verb Subcategorization"
Gibson & Loomis, "A Corpus Analysis of Recency Preference and Predicate Proximity"
Blackwell & Bates, "Inducing Agrammatic Profiles in Normals"
Pearlmutter, Daugherty, MacDonald, & Seidenberg, "Modeling the Use of Frequency and Contextual Biases in Sentence Processing"
Spivey-Knowlton & Tanenhaus, "Immediate Effects of Discourse and Semantic Context in Syntactic Processing: Evidence from Eye-Tracking"
Burgess, Tanenhaus, & Hoffman, "Parafoveal and Semantic Effects on Syntactic Ambiguity Resolution"
%TI SYMPOSIUM: Scientific Creativity: Multidisciplinary Perspectives
%AU N. Nersessian (chair)
%AU J. Clement
%AU K. Dunbar
%AU R. Jones
%AU R. Tweney
%SC Sunday, August 14, 11-12:30
%TI SYMPOSIUM: Animal Cognition
%AU A. Francis (chair)
%AU D. Rumbaugh
%AU M. Tomasello
%AU D. Washburn
%SC Sunday, August 14, 2-3:30
%TI SYMPOSIUM: Learning New Features of Representation
%AU R.L. Goldstone (chair)
%AU P. Schyns (chair)
%AU B. French
%AU D.L. Medin
%AU M. Mozer
%AU J.-P. Thibaut
%SC Sunday, August 14, 4-5:30
%TI SYMPOSIUM: Cognitive Science Meets Cognitive Engineering
%AU R. Catrambone (chair)
%AU S.T. Dumais
%AU J. Elkerton
%AU B.E. John
%AU M.G. Shafto
%SC Monday, August 15, 11-12:30
%TI SYMPOSIUM: Visual Reasoning in Discovery, Instruction and Problem Solving
%AU N.H. Narayanan (chair)
%AU M. Hegarty
%AU R. Hall
%AU N. Nersessian
%SC Monday, August 15, 2-3:30
%TI SYMPOSIUM: The Role of Cases in Learning
%AU T. Koschmann (chair)
%AU A. Collins
%AU K. Holyoak
%AU G. Klein
%AU J. Kolodner
%SC Monday, August 15, 4-5:30
%TI SYMPOSIUM: Collaborative Knowledge
%AU P. Thagard (chair)
%AU K. Dunbar
%AU E. Hutchins
%AU G. Olson
%SC Tuesday, August 16, 11-12:30
%TI Causal Attribution As Mechanism-Based Story Construction: An Explanation Of The Conjunction Fallacy And The Discounting Principle
%AU Woo-kyoung Ahn
%AU Jeremy Bailenson
%AU Brian Gordon
%PU Proc. CogSci-94, pp. 9-14
%SC Monday, August 15, 2-3:30
%AB We propose that causal attribution involves constructing a coherent
story using mechanism information (i.e., the processes underlying
the relationship between the cause and the effect). This processing
account can explain both the conjunction effect (i.e., conjunctive
explanations being rated more probable than their components) and
the discounting effect (i.e., the effect of one cause being
discounted when another cause is already known to be true). In the
current experiment, both effects occurred with mechanism-based
explanations but not with covariation-based explanations in which
the cause-effect relationship was phrased in terms of covariations
without referring to mechanisms. We discuss why the current results
pose difficulties for previous attribution models in Psychology and
Artificial Intelligence.
%TI Distribution and frequency: Modelling the effects of speaking rate on category boundaries using a recurrent neural network
%AU Mukhlis Abu-Bakar
%AU Nick Chater
%PU Proc. CogSci-94, pp. 3-8
%SC Sunday, August 14, 11-12:30
%AB We describe a recurrent neural network model of rate effects on the
syllable-initial voicing distinction, specified by voice-onset-time
(VOT). The stimuli were stylized /bi/ and /pi/ syllables covarying
in VOT and syllable duration. Network performance revealed a
systematic rate effect: as syllable duration increases, the category
boundary moves toward longer VOT values, mirroring human
performance. Two factors underlie this effect: the range of
training stimuli with each VOT and syllable duration, and their
frequency of occurrence. The latter influence was particularly
strong, consistent with exemplar-based accounts of human category
formation.
%TI Mental Models in Propositional Reasoning
%AU B.G. Bara
%AU M. Bucciarelli
%AU P.N. Johnson-Laird
%AU V. Lombardo
%PU Proc. CogSci-94, pp. 15-20
%SC Monday, August 15, 4-5:30
%AB A cognitive account of propositional reasoning must consider both the
representation of the propositions (premises and states of affairs) and
the context in which the propositions are used. This paper is concerned
with reasoning processes involving three different connectives
(conjunctive, conditional and disjunctive connectives) in three different
tasks (accomplishing a request for action expressed by a premise, judging
a state of affairs as true or false with respect to a premise, drawing an
inference from two premises). Our claim is that the ability to reason
with connectives is explained in terms of construction and manipulation
of mental models. We present a computer model that takes as input the
modelistic representations of the premises and the specific state of
affairs, compares such models and gives rise to a series of model
manipulations in order to produce a result, i.e. an action, a judgement
or an inference. A computer program reproduces the performances of
subjects of different age groups, predicting both correct and erroneous
inferences.
%TI Combining Simulative and Metaphor-Based Reasoning about Beliefs
%AU John A. Barnden
%AU Stephen Helmreich
%AU Eric Iverson
%AU Gees C. Stein
%PU Proc. CogSci-94, pp. 21-26
%SC Tuesday, August 16, 11-12:30
%AB An unprecedented combination of simulative and metaphor-based
reasoning about beliefs is achieved in an AI system, ATT-Meta. Much
mundane discourse about beliefs uses conceptual metaphors (e.g.,
MIND AS CONTAINER) productively, and ATT-Meta's metaphor-based
reasoning accordingly leads to crucial discourse comprehension
decisions. ATT-Meta's non-metaphorical mode of belief reasoning
includes simulative reasoning (SR). In ATT-Meta, metaphor-based
reasoning can block and otherwise influence the course of SR. Also,
ATT-Meta can nest SR and metaphor-based reasoning within themselves
and each other. As well as currently allowing ATT-Meta to
simulatively reason about beliefs about beliefs ..., the nesting
will in the near future allow the system to handle chained
metaphors, ascribe its own metaphor-based reasoning to other agents,
and apply simulative reasoning to purely metaphorical agents.
%TI Artificial Evolution of Syntactic Aptitude
%AU John Batali
%PU Proc. CogSci-94, pp. 27-32
%SC Monday, August 15, 4-5:30
%AB Populations of simple recurrent neural networks were subject to
simulations of evolution where the selection criterion was the
ability of a network to learn to recognize strings from context free
grammars. After a number of generations, networks emerged that use
the activation values of the units feeding their recurrent
connections to represent the depth of embedding in a string.
Networks inherited innate biases to accurately learn members of a
class of related context-free grammars, and, while learning, passed
through periods during which exposure to spurious input interfered
with their subsequent ability to learn a grammar.
%TI Interactive Model-Driven Case Adaptation for Instructional Software Design
%AU Benjamin Bell
%AU Smadar Kedar
%AU Ray Bareiss
%PU Proc. CogSci-94, pp. 33-38
%SC Monday, August 15, 7:30-9
%AB Research in case-based design has demonstrated some capability to retrieve
relevant designs and to adapt them automatically to satisfy new design
constraints. However, some domains are less amenable to automated
adaptation, particularly when the cases are very complex and when
relationships among the design components are difficult to express
formally. The design of interactive learning environments is one such
domain. We describe a case-based approach to instructional software design
which utilizes interactive, model-driven case adaptation. Our model for
computer-based instruction is Goal-Based Scenarios. We describe a tool,
Goal-Based Scenario Builder, which supports interactive adaptation of
instructional software using the model, and illustrate its use in adapting
an example case of a successful instructional software program, Sickle Cell
Counselor.
%TI Collaborative Explanations and Metacognition: Identifying Successful Learning Activities in the Acquisition of Cognitive Skills
%AU K. Bielaczyc
%AU P. Pirolli
%AU A. Brown
%PU Proc. CogSci-94, pp. 39-44
%SC Tuesday, August 16, 11-12:30
%AB Individual differences in collaborative explanations during
learning were analyzed to determine effects on problem solving.
Twenty-five university students with no prior programming experience
worked through a sequence of programming lessons. For the Target
lesson, subjects studied instructional texts and examples in either
mixed performance-level dyads (collaborative dyad group) or
individually (individual group) prior to individual programming
activities. The collaborative dyad subjects were divided into equal
sized groups of high-benefit and low-benefit dyad subjects based on
Target lesson programming performance. Between-group analyses of the
characteristics of the explanations generated by high-benefit and
low-benefit dyad subjects were investigated, including (a) explanation
and metacognitive strategies, (b) content of elaborations, and (c)
manner of generating elaborations. High-benefit dyad subjects were
found to generate both a higher quantity and higher quality of
elaborations. These results are compared to findings from prior
research on the self-explanation processes of solo learners.
%TI Inducing Agrammatic Profiles in Normals
%AU Arshavir Blackwell
%AU Elizabeth Bates
%PU Proc. CogSci-94, pp. 45-50
%SC Tuesday, August 16, 11-12:30
%AB The selective vulnerability of morphology in agrammatic aphasia is
often interpreted as evidence that closed-class items reside in a
particular part of the brain (i.e., Broca9s area); thus, damage to a
part of the language processor maps onto behavior in a transparent
fashion. We propose that the selective vulnerability of grammatical
morphemes in receptive processing may be the result of decrements in
overall processing capacity, and not the result of a selective
lesion. We demonstrate agrammatic profiles in healthy adults who
have their processing capacity diminished by engaging in a secondary
task during testing. Our results suggest that this selective
profile does not necessarily indicate the existence of a distinct
sub-system specialized for the implicated aspects of syntax, but
rather may be due to the vulnerability of these forms in the face of
global resource diminution, at least in grammaticality judgment.
%TI Problem Content Affects the Categorization and Solutions of Problems
%AU Stephen B. Blessing
%AU Brian H. Ross
%PU Proc. CogSci-94, pp. 51-55
%SC Monday, August 15, 2-3:30
%AB In many domains, the content of a problem (i.e., its surface cover
story) provides useful clues as to the type of problem it is and its
solution. Three experiments examined this role of problem content on
the problem categorization and solution of algebra word problems
with experienced subjects, by manipulating only the content of the
problems. When a problem's content was highly correlated with its
deep structure (e.g., a content of cars driving for a
distance-time-rate problem), people were able to categorize the
problem after seeing a smaller portion of it compared to a baseline
with contents uncorrelated to the problem deep structure. In
addition, for more complex problems in which irrelevant information
had been added, problem solving performance was higher and people
showed greater sensitivity to the relevance of the information. When
a problem's content suggested a different (inappropriate) type of
problem, people required a greater part of the problem to categorize
it and were slower and less accurate at solving the problem. These
results suggest that content may be influential even for experienced
problem solvers.
%TI On the Psychological Basis for Rigid Designation
%AU Nick Braisby
%AU Bradley Franks
%AU James Hampton
%PU Proc. CogSci-94, pp. 56-60
%SC Sunday, August 14, 4-5:30
%AB Kripke (1972) and Putnam (1975a; 1975b) have argued forcefully for
the philosophical view of word meaning known as rigid designation.
While certain psychological studies have appeared to offer this view
support (Keil, 1986; Rips, 1989), we argue that these have not
provided an exhaustive evaluation. In particular, the original
discussions of Kripke and Putnam reveal that their view rests on an
explicit appeal to intuition concerning word use in a range of
different scenarios. The study reported here investigates word use
under three such types of scenarios, for a variety of natural kind
terms, by investigating subjects' judgements of truth or falsity for
a range of statement types. We argue that the results obtained
indicate that the intuition on which rigid designation rests is not
one which is generally true of agents' language use. Further, we
obtain patterns of apparent contradiction which appear strictly
inconsistent with rigid designation and which require an account of
word meaning which allows that the sense of words may vary
systematically with context (Franks & Braisby, 1990).
%TI The Theory-Ladenness of Data: An Experimental Demonstration
%AU William F. Brewer
%AU Clark A. Chinn
%PU Proc. CogSci-94, pp. 61-65
%SC Monday, August 15, 7:30-9
%AB Most philosophers of science now believe that scientific data are
theory laden, i.e., the evaluation of data is influenced by prior
theoretical beliefs. Although there is historical and psychological
evidence that is consistent with the theory-laden position,
experimental evidence is needed to directly test whether prior
beliefs influence the evaluation of scientific data. In a fully
counterbalanced design, one group of subjects received evidence that
dinosaurs were cold-blooded, and another group of subjects received
evidence that dinosaurs were warm-blooded. The subjects reported a
strong belief in whichever theory they had read about. Then
subjects were presented with a piece of data that supported one
theory and contradicted the other theory. The identical piece of
data was rated as more believable when it was consistent with the
subject's theory than when it was inconsistent. These results
provide clear support for the position that scientific data are
theory laden.
%TI Kant and Cognitive Science
%AU Andrew Brook
%PU Proc. CogSci-94, pp. 66-71
%SC Monday, August 15, 7:30-9
%AB Some of Kant's ideas about the mind have had a huge influence on
cognitive science, in particular his view that sensory input has to be
worked up using concepts or concept-like states and his conception of the
mind as a system of cognitive functions. Other ideas of Kant's about the
mind have not been assimilated into cognitive science, including
important ideas about synthesis, mental unity and consciousness and
self-consciousness. Work of P. M. and P. S. Churchland, Dennett,
Flanagan, Jerry Fodor, Patricia Kitcher, Martindale, Sellars, and
Treisman is briefly discussed.
%TI A Connectionist Model of the Development of Velocity, Time, and Distance Concepts
%AU David Buckingham
%AU Thomas R. Shultz
%PU Proc. CogSci-94, pp. 72-77
%SC Monday, August 15, 7:30-9
%AB Connectionist simulations of children's acquisition of velocity (v),
time (t), and distance (d) concepts were conducted using a
generative algorithm, cascade-correlation (Fahlman & Lebiere, 1990).
Diagnosis of network rules were consistent with the developmental
course of children's concepts (Wilkening, 1981, 1982) and predicted
some new stages as well. Networks integrated the defining
dimensions of the concepts first by identity rules (e.g., v = d),
then additive rules (e.g., v = d-t), and finally multiplicative
rules (e.g., v = d/t). Psychological effects of differential memory
demands were also simulated. It is argued that cascade-correlation
implements an explicit mechanism of developmental change involving
incremental learning and qualitative increases in representational
power.
%TI Connectionist Modelling of Spelling
%AU John A. Bullinaria
%PU Proc. CogSci-94, pp. 78-83
%SC Monday, August 15, 2-3:30
%AB We present a new connectionist model of human spelling and
investigate some of its properties. Although based on Sejnowski &
Rosenberg's (1987) NETtalk model of reading, it requires no
pre-processing of the training data to align the phonemes and
letters. The model achieves 100% performance on the training data
(2837 monosyllabic words including many irregular words) and has a
generalization performance of about 89%. Under appropriate
conditions it exhibits symptoms similar to developmental surface
dyslexia and acquired surface dysgraphia. However, its inability to
account for phonological dysgraphia and lexical decision leads us to
believe that it is a promising candidate for the rule based part of
a dual route model but not a complete model of spelling on its own.
%TI Internal Representations of a Connectionist Model of Reading Aloud
%AU John A. Bullinaria
%PU Proc. CogSci-94, pp. 84-89
%SC Monday, August 15, 7:30-9
%AB We use hierarchical cluster analysis, principal component analysis,
multi-dimensional scaling and discriminant analysis to investigate
the internal representations learnt by a recent connectionist model
of reading aloud. The learning trajectories of these
representations may help us understand reading development in
children and the results of naming latency experiments in adults.
Studying the effects of network damage on these representations
seems to provide insight into the mechanisms underlying acquired
surface dyslexia. The discussion of the various techniques used may
also prove useful in analysing the functioning of other
connectionist systems.
%TI Multiple Constraints in Syntactic Ambiguity Resolution: A Connectionist Account of Psycholinguistic Data
%AU Curt Burgess
%AU Kevin Lund
%PU Proc. CogSci-94, pp. 90-95
%SC Monday, August 15, 11-12:30
%AB We implement a constraint satisfaction connectionist style model that
accounts for data from three psycholinguistics experiments investigating
the gardenpath effect with reduced relative constructions. Normative
data was collected on the stimuli used in experiments by Burgess and
Tanenhaus (1992) and Ferreira and Clifton (1986) and this data served as
the input for the simulation. We have demonstrated with this set of
simulations that a plausible theoretical framework for a range of these
results is a hierarchical connectionist network which is sensitive to a
number of constraints inherent in the input stimuli. The model accounts
for the top-down effect of context, the contribution of the bottom-up
morphological frequency asymmetry of the verb, and the probabilistic
nature of the disambiguating preposition. These effects are sensitive to
the timecourse of processing as well. The pattern of results from the
psycholinguistic data suggest that syntactic processing is a confluence
of multiple constraints that represent both bottom-up and top-down
influences in processing. These results are incompatible with a
deterministic parsing model. The hierarchical connectionist style model
presented in this paper is sensitive to the range of constraints
discussed above and is offered as a more adaptive theoretical model that
can capture the domain of effects found in the literature encompassing
local syntactic ambiguity resolution.
%TI Parafoveal and Semantic Effects on Syntactic Ambiguity Resolution
%AU Curt Burgess
%AU Michael K. Tanenhaus
%AU Miriam Hoffman
%PU Proc. CogSci-94, pp. 96-99
%SC Tuesday, August 16, 11-12:30
%AB Subjects were presented with strongly past-participle biased sentences,
The portrait sketched by the tree was very beautiful, in a self-paced
reading time task. Sentences were displayed two words at a time, (e.g.,
The portrait / sketched by ...) so that the verb and disambiguating
preposition were read together. In Experiment 1, a set of materials
constructed to minimize the past-tense bias with an inanimate NP was
compared with a less constraining set of sentences. The syntactic
gardenpath usually associated with the reduced-relative construction was
not present with the more constraining materials. In Experiment 2, using
the more constraining materials, preposition length was manipulated so
that subjects read sentences with both short (i.e., by) and long (i.e.,
underneath) prepositions. No syntactic gardenpaths occurred with
sentences with the past-participle bias and short prepositions; however,
when the same sentences were read with the long prepositions - the
syntactic gardenpath was present. This result is inconsistent with a
deterministic parser. We expand on our previous proposals that the parser
must be able to take into account both semantic and verb-form
information, as well as, parafoveal disambiguating information in the
form of the preposition.
%TI Competing Models of Analogy: ACME Versus Copycat
%AU Bruce D. Burns
%AU Keith J. Holyoak
%PU Proc. CogSci-94, pp. 97-100
%SC Sunday, August 14, 2-3:30
%AB ACME and Copycat have been viewed as competing models of analogy
making. Mitchell (1993) makes three major criticisms of ACME in
arguing for Copycat's superiority: that because ACME considers all
syntactically possible mappings it is psychologically implausible
and computationally infeasible; that its representations are rigid
and hand-tailored for each problem; and that ACME's representations
are semantically empty. To evaluate these criticisms we applied
ACME to simulating problems in the only domain addressed by Copycat,
letter-string analogies such as, "If abc is changed into abd, how
would you change kji in the same way?" Using representations that
include only knowledge available to Copycat, ACME generated the most
common solutions that people and Copycat produce. In addition, ACME
was able to generate some solutions produced by people but that are
impossible for Copycat, demonstrating that in some respects ACME is
a more flexible analogical reasoner than is Copycat. These
simulations answer each of Mitchell's criticisms of ACME. ACME can
incorporate domain-relevant knowledge to allow a principled
reduction in the number of mappings considered; it can generate
novel representations based on its domain-general constraints; and
it can incorporate semantic content into its representations. In
addition, ACME has the advantage of being applicable to many
different domains.
%TI Case Age: Selecting the Best Exemplars for Plausible Reasoning Using Distance in Time or Space
%AU Mark H. Burstein
%PU Proc. CogSci-94, pp. 106-111
%SC Monday, August 15, 11-12:30
%AB The age of a case (in the CBR sense) is the amount of time that has
elapsed between the time that the case originally occurred and the time
of the current reasoning activity. People engaged in plausible reasoning
tasks will, under appropriate circumstances, use the age of retrieved
prior cases to filter and discard them, or to select among alternatives
by their recency. This paper examines how the age of a case (and its
spatial analog) are used by people in plausible reasoning and case-based
reasoning tasks. I will argue that (1) the ate of a retrieved case is an
important factor in relevance judgements for certain kinds of inferences.
(2) When case age is relevant, more recent cases are usually, but not
always, preferred to older ones (the "all other things being equal"
caveat). Finally, I will argue that, somewhat surprisingly, (3) case age
cannot be used as in index into memory given some commonly held
assumptions about the nature of the retrieval process because it varies
with the time of retrieval. This limits its use to post-retrieval
processes, such as the filtering of already retrieved cases.
%TI The Implications of Corrections: Then Why Did You Mention It?
%AU Julie G. Bush
%AU Hollyn M. Johnson
%AU Colleen M. Seifert
%PU Proc. CogSci-94, pp. 112-117
%SC Sunday, August 14, 11-12:30
%AB How can misreported information be effectively corrected? Wilkes
and Leatherbarrow (1988) found that people relied upon invalidated
information to answer questions despite their awareness of its
inaccuracy, a phenomenon called the "continued influence effect"
(Johnson & Seifert, in press). But corrections in which an
assertion is made and then denied (e.g., "X is true ... actually, X
is untrue") may violate important conversational assumptions. Grice
(1967/1989) and others have argued that people expect speakers to
offer only information that is both truthful and conversationally
relevant; thus, people may seek interpretations for corrections that
will incorporate both the literal meaning and the conversational
implications of the contradictory statements. Our hypothesis was
that corrections would be more successful when they explained why
the original information was asserted. An empirical study showed
that corrections that accounted for conversational implications
(e.g., "X, which had originally been believed because of Y, is
actually untrue") could more effectively reduce the continued use of
discredited information. Additionally, the results show that
reiterating the literal content of a correction may actually be
perceived as implying that the correction statement should be
disbelieved. Since the conversational implications of corrections
critically shape comprehension, their examination is crucial in
domains (such as courtrooms, newspapers, and classrooms) where
informational updates frequently occur.
%TI Integrating, Not Debating, Situated Action and Computational Models: Taking the Environment Seriously
%AU Michael D. Byrne
%PU Proc. CogSci-94, pp. 118-123
%SC Sunday, August 14, 4-5:30
%AB A recent issue of the journal Cognitive Science (1993, vol. 17, no.
1) centered around a debate between two "camps" within the field,
the "situated action" (or SA) camp and the "traditional," symbol
processing camp. Though the debate in that journal suggests that, at
some levels, symbol processing and SA are incommensurable, this
paper disputes that view. If the message of the SA community is
taken to be that traditional approaches neglect the importance of
the environment, then not only is the message an important one, but
the typical symbol processing system is guilty as charged. However,
this does not mean that, in principle, symbol processing systems
must have this limitation. The two approaches can work hand-in-hand
to produce more general and more accurate computational models. A
framework of building models of the environment and having models of
cognitive agents work with those models is proposed, from which a
smooth integration of SA and symbol processing is not only possible,
but desirable. The framework proposed here is instantiated with a
production system called S-CAPS, and the efficacy of building models
of both the problem-solver and the problem environment is
demonstrated.
%TI Counterfactual Reasoning: Inferences from Hypothetical Conditionals
%AU Ruth M.J. Byrne
%AU Alessandra Tasso
%PU Proc. CogSci-94, pp. 124-129
%SC Sunday, August 14, 11-12:30
%AB Hypothetical reasoning -- thinking about what might happen in the
future or what might have happened in the past -- enables us to go
beyond factual reality. We suggest that human reasoners construct a
more explicit mental representation of hypothetical conditionals,
such as, If Linda were in Dublin then Cathy would be in Galway, than
of factual conditionals, such as, if Linda is in Dublin then Cathy
is in Galway. When people think about the factual conditional, they
keep in mind the affirmative situation -- Linda is in Dublin, Cathy
is in Galway, and they maintain only an implicit awareness that
there may be alternatives to this situation. In contrast, when they
think about the hypothetical conditional, they keep in mind not only
the affirmative situation, but also the presupposed negative one
(Linda is not in Dublin, Cathy is not in Galway). The postulated
differences in mental representations lead us to expect differences
in the frequency of inferences that people make from the two sorts
of conditionals, and we report the results of an experiment that
corroborates this prediction. The psychological data have
implications for philosophical and linguistic accounts of
counterfactual conditionals, and for artificial intelligence
programs designed to reason hypothetically.these results for
computational models of analogy are discussed.
%TI Functional and Conditional Equivalence: Conceptual Contributions from Behavior Analysis
%AU Angel Cabrera
%PU Proc. CogSci-94, pp. 130-135
%SC Sunday, August 14, 11:00am-12:30pm
%AB Behavior analysis has recently developed a new paradigm for the
study of categorization and language based on the mathematical
notion of equivalence. Inspired by this paradigm, this paper
presents a definitional framework that could be relevant for several
of the phenomena under study in Cognitive Science. First,
categories are viewed as classes of functional equivalence. By
doing so, results from behavior analysis and cognitive psychology
seem to converge towards an experience-based interpretation of
category basicness. Second, conditional equivalence is proposed as
the basis for symbol-meaning and symbol-symbol relationships.
Transfer of function through conditional links is suggested as the
mechanism of connection between language and other aspects of
cognition. The adoption and extension of these functionalist
formalisms provides us with significant methodological, conceptual
and even empirical advantages.
%TI Lexical Segmentation: the role of sequential statistics in supervised and un-supervised models
%AU Paul Cairns
%AU Richard Shillcock
%AU Nick Chater
%AU Joe Levy
%PU Proc. CogSci-94, pp. 136-141
%SC Monday, August 15, 7:30-9
%AB The use of transitional probabilities between phonetic segments as a
cue for segmenting words from English speech is investigated. We
develop a series of class-based n-gram and feature-based neural
network models that enable us to quantify the contribution of
low-level statistics to word boundary prediction. Training data for
our models is representative of genuine conversational speech: a
phonological transcription of the London-Lund corpus. These simple
models can be purely bottom-up and hence valid bootstrapping models
of infant development. We go on to demonstrate how the boostrapping
models mimic the Metrical Segmentation Strategy of Cutler and Norris
(1988), and we discuss the implications of this result.
%TI A Taxonomy for Planned Reading
%AU Tamitha Carpenter
%AU Richard Alterman
%PU Proc. CogSci-94, pp. 142-147
%SC Sunday, August 14, 4-5:30
%AB Early computational models of reading treated reading as a disembodied
process of examining a piece of text sequentially and in its entirety.
More recent work has shown that reading does not always occur
sequentially, and that embodying reading in a larger activity is
beneficial to the reading process. This paper will present a
cognitive model that uses reading plans to read instructional text
non-sequentially and in the context of an activity. To support this
model, we will discuss: 1) a taxonomy of reading plans and their
functions; 2) a taxonomy of reading sub-plans and their roles; and 3)
procedures for adapting reading plans. In addition, the results of a
protocol study are given which support planned reading as a cognitive
model.
%TI Modelling the Interaction between Speech and Gesture
%AU Justine Cassell
%AU Matthew Stone
%AU Brett Douville
%AU Scott Prevost
%AU Brett Achorn
%AU Mark Steedman
%AU Norm Badler
%AU Catherine Pelachaud
%PU Proc. CogSci-94, pp. 153-158
%SC Sunday, August 14, 4-5:30
%AB This paper describes an implemented system that generates spoken
dialogue, including speech, intonation, and gesture, using two
copies of an identical program that differ only in knowledge of the
world and which must cooperate to accomplish a goal. The output of
the dialogue generation is used to drive a three-dimensional
interactive animated model -- two graphic figures on a computer
screen who speak and gesture according to the rules of the system.
The system is based upon a formal, predictive and explanatory theory
of the gesture-speech relationship. A felicitous outcome is a
working system to realize autonomous animated conversational agents
for virtual reality and other purposes, and a tool for investigating
the relationship between speech and gesture.
%TI The Effects of Labels in Examples on Problem Solving Transfer
%AU Richard Catrambone
%PU Proc. CogSci-94, pp. 159-164
%SC Monday, August 15, 2-3:30
%AB It is hypothesized that labels in examples help learners group a set
of steps and to try to explain why those steps belong together. The
result of these grouping and self-explanation processes might be the
formation of a subgoal. It is conjectured that the meaningfulness
of the label itself might not be critical in order for the grouping
and self-explanation processes to occur. This conjecture is
supported in an experiment in which subjects studying examples in
probability that had steps labeled transferred to novel problems
more successfully than subjects whose examples did not contain
labels. Furthermore, subjects who saw less meaningful labels
transferred as successfully as subjects studying examples with more
meaningful labels. Thus, it appears that the meaningfulness of the
label does not seem to affect subgoal formation as much as the
presence of a label. This result supports the interpretation that
subgoal learning is affected by labels and that labels produce this
benefit by helping learners group the steps into a purposeful unit,
perhaps through a self-explanation process.
%TI SL: A Subjective, Intensional Logic of Belief
%AU Hans Chalupsky
%AU Stuart C. Shapiro
%PU Proc. CogSci-94, pp. 165-170
%SC Tuesday, August 16, 11-12:30
%AB Logics of belief are usually either quite complex, unintuitive, make
overly idealistic assumptions, or all of the above, because they
have to cope with the unusual characteristics of the belief operator
(relation, predicate). Some of these problematic characteristics are
referential opacity, the possible falsehood of objects of belief,
belief recursion, identification of referents from outside of the
belief operator in quantification contexts, etc. The difficulties
faced by traditional logical treatments seem to stem mainly from the
fact that an essentially subjective, intensional phenomenon gets
analyzed from an objective, outside observer's point of view in an
extensional, logical framework. As an alternative, we propose a
subjective, intensional logic SL, which takes seriously the usual
characterization of belief as a propositional attitude, that is, in
SL belief is treated as a relation between an agent and a
proposition (an intensional object). As results we gain technical
simplicity and a simple, intuitive semantics for belief sentences.
%TI An Empirical Investigation Of Law Encoding Diagrams For Instruction
%AU Peter C-H. Cheng
%PU Proc. CogSci-94, pp. 171-176
%SC Sunday, August 14, 4-5:30
%AB Law Encoding Diagrams, LEDs, are knowledge representations that
correctly encode systems of one or more laws using the geometric
and/or the topological structure of diagrams. In an instructional
role, LEDs aim to focus learning on the formal relations defined by
the correct laws, whilst using diagrammatic representations to aid
comprehension. LEDs can be viewed as intermediate representations
that aim to bridge the conceptual gulf between abstract laws and the
behaviour of phenomena. It is anticipated LEDs will be adopted as
key models in the foundation of expertise. This paper describes an
investigation in which LEDs for momentum and energy conservation
were used for instruction. The LEDs were implemented in a computer
based discovery learning environment and the subjects given only
minimal instruction on their use in problem solving. However, half
the subjects used the LEDs for successful post-test solutions of
different classes of problem and exhibited strategies that were
expert-like, in marked contrast to their novice-like pre-test
performance.
%TI Are Scientific Theories that Predict Data More Believable than Theories that Retrospectively Explain Data? A Psychological Investigation
%AU Clark A. Chinn
%PU Proc. CogSci-94, pp. 177-182
%SC Monday, August 15, 7:30-9
%AB Philosophers have disagreed about whether theories that make
successful predictions are more believable than theories that merely
explain data that have already been discovered. Predictivists
believe that theories that make successful predictions have an edge
over theories that offer only retrospective explanations of the same
data. Nonpredictivists maintain that whether a theory predicts data
or explains data retrospectively is irrelevant to the believability
of the theory. The purpose of this paper is to report on three
psychological experiments designed to determine whether
undergraduates behave as predictivists or nonpredictivists when they
evaluate theories. Results indicate that subjects behaved as
nonpredictivists when one theory predicted a body of data and a
second theory was devised later to explain the same data
retrospectively. However, subjects behaved as predictivists in the
situation in which a theory retreated in the face of anomalous data
by adding an auxiliary hypothesis; for instance, theories that
predicted data by adding the necessary auxiliary hypotheses before
the data came in were more believable than theories that added the
auxiliary hypothesis in reaction to the data. These results suggest
that cognitive models of theory choice that assume that people are
nonpredictivists may require modification.
%TI The Architecture of Intuition: Converging Views from Physics Education and Linguistics
%AU Ming Ming Chiu
%AU Joshua Gutwill
%PU Proc. CogSci-94, pp. 183-188
%SC Monday, August 15, 7:30-9
%AB This paper analyzes two converging views of the architecture of
intuition. A. diSessa and L. Talmy, working independently in
different fields (physics education and linguistics), have
formulated strikingly similar theories of intuition. Both view
people's intuitions about forces as simple pieces of knowledge
organized heterarchically. However, Talmy's force dynamic patterns
have more sys tem-wide structure than diSessa's phenomenological
primi tives. Using these primitives, people generate common sense
explanations for a wide variety of situations. Moreover, people may
build upon these intuitions while studying formal disciplines such
as physics. However, several primitives directly conflict with
physics concepts and may account for resilient misconceptions.
Finally, intuitions may also provide the basis for understanding so
cial and psychological phenomena.
%TI Commonsense Knowledge and Conceptual Structure in Container Metaphors
%AU Timothy C. Clausner
%PU Proc. CogSci-94, pp. 189-194
%SC Monday, August 15, 11-12:30
%AB Cognitive grammar provides an analytic framework in which the
semantic value of linguistic expressions is characterized relative
to domains of presupposed knowledge. Cognitive metaphor theory holds
that metaphorical language involves a mapping of conceptual
structure from a source domain to a target domain. Containers are
one such pervasive structure. This investigation proposes a
detailed representation for the domain CONTAINER and applies it in
the analysis of metaphorical expressions mapping CONTAINER onto
target domains ARGUMENT and LINGUISTIC EXPRESSION. Each source
domain word is analyzed with respect to which aspects of the
CONTAINER domain structure it refers, and whether it refers to a 2D
or 3D bounded region. The pattern of aspects mapped suggest that
spatial containment, content, and material container object comprise
major aspects of the 3D CONTAINER domain. The target domains are
demonstrated to be structured according this container organization.
The results demonstrate that cognitive semantic analysis can reveal
specific structures of commonsense knowledge which are prerequisite
for language use.
%TI A Descriptive Model of Question Asking During Story Acquistion Interviews
%AU Chip Cleary
%AU Ray Bareiss
%PU Proc. CogSci-94, pp. 195-200
%SC Monday, August 15, 7:30-9
%AB In this paper, we provide a taxonomy of the processes which people
use to generate questions for a type of interviewing task.
Specifically, we analyze "story acquisition interviews" in which the
interviewer is a knowledge engineer who asks questions of a domain
expert to acquire material for a conversational hypermedia system.
Such interviews have proven to be surprisingly difficult to conduct
successfully. We have identified a number of "local" strategies
which successful interviewers use to develop coherent, interesting
sequences of questions and we have positioned these strategies
within a model which describes the global interviewing process. This
descriptive model is an initial step towards a methodology
prescribing how to perform these interviews effectively.
%TI Imagistic Simulation and Physical Intuition in Expert Problem Solving
%AU John Clement
%PU Proc. CogSci-94, pp. 201-206
%SC Sunday, August 14, 4-5:30
%AB This paper discusses evidence from thinking aloud case studies indicating
that part of the knowledge used by expert problem solvers consists of
concrete physical intuitions rather than abstract verbal principles or
equations. One purpose of the paper is to provide empirical
documentation of behaviors such as spontaneous references to using
intuition, depictive hand motions, and dynamic imagery reports. Although
the role of imagery in lower level tasks is becoming more accepted, we
currently lack sufficient empirical evidence for its use in higher level
thinking. In order to account for cases where subjects appear to be
"running a simulation" of an event on the basis of a physical intuition,
a model is presented in which a somewhat general and permanent perceptual
motor schema controls a more specific and temporary image of a situation.
This process is termed "imagistic simulation". The imagery can be
kinesthetic as well as visual, and dynamic rather than static, suggesting
the involvement of the motor system. Although rules for making
inferences from networks of causal relations have been studied, we lack
models which analyze the nature of mental simulations underlying a single
causal relationship. Such physical intuitions and simulations may
provide basic building blocks for constructing visualizable models in
science.
%TI Modeling Retroactive Context Effects in Spoken Word Recognition with a Simple Recurrent Network
%AU Alain Content
%AU Pascal Sternon
%PU Proc. CogSci-94, pp. 207-212
%SC Sunday, August 14, 11-12:30
%AB We present a new variant of a simple recurrent network to model
auditory word recognition in continuous speech and address the issue
of lexical segmentation. Simulations based on small word sets show
that the system provides a near-optimal solution to the opposite
constraints of speed, which requires that lexical processing be
immediate, and reliability, which imposes that identification
decisions be postponed until unambiguous information is available.
Contrary to an often-heard statement, the simulations show that the
existence of embedded words is not incompatible with the notion of
continuous on-line lexical processing.
%TI Individual Differences and Predictive Validity in Student Modeling
%AU Albert T. Corbett
%AU John R. Anderson
%AU Valerie H. Carver
%AU Scott A. Brancolini
%PU Proc. CogSci-94, pp. 213-218
%SC Monday, August 15, 7:30-9
%AB This paper evaluates the student modeling procedure in the ACT
Programming Tutor (APT). APT is a practice environment that
provides assistance to students as they write short programs. The
tutor is constructed around a set of several hundred programming
rules called the ideal student model, that allows the program to
solve exercises along with the student. As the student works the
tutor maintains an estimate of the probability that the student has
learned the rules in the ideal model, in a process we call knowledge
tracing. The cognitive model, and the learning and performance
assumptions that underlie knowledge tracing are described. The
assumptions that underlie knowledge tracing also yield performance
predictions. These predictions provide a good fit to students'
performance in completing tutor exercises, but a more important
issue is how well the model predicts students' performance outside
the tutor environment. A previous study showed that the model
provides a good fit to average posttest performance across students,
but is less sensitive to individual differences. This paper
describes a method of individualizing learning and performance
estimates on-line in the tutor and assesses the validity of the
resulting performance predictions.
%TI Rational choice and framing devices: Argumentation and computer programming
%AU Seana Coulson
%AU Nick V. Flor
%PU Proc. CogSci-94, pp. 219-224
%SC Sunday, August 14, 2-3:30
%AB The argumentative discourse of computer programmers engaged in a
collaborative programming task were analyzed as instances of ecologically
valid reasoning behavior. Teams of expert programmers were brought into
a laboratory setting to work cooperatively on a software maintenance
task. Arguments which occurred spontaneously in the course of the task
were examined with respect to: (a) their effect on task performance; and
(b) to reveal the sorts of inferential machinery programmers use when
they reason with one another. Arguments were found to be important in
the formulation of plans as well as the negotiation of strategic
priorities with respect to the task. Pragmatic features of the
programmers' discourse revealed extensive use of framing devices whose
efficacy depended upon interpretation in the context of linked pragmatic
scales.
%TI Graphical effects in learning logic: reasoning, representation and individual differences
%AU Richard Cox
%AU Keith Stenning
%AU Jon Oberlander
%PU Proc. CogSci-94, pp. 237-242
%SC Sunday, August 14, 4-5:30
%AB Hyperproof is a computer program created by Barwise and Etchemendy
for teaching logic using multimodal graphical and sentential
methods, inspired by their theories of heterogeneous reasoning
(Barwise and Etchemendy 1994). Elsewhere, we have proposed a theory
of the cognitive impact of assigning information to different
modalities (Stenning and Oberlander 1992). Our view is that where
diagrams are advantageous, it is because they enforce the
representation of information, leading to *weak* expressiveness,
thereby facilitating inference. The present study tests and
develops these claims by comparing the effects of teaching
undergraduate logic classes using Hyperproof and a control syntactic
teaching method. Results indicate that there is significant
transfer from the logic courses to logical and analytical reasoning
problems. There are also significant interactions between
theoretically motivated pre-course aptitude measures and teaching
method; the interactions influence post-course reasoning performance
in transfer domains. Hyperproof boosts students previously weak on
items which benefit from diagram use, whereas the syntactic course
appears to degrade the same group of students' graphical strategies.
As well as being theoretically interesting, these results provide
support for the important practical conclusion that individual
differences in aptitude should be taken into account in choosing
teaching technique.
%TI Failure-Driven Learning as Input Bias
%AU Michael T. Cox
%AU Ashwin Ram
%PU Proc. CogSci-94, pp. 231-236
%SC Monday, August 15, 2-3:30
%AB Self-selection of input examples on the basis of performance failure
is a powerful bias for learning systems. The definition of what
constitutes a learning bias, however, has been typically restricted
to bias provided by the input language, hypothesis language, and
preference criteria between competing concept hypotheses. But if
bias is taken in the broader context as any basis that provides a
preference for one concept change over another, then the paradigm of
failure-driven processing indeed provides a bias. Bias is exhibited
by the selection of examples from an input stream that are examples
of failure; successful performance is filtered out. We show that the
degrees of freedom are less in failure-driven learning than in
success-driven learning and that learning is facilitated because of
this constraint. We also broaden the definition of failure, provide
a novel taxonomy of failure causes, and illustrate the interaction
of both in a multistrategy learning system called Meta-AQUA.
%TI Machines that Forget: Learning from retrieval failure of mis-indexed explanations
%AU Michael T. Cox
%PU Proc. CogSci-94, pp. 225-230
%SC Tuesday, August 16, 11-12:30
%AB A reasoner may fail at a cognitive task, not because it does not
have appropriate knowledge with which to reason, but instead because
it does not have the proper index or cue with which to retrieve such
knowledge from memory. The reasoner knows this memory item; it
simply cannot remember the item. This paper argues that forgetting
provides an opportunity for learning through memory reorganization.
A reasoner that takes full advantage of such opportunities, however,
must be able to reason about its own memory system. To do so, it
must possess a language for declaratively representing its reasoning
failures and must reflectively inspect such representations if it is
to fully explain the reason for its failure. Once such an error is
understood as a memory failure, the problem of forgetting is to
re-adjust the indexes so that the knowledge is properly retrieved in
similar, future situations.
%TI The Null List Strength Effect in Recognition Memory: Environmental Statistics and Connectionist Accounts
%AU Simon Dennis
%PU Proc. CogSci-94, pp. 243-247
%SC Sunday, August 14, 2-3:30
%AB In recognition paradigms, increasing the number of occurrences or
presentation time in a study list of some words improves performance
on these words (the item strength effect), but does not affect the
performance on other words (null list strength effect). In contrast,
adding new items results in a deterioration of performance on the
other words (list length effect). Taken together these results place
strong constraints on models of recognition memory. To explain
these data an account based on optimisation to the environment is
presented. A summary is given of environmental analyses which
suggest that (1) the likelihood of recurrence of a word within a
context increases as the number of occurrences increases; (2) the
repetition rates of other words in a context has no significant
effect on the recurrence probability of a word; and (3) the
recurrence probability of a word drops as a function of the number
of words since the last occurrence of that word. A training set
which reflected these constraints was constructed and presented to
an optimising connectionist network which was designed to extract
recurrence statistics (the Hebbian Recurrent Network). The resultant
model is able to model all three of the effects outlined above.
%TI Effects of Collaborative Interaction and Computer Tool Use
%AU Sharon Derry
%AU Keith Tookey
%PU Proc. CogSci-94, pp. 248-253
%SC Sunday, August 14, 2-3:30
%AB We compared cognitive processing of two complex arithmetic word
problems by college students randomly assigned to four different
situating tool and social contexts: individualized problem solving
with pen and paper; pair problem solving with pen and paper;
individualized problem solving on TAPS, a computer-based problem
solving tool; and collaborative problem solving on TAPS. TAPS users
differed from users of conventional tools in that they required
relatively more time for problem solving, spent more time in
planning activity, and proportionately less time reading. With
respect to the influences of social (versus individual) problem
solving, collaboration also produced significantly more planning
behavior, such that the combined use of TAPS and collaboration
produced a marked increase in planning. Also, significantly more
behavior associated with metacognitive monitoring occurred in the
protocols for pairs. There was no evidence that use of the TAPS
tool changed the social nature of the collaboration. However, a
qualitative analysis yielded interesting information regarding
negotiation processes underlying pair problem solving. For example,
we saw specifically some reasons why untrained pair problem solving
does not proceed naturally and smoothly. Results are interpreted in
terms of situated cognition theory, although symbolic processing
theories also can explain much of the data.
%TI Learning from Instruction: A Comprehension-Based Approach
%AU Stephanie M. Doane
%AU Young Woo Sohn
%AU David Adams
%AU Danielle S. McNamara
%PU Proc. CogSci-94, pp. 254-259
%SC Tuesday, August 16, 11-12:30
%AB A comprehension-based approach to learning assumes that incoming
information and background knowledge are integrated to form a mental
represention which is subsequently used to incorporate new
knowledge. We demonstrate that this approach can indicate when
people will learn from instructions. Specifically, we show that a
computational model based on the construction-integration theory of
comprehension (Kintsch, 1988) can explain and predict how individual
users will comprehend help prompts that guide their generation of
successful complex commands within an operating system. In previous
empirical studies, we asked users whose UNIX operating system
experience varied to produce complex UNIX commands, and then
provided prompts when the commands they produced were erroneous. The
prompts were designed to assist subjects with both knowledge and
processes that our previous efforts have suggested are lacking in
less expert users. The empirical results showed significant
differences in response to different prompts as a function of
background knowledge about UNIX. In the present work, we extended
our computational model to include comprehension-based learning
mechanisms. We modeled a subset of the individuals in the prompting
study by representing each subject's initial knowledge base, then
simulating each user's run through the prompting experiment. The
results show that the modeled performance matches individual
performance quite well both quantitatively and qualitatively. This
work has implications for the development of instructional systems,
and theoretical implications for the construction-integration theory
of comprehension.
%TI An Experiment to Determine Improvements in Automated Problem Solving in a Complex Problem Domain
%AU M. Van Dyne
%AU C. Tsatsoulis
%PU Proc. CogSci-94, pp. 899-904
%SC Monday, August 15, 2-3:30
%AB A previously constructed prototype expert system was extended to
include case-based reasoning/learning, in order to determine if the
automated problem solving behavior could be improved. The initial
expert system was developed by using an inductive machine learning
technique on 9,445 data records of pregnant women, providing
production rules to predict preterm delivery. Its predictive
accuracy was tested on a separate set of 9,445 data records. Next,
the capability to reason from both production rules and input test
cases was added to the system, in addition to the capability to
internally modify its confidence in each piece of knowledge (rule or
case) and the relative importance of patient attributes which appear
to be predictive of preterm delivery. The system was structured such
that the accuracy of either type of reasoning could be measured
individually to determine how rule-based and case-based reasoning
perform alone, and to determine how they perform together. Results
show that the predictive accuracy of the system was improved, with
different trends emerging, dependent on the bias of the learning
data. Neither system performed as well alone as did both together.
%TI Managing Disagreement in Intellectual Conversations: Coordinating Interpersonal and Conceptual Concerns in the Collaborative Construction of Mathematical Explanations
%AU Randi A. Engle
%AU James G. Greeno
%PU Proc. CogSci-94, pp. 266-271
%SC Sunday, August 14, 2-3:30
%AB This paper reports research into how mathematical explanations are
constructed during conversation based on videotapes of pairs of
student math teachers collaboratively writing explanations in
geometry. In particular, we analyzed how disagreements about parts
of their explanations were managed in these conversations. In
contrast to research on disagreement in everyday conversation,
explanation disagreements were more likely to overlap with preceding
turns and to be stated baldly without prefaces, token agreements or
qualifications. However, the observed frequencies of different
kinds of disagreements were not consistent with a model favoring
explicit substantive disgreement either. Instead, it is proposed
that both the interpersonal concerns that would motivate a
preference for agreement and the conceptual concerns for a quality
explanation that would motivate a preference for substantive
disagreement are being managed by participants. Disagreements are
co-constructed, and conversants are seen to jointly employ complex
devices for introducing and managing disagreement across turns that
can satisfy both kinds of concerns with much less conflict between
them than might have been expected.
%TI Natural Oculomotor Performance in Looking and Tapping Tasks
%AU Julie Epelboim
%AU Eileen Kowler
%AU Mark Edwards
%AU Han Collewijn
%AU Casper J. Erkelens
%AU Zygmunt Pizlo
%AU Robert M. Steinman
%PU Proc. CogSci-94, pp. 272-277
%SC Monday, August 15, 7:30-9
%AB A unique apparatus recorded eye and head movements of subjects as
they tapped or only looked at sequences of 2, 4 or 6 nearby, 3-D
targets. Each sequence was repeated 10 times to allow an
opportunity for learning. A stereotypical pattern of movements
was established after 2-3 repetitions. Subjects almost always
looked at each target just before tapping it. Looking-only was more
difficult than tapping in that it took more time and, unlike
tapping, usually did not benefit from practice. The number of
targets in a sequence affected time/target in both tasks. Sequence
length and practice effects show that memory was involved. The
persistent strategy of looking before tapping and the subjects'
inability to tap a well-learned pattern with eyes closed, show that
visual cues were also important. We conclude that motor planning
occurred first at the level of the task and then at the level of
specific motor programs. The relative difficulty of the less
natural, looking-only task, in which the eyes worked without a
meaningful cognitive or motor purpose, suggests that efficient eye
movement programming requires a natural task of the kind eye
movements evolved to serve.
%TI The Effect of Similarity on Memory for Prior Problems
%AU Jeremiah M. Faries
%AU Karen R. Schlossberg
%PU Proc. CogSci-94, pp. 278-282
%SC Monday, August 15, 11-12:30
%AB Students often rely on prior work or previously studied examples to
help them solve their current problems. In this paper we
investigate the relative contributions of easily accessed
superficial similarity and deep, solution relevant, structural
similarity to memory for prior problems. Some models of memory for
analogy suggest that superficial similarity initially selects or
constrains memory for prior examples and predicts that analogs that
share both surface and structural similarities will be more likely
noticed by novices. An experiment is reported in which subjects are
observed as they learn how to program. We find that people remember
the examples that are related in terms of structural features alone
as frequently as those that are related in terms of both structural
and superficial features but there is no advantage to having
superficial similarities as well. Moreover, even though superficial
features sometimes are associated with helpful similarities and
sometimes associated with unhelpful similarities people still do not
get misled by superficial similarity when that is the only basis for
similarity. This finding suggests that models that require
superficial similarity as a major selection procedure for analogical
reminding may need to be modified for conditions in which people are
learning a new skill.
%TI MAGI: Analogy-based Encoding Using Regularity and Symmetry
%AU Ronald W. Ferguson
%PU Proc. CogSci-94, pp. 283-288
%SC Sunday, August 14, 2-3:30
%AB Analogy has always been considered a mechanism for interrelating distinct
parts of the world, but it is perhaps just as important to consider how
analogy might be used to break the world into comprehensible parts. The
MAGI program uses the Structure-Mapping Engine (SME) to flexibly and
reliably match a description against itself. The resulting mapping pulls
out the two maximally consistent parts of the given description. MAGI
then divides out the parts of the mapping and categorizes the mapping as
symmetrical or regular. These parts may then be used as the basis for
new comparisons. We theorize that MAGI models how people use symmetry
and regularity to facilitate the encoding task. We demonstrate this with
three sets of examples. First, we show how MAGI can augment traditional
axis detection and reference frame adjustment in geometric figures.
Next, we demonstrate how MAGI detects visual and functional symmetry in
logic circuits, where symmetry of form aids encoding symmetry of
function. Finally, to emphasize that regularity and symmetry detection
is not simply visual, we show how MAGI models some aspects of expectation
generation in story understanding. In general, MAGI shows symmetry and
regularity to be not only pretty, but also cognitively valuable.
%TI Context Effects in Syntactic Ambiguity Resolution: The Location of Prepositional Phrase Attachment
%AU Evelyn Ferstl
%PU Proc. CogSci-94, pp. 295-300
%SC Tuesday, August 16, 11-12:30
%AB Two experiments are reported to test whether the location of
prepositional phrase attachment can be influenced by syntactic and
contextual factors. The first experiment tested the hypothesis that
attachment is delayed until the word after the prepositional phrase.
Replicating the results of Taraban and McClelland (1988), this
experiment showed that sentence bias rather than syntactic structure
determines the ease of processing; attachment effects were observed
on the words after the noun filler. In addition, using sentences in
which the noun filler consisted of a compound noun, we also found
evidence for delayed attachment. Using sentences in which the noun
filler was modified by an adjective, we found evidence for early
attachment. In the second experiment, we used context paragraphs to
induce earlier attachment for the compound noun sentences. When the
first noun of the compound was mentioned in the prior discourse,
attachment effects were observed on the disambiguating noun filler.
When the first noun was not mentioned, attachment effects were
observed, as in Experiment 1, on the words after the prepositional
phrase. Thus, the study supports the idea of a context-dependent
delay strategy for prepositional phrase attachment.
%TI The Construction-Integration Model: A Framework for Studying Context Effects in Sentence Processing
%AU Evelyn Ferstl
%PU Proc. CogSci-94, pp. 289-294
%SC Monday, August 15, 11-12:30
%AB Contextual and pragmatic knowledge facilitates the eventual
interpretation of a syntactically ambiguous sentence. However,
psycholinguistic studies have not provided a clear answer to when
and how this non-syntactic knowledge is used. One explanation for
the discrepancy of the results is that the predictions for parsing
processes in context cannot be specified unless they are based on a
theory of text comprehension. The construction-integration model of
discourse comprehension (Kintsch, 1988) is proposed as an example
for such a theory. The model is parallel and weakly interactive,
and its psychological validity has been shown in a variety of
applications. Three simulations for syntactic ambiguity resolutions
are presented. In the first, syntactic constraints are used to
account for the correct interpretation of a garden-path sentence, as
well as for common misparses. In the second example, pragmatic
knowledge is used to disambiguate a prepositional phrase attachment.
In the final example, it is shown that the model can also account
for effects of discourse context in the resolution of prepositional
phrase attachment ambiguities.
%TI Attention Allocation During Movement Preparation
%AU Martin H. Fischer
%PU Proc. CogSci-94, pp. 307-312
%SC Monday, August 15, 4-5:30
%AB Identification performance was measured for letters which were
briefly presented at different spatial locations and time delays
relative to the beginning of manual movement preparation.
Identification performance depended on the complexity of the
upcoming movement and decreased prior to movement onset. Further
findings of similar identification performance with differerent
spatial relations between probe location and manual movement
direction cast doubt on the generality of a premotor theory of
attention.
%TI Incremental Structure Mapping
%AU Kenneth D. Forbus
%AU Ronald W. Ferguson
%AU Dedre Gentner
%PU Proc. CogSci-94, pp. 313-318
%SC Monday, August 15, 11-12:30
%AB Many cognitive tasks involving analogy, such as understanding
metaphors, problem-solving, and learning, require the ability to
extend mappings as new information is found. This paper describes a
new version of SME, called I-SME, that operates incrementally.
I-SME is inspired by Keane's IAM model and the use of incremental
mapping in Falkenhainer's PHINEAS learning system. We describe the
I-SME algorithm and discuss tradeoffs introduced by incremental
mapping, including parallel versus serial processing and pragmatic
influences. The utility of I-SME is illustrated by two examples.
First, we show that I-SME can account for the psychological results
found by Keane on a serial version of the Holyoak & Thagard
attribute mapping task. Second, we describe how I-SME is used in
the Minimal Analogical Reasoning System (MARS), which uses analogy
to solve engineering thermodynamics problems.
%TI Learning the Arabic Plural: The Case for Minority Default Mappings in Connectionist Networks.
%AU Neil Forrester
%AU Kim Plunkett
%PU Proc. CogSci-94, pp. 319-323
%SC Monday, August 15, 7:30-9
%AB Connectionist accounts of inflectional morphology have focussed on
the domain of the English Past Tense (e.g. Rumelhart & McClelland
1986; Plunkett & Marchman 1993). In this inflectional domain, the
default mapping process (add /ed/) reflects the process of
suffixation adopted by the majority of the forms in the language.
Connectionist models exploit the imbalance between English regular
and irregular verbs when learning the past tense and when responding
to novel forms in a default fashion. Not all inflectional systems
have a default mapping which is characterized by a majority of forms
in the language. The Arabic Plural System has been cited (Marcus et
al. 1993) as one such system where a minority default mapping
process operates. The Sound Plural in Arabic applies to only a
minority of forms in the lexicon (~10%), yet it appears to adopt the
role of a default mapping for novel nouns. We describe a
connectionist model that can learn a minority default mapping
analogous to the Arabic plural and discuss its performance in
relation to type and token frequency effects, and their distribution
within phonetic space.
%TI Using Introspective Reasoning to Guide Index Refinement in Case-Based Reasoning
%AU Susan Fox
%AU David Leake
%PU Proc. CogSci-94, pp. 324-329
%SC Monday, August 15, 2-3:30
%AB Case-based reasoning research on indexing and retrieval focuses
primarily on developing specific retrieval criteria, rather than on
developing mechanisms by which such criteria can be learned as
needed. This paper presents a framework for learning to refine
indexing criteria by introspective reasoning. In our approach, a
self-model of desired system performance is used to determine when
and how to refine retrieval criteria. We describe the advantages of
this approach for focusing learning on useful information even in
the absence of explicit processing failures, and support its
benefits with experimental results on how an implementation of the
model affects performance of a case-based planning system.
%TI How do representations of visual form organize our percepts of visual motion?
%AU Gregory Francis
%AU Stephen Grossberg
%PU Proc. CogSci-94, pp. 330-334
%SC Monday, August 15, 4-5:30
%AB How does the visual system generate percepts of moving forms? How
does this happen when the forms are emergent percepts (such as
illusory contours or segregated textures) and the motion percept is
apparent motion between the emergent forms? A neural model of
form-motion interactions is developed to explain parametric
properties of psychophysical motion data and to make predictions
about the parallel cortical processing streams V1 --> MT and V1 -->
V2 --> MT. The model simulates many parametric psychophysical data
arising from form-motion interactions. A key linkage between form
and motion data is articulated in terms of properties of visual
persistence and properties of apparent motion. The model explains
how an illusory contour can move in apparent motion to another
illusory contour or to a luminance-derived contour; how illusory
contour persistence relates to the upper ISI threshold for apparent
motion; and how upper and lower ISI thresholds for seeing apparent
motion between two flashes decrease with stimulus duration and
narrow with spatial separation (Korte's laws). Psychophysical data
are derived from an analysis of how orientationally tuned form
perception mechanisms and directionally tuned motion perception
mechanisms interact to generate consistent percepts of moving forms.
%TI Dynamically constraining connectionist networks to produce distributed, orthogonal representations to reduce catastrophic forgetting
%AU Robert M. French
%PU Proc. CogSci-94, pp. 335-340
%SC Sunday, August 14, 2-3:30
%AB It is well known that when a connectionist network is trained on one
set of patterns and then attempts to add new patterns to its
repertoire, catastrophic interference may result. The use of
sparse, orthogonal hidden-layer representations has been shown to
reduce catastrophic interference. The author demonstrates that the
use of sparse representations may, in certain cases, actually result
in worse performance on catastrophic interference. This paper
argues for the necessity of maintaining hidden-layer representations
that are both as highly distributed and as highly orthogonal as
possible. The author presents a learning algorithm, called
context-biasing, that dynamically solves the problem of constraining
hidden-layer representations to simultaneously produce good
orthogonality and distributedness. On the data tested for this
study, context-biasing is shown to reduce catastrophic interference
by more than 50% compared to standard backpropagation.
%TI Inference Processes in Speech Perception
%AU Gareth Gaskell
%AU William Marslen-Wilson
%PU Proc. CogSci-94, pp. 341-345
%SC Sunday, August 14, 11-12:30
%AB Cross-modal priming experiments have shown that surface variations
in speech are perceptually tolerated as long as they occur in
phonologically viable contexts. For example, [klim] (cleam) gains
access to the mental representation of clean when in the context of
[klimpaks] (cleam parks), since the change is a natural one,
reflecting the phonological process of place assimilation. This
implies that speech perception involves processes of phonological
inference, which recover the underlying form of speech. Here we
investigate the locus of these inference processes, using the
phoneme monitoring task. A set of stimulus sentences were created
containing deviations that were either phonologically viable (as in
cleam parks above) or unviable. In Experiment 1, subjects monitored
for the segment underlying the surface change (in the above example,
/n/) and in Experiment 2 the following segment (/p/) was the target.
In addition, the lexical status of the carrier word was manipulated
(e.g., clean vs threan), contrasting lexical and non- lexical
theories of phonological inference. Both experiments showed strong
effects of phonological viability for real words, with weaker
effects for the non-word stimuli. These results suggest that
phonological inference can occur non-lexically, but that it
interacts strongly with the process of lexical access.
%TI How Graphs Mediate Analog and Symbolic Representation
%AU Merideth Gattis
%AU Keith Holyoak
%PU Proc. CogSci-94, pp. 346-350
%SC Sunday, August 14, 4-5:30
%AB Three experiments are reported that examine the impact of people's
goals and conceptual understanding on graph interpretation, in order
to determine how people use graphical representations to evaluate
functional dependencies between continuous variables. Subjects made
inferences about the relative rate of two continuous linear
variables (altitude and temperature). We varied the assignments of
variables to axes, the perceived cause-effect relation between the
variables, and the causal status of the variable being queried. The
most striking finding was that accuracy was greater when the
Slope-Mapping Constraint was honored, which requires that the
variable being queried -- usually the effect or dependent variable,
but potentially the cause instead -- is assigned to the vertical
axis, so that steeper lines map to faster changes in the queried
variable. This constraint dominates when it conflicts with others,
such as preserving the low-level mapping of altitude onto the
vertical axis. Our findings emphasize the basic conclusion that
graphs are not pictures, but rather symbolic systems for
representing higher-order relations. We propose that graphs provide
external instantiations of intermediate mental representations,
which enable people to move from pictorial representations to
abstractions through the use of natural mappings between perceptual
properties and conceptual relations.
%TI Classicalism and Cognitive Architecture
%AU Tim van Gelder
%AU Lars Niklasson
%PU Proc. CogSci-94, pp. 905-909
%SC Sunday, August 14, 4-5:30
%AB This paper challenges the widely accepted claim that "classical"
cognitive architectures can explain the systematicity of cognition
(Fodor & Pylyshyn, 1988) . There are plausible ways of rendering
more precise the systematicity hypothesis (as standardly formulated)
in which it is entailed by classical architectures, and other
plausible ways in which it is not. Therefore, it is not a
determinate issue whether systematicity is entailed, and hence
explained, by classical architectures. The general argument is
illustrated in a particular domain, the systematicity of deductive
inference. In the case of the capacity to carry out the inference
modus tollens, the systematicity hypothesis can be made precise in
two ways, one entailed by classical architectures, another which is
not. Further, the latter, but not the former, accurately describes
the actual empirical phenomenon. Put another way, the clumps that
these deductive inference capacities come in are not the clumps that
are entailed by classical architectures. Therefore, in this area at
least, systematicity considerations count against the classical
conception of cognitive architecture.
%TI The Coherence Imbalance Hypothesis: A Functional Approach to Asymmetry in Comparison
%AU Dedre Gentner
%AU Brian F. Bowdle
%PU Proc. CogSci-94, pp. 351-356
%SC Monday, August 15, 11-12:30
%AB Directional asymmetry is a well-documented phenomenon in research on
similarity, metaphor, and analogy. In this paper, we present an
account of this phenomenon based on structural alignment. We
propose that a major source of asymmetry is coherence imbalance:
that is, a difference in the degree of systematicity of the
relational structures being compared. These claims are tested in
three experiments which examine the relationship between asymmetry,
informativity, and conceptual coherence. The results support the
hypothesis that coherence imbalance is a key factor in directional
comparison processes. Further, by incorporating the insights
offered by structural alignment, coherence imbalance advances a more
functional account of asymmetry.
%TI A Corpus Analysis of Recency Preference and Predicate Proximity
%AU Edward Gibson
%AU Jacob Loomis
%PU Proc. CogSci-94, pp. 357-362
%SC Tuesday, August 16, 11-12:30
%AB The recent availability of large on-line parsed corpora makes it possible
to test theories of psycholinguistic complexity by comparing the
frequency distributions of closely related constructions. In this paper,
we use this technique to test the psycholinguistic theory proposed by
Gibson et al. (1993), which includes two independently motivated
attachment principles: Recency Preference and Predicate Proximity. In
order to test this theory, we examined two general classes of attachment
ambiguities from the parsed Wall Street Journal corpus from the Penn
Treebank: 1) ambiguities which involve three prospective noun phrase
attachment sites; and 2) ambiguities which involve three prospective verb
phrase attachment sites. Given three prospective noun phrase (NP) sites
in English, the theory most naturally predicts a complexity ordering of
NP3 (easiest, most recent), NP1, NP2, but a ranking of VP3, VP2, VP1 for
verb phrase attachments. Our corpus analyses support both of these
predictions.
%TI Using Trajectory Mapping to Analyze Musical Intervals
%AU Stephen A. Gilbert
%AU Whitman Richards
%PU Proc. CogSci-94, pp. 363-368
%SC Monday, August 15, 11-12:30
%AB Cognitive scientists have often pondered the question of perceptual
spaces, that is, the question of how a certain gamut of familiar
stimuli might be organized in the mind. We present Trajectory
Mapping as an alternative clustering method to the traditional
algorithm of Multi-Dimensional Scaling. We suggest that given data
about the relationships among stimuli, Multi-Dimensional Scaling
provides one type of information (geometric), while Trajectory
Mapping offers a second type (relational). As an illustration we
present the initial results of applying both clustering techniques
to subjects' perceptions of musical intervals. While an
interpretation of the Multi-Dimensional Scaling requires a priori
knowledge of music theory, Trajectory Mapping directly reveals the
music theory that has been internalized by subjects.
%TI Are Children Lazy Learners? A comparison of natural and machine learning of stress
%AU Steven Gillis
%AU Walter Daelemans
%AU Gert Durieux
%PU Proc. CogSci-94, pp. 369-374
%SC Monday, August 15, 4-5:30
%AB Do children acquire rules for main stress assignment or do they
learn stress in an exemplar-based way? In the language acquisition
literature, the former approach has been advocated without
exception: although they hear most words produced with their
appropriate stress pattern, children are taken to extract rules, and
do not store stress patterns lexically. The evidence for a
rule-based approach is investigated, and it will be argued that in
the literature such an approach is preferred due to an
oversimplification of exemplar-based models. We will report
experiments showing that Instance-Based Learning, an exemplar-based
model, makes the same kinds of stress related errors in production
that children make: (i) the amount of production errors is related
to metrical markedness, and (ii) stress shifts and errors with
respect to the segmental and syllabic structure of words, typically
take the form of a regularization of stress patterns. Instance-Basd
Learning belongs to a class of Lazy Learning algorithms. In these
algorithms, no explicit abstractions in the form of decision trees
or rules are derived; abstraction is driven by similarity during
performance. Our results indicate that at least for this domain,
this kind of lazy learning is a valid alternative to rule-based
learning. Moreover, the results plead for a reanalysis of language
acquisition data in terms of exemplar-based models.
%TI Array Representations for Model-Based Spatial Reasoning
%AU Janice Glasgow
%PU Proc. CogSci-94, pp. 375-380
%SC Monday, August 15, 4-5:30
%AB To date, the major focus of research in knowledge representations
for artificial intelligence has been on sentential or linguistic
formalisms involving logic and rule-based reasoning. There is a
growing body of evidence suggesting, however, that much of human
problem solving is achieved, not through the application of rules of
inference, but rather through the manipulation of mental models.
Such a model is represented by a system with a similar relational
structure to the reality it represents. Moreover, spatial reasoning
with models involves the inspection and transformation of
representations in ways that are analogous to visually inspecting
and physically transforming entities in the world. Since a crucial
component of knowledge acquisition is to capture an expert's mental
state and reasoning strategies, it is important to shift some of the
attention of AI research to the study of representation techniques
that correspond to the mental models used by humans. The paper
begins with a cognitive perspective on model-based reasoning. A
knowledge representation scheme for spatial reasoning with models is
then presented. In this scheme, which has evolved from research in
computational imagery, spatial models are represented as symbolic
arrays where dimensions of the array correspond to transitive order
relations among entities.
%TI Binding of Object Representations by Synchronous Cortical Dynamics Explains Temporal Order and Spatial Pooling Data
%AU Alexander Grunewald
%AU Stephen Grossberg
%PU Proc. CogSci-94, pp. 387-391
%SC Monday, August 15, 2-3:30
%AB A key problem in cognitive science concerns how the brain binds
together parts of an object into a coherent visual object
representation. One difficulty that this binding process needs to
overcome is that different parts of an object may be processed by
the brain at different rates and may thus become desynchronized.
Perceptual framing is a mechanism that resynchronizes cortical
activities corresponding to the same retinal object. A neural
network model based on cooperation between oscillators via feedback
from a subsequent processing stage is presented that is able to
rapidly resynchronize desynchronized featural activities. Model
properties help to explain perceptual framing data, including
psychophysical data about temporal order judgments. These
cooperative model interactions also simulate data concerning the
reduction of threshold contrast as a function of stimulus length.
The model hereby provides a unified explanation of temporal order
and threshold contrast data as manifestations of a cortical binding
process that can rapidly resynchronize image parts which belong
together in visual object representations.
%TI Using Connectionist Networks to Examine the Role of Prior Constraints in Human Learning
%AU Michael Harm
%AU Lori Altmann
%AU Mark S. Seidenberg
%PU Proc. CogSci-94, pp. 392-396
%SC Sunday, August 14, 11-12:30
%AB This research investigated the effects of prior knowledge on
learning in psychologically-plausible connectionist networks. This
issue was examined with respect to the benchmark
orthography-to-phonology mapping task (Sejnowski & Rosenberg, 1986;
Seidenberg & McClelland, 1989). Learning about the correspondences
between orthography and phonology is a critical step in learning to
read. Children (unlike the networks mentioned above) bring to this
task extensive knowledge about the sound-structure of their
language. We first describe a simple neural network that acquired
some of this phonological knowledge. We then summarize simulations
showing that having this knowledge in place facilitates the
acquisition of orthographic-phonological correspondences, producing
a higher level of asymptotic performance with fewer implausible
errors and better nonword generalization. The results suggest that
connectionist networks may provide closer approximations to human
performance if they! incorporate more realistic assump tions about
relevant sorts of background knowledge.
%TI Objects, actions, nouns, and verbs
%AU Peter M. Hastings
%AU Steven L. Lytinen
%PU Proc. CogSci-94, pp. 397-402
%SC Monday, August 15, 4-5:30
%AB This paper describes a lexical acquisition mechanism that was
implemented in order to increase the robustness of a Natural
Language Processing system. Although the mechanism was not intended
to be a cognitive model of children's language acquisition, it
demonstrates many similarities with psycholinguistic findings. In
particular, the structure of the domain knowledge representation
forces the system to take a bipolar approach to learning nouns and
verbs. Psycholinguistic studies demonstrate differing treatment of
nouns and verbs by children and suggest a structural basis for this
difference. The knowledge-level similarities between our system and
human linguistic knowledge make it possible to infer that children
must adopt a similar strategy to effectively learn word meanings.
%TI Psychological Evidence for Assumptions of Path-Based Inheritance Reasoning
%AU Claire Hewson
%AU Carl Vogel
%PU Proc. CogSci-94, pp. 409-414
%SC Monday, August 15, 7:30-9
%AB The psychological validity of inheritance reasoners is clarified.
Elio and Pelletier (1993) presented the first pilot experiment
exploring some of these issues. We investigate other foundational
assumptions of inheritance reasoning with defaults: transitivity,
blocking of transitivity by negative defaults, preemption in terms
of structurally defined specificity and structurally defined
redundancy of information. Responses were in accord with the
assumption of at least limited transitivity, however, reasoning with
negative information and structurally defined specificity conditions
did not support the predictions of the literature. `Preemptive'
links were found to provide additional information leading to
indeterminacy, rather than providing completely overriding
information as the literature predicts. On the other hand, results
support the structural identification of certain links as redundant.
Other findings suggest that inheritance proof-theory might be
excessively guided by its syntax.
%TI Empirical Evidence Regarding the Folk Psychological Concept of Belief
%AU Claire Hewson
%PU Proc. CogSci-94, pp. 403-408
%SC Tuesday, August 16, 11-12:30
%AB This paper presents empirical evidence regarding the nature of our
commonsense concept of belief. The findings have significant bearing
upon claims made by authors concerned with the Folk Psychology
Debate---in particular, they challenge Stephen Stich's (1983) claim
that folk psychology is committed to a *broad* account of belief
states. In contrast it is found that folk psychology favours a
*narrow* account of belief. This result is important in refuting
Stich's claim that the folk psychological concept of belief has no
role to play in a developed cognitive science. The paper also
presents evidence regarding the influence of several factors on folk
psychological judgements of belief individuation (emphasised
similarities/differences between the referents of beliefs, nature of
past beliefs, goal of classification), and introduces a methodology
by which to investigate further factors. It is argued that the
observed conflict between individual speculations about likely folk
psychological intuitions within the philosophical literature and
actual empirical data regarding subjects' responses highlights the
important contribution of experimental psychology in exploring such
philosophical issues.
%TI Abstraction of Sensory-Motor Features
%AU Kazuo Hiraki
%PU Proc. CogSci-94, pp. 415-420
%SC Monday, August 15, 2-3:30
%AB This paper presents a way that enables robots to learn abstract
concepts from sensory/perceptual data. In order to overcome the gap
between the low-level sensory data and higher-level concept
description, a method called "feature abstraction" is used. Feature
abstraction dynamically defines abstract sensors from primitive
sensory devices and makes it possible to learn appropriate
sensory-motor constraints. This method has been implemented on a
real mobile robot as a learning system called ACORN2. ACORN2 was
evaluated with some empirical results and shown that the system can
learn some abstract concepts more accurately than other existing
systems.
%TI WanderECHO: A Connectionist Simulation of Limited Coherence
%AU Christopher M. Hoadley
%AU Michael Ranney
%AU Patricia Schank
%PU Proc. CogSci-94, pp. 421-426
%SC Monday, August 15, 7:30-9
%AB The Theory of Explanatory Coherence, or TEC, (Ranney & Thagard,
1988; Thagard, 1989, 1992) and ECHO, a connectionist implementation
of TEC, attempt to model human reasoning about evidence and
hypotheses. The ECHO model is based on the simultaneous satisfaction
of multiple constraints. This yields predicted activations
("believabilities") for propositions, which are based on the
propositions' evidential status, their explanatory relationships,
and their contradictory relationships. While ECHO has been
demonstrated to usefully model human reasoning, it does not model
processing limitations on the maintenance of coherence. WanderECHO
is a variation on the ECHO model that attempts to simulate
attentional and memorial limitations with a stochastic updating
algorithm that is based on a traveling focus of attention. Several
variants of the WanderECHO simulation were applied to Schank and
Ranney's (1991) data, and were found to generally simulate subjects'
mean believability ratings better than standard ECHO.
%TI PROVERB - A System Explaining Machine-Found Proofs
%AU Xiaorong Huang
%PU Proc. CogSci-94, pp. 427-432
%SC Monday, August 15, 7:30-9
%AB This paper outlines an implemented system called PROVERB that
explains machine-found natural deduction proofs in natural language.
Different from earlier works, we pursue a reconstructive approach.
Based on the observation that natural deduction proofs are at a too
low level of abstraction compared with proofs found in mathematical
textbooks, we define first the concept of so-called assertion level
inference rules. Derivations justified by these rules can
intuitively be understood as the application of a definition or a
theorem. Then an algorithm is introduced that abstracts
machine-found ND proofs using the assertion level inference rules.
Abstracted proofs are then verbalized into natural language by a
presentation module. The most significant feature of the
presentation module is that it combines standard hierarchical text
planning and techniques that locally organize argumentative texts
based on the derivation relation under the guidance of a focus
mechanism. The behavior of the system is demonstrated with the help
of a concrete example throughout the paper.
%TI Mapping Hierarchical Structures with Synchrony for Binding: Preliminary Investigations
%AU John E. Hummel
%AU Eric R. Melz
%AU Jeff Thompson
%AU Keith J. Holyoak
%PU Proc. CogSci-94, pp. 433-438
%SC Sunday, August 14, 2-3:30
%AB Synchrony of firing has recently become a popular technique for
dynamic binding in neural networks, and has been applied to numerous
problem domains. However, hierarchical structures are difficult to
represent using synchrony for binding. This paper presents our
progress toward a framework for representing hierarchies in a neural
network using synchrony for dynamic binding. We illustrate the
approach with a model of analogical mapping. The model (IMM2) uses
synchrony to bind case roles to objects within propositions.
Hierarchies are established by allowing units representing
propositions to play a dual role, acting both as the argument of one
proposition and as a pointer to another.
%TI Lexical Disambiguation Based on Distributed Representations of Context Frequency
%AU Marshall R. Mayberry, III
%AU Risto Miikkulainen
%PU Proc. CogSci-94, pp. 601-606
%SC Monday, August 15, 11-12:30
%AB A model for lexical disambiguation is presented that is based on
combining the frequencies of past contexts of ambiguous words. The
frequencies are encoded in the word representations and define the words'
semantics. A Simple Recurrent Network (SRN) parser combines the context
frequencies one word at a time, always producing the most likely
interpretation of the current sentence at its output. This disambiguation
process is most striking when the interpretation involves semantic
flipping, that is, an alternation between two opposing meanings as more
words are read in. The sense of "throwing a ball" alternates between
"dance" and "baseball" as indicators such as the agent, location, and
recipient are input. The SRN parser demonstrates how the context
frequencies are dynamically combined to determine the interpretation of
such sentences. We hypothesize that several other aspects of ambiguity
resolution are based on similar mechanisms, and can be naturally
approached from the distributed connectionist viewpoint.
%TI The Curtate Cycloid Illusion: Cognitive Constraints on the Processing of Rolling Motion
%AU Matthew I. Isaak
%AU Marcel Adam Just
%PU Proc. CogSci-94, pp. 439-444
%SC Monday, August 15, 4-5:30
%AB When a wheel rolls along a flat surface, a point on the wheel's
perimeter follows a cycloid trajectory. Subjects, however draw the
curtate cycloid, characterized by bottom loops, rather than the cycloid
to depict the path that a point on a static wheel's perimeter would
trace if the wheel were rolling. This is the curtate cycloid illusion.
In Experiment 1, we show that animating the wheel does not dispel the
illusion and that subjects high in spatial ability are less susceptible
to the illusion than are low-spatials. Experiments 2, 3a, and 3b
supported the hypothesis that the illusion occurs when subjects
reallocate cognitive resources from processing a rolling wheel's
translation to computing its instant centers, the point about which the
wheel is rotating at a given instant in time. This reallocation occurs
only when a reference point on the wheel's perimeter contacts and leaves
the surface. We conclude that the illusion does not reflect fundamental
perceptual biases, but rather stems from transient shortages of
cognitive resources during the higher-level processing of the wheel's
translation and rotation.
%TI Direct and Indirect Measures of Implicit learning
%AU Luis Jimenez
%AU Axel Cleeremans
%PU Proc. CogSci-94, pp. 445-450
%SC Monday, August 15, 2-3:30
%AB Comparing the relative sensitivity of direct and indirect measures
of learning is proposed as the best way to provide evidence for
unconscious learning when both conceptual and operative definitions
of awareness are lacking. This approach was first proposed by
Reingold & Merikle (1988) in the context of subliminal perception.
In this paper, we apply it to a choice reaction task in which the
material is generated based on a probabilistic finite-state grammar
(Cleeremans, 1993). We show (1) that subjects progressively learn
about the statistical structure of the stimulus material over
training with the choice reaction task, and (2) that they can use
some of this knowledge to predict the location of the next stimulus
in a subsequent explicit prediction task. However, detailed partial
correlational analyses of the correspondence between CRT performance
and the conditional probabilities of each stimulus showed that large
effects remained even when controlling for explicit knowlede as
assessed by the prediction task. Hence we conclude (1) that at least
some of the knowledge expressed in CRT performance can not be
characterized as conscious, and (2) that even when associations are
found at a global level of analysis, dissociations can still be
obtained when more detailed analyses are conducted. Finally, we also
show that subjects are limited in the depth of the contingencies
they can learn about, and that these limitations are shared by the
Simple Recurrent Network model of Cleeremans & McClelland (1991).
%TI Computational Simulation of Depth Perception in the Mammalian Visual System
%AU Jesse S. Jin
%PU Proc. CogSci-94, pp. 451-456
%SC Monday, August 15, 4-5:30
%AB This paper presents a computational model for stereopsis. Laplacian
of Gaussian filters are used to simulate ganglion cells and LGN
cells and zero-crossings extracted provide spatial features in the
visual scene. A set of one-octave Gabor filters is used to extract
orientation information, which cover 0 to 60 cycles/degree interval
in the human visual system. A Gaussian sphere model is used to map
a 3D space onto two 2D image planes, which combines monocular cues
with binocular cues in stereo matching. The determinant of the
Jacobian of the mapping is derived and matching is performed using
zero-crossings associated with their orientation information. The
possibility of transferring the knowledge such as the probability of
occurrence of visual scenes to the matching process from the mapping
is discussed. Relaxation labelling is used as a co-operative
process, which simulates binocular fusion and rivalry in the human
visual process.
%TI Bottom-up recognition learning: A compilation based model of limited-lookahead learning
%AU Todd R. Johnson
%AU Jiajie Zhang
%AU Hongbin Wang
%PU Proc. CogSci-94, pp. 469-474
%SC Monday, August 15, 7:30-9
%AB When faced with a novel problem, people can sometimes decide what to
do by imagining alternative sequences of actions and then taking the
sequence that solves the problem. In many problems, however, various
constraints, such as working memory capacity, limit the amount of
internal lookahead that people can do. This paper describes
Bottom-Up Recognition Learning (BURL), a model of limited-lookahead
learning based on final first learning and knowledge compilation. In
BURL, knowledge compilation of limited-lookahead search over
successive problem-solving trials transfers knowledge from the leaf
nodes of a problem space to the top node. Two experiments test
BURL's predictions. The first compares the Soar implementation of
BURL to human subjects learning to play two Tic-Tac-Toe isomorphs.
This experiment shows that BURL can account for learning that occurs
when subjects can perform a limited lookahead. The second experiment
studies transfer between two strategy acquisition tasks for one
isomorph. This experiment shows that BURL must be used in
conjunction with other learning methods to fully explain skill
acquisition on limited-lookahead tasks.
%TI A computational model of human abductive skill and its acquisition
%AU Todd R. Johnson
%AU Josef Krems
%AU Nasir K. Amra
%PU Proc. CogSci-94, pp. 463-468
%SC Monday, August 15, 7:30-9
%AB Abduction is the process of constructing a plausible explanation for
a set of observations. It is the fundamental type of reasoning in
many complex tasks such as scientific discovery and diagnosis. This
paper presents a mental-model theory of human abductive skill and
its acquisition in which abduction is viewed as the sequential
comprehension and integration of data into a single situation model.
Comprehension and integration are accomplished using satisficing
search of multiple problem spaces. The model has been implemented in
Soar and has been tested by comparing its predictions to those of
human subjects. The experimental results show that the model can
account for several important behavioral regularities, including
power-law speed-up, how the order of data presentation affects a
response, deviation of responses from probability theory, and how
the task and domain characteristics affect a person's response.
%TI Adaptive learning of Gaussian categories leads to decision bounds and response surfaces incompatible with optimal decision making
%AU Michael Kalish
%PU Proc. CogSci-94, pp. 479-484
%SC Monday, August 15, 7:30-9
%AB Two experiments in category learning are used to examine two types
of categorization models. In both a two and four choice experiment,
subjects are shown to fail to learn to optimally classify two
dimensional stimuli. The general recognition theory (GRT) of Ashby
& Maddox (1990) predicts quadratic decision bounds. The first
experiment disconfirms this. The extended GRT predicts that
learners adopt a bound of complexity equivalent to the optimal one.
The second experiment disconfirms this as well. Both experiments
support the idea that general resources of adaptive systems can
provide explanations of observed sub-optimal behavior.
%TI Coping with the Complexity of Design: Avoiding Conflicts and Prioritizing Constraints
%AU Irvin R. Katz
%PU Proc. CogSci-94, pp. 485-489
%SC Monday, August 15, 7:30-9
%AB Design is a complex cognitive task that pushes the limits of human
information processing. How do expert designers handle this
complexity? Professional and student architects solved a real-world
diagram construction task that required satisfying multiple,
sometimes conflicting, constraints to achieve an acceptable design.
Professionals' initial designs were more consistent with task
constraints and remained more consistent throughout problem
solution. Students restructured their designs more often in their
unsuccessful attempts to satisfy the multiple constraints imposed by
the task. Analysis of subjects' verbal and action protocols
suggests that one aspect of professionals' superior performance is
their early recogni-tion of the critical constraints on a design.
Professionals handle these constraints before others to structure
the remaining, more negotiable, constraints. By properly ordering
constraints, professionals effectively minimize constraint
conflicts. As conflict resolution has high processing costs,
constraint prioritization may be one way that professionals cope
with the complexity of design.
%TI Adaptation as a Selection Constraint on Analogical Mapping
%AU Mark T. Keane
%PU Proc. CogSci-94, pp. 490-495
%SC Sunday, August 14, 2-3:30
%AB In any given analogy, there are potentially a large number of
possible mapping interpretations. One of the key issues in analogy
research is how one of these mappings comes to be selected as
optimal and used as the basis for the analogical comparison. It is
well-established that structural factors, notably systematicity, can
act as selection constraints on mapping. The present work tests if
pragmatic and adaptation factors can also act as selection
constraints on mapping. The selection of a mapping based on
pragmatic factors proposes that people can exploit the higher-order
schematic structure of a domain to select one mapping over another.
With respect to adaptation factors, the proposal is that a mapping
will be selected if it is evaluated as being more adaptable than
other competing mappings. Both of these predictions are tested in a
novel, problem solving paradigm. The main finding is that
adaptation factors act as a selection constraint but that pragmatic
factors do not. The implications of these results for computational
models of analogy are discussed.
%TI Semantics and Pragmatics of Vague Probability Expressions
%AU Bernhard Kipper
%AU Anthony Jameson
%PU Proc. CogSci-94, pp. 496-501
%SC Monday, August 15, 7:30-9
%AB Two experiments assessed the membership functions that German speakers
assign to 12 adverb phrases and 17 modal verb forms that express
probability assessments. These expressions fall largely into three rather
homogeneous classes. The membership functions are used as part of the
semantic knowledge base of the natural language dialog system PRACMA, one
of whose purposes is to model pragmatic and contextual influences on the
use of vague expressions. The system's normative model accounts for the
role, in the selection and interpretation of vague probability
expressions, of the listener's prior expectations, the speaker's dialog
motivation, and the expressions that the speaker could have used but did
not.
%TI Immediate Effects of Discourse and Semantic Context in Syntactic Processing: Evidence from Eye-Tracking
%AU Michael Spivey-Knowlton
%AU Michael Tanenhaus
%PU Proc. CogSci-94, pp. 812-817
%SC Tuesday, August 16, 11-12:30
%AB We monitored readers' eye-movements to examine the time-course of
discourse and semantic influences in syntactic ambiguity resolution.
Our results indicate immediate and simultaneous influences of
referential context and local semantic fit in the reading of reduced
relative clauses (i.e., The horse raced past the barn fell.). These
results support a model of sentence processing in which alternatives
of a syntactic ambiguity are differentially activated by the
bottom-up input, and syntactically-relevant contextual constraints
simultaneously add activation to their supported alternatives.
Competition between comparably active alternatives may then cause
slowed reading times in regions of ambiguity.
%TI The Context-Sensitive Cognitive Architecture DUAL
%AU Boicho Kokinov
%PU Proc. CogSci-94, pp. 502-507
%SC Monday, August 15, 7:30-9
%AB Context-sensitivity is an important characteristic feature of every
cognitive process and therefore should be reflected in every
architecture pretending to explain human cognition. In this paper
some experimental facts demonstrating context effects on various
cognitive processes are reviewed and an attempt at context modeling
is described. A hybrid (symbolic/connectionist) cognitive
architecture, DUAL, is proposed. It consists of a multitude of
agents having both a symbolic and a connectionist part. The symbolic
part represents some knowledge structure, while the connectionist
part represents its relevance to the current context. The
performance of the cognitive system emerges as result of the work
and interaction of the currently active agents, where the set of
active agents is not predefined for a specific task but is dynamic
and reflects the specific context. So particular symbolic operations
and data structures may be supported or suppressed depending on the
particular activation pattern of the connectionist parts which
represent the context-dependent relevance of the operations and
structures. In this way a context-sensitive computation emerges. An
example of context-sensitive deductive reasoning is described.
%TI Learning of rules that have high-frequency exceptions: New empirical data and a hybrid connectionist model
%AU John K. Kruschke
%AU Michael E. Erickson
%PU Proc. CogSci-94, pp. 514-519
%SC Sunday, August 14, 11-12:30
%AB Theorists of human learning, in domains as various as category
learning and language acquisition, have grappled with the issue of
whether learners induce rules or remember exemplars, or both. In
this article we present new data that reflect both rule induction
and exemplar encoding, and we present a new connectionist model that
specifies one way in which rule-based and exemplar-based mechanisms
might interact. Our empirical study was motivated by analogy to past
tense acquisition, and specifically by the previous work of Palermo
& Howe (1970). Human subjects learned to categorize items, most of
which could be classified by a simple rule, except for a few
frequently recurring exceptions. The modeling was motivated by the
idea of combining an exemplar-based module (ALCOVE, Kruschke 1992)
and a rule-based module in a connectionist architecture, and
allowing the system to learn which module should be responsible for
which instances, using the competitive gating mechanism introduced
by Jacobs, Jordan, Hinton & Nowlan (1991). We report quantitative
fits of the model to the learning data.
%TI Recurrent Natural Language Parsing
%AU Stan C. Kwasny
%AU Sahnny Johnson
%AU Barry L. Kalman
%PU Proc. CogSci-94, pp. 525-530
%SC Monday, August 15, 7:30-9
%AB A recurrent network was trained from sentence examples to construct
symbolic parses of sentence forms. Hundreds of sentences,
representing significant syntactic complexity, were formulated and
then divided into training and testing sets to evaluate the ability
of a recurrent network to learn their structure. The network is
shown to generalize well over test sentences and the errors that do
remain are found to be of a single type and related to human
limitations of sentence processing.
%TI When 'Or' Means 'And': A Study in Mental models
%AU Philip N. Johnson-Laird
%AU Patricia E. Barres
%PU Proc. CogSci-94, pp. 475-478
%SC Monday, August 15, 4-5:30
%AB We describe an algorithm that constructs mental models of assertions
containing sentential connectives, such as and, if, and or. It
performs at three levels of expertise depending on the completeness
of the models it constructs. At a rudimentary level of performance,
it constructs models that make explicit as little as possible. One
unexpected consequence is that it produces the same explicit models
for assertions of the form:
if p then q, and if r then s
if p then q, or if r then s
p and q, or r and s.
We initially suspected that there was a bug in the algorithm (or
theory), but there was not. We therefore carried out two
experiments with logically-untrained subjects. Their results
confirmed the phenomena: for many individuals, a conjunction of
conditionals is equivalent to their disjunction, which in turn is
equivalent to a disjunction of conjunctions.
%TI Levels of Semantic Constraint and Learning Novel Words
%AU James M. Lampinen
%AU Jeremiah M. Faries
%PU Proc. CogSci-94, pp. 531-536
%SC Monday, August 15, 4-5:30
%AB A common method of teaching vocabulary involves presenting students
with new words in context and having the students derive the meaning
of these words based on contextual cues. Beck, McKeown and McCaslin
(1983) have argued that the contexts used to teach new words should
be highly constraining. Although highly constraining contexts avoid
ambiguity they do not present the learner with the necessity of
combining contextual and word specific information and thus
practicing skills needed for general comprehension. We suggest that
a superior method of teaching is to relax the amount of contextual
constraint because to optimize the learning from the presentation of
a sentence the student must use both top down and bottom up
processes to discover the meaning of the sentence, thus integrating
two sources of knowledge about the word. The present research
compares knowledge and use of newly learned words between students
who learned the new words using three encounters with highly
constraining contexts, three encounters with moderately constraining
contexts or three progressively less constraining contexts.
Students were given definitional and comprehension tests both
immediately after study and at a one week delay. The results
suggest that repeated encounters with moderately constraining
contexts are superior to repeated encounters with highly
constraining contexts.
%TI Models of Metrical Structure in Music
%AU Edward W. Large
%PU Proc. CogSci-94, pp. 537-542
%SC Monday, August 15, 11-12:30
%AB Recent models of metrical structure in music rely upon notions of
oscillation and synchronization. Such resonance models treat the
perception of metrical structure as a dynamic process in which the
temporal organization of musical events synchronizes, or entrains, a
listener's internal processing mechanisms. The entrainment of a
network of oscillators to an afferent rhythmic pattern models the
perception of metrical structure. In this paper, I compare one
resonance model with several previously proposed models of meter
perception. Although the resonance model is consistent with previous
models in a number of ways, mathematical analysis reveals properties
that are quite distinct from properties of the previously proposed
models.
%TI Simulating Similarity-Based Retrieval: A Comparison of ARCS and MAC/FAC
%AU Keith Law
%AU Kenneth D. Forbus
%AU Dedre Gentner
%PU Proc. CogSci-94, pp. 543-548
%SC Sunday, August 14, 2-3:30
%AB Current theories and supporting simulations of similarity-based
retrieval disagree in their process model of semantic similarity
decisions. We compare two current computational simulations of
similarity-based retrieval, MAC/FAC and ARCS, with particular
attention to the semantic similarity models used in each. Four
experiments are presented comparing the performance of these
simulations on a common set of representations. The results suggest
that MAC/FAC, with its identicality-based constraint on semantic
similarity, provides a better account of retrieval than ARCS, with
its similarity-table based model
%TI Towards A Computer Model of Memory Search Strategy Learning
%AU David B. Leake
%PU Proc. CogSci-94, pp. 549-554
%SC Tuesday, August 16, 11-12:30
%AB Much recent research on modeling memory processes has focused on
identifying useful indices and retrieval strategies to support
particular memory tasks. Another important question concerning
memory processes, however, is how retrieval criteria are learned.
This paper examines the issues involved in modeling the learning of
memory search strategies. It discusses the general requirements for
appropriate strategy learning and presents a model of memory search
strategy learning applied to the problem of retrieving relevant
information for adapting cases in case-based reasoning. It
discusses an implementation of that model, and, based on the lessons
learned from that implementation, points towards issues and
directions in refining the model.
%TI Error Modeling in the ACT-R Production System
%AU Christian Lebiere
%AU John R. Anderson
%AU Lynne M. Reder
%PU Proc. CogSci-94, pp. 555-559
%SC Monday, August 15, 7:30-9
%AB We describe how to extend the ACT-R production system to model human
errors in the performance of a high-level cognitive task: to solve
simple linear algebra problems while memorizing a digit span.
Errors of omission are produced by introducing a cutoff on the
latency of memory retrievals. If a memory chunk cannot gather
enough activation to be retrieved before the threshold is reached,
retrieval fails. Adding Gaussian noise to chunk activation produces
a pattern quantitatively similar to subject errors. Errors of
commission are introduced by allowing imperfect matching in the
condition side of productions. The wrong memory chunk can be
retrieved if its activation is large enough to allow it to overcome
the mismatch penalty. This mechanism provides a qualitative and
quantitative fit to subject errors. In conclusion, this paper
demonstrates that human-like errors, sometimes thought of as the
exclusive domain of connectionist models, can be successfully
duplicated in production system models.
%TI Priming, Perceptual Reversal, and Circular Reaction in a Neural Network Model of Schema-Based Vision
%AU Wee Kheng Leow
%AU Risto Miikkulainen
%PU Proc. CogSci-94, pp. 560-565
%SC Monday, August 15, 7:30-9
%AB VISOR is a neural network system for object recognition and scene
analysis that learns visual schemas from examples. Processing in VISOR is
based on cooperation, competition, and parallel bottom-up and top-down
activation of schema representations. Similar principles appear to
underlie much of human visual processing, and VISOR can therefore be used
to model various perceptual phenomena. This paper focuses on analyzing
three phenomena through simulation with VISOR: (1) priming and mental
imagery, (2) perceptual reversal, and (3) circular reaction. The results
illustrate similarity and subtle differences between the mechanisms
mediating priming and mental imagery, show how the two opposing accounts
of perceptual reversal (neural satiation and cognitive factors) may both
contribute to the phenomenon, and demonstrate how intentional actions can
be gradually learned from reflex actions. Successful simulation of such
effects suggests that similar mechanisms may govern human visual
perception and learning of visual schemas.
%TI Understanding Diagrammatic Demonstrations
%AU Robert K. Lindsay
%PU Proc. CogSci-94, pp. 572-576
%SC Sunday, August 14, 4-5:30
%AB In this paper I examine the question of how a diagrammatic
demonstration (a "proof without words") could be understood by a
computational model. The computational model (a) has a means of
representing geometric diagrams composed exclusively of points, line
segments, triangles, and quadrilaterals, including the special cases
of parallelograms, rhombuses, rectangles, and squares; (b) accepts
step-by-step descriptions of specific diagrams, and constructs in
computer memory a representation of the diagram as it is described;
(c) includes the ability to make modifications to the diagram by
construction steps that specify movement of previously constructed
components; (d) after each construction step notices any new objects
(line segments, triangles, etc.) that are created by the step; (e)
accepts a goal statement that the construction sequence is allegedly
demonstrating; and (f) attempts to find a justification that
confirms the goal statement.
%TI Predicting Irregular Past Tenses: Comparing Symbolic and Connectionist Models Against Native English Speakers
%AU Charles X. Ling
%PU Proc. CogSci-94, pp. 577-582
%SC Monday, August 15, 4-5:30
%AB Learning the past tense of English verbs has become a landmark
task for testing the adequacy of cognitive modeling.
We review a set of intriguing psychological phenomena
that any modeling of past-tense acquisition has to account for.
Traditional grammatical theories fail to explain phenomena
of irregular verbs, while connectionist models, which require no
symbols and explicit rules, fail on regular verbs.
We present a general-purpose symbolic pattern associator (SPA)
which learns a set of sufficient and necessary symbolic rules for
both distinguishing and predicting regular and irregular verbs.
Our all-rule theory is similar in spirit to Pinker's (1991, 1993)
modular hypothesis, and is able to account for most
psychological phenomena in the past-tense acquisition.
Even for the task of irregular past-tense generalization,
the SPA is judged to be more psychologically plausible than
the connectionist model by adult native English speakers.
Our results support the view that language acquisition and processing
should be better modeled by symbolic, rather than connectionist, systems.
%TI Distributed Meeting Scheduling
%AU JyiShane Liu
%AU Katia Sycara
%PU Proc. CogSci-94, pp. 583-588
%SC Sunday, August 14, 2-3:30
%AB Meeting scheduling takes place when a group of people intend to meet
with each other. Since each person has individual availability
constraints and preferences, meeting scheduling is naturally
distributed and there is a need to schedule the meeting in such a
way as to consider the preferences of the set of meeting
participants. In addition, individual meeting constraints and
preferences may change both as a result of an agent's situation or
as a result of other agents' scheduling decisions. Therefore, there
is a need for distributed reactive schedule revision in response to
changing requirements and constraints. We present an approach to
distributed meeting scheduling based on modeling and communication
of constraints and preferences among the agents. When a feasible
global schedule cannot be found, agents enter a negotiation and
relax their constraints. The approach enables the agents to find and
reach agreement on the schedule with the highest joint utility and
to reactively revise the schedule in response to new information.
%TI Uniform Representations for Syntax-Semantics Arbitration
%AU Kavi Mahesh
%AU Kurt P. Eiselt
%PU Proc. CogSci-94, pp. 589-594
%SC Monday, August 15, 11-12:30
%AB Psychological investigations have led to considerable insight into the
working of the human language comprehension system. In this article, we
look at a set of principles derived from psychological findings to argue
for a particular organization of linguistic knowledge along with a
particular processing strategy and present a computational model of
sentence processing based on those principles. Many studies have shown
that human sentence comprehension is an incremental and interactive
process in which semantic and other higher-level information interacts
with syntactic information to make informed commitments as early as
possible at a local ambiguity. Early commitments may be made by using
top-down guidance from knowledge of different types, each of which must
be applicable independently of others. Further evidence from studies of
error recovery and delayed decisions points toward an arbitration
mechanism for combining syntactic and semantic information in resolving
ambiguities. In order to account for all of the above, we propose that
all types of linguistic knowledge must be represented in a common form
but must be separable so that they can be applied independently of each
other and integrated at processing time by the arbitrator. We present
such a uniform representation and a computational model called COMPERE
based on the representation and the processing strategy.
%TI Acoustic-based syllabic representation and articulatory gesture detection: Prerequisites for early childhood phonetic and articulatory development
%AU Kevin L. Markey
%PU Proc. CogSci-94, pp. 595-600
%SC Sunday, August 14, 11-12:30
%AB We describe the perceptual foundations of a sensorimotor model of
early childhood phonetic and articulatory development. The model's
auditory perception is sensitive to prosodic and syllabic structure
and simulates the categorical phonetic perception of late infancy.
Importantly, the model relies on exclusively acoustic cues and their
statistical distribution in the linguistic environment, avoiding
prior assumptions of articulatory-acoustic correlations or
linguistic contrasts which are inappropriate for a model of
perceptual development. The model detects and categorizes speech
segments, which, despite their acoustic basis, correlate with
linguistic events and articulatory gestures. The resulting
representation supports not only word recognition but also the
unique demands of articulatory motor control and its development.
In simulations examining the distinctiveness and faithfulness of the
representation, we find that it preserves and makes explicit
information about the phonetic properties of the acoustic signal.
%TI Time as Phase: A Dynamic Model of Time Perception
%AU J. Devin McAuley
%PU Proc. CogSci-94, pp. 607-612
%SC Monday, August 15, 11-12:30
%AB In this paper, a dynamic model of human time perception is presented
which treats time as phase, relative to the period of an oscillator
that adapts its oscillation rate in response to an input rhythm.
The adaptive oscillator mechanism is characterized by four
fundamental properties: (1) a preferred oscillation rate which
captures the notion of a preferred tempo, (2) a fast-acting
synchronization procedure which models our ability to perceptually
lock onto salient aspects of a rhythm, (3) a decay process to oppose
synchronization, and (4) a drift process which causes the preferred
rate to gradually drift towards the adapted rate, thereby modeling
the context effects of long-term pattern exposure. By assuming that
sensitivity to duration is a function of oscillator entrainment to
the contextual rhythm, the model provides a qualitative match to
data on tempo discrimination, and predicts the types of errors
subjects would make on such tasks. These predictions are in
agreement with data showing that subjects overestimate short
intervals and underestimate long intervals.
%TI Letter Perception: Toward a conceptual approach
%AU Gary McGraw
%AU John Rehling
%AU Rob Goldstone
%PU Proc. CogSci-94, pp. 613-618
%SC Monday, August 15, 11-12:30
%AB We present the results of a simple experiment in lowercase letter
recognition. Unlike most psychology studies of letter recognition,
we include in our data set letters at the extremes of their
categories and investigate the recognition of letters of multiple
typefaces. We are interested in the relationship between the
recognition of normal letters and the recognition of non-standard
letters. Results provide empirical evidence for top-down conceptual
constraints on letter perception in the form of roles and relations
between perceptually-based structural subcomponents. A process
model based on the hypothesis developed below is currently being
implemented.
%TI Towards a New Model of Phonological Encoding
%AU Drs. Paul J. A. Meijer
%PU Proc. CogSci-94, pp. 619-623
%SC Sunday, August 14, 11-12:30
%AB The sound-form generation of a word in speech production involves
the retrieval of segmental and suprasegmental information from the
mental lexicon. A translation task experiment showed that the
naming latencies of target items can be reduced when prime words are
presented that have the same placement of the lexical stress as the
target. However, this reduction will only occur when primes and
targets have the same word onset. A second experiment showed that
primes that have the same number of segments as the targets will
cause naming facilitation compared to primes that have different
numbers of segments. I have developed a new model of phonological
encoding that incorporates ordered selection of the various
elements. Lexical stress is chosen first, followed by information
about the number of slots, the word onset, the second segment, and
the other segments, until all segments have been selected. The
model further employs mechanisms that allow for the retrieval of the
initial segment to influence the retrieval of lexical stress.
Various simulations show that the model can replicate the findings
of the two experiments. Other models of phonological encoding
largely neglect suprasegmental retrieval and cannot explain these
results.
%TI How Mathematicians Prove Theorems
%AU Erica Melis
%PU Proc. CogSci-94, pp. 624-628
%SC Sunday, August 14, 11-12:30
%AB This paper analyzes how mathematicians prove theorems. The analysis
is based upon several empirical sources such as reports of
mathematicians and mathematical proofs by analogy. In order to
combine the strength of traditional automated theorem provers with
human-like capabilities, the questions arise: Which problem solving
strategies are appropriate? Which representations have to be
employed? As a result of our analysis, the following reasoning
strategies are recognized: proof planning with partially
instantiated methods, structuring of proofs, the transfer of
subproofs and of reformulated subproofs. We discuss the
representation of a component of these reasoning strategies, as well
as its properties. We find some mechanisms needed for theorem
proving by analogy, that are not provided by previous approaches to
analogy. This leads us to a computational representation of new
components and procedures for automated theorem proving systems.
%TI Scaffolding Effective Problem Solving Strategies in Interactive Learning Environments
%AU Douglas C. Merrill
%AU Brian J. Reiser
%PU Proc. CogSci-94, pp. 629-634
%SC Sunday, August 14, 4-5:30
%AB Novices often experience great difficulty learning new domains.
Thus, understanding how best to scaffold novice problem solving has
potentially tremendous importance for learning in formal domains.
In this paper, we present results from an experimental study that
compared learning outcomes of students solving introductory
programming problems in three different learning environments. This
range of environments varies in two ways. First, the notations used
in the environments vary between diagrammatic and textual. More
importantly, the environments differ in the cognitive activities
students are led to perform while solving problems, such as
prediction of intermediate results and noting future goals to
achieve. This experiment demonstrated that environments that
scaffold more of the important cognitive activities lead to superior
performance, regardless of whether the environments are textual or
diagrammatic.
%TI Modeling Inter-Category Typicality within a Symbolic Search Framework
%AU Craig S. Miller
%PU Proc. CogSci-94, pp. 635-639
%SC Sunday, August 14, 11-12:30
%AB This paper addresses category typicality in the context of a
category naming task. In contrast to the predominant effort with
gradient models, a symbolic search framework is taken. Within this
framework, the SCA (Symbolic Concept Acquisition) model demonstrates
varying response times as a function of an instance's intra-category
typicality. Here its coverage is expanded to inter-category
typicality. A functionally motivated extension for SCA is advanced
that pursues search backtracking under ambiguous cases. I explain
how the backtracking extension accounts for inter-category
typicality effects, and support it with some empirical evidence. I
discuss how the effect generalizes to a larger class of symbolic
search models.
%TI Mental models for proportional reasoning
%AU Joyce L. Moore
%AU Daniel L. Schwartz
%PU Proc. CogSci-94, pp. 640-645
%SC Monday, August 15, 4-5:30
%AB Three studies investigated the role of perceptual and quantitative
situational factors on the structure of 5th- and 6th-graders' mental
models. A task involved a carton of orange juice made from
concentrate and water, and two glasses of different sizes filled
from the carton. The children had to predict whether the two
glasses would taste the same. We manipulated whether students were
presented with physical, diagrammatic, photographic, or textual
information. We also manipulated the type of relationship specified
between quantities: qualitative, easy numerical, or difficult
numerical. We found that for the diagram condition, difficult
numerical relationships yielded poor performance, whereas the easy
numerical and qualitative relationships yielded excellent
performance. In contrast, in the physical condition, the easy
numerical relationships yielded poor performance, whereas the
difficult numerical and qualitative relationships yielded excellent
performance. These and other results are interpreted by developing
a sketch of the mental models pre-proportional children construct to
reason about this quantitative situation, and describing how
situational factors influence the construction of the models. For
example, physical features led to models that captured the identity
relationship between the juice in the glasses (e.g., the juice came
from the same carton) whereas numerical features led to models that
captured the relationship between the constituents of concentrate
and water in each glass (e.g., within a glass there is more water
than concentrate).
%TI Integrating Creativity and Reading: A Functional Approach
%AU Kenneth Moorman
%AU Ashwin Ram
%PU Proc. CogSci-94, pp. 646-651
%SC Sunday, August 14, 4-5:30
%AB Reading has been studied for decades by a variety of cognitive
disciplines, yet no theories exist which sufficiently describe and
explain how people accomplish the complete task of reading
real-world texts. In particular, a type of knowledge intensive
reading known as creative reading has been largely ignored by the
past research. We argue that creative reading is an aspect of
practically all reading experiences; as a result, any theory which
overlooks this will be insufficient. We have built on results from
psychology, artificial intelligence, and education in order to
produce a functional theory of the complete reading process. The
overall framework describes the set of tasks necessary for reading
to be performed. Within this framework, we have developed a theory
of creative reading. The theory is implemented in the ISAAC
(Integrated Story Analysis And Creativity) system, a reading system
which reads science fiction stories.
%TI A Study of Diagrammatic Reasoning from Verbal and Gestural Data
%AU N. Hari Narayanan
%AU Masaki Suwa
%AU Hiroshi Motoda
%PU Proc. CogSci-94, pp. 652-657
%SC Tuesday, August 16, 11-12:30
%AB This paper reports on an exploratory study of diagrammatic
reasoning. Concurrent think-aloud protocols and gestures of
subjects solving a set of device behavior hypothesis problems
presented as labeled diagrams were collected. In addition to
analyzing verbal protocols, the gestures and marks made by the
subjects were examined and used to annotate encoded verbal data. A
model of diagrammatic reasoning in this task is proposed and
compared with results of analyzing the protocols. Besides lending
support to results of previous experimental studies, this study also
revealed some interesting aspects of diagrammatic reasoning that
merit further investigation.
%TI Integrating Cognitive Capabilities in a Real-Time Task
%AU Greg Nelson
%AU Jill Fain Lehman
%AU Bonnie E. John
%PU Proc. CogSci-94, pp. 658-663
%SC Sunday, August 14, 4-5:30
%AB NTD-Soar is a model of the perceptual, cognitive, and motor actions
performed by the NASA Test Director as he utilizes the materials in
his surroundings and communicates with others to prepare for a Space
Shuttle Launch. The model, built within the framework of a serial
symbolic architecture, is based on a number of independently
designed general cognitive capabilities as well as a cognitive
analysis of a particular task. This paper presents a detailed
description of the model and an assessment of its performance when
compared to human data. NTD-Soar's ability to display human-like
real-time performance demonstrates that symbolic models with a
serial bottleneck can account for complex behaviors which appear to
happen in parallel, simply by opportunistically interleaving small
elements of the different subtasks.
%TI Can Connectionist Models Exhibit Non-Classical Structure Sensitivity?
%AU Lars Niklasson
%AU Tim van Gelder
%PU Proc. CogSci-94, pp. 664-669
%SC Sunday, August 14, 2-3:30
%AB Several connectionist models have been supplying non-classical
explanations to the challenge of explaining systematicity, i.e.,
structure sensitive processes, without merely being implementations
of classical architectures. However, lately the challenge has been
extended to include learning related issues. It has been claimed
that when these issues are taken into account, only a restricted
form of systematicity could be claimed by the connectionist models
put forward so far. In this paper we investigate this issue further,
and supply a model and results that satisfies even the revised
challenge.
%TI Cognitive Development and Infinity in the Small: Paradoxes and Consensus
%AU Rafael Nunez
%PU Proc. CogSci-94, pp. 670-674
%SC Monday, August 15, 7:30-9
%AB Throughout history the concept of infinity has played an important
role in almost every branch of human knowledge. Paradoxically, very
little effort has been made by the various theoretical schools in
Cognitive Science to study this fascinating aspect of human mental
activity. The study of subdivision offers an interesting subject
matter to address the question of how the idea of infinity in the
small emerge in our minds. 32 students, aged 8, 10, 12 and 14 (high
and low intellectual*academic performers), participated in this
study, in which a version of one of Zeno's paradoxes was analyzed by
means of individual interviews. Results suggest that between ages 10
and 12, a certain intuition of the entailments of subdivision
emerges, remaining very labile afterwards and being very influenced
by the context. 66% of the 12- and 14-year-old children said that
the process involved in the paradox comes to an end. Less than 25%
considered (with deep hesitations) the possibility that the process
might continue endlessly. This suggests that the classic piagetian
view that the indefinite subdivision is mastered at the period of
formal operations must be reassessed. Some epistemological
consequences based on an embodied- cognition oriented perspective
are discussed.
%TI Changing the Viewpoint: Re-Indexing by Introspective Questioning
%AU Ruediger Oehlmann
%AU Pete Edwards
%AU Derek Sleeman
%PU Proc. CogSci-94, pp. 675-680
%SC Monday, August 15, 2-3:30
%AB Various cognitive and computational models have addressed the use of
previous experience to understand a new domain. In particular,
research in case-based reasoning has explored the ideas of
retrieving and adapting previous experience in the form of cases,
which can only be retrieved when they are appropriately indexed. In
contrast to learning new indexes, re-indexing of existing cases has
received little attention. The need for re-indexing a case arises
when a previous situation has been incorrectly or incompletely
understood. We describe a novel approach to re-indexing which
integrates results from two different areas: multiple viewpoints
used in intelligent tutoring systems and introspective questioning
used in metacognitive activities. Furthermore, we apply ideas from
Case-Based Reasoning to the re-indexing process itself. The revised
index can be tested by active interaction with the agent's
environment. An example of our implementation, IULIAN, will
illustrate the re-indexing process.
%TI The Power of Negative Thinking: The Central Role of Modus Tollens in Human Cognition
%AU Stellan Ohlsson
%AU Nina Robin
%PU Proc. CogSci-94, pp. 681-686
%SC Sunday, August 14, 11-12:30
%AB Thinking is governed by abstract schemas. Verbal protocols
illustrate spontaneous use, by logically unsophisticated subjects,
of the schema known as modus tollens. The tollens inference schema
appeared embedded within two reasoning strategies, the classical
reductio ad absurdum and reasoning by elimination. The psychological
reality of modus tollens is implicitly assumed by many theories in
cognitive science and the hypothesis that it is a basic component of
human cognition cannot be dismissed.
%TI Similarity by feature creation: Reexamination of the asymmetry of similarity
%AU Hitoshi Ohnishi
%AU Hiroaki Suzuki
%AU Kazuo Shigemasu
%PU Proc. CogSci-94, pp. 687-692
%SC Sunday, August 14, 2-3:30
%AB We developed a computational model of similarity judgment in
problem-solving contexts. The model first attempts to transform an
object to another using the knowledge of the domain, the strategy,
and the goal. If the transformation succeeds, new feature about
transformability is created. A similarity of an object to another is
computed, based on the created features. If the model fails to
create a new feature, it computes a similarity by feature comparison
in the same way as the contrast model. An important prediction of
the model is that the asymmetry of similarity judgments is caused by
the directionality of the problem-solving skills. We examined the
model's prediction. The material was the Tower of Hanoi puzzle.
Subjects were required to rate the similarities of one state to the
goal as well as those of the goal to a state. In Experiment 1, we
taught one group of subjects the `move-pattern strategy' that
induced learners to acquire highly directional skills, and compared
their judgments with those by naive subjects. The asymmetry was
observed only in the judgments by the trained subjects. The second
experiment showed that the results of the experiment 1 could not be
attributed to the `prototypicality' of the goal.
%TI A connectionist account of Global Precedence: Theory and data
%AU Elizabeth M. Olds
%PU Proc. CogSci-94, pp. 693-698
%SC Monday, August 15, 11-12:30
%AB A connectionist model was developed to investigate the relationship
between global and local information in visual perception, and an
experiment tested a prediction generated by the model. The research
focused on the fact that processing of global information is found
to dominate processing of local information in many tasks ("global
precedence"). The connectionist model demonstrated that global
precedence can arise out of simple parallel processing. The
experiment demonstrated that rotating global elements eliminates
Global Precedence. This empirical result supports the possibility,
raised by the model, that Global Precedence is due in part to
simplicity of input-output mapping.
%TI Modeling the Use of Frequency and Contextual Biases in Sentence Processing
%AU Neal J. Pearlmutter
%AU Kim G. Daugherty
%AU Maryellen C. MacDonald
%AU Mark S. Seidenberg
%PU Proc. CogSci-94, pp. 699-704
%SC Tuesday, August 16, 11-12:30
%AB MacDonald, Pearlmutter, and Seidenberg (1993) propose an alternative
to the dominant view in sentence processing that syntactic
ambiguities are resolved by heuristics based on structural
simplicity. MacDonald et al. argue that such ambiguities can be
defined in terms of alternatives associated with information in
individual lexical items, and thus that syntactic ambiguities can be
resolved by lexical disambiguation mechanisms relying on access to
the relative frequencies of alternatives and to biases created by
contextual constraints. We present evidence from a computer
simulation of the use of frequency-based and contextual constraints
in the processing of the main verb/reduced relative syntactic
ambiguity, showing that frequency and relatively limited contextual
information from a sample of natural language can interact
sufficiently to model basic results in the literature.
%TI KA: Situating Natural Language Understanding in Design Problem Solving
%AU Justin Peterson
%AU Kavi Mahesh
%AU Ashok Goel
%AU Kurt Eiselt
%PU Proc. CogSci-94, pp. 711-716
%SC Sunday, August 14, 4-5:30
%AB In this paper, we investigate the interaction between linguistic and
non-linguistic processes by considering the role of functional reasoning
in understanding design specifications written in natural language. We
describe KA, an experimental model-based interpretation and design
system which understands English language descriptions of the design
problems it solves, and examine whether KA's problem-solving
capabilities help i) ascertain the relevance of ambiguous design
specifications and ii) identify unspecified relations between design
requirements. Our results demonstrate that augmenting language
processing with the ability to reason about function along the lines
suggested in KA provides effective solutions to these problems in
particular as well as to other problems in natural language
understanding.
%TI Correspondences between Syntactic Form and Meaning From Anarchy to Hierarchy
%AU Justin Peterson
%AU Dorrit Billman
%PU Proc. CogSci-94, pp. 705-710
%SC Monday, August 15, 4-5:30
%AB If we are to develop language processing systems that model human
capabilities and performance, we must identify correspondences between
the grammatical features and meaning of language and employ them in our
computational models of sentence interpretation. In this paper, we
present a computational model of sentence interpretation and a theory of
compositional semantics. Our model provides a method for addressing a
range of lexical novelty (e.g., novel verbs, novel uses of known
verbs), relying on a semantic representation that maintains principled
correspondences with syntactic form. In our approach, syntactic
structure preserves critical information about the hierarchical
structure of semantic interpretations. This property of the semantic
representation along with restrictions on semantic interpretations
enable the model to infer the semantics of novel verbs, disambiguate the
semantics of known verbs, and determine the contributions that verb
arguments make to sentence interpretation in a constrained and
principled manner. This research offers a fruitful approach for using
linguistic analysis to address the recovery of meaning in natural language
processing systems.
%TI Categorization and the Parsing of Objects
%AU Rachel Pevtzow
%AU Robert L. Goldstone
%PU Proc. CogSci-94, pp. 717-722
%SC Sunday, August 14, 11:00am-12:30pm
%AB Several models of categorization suggest that fixed inputs
(features) are combined together to create categorization rules. It
is also possible that categorization influences what features are
perceived and used. This experiment explored the possibility that
categorization training influences how an object is decomposed into
parts. In the first part of this experiment, subjects learned to
categorize objects based on particular sets of line segments.
Following categorization training, subjects were tested in a
whole-part decomposition task, making speeded judgments of "does
whole X contain probe Y". All diagnostic and nondiagnostic category
parts were used as parts within the whole objects, and as probes.
Categorization training in the first part of the experiment affected
performance on the second task. In particular, subjects were faster
to respond when the whole object contained a part that was
diagnostic for categorization than when it contained a nondiagnostic
part. When the probe was a diagnostic category part, subjects were
faster to respond that it was present than absent, and when the
probe was a nondiagnostic part, subjects were faster to respond that
it was absent than that it was present. These results are discussed
in terms of perceptual sensitivity, response bias, and the
modulating influence of experience.
%TI Strong Systematicity within Connectionism: The Tensor-Recurrent Network
%AU Steven Phillips
%PU Proc. CogSci-94, pp. 723-727
%SC Sunday, August 14, 2-3:30
%AB Systematicity, the ability to represent and process structurally
related objects, is a significant and pervasive property of
cognitive behaviour, and clearly evident in language. In the case of
Connectionist models that learn from examples, systematicity is
generalization over examples sharing a common structure. Although
Connectionist models (e.g., the recurrent network and its variants)
have demonstrated generalization over structured domains, there has
not been a clear demonstration of strong systematicity (i.e.,
generalization across syntactic position). The tensor has been
proposed as a way of representing structured objects, however, there
has not been an effective learning mechanism (in the strongly
systematic sense) to explain how these representations may be
acquired. I address this issue through an analysis of tensor
learning dynamics. These ideas are then implemented as the
tensor-recurrent network which is shown to exhibit strong
systematicity on a simple language task. Finally, it is suggested
that the properties of the tensor-recurrent network that give rise
to strong systematicity are analogous to the concepts of variables
and types in the Classical paradigm.
%TI A Simple Co-Occurrence Explanation for the Development of Abstract Letter Identities
%AU Thad A. Polk
%AU Martha J. Farah
%PU Proc. CogSci-94, pp. 728-732
%SC Monday, August 15, 4-5:30
%AB Evidence suggests that an early representation in the visual
processing of orthography is neither visual nor phonological, but
codes abstract letter identities (ALIs) independent of case, font,
size, etc. How could the visual system come to develop such a
representation? We propose that, because many letters look similar
regardless of case, font, etc., different visual forms of the same
letter tend to appear in visually similar contexts (e.g., in the
same words written in different ways) and that correlation-based
learning in visual cortex picks up on this similarity among contexts
to produce ALIs. We present a simple self-organizing Hebbian neural
network that illustrates how this idea could work and that produces
ALIs when presented with appropriate input.
%TI Probabilistic Reasoning under Ignorance
%AU Marco Ramoni
%AU Alberto Riva
%AU Vimla L. Patel
%PU Proc. CogSci-94, pp. 733-738
%SC Monday, August 15, 7:30-9
%AB The representation of ignorance is a long standing challenge for
researchers in probability and decision theory. During the past
decade, Artificial Intelligence researchers have developed a class
of reasoning systems, called Truth Maintenance Systems, which are
able to reason on the basis of incomplete information. In this paper
we will describe a new method for dealing with partially specified
probabilistic models, by extending a logic-based truth maintenance
method from Boolean truth-values to probability intervals. Then we
will illustrate how this method can be used to represent Bayesian
Belief Networks - one of the best known formalisms to reason under
uncertainty - thus producing a new class of Bayesian Belief
Networks, called Ignorant Belief Networks, able to reason on the
basis of partially specified prior and conditional probabilities.
Finally, we will discuss how this new method relates to some
theoretical intuitions and empirical findings in decision theory and
cognitive science.
%TI Troubleshooting Strategies in a Complex, Dynamical Domain
%AU Margaret M. Recker
%AU T. Govindaraj
%AU Vijay Vasandani
%PU Proc. CogSci-94, pp. 739-744
%SC Monday, August 15, 2-3:30
%AB In this paper, we present results from two empirical studies in
which subjects diagnosed faults that occurred in a computer-based,
dynamical simulation of an oil-fired marine power plant, called
Turbinia. Our results were analyzed in the framework of dual
problem space search (DPSS), in which non-routine diagnosis was
characterized as a process of generating hypotheses to explain the
observed faults, and testing these hypotheses by conducting
experiments. In the first study, we found that the less-efficient
subjects conducted significantly more experiments, indicating a
strong bottom-up bias in their diagnostic strategy. In the second
study, we examined the effects of imposing external resource bounds
on subjects' diagnostic strategies. Results indicated that
constraints on diagnosis time led to a reduction in the number of
actions performed and components viewed, without appearing to affect
diagnostic performance. Constraints on the number of diagnostic
tests reduced search in the experiment space, which appeared to
negatively affect performance. Taken together, these suggest
results that subjects' diagnostic strategies were sensitive to
constraints in the external task environment. We close with a
sketch of how DPSS might be augmented to include effects due to
external resource bounds.
%TI The Guessing Game: A Paradigm for Artificial Grammar Learning
%AU Martin Redington
%AU Nick Chater
%PU Proc. CogSci-94, pp. 745-749
%SC Monday, August 15, 7:30-9
%AB In a guessing game, subjects reconstruct a sequence by guessing each
successive element of the sequence from a finite set of
alternatives, receiving feedback after each guess. An upper bound
on Ss knowledge of the sequence is given by H, the estimated entropy
of the numbers of guesses. The method provides a measure of
learning independent of material type and distractors, and the
resulting data set is very rich. Here, the method is applied to
artificial grammar learning; subjects were exposed to strings from a
finite state grammar and subsequently distinguished between strings
that followed or violated the grammar reliably better than subjects
who had not seen the learning strings (but who themselves performed
at above chance levels). subjects knowledge of the strings, H,
reflected both grammaticality and exposure to learning strings, and
was correlated with overall judgement performance. For
non-grammatical strings, the strings that Ss knew most about were
those they found most difficult to classify correctly. These
results support the hypothesis that fragment knowledge plays an
important part in artificial grammar learning, and we suggest that
the guessing game paradigm is a useful tool for studies of learning
and memory in general.
%TI Educational Implications of CELIA: Learning by Observing and Explaining
%AU Michael Redmond
%PU Proc. CogSci-94, pp. 750-755
%SC Monday, August 15, 7:30-9
%AB CELIA is a computational model of how a novice student can quickly
become competent at a procedural task through observing and
understanding an expert's problem solving. This model was inspired
by protocol studies, and implemented in a computer program. This
model of a student's effective learning suggests some implications
for teaching novices in a new domain. These may be relevant for both
human teaching and intelligent tutoring. The implications include:
encourage the student to predict, interactive step-by-step
presentation of example steps, encourage self-explanation by the
student, order example steps to match their logical order, give a
variety of examples in early instruction, allow flexible interaction
with the student, and present basic background concepts prior to
examples. These implications represent hypotheses that follow from
the learning model; they suggest further research.
%TI Improving Design with Artifact History
%AU Brent Neal Reeves
%PU Proc. CogSci-94, pp. 756-761
%SC Monday, August 15, 7:30-9
%AB History tools play an important part in supporting human computer
interaction. Most research in history tools has focussed on user
interaction histories. In contrast, this paper presents a
theoretical framework for artifact history and describes a computer
based design environment which implements embedded artifact history.
The most promising area for history tools is in collaborative
design, helping users to understand others' as well as one's own
previous work.
%TI Explanatory AI, Indexical Reference, and Perception
%AU Lawrence D. Roberts
%PU Proc. CogSci-94, pp. 762-765
%SC Monday, August 15, 7:30-9
%AB Researchers in AI often say that certain types of reference are
based on perception. Their models, however, do not reflect
perceptual functioning, but instead represent denota- tion, an
intellectually modeled relation, by using exact fea- ture matching
in a serial device as the basic mechanism for reference. I point
out four problems in this use of denota- tion: substitution of an
intellectual model for a perceptual one; unclarity about the nature
of referential identification; relative neglect of the role of
contrast in reference; and inexact matches. I then suggest an
alternative theoretical account for perceptually based indexical
reference, the figure-ground model, and I explain how this model
handles the four problems.
%TI Learning Features of Representation in Conceptual Context
%AU Luc Rodet
%AU Philippe G. Schyns
%PU Proc. CogSci-94, pp. 766-771
%SC Monday, August 15, 7:30-9
%AB When people categorize an object, they often encode a certain number of
its properties for later classification. In Schyns and Murphy (in
press), we suggested that the way people group objects into categories
could induce the learning of new dimensions of categorization--i.e.,
dimensions that did not exist prior to the experience with the
categorization system. In this research, we examine whether the context
of known concepts can influence feature extraction. The first experiment
simply tested whether the context of different object categories could
change the perception of the same target stimuli. The second experiment
examined whether learning category B given the concept of category A may
result in different features being learned that learning A given B. The
results showed that the context of known concepts influence the features
people learn to represent object categories.
%TI On-line versus Off-line Priming of Word-Form Encoding in Spoken Word Production
%AU Ardi Roelofs
%PU Proc. CogSci-94, pp. 772-777
%SC Sunday, August 14, 11-12:30
%AB The production of a disyllabic word is speeded up by advance
(off-line) knowledge of the first syllable, but not by knowledge
about the second syllable (Meyer, 1990). By contrast, when first
syllable or second-syllable primes are presented during the
production of a disyllabic word (on-line), both primes yield a
facilitatory effect (Meyer & Schriefers, 1991). In this paper, the
computational model of word-form encoding in speaking developed in
Roelofs (1992b, submitted) is applied to these contradictory
findings. Central to the model is the proposal by Levelt (1992) that
morphemic representations are mapped onto stored syllable programs
by serially grouping the morphemes' segments into phonological
syllables, which are then used to address the programs in a
syllabary. Results of computer simulations reported in this paper
show that the model resolves the empirical discrepancy.
%TI Do Children have Epistemic Constructs about Explanatory Frameworks: Examples from Naive Ideas about the Origin of Species
%AU Ala Samarapungavan
%AU Reinout Wiers
%PU Proc. CogSci-94, pp. 778-783
%SC Monday, August 15, 4-5:30
%AB This paper presents the results of a study which examined children's
ideas about the origin and differentiation of species. The focus of
this paper is on the epistemic constructs associated with children's
explanatory frameworks. Two groups of elementary school students,
9-year-olds and 12-year-olds, were interviewed using a
semi-structured questionnaire. The results indicate that most
children explain the phenomena of speciation in terms of a
conceptual framework that strongly resembles either early Greek or
later renaissance variants of Essentialist theories in biology.
Children also demonstrate a spontaneous understanding of important
epistemic constructs associated with theoretical frameworks. For
example, most children show an explicit awareness of the boundaries
of their theoretical frameworks and have some idea of the phenomena
that such a framework can and should explain. Many children treat
questions about the origins of the first animal and plant species as
"first questions," or questions which are in principle unanswerable.
The children appear to distinguish between facts that they as
individuals lack but that are probably known by experts, domain
problems that are unsolved but could in principle be answered by
biological theories, and problems that are beyond the explanatory
scope of biological theories.
%TI A Connectionist Model of Verb Subcategorization
%AU Hinrich Schutze
%PU Proc. CogSci-94, pp. 784-788
%SC Tuesday, August 16, 11-12:30
%AB Much of the debate on rule-based vs. connectionist models in
language acquisition has focussed on the English past tense. This
paper investigates a new area, the acquisition of verb
subcategorization. Verbs differ in how they express their arguments
or subcategorize for them. For example, ``She gave him a book.'' is
good, but ``She donated him a book.'' sounds odd. The paper
describes a connectionist model for the acquisition of verb
subcategorization and how it accounts for overgeneralization and
learning in the absence of explicit negative evidence. It is argued
that the model presents a better explanation for the transition from
the initial rule-less state to final rule-like behavior for some
verb classes than the symbolic account proposed by Pinker (1989).
%TI Viewpoint dependence and face recognition
%AU Philippe G. Schyns
%AU Heinrich H. Bulthoff
%PU Proc. CogSci-94, pp. 789-793
%SC Monday, August 15, 11-12:30
%AB Face recognition stands out as a singular case of object recognition:
although most faces are very much alike, people discriminate between
many different faces with outstanding efficiency. Even though little
is known about the mechanisms of face recognition, viewpoint
dependence, a recurrent characteristic of many research on faces,
could inform algorithms and representations. Poggio and Vetter's symmetry
argument predicts that learning only one view of a face may be sufficient
for recognition, ifthis view allows the computation of a symmetric,
``virtual,'' view. More specifically, as faces are roughly bilaterally
symmetric objects, learning a side-view--which always has a symmetric
view--should give rise to better generalization performances than
learning the frontal view. It is also predicted that among all
new views, a virtual view should be best recognized. We ran two
psychophysical experiments to test these predictions. Stimuli
were views of 3D models of laser-scanned faces. Only shape was available
for recognition; all other face cues--texture, color, hair, etc.--were
removed from the stimuli. The first experiment tested whether a particular
view of a face was canonical. The second experiment tested which single
views of a face give rise to best generalization performances. The
results were compatible with the symmetry argument: face recognition
from a single view is always better when the learned view allows the
computation of a symmetric view.
%TI Multiple Learning Mechanisms Within Implicit Learning
%AU Carol Augart Seger
%PU Proc. CogSci-94, pp. 794-799
%SC Monday, August 15, 2-3:30
%AB The experiment reported in this paper provides evidence that there
are at least two independent implicit learning mechanisms in
implicit learning: an efficiency mechanism, which underlies changes
in reaction time to patterned stimuli, and a conceptual fluency
mechanism, which underlies the ability to make judgments about
stimuli based on implicit knowledge. Each of these implicit
mechanisms is independent of explicit learning. Subjects performed
a serial reaction time task under one of three learning conditions
(nonattentional, attentional and observational) for one of three
study lengths (2, 6 or 12 blocks). Subjects then completed five
tests of their knowledge: attentional and nonattentional reaction
time tasks (measuring two kinds of efficiency learning), awareness
questionnaire (measuring explicit knowledge) , a generation task,
and a conceptual fluency task. Correlation analyses and criterion
analyses found no dependencies between the measures in low awareness
subjects. In addition, the measures were influenced differently by
the independent variables of learning condition and study length;
these dissociations indicate separate underlying mechanisms.
Implications of the existence of multiple implicit mechanisms for
connectionist modeling of implicit learning are drawn.
%TI Learning with friends and foes
%AU Mahendra Sekaran
%AU Sandip Sen
%PU Proc. CogSci-94, pp. 800-805
%SC Tuesday, August 16, 11-12:30
%AB Social agents, both human and computational, inhabiting a world
containing multiple active agents, need to coordinate their
activities. This is because agents share resources, and without
proper coordination or ``rules of the road'', everybody will be
interfering with the plans of others. As such, we need coordination
schemes that allow agents to effectively achieve local goals without
adversely affecting the problem-solving capabilities of other
agents. Researchers in the field of stributed Artificial
Intelligence (DAI) have developed a variety of coordination schemes
under different assumptions about agent capabilities and
relationships. Whereas some of these research have been motivated
by human cognitive biases, others have approached it as an
engineering problem of designing the most effective coordination
architecture or protocol. We propose reinforcement learning as a
coordination mechanism that imposes little cognitive burden on
agents. More interestingly, we show that a uniform learning
mechanism suffices as a coordination mechanism in both cooperative
and adversarial situations. Using an example block-pushing problem
domain, we demonstrate that agents can use reinforcement learning
algorithms, without explicit information sharing, to develop
effective policies to coordinate their actions both with agents
acting in unison and with agents acting in opposition.
%TI Tractable Learning of Probability Distributions Using the Contrastive Hebbian Algorithm
%AU Craig E. L. Stark
%AU James L. McClelland
%PU Proc. CogSci-94, pp. 818-823
%SC Monday, August 15, 7:30-9
%AB In some tasks (e.g., assigning meanings to ambiguous words) humans
produce multiple distinct alternatives in response to a particular
stimulus, apparently mirroring the environmental probabilities
associated with each alternative. For this purpose, a network
architecture is needed that can produce a distribution of outcomes,
and a learning algorithm is needed that can lead to the discovery of
ensembles of connection weights that reproduce the environmentally
specified probabilities. Stochastic symmetric networks such as
Boltzmann machines and networks that use graded activations
perturbed with Gaussian noise can exhibit such distributions at
equilibrium, and they can be trained to match environmentally
specified probabilities using Contrastive Hebbian Leaning, the
generalized form of the Boltzmann Learning algorithm. Learning
distributions exacts a considerable computational cost as processing
time is used both in settling to equilibrium and in sampling
equilibrium statistics. The work presented here examines tge extent
of this cost and how it may be minimized, and produces speedups of
roughly a foactor of 5 compared to previously published results.
%TI A Unified Model of Preference and Recovery Mechanisms in Human Parsing
%AU Suzanne Stevenson
%PU Proc. CogSci-94, pp. 824-829
%SC Monday, August 15, 11-12:30
%AB Models of human parsing typically focus on explaining syntactic
preferences and garden-path phenomena. This paper explores another
aspect of the processing of syntactic ambiguity---the successful
revision of previously preferred structure. In the competitive
attachment model of parsing, a hybrid connectionist network directly
represents the attachment structure among phrasal nodes in a parse
tree. A syntactic ambiguity leads to a network of alternative
attachments that compete for numeric activation. The winning
attachments are determined within a parallel operation that
simultaneously revises earlier attachments as needed when initially
attaching a new phrase to the developing parse tree. Because of the
unique parallel structuring operation, the competitive attachment
model provides a unified explanation of human preference and
recovery mechanisms in parsing. The paper demonstrates this ability
by showing how the model accounts for recency effects in human
syntactic processing. In the parsing network, a mechanism of decay,
which is independently needed to manage the finite pool of
processing nodes, allows more recent phrases to compete more
effectively than less recent phrases for new attachments. The
effect of decay on the attachment competition underlies a unified
account of psycholinguistic observations of recency, both in initial
syntactic preferences and in the revision of erroneous attachments.
%TI PCLEARN: A model for learning perceptual-chunks
%AU Masaki Suwa
%AU Hiroshi Motoda
%PU Proc. CogSci-94, pp. 830-835
%SC Monday, August 15, 2-3:30
%AB Past research in cognitive science reveals that prototypical
configurations of domain objects, called perceptual-chunks, underlie
the abilities of experts to solve problems efficiently. Little
research, however, has been carried out on the mechanism used for
learning perceptual-chunks from solving problems. The present paper
addresses this issue in the domain of geometry proof
problem-solving. We have developed a computational model that
chunks, from problem diagrams, configuration of the elements which
are visually grouped together, based on perceptual chunking
criterion. This criterion, called recognition rules, reflects how
people see problem diagrams and thus works effectively to determine
which portion of problem diagrams are more likely to be grouped as a
chunk. This distinguishes the proposed method from the
goal-oriented chunking techniques used in machine-learning
community. Experiments on solving geometry problems show that our
technique can detect essential diagram configurations common to many
problems. Additionally, implications of the recognition rules are
discussed from a cognitive point of view.
%TI Toward A Theoretical Account of Strategy Use and Sense-Making in Mathematics Problem Solving
%AU Hermina J.M. Tabachneck
%AU Kenneth R. Koedinger
%AU Mitchell J. Nathan
%PU Proc. CogSci-94, pp. 836-841
%SC Sunday, August 14, 11-12:30
%AB Much problem solving and learning research in math and science has
focused on formal representations. Recently researchers have documented
the use of unschooled strategies for solving daily problems -- informal
strategies which can be as effective, and sometimes as sophisticated, as
school-taught formalisms. Our research focuses on how formal and
informal strategies interact in the process of doing and learning
mathematics. We found that combining informal and formal strategies is
more effective than single strategies. We provide a theoretical account
of this multiple strategy effect and have begun to formulate this theory
in an ACT-R computer model. We show why students may reach common
impasses in the use of written algebra, and how subsequent or concurrent
use of informal strategies leads to better problem-solving performance.
Formal strategies facilitate computation because of their abstract and
syntactic nature; however, abstraction can lead to nonsensical
interpretations and conceptual errors. Reapplying the formal strategy
will not repair such errors; switching to an informal one may. We
explain the multiple strategy effect as a complementary relationship
between the computational efficiency of formal strategies and the
sense-making function of informal strategies.
%TI How Does an Expert Use a Graph? A Model of Visual and Verbal Inferencing in Economics
%AU Hermina J.M. Tabachneck
%AU Anthony M. Leonardo
%AU Herbert A. Simon
%PU Proc. CogSci-94, pp. 842-847
%SC Sunday, August 14, 4-5:30
%AB This research aims to clarify, by constructing and testing a computer
simulation, the use of multiple representations in problem solving,
focusing on the role of visual representations. We model the behavior of
an economics expert as he teaches some economics principles while drawing
a graph on a blackboard. Concurrent verbal protocols are used to guide
construction of a production system. The model employs
representation-specific data structures and rules. The graph on the
blackboard is represented by a bit map; the pictorial working memory (WM)
and long term memory (LTM) representations are node-link structures of a
pictorial nature; the auditory WM and LTM representations are node-link
structures of a verbal-semantic nature. Pieces from the different
representations are linked together on a sequential and temporary basis
to form a reasoning and inferencing chain, using cues from LTM and from
the external graph. The expert used two representations so as to exploit
the unique advantages of each. The graphical representation served as a
place holder during reasoning, as well as a summary. The verbal-semantic
representation served to give semantic meaning and causal background.
Both could initiate reasoning chains. We compare the expert behavior
with novices trying to learn the same principles.
%TI A Lexical Model of Learning to Read Single Words Aloud
%AU Roman Taraban
%AU Carolyn Beth Taraban
%PU Proc. CogSci-94, pp. 848-853
%SC Monday, August 15, 4-5:30
%AB Three principles governing the operation of the lexical pathway in a
model of reading single words aloud were applied to the question of
learning, as measured by times to initiate correct pronunciations.
I. At the lexical level, a target word activates a neighborhood of
orthographically similar entries in the lexicon. II. At the phoneme
level, the correct phonemes in the phonemic spelling of the word
compete with the other active phonemes. III. At the naming level,
the pronunciation is composed of a conjunction of phonemes. These
principles were tested using the data from a 4-year-old beginning
reader (LT), resulting in a goodness-of-fit R2 = .44. When a rule
pathway using grapheme-phoneme correspondences was added to the
lexical pathway, the goodness-of-fit was comparable (R2 = .46). When
single entries were accessed along the lexical pathway, instead of
word neighborhoods, and grapheme-phoneme correspondences were
accessed along the rule pathway, as in standard dual-route models,
the goodness-of-fit R2 fell to .27. Although the model- fitting
supported the importance of neighborhood activation and failed to
support the importance of rules, grapheme-phoneme correspondences
were overtly used by LT in the initial trials with words and when
feedback indicated an errorful pronunciation. Thus, rule
application may be relatively slow in normal fluent word naming, but
may still play a strategic role in attempts to initially decode
letter strings or to correct errors.
%TI Formal Rationality and Limited Agents
%AU Jonathan King Tash
%PU Proc. CogSci-94, pp. 854-857
%SC Sunday, August 14, 4-5:30
%AB Many efforts have been made to use normative theories of rational
decision-making, such as Bayesian decision theory, to construct and
model agents exhibiting intelligent behavior. In order to
accommodate agents possessing only limited computational resources
to apply to their decision making, however, a significant change is
required in how the role of formal rationality is to be viewed.
This paper argues that rationality is best seen as a property of the
relationship between the agent and a designer. Such a perspective
has several consequences for the design and modelling of agents,
bearing on assessment of rationality, induction, reactivity, and
metalevel control. It also illuminates several concerns put forth
by critics of the work of the artificial intelligence community.
%TI Limiting nested beliefs in cooperative dialogue
%AU Jasper Taylor
%AU Jean Carletta
%PU Proc. CogSci-94, pp. 858-863
%SC Monday, August 15, 7:30-9
%AB Models of rationality typically rely on underlying logics that allow
simulated agents to entertain beliefs about one another to any depth
of nesting. We argue that representations of individual deeply
nested beliefs are in principle unnecessary for any cooperative
dialogue. We describe a simulation of such dialogues in a simple
domain, and attempt to generalize the principles of this simulation,
first to explain features of human dialogue in this domain, then
those of cooperative dialogues in general. We propose that for the
purposes of cooperative interaction, the status of all deeply-nested
beliefs about each concept can be conjoined into a single
represented value, which will be affected by reasoning that might be
expected to lead to conclusions in terms of deeply-nested beliefs.
We concede that people are capable of using individual deeply-nested
beliefs to some degree, but such beliefs need only be handled
explicitly in dialogues involving secrecy or deception.
%TI Functional Parts
%AU Joshua Tenenbaum
%PU Proc. CogSci-94, pp. 864-869
%SC Monday, August 15, 7:30-9
%AB Previous work in visual cognition has extensively explored the power
of parts-based representations of objects for recognition,
categorization, and functional reasoning. We propose a novel,
parts-based representation of objects, where the parts of an object
are found by grouping together object elements that move together
over a set of images. The distribution of object configurations is
then succinctly descibed in terms of these functional parts and an
orthogonal set of modal transformations of these parts. If the
distribution has a natural set of principal axes, the computed modes
are stable and functionally significant. Moreover, the
representation is always unique and robustly computable because it
does not rely critically on the properties of any particular element
in any particular instance of the object. Most importantly, the
representation provides a set of direct cues to object funtionality
without making any assumptions about object geometry or invoking any
high-level domain knowledge. This robustness and functional
transparency may be contrasted with standard representations based
on geometric parts, such as generalized cylinders (Marr and
Nishihara, 1978) or geons (Biederman, 1987), which are sensitive to
accidental alignments and occlusions (Biederman, 1987), and which
only support functional reasoning in conjunction with high-level
domain knowledge (Tversky and Hemenway, 1984).
%TI Simulated Perceptual Grouping: An Application to Human-Computer Interaction
%AU Kristinn R. Thorisson
%PU Proc. CogSci-94, pp. 876-881
%SC Monday, August 15, 11-12:30
%AB The perceptual principles that allow people to group visually
similar objects into entities, or groups, have been called the
Gestalt Laws of perception. Two well known principles of perceptual
grouping are proximity and similarity: objects that lie close
together are perceived to fall into groups; objects of similar
shape, size or color are more likely to form groups than objects
differing along these dimensions. While the primary function of
these "laws" is to help us perceive the world, they also enter into
our communications. People can build on assumptions about each
other's perception of the world as a basis for simplifying
discourse: for example, we invariably refer to collections of
objects simply by gesturing in their direction and uttering "those."
The current work describes an algorithm that simulates parts of the
visual grouping mechanism at the object level. The system uses
feature spaces and simple ranking methods to produce object
groupings. Computational aspects of this system are described in
detail and its uses for enhancing multi-modal interfaces are
explained.
%TI Handling Unanticipated Events During Collaboration
%AU Roy M. Turner
%AU Peggy S. Eaton
%PU Proc. CogSci-94, pp. 887-892
%SC Sunday, August 14, 2-3:30
%AB Handling unanticipated events during problem solving is difficult
enough when an agent is operating by itself. When the agent is part
of a cooperative distributed problem solving (CDPS) system, the
task's difficulty increases dramatically. Now the agent is forced
to consider the effect of the event not only on itself, but also on
others and the group as a whole. It must also consider who should
handle the event and the likely impact that actions taken to
diagnose the event or respond to it may have on other agents. In
this paper, we discuss preliminary work aimed at developing a
process for handling events during multiagent cooperative problem
solving. The domain in which the work is being done is cooperating
multiple autonomous underwater vehicles (AUVs). However, the
approach should have broader applicability to almost any real-world
cooperative problem solving task involving autonomous or nearly
autonomous agents.
%TI Exploiting Problem Solving to Select Information to Include in Dialogues between Cooperating Agents
%AU Elise H. Turner
%PU Proc. CogSci-94, pp. 882-886
%SC Monday, August 15, 7:30-9
%AB When agents cooperate to solve complex problems in the real-world,
they must choose which information to communicate from the mass of
information that might affect the problem. A speaker should
communicate the information that will be most helpful to the other
agent. However, the speaker may not have a great deal of knowledge
about the other. In addition, the speaker is also involved in
reasoning about the collaborative problem solving task. So,
processing that is done solely to select information will be taken
from the resources available to work on the primary problem. In
this paper, we present preliminary work on a new approach to
selecting information that should be included in a dialogue. Our
approach uses the speaker's knowledge of its own problem solving to
determine how useful some piece of information might be to other
agents. Consequently, the speaker can make its decision to include
information in the dialogue using no additional knowledge and few
additional computational resources beyond those required to reason
about the primary problem solving task. We suggest heuristics which
translate problem solving into estimates of how useful information
will be for others.
%TI STEPS: A Preliminary Model of Learning from a Tutor
%AU Sigalit Ur
%AU Kurt VanLehn
%PU Proc. CogSci-94, pp. 893-898
%SC Monday, August 15, 7:30-9
%AB This paper describes a prototype of a simulated physics student that
learns by interacting with a human tutor. The system solves physics
problems while showing its work on a workstation screen, and the
tutor can intervene at certain points during problem-solving to
advise the simulated student. This prototype constitutes an initial
cognitive task analysis of the skill of learning from a tutor, which
prescribes several tutoring practices that appear to be plausible
for both human and computer tutors.
%TI Belief Modelling, Intentionality and Perlocution in Metaphor Comprehension
%AU Tony Veale
%AU Mark T. Keane
%PU Proc. CogSci-94, pp. 910-915
%SC Tuesday, August 16, 11-12:30
%AB Metaphor is an elegant, concise, often startling communicative form
which is employed by a speaker as a means of conveying a state of
affairs to a hearer; as such, it deserves to be analysed as a
speech-act, with a particular illocutionary intent and
perlocutionary effect. This paper describes a hybrid
symbolic/connectionist model of metaphor (SAPPER by Veale & Keane,
1993), which incorporates elements of the belief ascription model of
Wilks, Barnden & Wang (1991). This extended framework provides a
suitable computational environment for analysing the illocutionary
intent of the speaker, and perlocutionary effect upon the hearer's
belief space, of a broad class of metaphors with an observable
ameliorative/pejorative connotation.
%TI Goal Specificity in Hypothesis Testing and Problem Solving
%AU Regina Vollmeyer
%AU Keith J. Holyoak
%AU Bruce D. Burns
%PU Proc. CogSci-94, pp. 916-921
%SC Monday, August 15, 2-3:30
%AB Theories of skill acquisition have made radically different
predictions about the role of means-ends analysis in acquiring
general rules that promote effective transfer to new problems.
Under one view, means-ends analysis is assumed to provide the basis
for efficient knowledge compilation (Anderson, 1987), whereas under
the alternative view means-ends analysis is believed to disrupt rule
induction (Sweller, 1988). We suggest that in the absence of a
specific goal people are more likely to use a rule-induction
learning strategy, whereas providing a specific goal fosters use of
means-ends analysis, which is a non-rule-induction strategy. We
performed an experiment to investigate the impact of goal
specificity and systematicity of rule-induction strategies in
learning and transfer within a complex dynamic system. Subjects who
were provided with a specific goal were able to solve the initial
problem, but were impaired on a transfer test using a similar
problem with a different goal, relative to subjects who were
encouraged to use a systematic rule-induction strategy to freely
explore the problem space. Our results support Sweller's proposal
that means-ends analysis leads to specific knowledge of an isolated
solution path, but does not provide an effective method for learning
the overall structure of a problem space.
%TI Computing Goal Locations from Place Codes
%AU Hank S. Wan
%AU David S. Touretzky
%AU A. David Redish
%PU Proc. CogSci-94, pp. 922-927
%SC Monday, August 15, 2-3:30
%AB A model based on coupled mechanisms for place recognition, path
integration, and maintenance of head direction in rodents replicates
a variety of neurophysiological and behavioral data. Here we
consider a task described in [Collett et. al. 86] in which gerbils
were trained to find food equidistant from three identical landmarks
arranged in an equilateral triangle. In probe trials with various
manipulations of the landmark array, the model produces behaviors
similar to those of the animals. We discuss computer simulations
and an implementation of portions of the model on a mobile robot.
%TI Verb Inflections in German Child Language: A Connectionist Account
%AU Gert Westermann
%AU Risto Miikkulainen
%PU Proc. CogSci-94, pp. 928-933
%SC Monday, August 15, 4-5:30
%AB The emerging function of verb inflections in German language acquisition
is modeled with a connectionist network. A network that is initially
presented only with a semantic representation of sentences uses the
inflectional verb ending -t to mark those sentences that are low in
transitivity, whereas all other verb endings occur randomly. This
behavior matches an early stage in German language acquisition where verb
endings encode a similar semantic rather than a grammatical function.
When information about the surface structure of the sentence is added to
the input data, the network learns to use the correct verb inflections in
a process very similar to children's learning. This second phase is
facilitated by the semantic phase, suggesting that there is no shift from
semantic to grammatical encoding, but rather an extension of the initial
semantic encoding to include grammatical information. This can be seen
as evidence for the strong version of the functionalist hypothesis of
language acquisition.
%TI Analogical Transfer Through Comprehension and Priming
%AU Charles M. Wharton
%AU Trent E. Lange
%PU Proc. CogSci-94, pp. 934-939
%SC Sunday, August 14, 2-3:30
%AB An unexplored means by which analogical transfer might take place is
through indirect priming through the interaction of text
comprehension and memory retrieval processes. REMIND is a structured
spreading- activation model of language understanding and reminding
in which simple transfer can result from indirect priming from
previously processed source analogs. This paper describes two
experiments based on REMIND's priming-based transfer framework. In
Experiment 1, subjects (1) summarized analogous source stories'
common plot; (2) rated the comprehensibility of targets related to
sources by similar themes, contexts, or themes and contexts; then
(3) described any sources incidentally recalled during target
rating. Source/target similarity influenced comprehensibility and
reminding without any explicit mapping or problem-solving. In
Experiment 2, subjects (1) rated each story's com- prehensibility in
source/target pairs having similar relationships to each other as in
Experiment 1; then (2) rated source/target similarity. Analogous
targets were rated as more comprehensible than non-analogous
targets. Both experiments imply that transfer can be caused by
activation of abstract knowledge representations without explicit
mapping.
%TI Explaining Serendipitous Recognition in Design
%AU Linda M. Wills
%AU Janet L. Kolodner
%PU Proc. CogSci-94, pp. 940
%SC Monday, August 15, 7:30-9
%AB Creative designers often see solutions to pending design problems in the
everyday objects surrounding them. This can often lead to innovation and
insight, sometimes revealing new functions and purposes for common design
pieces in the process. We are interested in modeling serendipitous
recognition of solutions to pending problems in the context of creative
mechanical design. This paper characterizes this ability, analyzing
observations we have made of it, and placing it in the context of other
forms of recognition. We propose a computational model to capture and
explore serendipitous recognition which is based on ideas from
reconstructive dynamic memory and situation assessment in case-based
reasoning.
%TI Towards a Principled Representation of Discourse Plans
%AU R. Michael Young
%AU Johanna D. Moore
%AU Martha E. Pollack
%PU Proc. CogSci-94
%SC Monday, August 15, 7:30-9
%AB We argue that discourse plans must capture the intended causal and
decompositional relations between communicative actions. We present
a planning algorithm, DPOCL, that builds plan structures that
properly capture these relations, and show how these structures are
used to solve the problems that plagued previous discourse planners,
and allow a system to participate effectively and flexibly in an
ongoing dialogue.
%TI The Representation of Relational Information
%AU Jiajie Zhang
%AU Donald A. Norman
%PU Proc. CogSci-94
%SC Monday, August 15, 7:30-9
%AB Most graphic and tabular displays are relational information
displays--displays that represent relational information, which is a
relation on a set of dimensions. In this paper, we argue that
relational information displays are distributed representations --
representations that are distributed cross the internal mind and the
external environment, and display-based tasks are distributed
cognitive tasks--tasks that require the interwoven processing of
internal and external information. The basic components of
relational information displays are dimensions. Through a
theroretical analysis of dimensional representations, we identified
four major factors that affect the representational efficiencies of
relational information displays: the distributed representation of
scale information, the relation between psychological and physical
measurements, the interaction between dimensions, and the visual and
spatial properties of dimenisions. Based on the representational
analysis of relational information displays, we proposed a
representational taxonomy of relational information displays. This
taxonomy can be used to classify most types of relational
information displays. In addition, it can be used as a theoretical
framework to study the empirical issues of relational information
displays in a systematic way.
%TI Segmenting Speech without a Lexicon: Evidence for a Bootstrapping Model of Lexical Acquisition
%AU Timothy A. Cartwright
%AU Michael R. Brent
%PU Proc. CogSci-94, pp. 148-152
%SC Monday, August 15, 4-5:30
%AB Infants face the difficult problem of segmenting continuous speech
into words without the benefit of a fully developed lexicon.
Several information sources in speech---prosody, semantic
correlations, phonotactics, and so on---might help infants solve
this problem. Research to date has focused on determining to which
of these information sources infants might be sensitive, but little
work has been done to determine the usefulness of each source. The
computer simulations reported here are a first attempt to measure
the usefulness of distributional and phonotactic information in
adult- and child-directed speech. The simulations hypothesize
segmentations of speech into words; the best segmentation hypothesis
is selected using the Minimum Description Length paradigm. Our
results indicate that while there is some useful information in both
phoneme distributions and phonotactic rules, the combination of both
sources is most useful. Further, this combination of information
sources is more useful for segmenting child-directed speech than
adult-directed speech. The implications of these results for
theories of lexical acquisition are discussed.
%TI The Effect of Syntactic Form on Simple Belief Revisions and Updates
%AU Renee Elio
%AU Francis Jeffry Pelletier
%PU Proc. CogSci-94, pp. 260-265
%SC Tuesday, August 16, 11-12:30
%AB In this paper we report preliminary results on how people revise or
update a previously held set of beliefs. When intelligent agents
learn new things which conflict with their current belief set, they
must revise their belief set. When the new information does not
conflict, they merely must update their belief set. Various AI
theories have been proposed to achieve these processes. There are
two general dimensions along which these theories differ: whether
they are syntactic-based or model-based, and what constitutes a
minimal change of beliefs. This study investigates how people
update and revise semantically equivalent but syntactically distinct
belief sets, both in symbolic-logic problems and in quasi-real-world
problems. Results indicate that syntactic form affects belief
revision choices. In addition, for the symbolic problems, subjects
update and revise semantically-equivalent belief sets identically,
whereas for the quasi-real-world problems they both update and
revise differently. Further, contrary to earlier studies, subjects
are sometimes reluctant to accept that a sentence changes from false
to true, but they are willing to accept that it would change from
true to false.
%TI Distributional Bootstrapping: From Word Class to Proto-Sentence
%AU S. Finch
%AU N. Chater
%PU Proc. CogSci-94, pp. 301-306
%SC Monday, August 15, 4-5:30
%TI Scientific Discovery in a Space of Structural Models: An Example from the History of Solution Chemistry
%AU Adrian Gordon
%AU Peter Edwards
%AU Derek Sleeman
%AU Yves Kodratoff
%PU Proc. CogSci-94, pp. 381-386
%SC Monday, August 15, 7:30-9
%AB Much previous work in developing computational models of scientific
discovery has concentrated on the formation of basic laws. The
important role played by additional assumptions in this process is a
neglected research topic. We argue that hypotheses about structure
are an important source of such additional assumptions, and that
knowledge of this type can be embodied in the notion of Informal
Qualitative Models (IQMs). In this paper, we demonstrate that such
models can be synthesised by applying a set of operators to the most
fundamental model in a domain. Heuristics are employed to control
this process, which forms the basis of an architecture for
model-driven scientific discovery. Conventional data-driven
discovery techniques can be integrated into this architecture,
resulting in laws which depend crucially on the model that is
applied to a problem. This approach is illustrated by an historical
survey of eighteenth and nineteenth century solution chemistry,
which focuses on the evolution of the models employed by
scientists. A series of models are synthesised which reflect these
historical developments, showing the importance of structural models
both in understanding certain aspects of the scientific discovery
process, and as a basis for practical discovery systems.
%TI The Origin of Clusters in Recurrent Neural Network State Space
%AU J.F. Kolen
%PU Proc. CogSci-94, pp. 508-513
%SC Monday, August 15, 7:30-9
%TI Categorization, Typicality, and Shape Similarity
%AU M.A. Kurbat
%AU E.E. Smith
%AU D.L. Medin
%PU Proc. CogSci-94, pp. 520-524
%SC Sunday, August 14, 11-12:30
%TI Variation in Unconscious Lexical Processing: Education and Experience Make a Difference
%AU G. Libben
%AU L. Sveinson
%PU Proc. CogSci-94, pp. 566-571
%SC Monday, August 15, 7:30-9
%TI Situated Cognition: Empirical Issue, "Paradigm Shift" or Conceptual Confusion?
%AU P. Slezak
%PU Proc. CogSci-94, pp. 806-811
%SC Sunday, August 14, 4-5:30