bluuebunny/crossref_metadata_2025 huggingface dataset:
Complex Concepts
Authors: Keller Janet Dixon , Lehman F.K.
Published: August 2005

For some time cognitive anthropology, and indeed cognitive science generally, have been concerned with issues of conceptual representation. Inadequacies in classical and more recent approaches, in particular, prototype theories, have motivated a search for alternative accounts. We propose an approach to conceptual representation that requires the specification of domain theories, from which conceptual definitions are generated, and within which the relations among concepts can be formalized. We demonstrate the utility of a formal characterization of this view of domains and concepts in the analysis of an aspect of the basic metaphysics constructed by the people of West Futuna, Vanuatu. In the process, we expand the set of formal devices traditionally relied upon by anthropologists and others involved in cognitive studies, and thereby gain insight into the complexity of the metaphysical beliefs of an Oceanic people.
A Modular Neural Network Model of Concept Acquisition
Authors: Schyns Philippe G.
Published: August 2005

Previous neural network models of concept learning were mainly implemented with supervised learning schemes. However, studies of human conceptual memory have shown that concepts may be learned without a teacher who provides the category name to associate with exemplars. A modular neural network architecture that realizes concept acquisition through two functionally distinct operations, categorizing and naming, is proposed as an alternative. An unsupervised algorithm realizes the categorizing module by constructing representations of categories compatible with prototype theory. The naming module associates category names to the output of the categorizing module in a supervised mode. In such a modular architecture, the interface between the modules can be conceived of as an “information relay” that encodes, constrains, and propagates important information. Five experiments were conducted to analyze the relationships among internal conceptual codes and simple conceptual and lexical development. The first two experiments show a prototype effect and illustrate some basic characteristics of the system. The third experiment presents a bottom‐up model of the narrowing down of children's early lexical categories that honors mutual exclusivity. The fourth experiment introduces top‐down constraints on conceptual coding. The fifth experiment exhibits how hierarchical relationships between concepts are learned by the architecture, and also demonstrates how a spectrum of conceptual expertise may gradually emerge as a consequence of experiencing more with certain categories than with others.
Goal‐Based Explanation Evaluation
Authors: Leake David B.
Published: August 2005

Many theories of explanation evaluation are based on context‐independent criteria. Such theories either restrict their consideration to explanation towards a fixed goal, or assume that all valid explanations are equivalent, so that evaluation criteria can be neutral to the goals underlying the attempt to explain. However, explanation can serve a range of purposes that place widely divergent requirements on the information an explanation must provide. It is argued that understanding what determines explanations' goodness requires a dynamic theory of evaluation, based on analysis of the information needed to satisfy the many goals that can prompt explanation; this view conforms to the commonsense idea that people accept and apply explanations precisely if those explanations give the information they need. A range of goals that can underly explanation is examined, and a theory is presented for evaluating whether an explanation provides the information an explainer needs for these goals. The theory is illustrated by sketching its implementation in the computer program, ACCEPTER, which does goal‐based evaluation of the goodness of explanations for surprising events in news stories.
The Role of Notation and Knowledge Representation in the Determination of Programming Strategy: A Framework for Integrating Models of Programming Behavior
Authors: Davies Simon P.
Published: August 2005

A number of accounts of expert programming behavior have been advanced. These models of the programming activity have served to highlight the range of factors that are thought to underpin programming strategy. However, such accounts have tended to emphasize either the effects of the organization of the programmer's knowledge representation or the role played by features of the notation of the task language on the emergence, development, and support of particular forms of strategy. Such work has neglected to (a) provide an account of the way in which these factors might interact to determine programming strategy, and (b) shed light upon the nature of the development of particular strategies as programming skill increases.

This article presents the results of an empirical analysis of the strategies employed by programmers of varying skill levels using different programming languages. The development of particular forms of strategy is hypothesized to be related both to the development of systematic asymmetries in programmers' generic plan‐based representations of programming knowledge and to the way in which features of the notation of particular programming languages might differentially support particular strategies. However, the results of the study reported here suggest that these effects must be interpreted within a broad developmental context. Hence, as programming skill develops, it appears that the notation of the task language tends to take precedence as a determinant of strategy, but has less relevance at beginning stages of skill development and within the context of expert performance. This finding would not be predicted by current models of the programming activity because such models suggest that any notational effects will tend to be consistent regardless of the skill level attained by the programmer. This article endeavors to extend current models of programming by emphasizing the need to consider in detail not only the development of programming strategy, but also the way in which knowledge representation and features of the task langauge interact to give rise to particular forms of programming behavior.
The Case for Rules in Reasoning
Authors: Smith Edward E. , Langston Christopher , Nisbett Richard E.
Published: August 2005

A number of theoretical positions in psychology—including variants of case‐based reasoning, instance‐based analogy, and connectionist models—maintain that abstract rules are not involved in human reasoning, or at best play a minor role. Other views hold that the use of abstract rules is a core aspect of human reasoning. We propose eight criteria for determining whether or not people use abstract rules in reasoning, and examine evidence relevant to each criterion for several rule systems. We argue that there is substantial evidence that several different inferential rules, including modus ponens, contractual rules, causal rules, and the law of large numbers, are used in solving everyday problems. We discuss the implications for various theoretical positions and consider hybrid mechanisms that combine aspects of instance and rule models.
Connectionist and Memory‐Array Models of Artificial Grammar Learning
Authors: Dienes Zoltan
Published: August 2005

Subjects exposed to strings of letters generated by a finite state grammar can later classify grammatical and nongrammatical test strings, even though they cannot adequately say what the rules of the grammar are (e.g., Reber, 1989). The MINERVA 2 (Hintzman, 1986) and Medin and Schaffer (1978) memory‐array models and a number of connectionist outoassociator models are tested against experimental data by deriving mainly parameter‐free predictions from the models of the rank order of classification difficulty of test strings. The importance of different assumptions regarding the coding of features (How should the absence of a feature be coded? Should single letters or digrams be coded?), the learning rule used (Hebb rule vs. delta rule), and the connectivity (Should features be predicted only by previous features in the string, or by all features simultaneously?) is investigated by determining the performance of the models with and without each assumption. Only one class of connectionist model (the simultaneous delta rule) passes all the tests. It is shown that this class of model can be regarded by abstracting a set of representative but incomplete rules of the grammar.
A Two‐Stage Model of Category Construction
Authors: Ahn Woo‐Kyoung , Medin Douglas L.
Published: August 2005

The current consensus is that most natural categories are not organized around strict definitions (a list of singly necessary and jointly sufficient features) but rather according to a family resemblance (FR) principle: Objects belong to the same category because they are similar to each other and dissimilar to objects in contrast categories. A number of computational models of category construction have been developed to provide an account of how and why people create FR categories (Anderson, 1990; Fisher, 1987). Surprisingly, however, only a few experiments on category construction or free sorting have been run and they suggest that people do not sort examples by the FR principle. We report several new experiments and a two‐stage model for category construction. This model is contrasted with a variety of other models with respect to their ability to account for when FR sorting will and will not occur. The experiments serve to identify one basis for FR sorting and to support the two‐stage model. The distinctive property of the two‐stage model is that it assumes that people impose more structure than the examples support in the first stage and that the second stage adjusts for this difference between preferred and perceived structure. We speculate that people do not simply assimilate probabilistic structures but rather organize them in terms of discrete structures plus noise.
Adversarial Problem Solving: Modeling an Opponent Using Explanatory Coherence
Authors: Thagard Paul
Published: August 2005

In adversarial problem solving (APS), one must anticipate, understand and counteract the actions of an opponent. Military strategy, business, and game playing all require an agent to construct a model of an opponent that includes the opponent's model of the agent. The cognitive mechanisms required for such modeling include deduction, analogy, inductive generalization, and the formation and evaluation of explanatory hypotheses. Explanatory coherence theory captures part of what is involved in APS, particularly in cases involving deception.
On the Role of Biomedical Knowledge in Clinical Reasoning by Experts, Intermediates and Novices
Authors: Boshuizen Henny P.A. , Schmidt Henk G.
Published: August 2005

In two studies the role of biomedical knowledge in the diagnosis of clinical cases was explored. Experiment 1 demonstrated a decrease in the use of biomedical knowledge with increasing expertise. This result appeared to be at variance with some findings reported in the literature (e.g., Lesgold, 1984), but supported those of others (e.g., Patel, Evans, & Groen, 1989). In Experiment 2, three possible explanations for this phenomenon were investigated: (1) rudimentation of biomedical knowledge, (2) inertia, and (3) encapsulation of biomedical knowledge under higher order concepts. Using a combined think‐aloud and post‐hoc explanation methodology, it was shown that experts have more in‐depth biomedical knowledge than novices and subjects at intermediate levels of expertise. The findings generally support a three‐stage model of expertise development in medicine consisting of acquisition of biomedical knowledge, practical experience, and integration of theoretical and experiental knowledge resulting in knowledge encapsulation.
Propositional Versus Structural Semantic Analyses of Medical Diagnostic Thinking
Authors: Lemieux Madeleine , Bordage Georges
Published: August 2005

Two approaches to the study of diagnostic thinking are compared, one mainly propositional, namely that of Patel and Groen (1986), the other mainly semantic, that of Lemieux and Bordage (1986). Patel and Groen analyzed the linear dimension of cardiologists' discourses while solving a case of acute bacterial endocarditis, that is, the before and after propositional rules. A secondary analysis of two of their pothophysiological protocols is done using structural semantic techniques from Lemieux and Bordage where the vertical dimension of the discourses is analyzed, that is, the levels of meaning or semantic axes. Contrary to Patel and Groen's position, making an accurate diagnosis is not explained in terms of pure forward reasoning through networks of causal rules, but by means of networks of semantic qualities abstracted from the symptoms and signs. The semantic qualities operate in the clinician's mind in terms of binary oppositions (e.g., sudden‐gradual, unilateral‐bilateral) whereby each pair of properties constitutes a semantic axis (e.g., sudden‐gradual). The successful diagnosticians are those who use the most diversified and pertinent set of semantic axes and, therefore, have a deeper representation of the problem.
Reasoning About a Semantic Memory Encoding of the Connectivity of Events
Authors: Alterman Richard , Bookman Lawrence A.
Published: August 2005

In artificial intelligence, human understanding of text is modeled by programs that construct representations. A critical question concerns determining the form of the representation (hence, the form of the understanding). This article explores the encoding relationship between the semantic memory and the representation/understanding. An important feature of this relationship is that semantic memory and the constructed representation share the same structure, that is, the structure of the “understanding” is a “copy” of some piece of structure taken from semantic memory. A key idea developed is the notion of conceptual roots, the basic framework of the semantic memory‐encoded understanding/representation.

The research reported in this article examined the twin themes of “copybased” representation/understanding and the conceptual roots by exploring their roles In several reasoning tasks: The conceptual roots can be used to explain succinctly the connection between any two concept‐coherent events in an understanding. The semantic memory encoding provides a measure of importance that quantifies the conceptual emphasis of the understanding; given this measure of importance, It is easy to show that each of the important nodes is either a conceptual root or covered by one of the conceptual roots. A combination of techniques can be used to generate a description of the basic event content of the narrative (i.e., a basic summary): This last task reveals some other interesting properties of the conceptual roots.
Computer Understanding of Conventional Metaphoric Language
Authors: Martin James H.
Published: August 2005

Metaphor is a conventional and ordinary part of language. An approach to metaphor, based on the explicit representation of knowledge about metaphors, has been developed. This approach asserts that the interpretation of conventional metaphoric language should proceed through the direct application of specific knowledge about the metaphors in the language. MIDAS (Metaphor Interpretation, Denotation, and Acquisition System) is a computer program that has been developed based upon this approach. MIDAS can be used to represent knowledge about conventional metaphors, interpret metaphoric language by applying this knowledge, and dynamically learn new metaphors as they are encountered during normal processing.
The Story Gestalt: A Model Of Knowledge‐Intensive Processes in Text Comprehension
Authors: John Mark F.
Published: August 2005

How are knowledge‐intensive, text‐comprehension processes computed? Specifically, how are (1) explicit propositions remembered correctly, (2) pronouns resolved, (3) coherence and prediction inferences drawn, (4) on‐going interpretations revised as more information becomes available, and (5) information learned in specific contexts generalized to novel texts? A constraint satisfaction model is presented that offers a number of advantages over previous models: Each of the previous processes can be seen as examples of the same process of constraint satisfaction, constraints can have strengths to represent the degrees of correlation among information, and the independence of constraints provides insight into generalization. In the model, propositions describing a simple event, such as going to the beach or a restaurant, are sequentially presented to a recurrent PDP network. The model is trained through practice processing a large number of example texts and answering questions. Questions are predicates from propositions explicit or inferable from the text, and the model has to answer with the proposition that fits that predicate. The model learns to perform well, though some processes require substantial training. A second simulation shows how the combinatorics in the training corpus can increase generalization. This effect is explained by introducing the concept of identity and associative constraints that are learned from a corpus. Overall, the model provides a number of insights into how a graded constraint‐satisfaction model can compute knowledge‐intensive processes in text comprehension.
Forward Models: Supervised Learning with a Distal Teacher
Authors: Jordan Michael I. , Rumelhart David E.
Published: August 2005

Internal models of the environment have an important role to play in adaptive systems, in general, and are of particular importance for the supervised learning paradigm. In this article we demonstrate that certain classical problems associated with the notion of the “teacher” in supervised learning can be solved by judicious use of learned internal models as components of the adaptive system. In particular, we show how supervised learning algorithms can be utilized in cases in which an unknown dynamical system intervenes between actions and desired outcomes. Our approach applies to any supervised learning algorithm that is capable of learning in multilayer networks.
Computational Imagery
Authors: Glasgow Janice , Papadias Dimitri
Published: August 2005

After many years of neglect, the topic of mental imagery has recently emerged as an active area of research and debate in the cognitive science community. This article proposes a concept of computational imagery, which has potential applications to problems whose solutions by humans involve the use of mental imagery. Computational imagery can be defined as the ability to represent, retrieve, and reason about spatial and visual information not explicitly stored in long‐term memory.

The article proposes a knowledge representation scheme for computational imagery that incorporates three representations: a long‐term memory, descriptive representation and two working‐memory representations, corresponding to the distinct visual and spatial components of mental imagery. The three representations, and a set of primitive functions, are specified using a formal theory of arrays and implemented in the array‐based language Nial. Although results of studies in mental imagery provide initial motivation for the representations and functionality of the scheme, our ultimate concerns are expressive power, inferential adequacy, and efficiency.
The structure of Design Problem Spaces
Authors: Goel Vinod , Pirolli Peter
Published: August 2005

It is proposed that there are important generalizations about problem solving in design activity that reach across specific disciplines. A framework for the study of design is presented that (a) characterizes design as a radial category and fleshes out the task environment of the prototypical cases; (b) takes the task environment seriously; (c) shows that this task environment occurs in design tasks, but does not occur in every nondesign task; (d) explicates the impact of this task environment on the design problem space; and (e) demonstrates that, given the structure of the information‐processing system, the features noted in the problem spaces of design tasks will not all occur in problem spaces where the task environment is vastly different. This analysis leads to the claim that there are a set of invariant features in the problem spaces of design situations that collectively constitute a design problem space. Protocol studies are reported in which the problem spaces of three design tasks in architecture, mechanical engineering, and instructional design are explored and compared with several protocols from nondesign problem‐solving tasks.
Memory‐Based Hypothesis Formation: Heuristic Learning of Commonsense Causal Relations from Text
Authors: Bozsahin H. Cem , Findler Nicholas V.
Published: August 2005

We present a memory‐based approach to learning commonsense causal relations from episodic text. The method relies on dynamic memory that consists of events, event schemata, episodes, causal heuristics, and causal hypotheses. The learning algorithms are based on applying causal heuristics to precedents of new information. The heuristics are derived from principles of causation, and, to a limited extent, from domain‐related causal reasoning. Learning is defined as finding—and later augmenting—inter‐episodal and intea‐episodal causal connections. The learning algorithms enable inductive generalization of causal associations into AND/OR graphs. The methodology has been implemented and tested in the program NEXUS.
Question‐Answering for Intelligent On‐Line Help: The Process of Intelligent Responding
Authors: Pilkington Rachel M.
Published: August 2005

This article examines the process of specifying a question‐answering help facility in the context of UNIX mail. The specification was based upon experimental expert‐user facilitative dialogues. These dialogues were analyzed using a classification scheme developed for the purpose. The scheme provides a metalanguage for describing patterns of intent and rhetorical structure in dialogue. Using the scheme as a tool, common patterns in expert‐user dialogue emerged, providing insights into both tutoring strategy and the linguistic forms required to generate help output. These were refined to form a set of frames consisting of stereotypical patterns of rhetorical predicates to answer different types of questions which could be adapted to the needs of a particular user. The significance of the system developed from this specification is discussed.
Intermediate Vision: Architecture, Implementation, and Use
Authors: Chapman David
Published: August 2005

This article describes an implemented architecture for intermediate vision. By integrating a variety of Intermediate visual mechanisms and putting them to use in support of concrete activity, the implementation demonstrates their utility. The sytem, SIVS, models psychophysical discoveries about visual attention and search. It is designed to be efficiently implementable in slow, massively parallel, locally connected hardware, such as that of the brain.

SIVS addresses five fundamental problems. Visual attention is required to restrict processing to task‐relevant locations in the image. Visual search finds such locations. Visual routines are a means for nonuniform processing based on task demands. Intermediate objects keep track of intermediate results of this processing. Visual operators are a set of relatively abstract, general‐purpose primitives for spatial analysis, out of which visual routines are assembled.
Defining Emotion Concepts
Authors: Wierzbicka Anna
Published: August 2005

This article demonstrates that emotion concepts—including the so‐called basic ones, such as anger or sadness—can be defined in terms of universal semantic primitives such as “good”, “bad”, “do”, “happen”, “know”, and “want”, in terms of which all areas of meaning, in all languages, can be rigorously and revealingly portrayed.

The definitions proposed here take the form of certain prototypical scripts or scenarios, formulated in terms of thoughts, wants, and feelings. These scripts, however, can be seen as formulas providing rigorous specifications of necessary and sufficient conditions (not for emotions as such, but for emotion concepts), and they do not support the idea that boundaries between emotion concepts are “fuzzy.” On the contrary, the small set of universal semantic primitives employed here (which has emerged from two decades of empirical investigations by the author and colleagues) demonstrates that even apparent synonyms such as sad and unhappy embody different—and fully specifiable—conceptual structures.
Structure and Content in Language Production: A Theory of Frame Constraints in Phonological Speech Errors
Authors: Dell Gary S. , Juliano Cornell , Govindjee Anita
Published: August 2005

Theories of language production propose that utterances are constructed by a mechanism that separates linguistic content from linguistic structure, Linguistic content is retrieved from the mental lexicon, and is then inserted into slots in linguistic structures or frames. Support for this kind of model at the phonological level comes from patterns of phonological speech errors. W present an alternative account of these patterns using a connectionist or parallel distributed proceesing (PDP) model that learns to produce sequences of phonological features. The model's errors exhibit some of the properties of human speech errors, specifically, properties that have been attributed to the action of phonological rules, frames, or other structural generalizations.
Conversation and Behavior Games in the Pragmatics of Dialogue
Authors: Airenti Gabriella , Bara Bruno G. , Colombetti Marco
Published: August 2005

In this article we present the bases for a computational theory of the cognitive processes underlying human communication. The core of the article is devoted to the analysis of the phases in which the process of comprehension of a communicative act can be logically divided: (1) literal meaning, where the reconstruction of the mental states literally expressed by the actor takes place: (2) speaker's meaning, where the partner reconstructs the communicative intentions of the actor; (3) communicative effect, where the partner possibly modifies his own beliefs and intentions; (4) reaction, where the intentions for the generation of the response are produced; and (5) response, where an overt response is constructed. The model appears to be compatible with relevant facts about human behavior. Our hypothesis is that, through communication, an actor tries to exploit the motivational structures of a partner so that the desired goal is generated. A second point is that social behavior requires that cooperation be maintained at some level. In the case of communication, cooperation is, in general, pursued even when the partner does not adhere to the actor's goals, and therefore no cooperation occurs at the behavioral level. This important distinction is reflected in the two kinds of game we introduce to account for communication. The main concept implied in communication is that two agents overtly reach a situation of shared mental states. Our model deals with sharedness through two primitives: shared beliefs and communicative intentions.
A Bayesian‐Network Approach to Lexical Disambiguation
Authors: Eizirik Leila M.R. , Barbosa Valmir C. , Mendes Sueli B.T.
Published: August 2005

Lexical ambiguity can be syntactic if it involves more than one grammatical category for a single word, or semantic if more than one meaning can be associated with a word. In this article we discuss the application of a Bayesian‐network model in the resolution of lexical ambiguities of both types. The network we propose comprises a parsing subnetwork, which can be constructed automatically for any context‐free grammar, and a subnetwork for semantic analysis, which, in the spirit of Fillmore's (1968) case grammars, seeks to fulfill the required cases of all candidates for verb of the sentence. Solving for the highest joint probability of the variables conditioned upon the evidences to the network yields the most likely candidate with its meaning, along with its cases and respective meanings. This is achieved by fixing the values of all evidence nodes concurrently, and then performing a stochastic simulation in which the remaining nodes are updated probabilistically with a high degree of parallelism. The process of disambiguation is directed neither by the syntax nor the semantics, but rather by the interrelation between the two subnetworks. The use of a Bayesian‐network model allows us to express this interrelation between the two subnetworks and among their constituents in a rather direct and rigorous way that, in connection with the convergence properties of the stochastic simulation, reveals a very robust model.
How Experts Solve a Novel Problem in Experimental Design
Authors: Schraagen Jan Maarten
Published: August 2005

Research on expert‐novice differences has mainly focused on how experts solve familiar problems. We know far less about the skills and knowledge used by experts when they are confronted with novel problems within their area of expertise. This article discusses a study in which verbal protocols were taken from subjects of various expertise designing an experiment in an area with which they were unfamiliar. The results showed that even when domain knowledge is lacking, experts solve a novel problem within their area of expertise by dividing the problem into a number of subproblems that are solved in a specified order. The lack of domain knowledge is compensated for by using abstract knowledge structures and domain‐specific heuristic strategies. However, the quality of their solutions is considerably lower than the quality attained by experts who were familiar with the type of problem to be solved. The results suggest that when experts are confronted with novel problems as compared with familiar problems, their form of reasoning remains intact, but the content of their reasoning suffers due to lack of domain knowledge.
Learning Continuous Probability Distributions with Symmetric Diffusion Networks
Authors: Movellan Javier R. , McClelland James L.
Published: August 2005

In this article we present symmetric diffusion networks, a family of networks that instantiate the principles of continuous, stochastic, adaptive and interactive propagation of information. Using methods of Markovion diffusion theory, we formalize the activation dynamics of these networks and then show that they can be trained to reproduce entire multivariate probability distributions on their outputs using the contrastive Hebbion learning rule (CHL). We show that CHL performs gradient descent on an error function that captures differences between desired and obtained continuous multivariate probability distributions. This allows the learning algorithm to go beyond expected values of output units and to approximate complete probability distributions on continuous multivariate activation spaces. We argue that learning continuous distributions is an important task underlying a variety of real‐life situations that were beyond the scope of previous connectionist networks. Deterministic networks, like back propagation, cannot learn this task because they are limited to learning average values of independent output units. Previous stochastic connectionist networks could learn probability distributions but they were limited to discrete variables. Simulations show that symmetric diffusion networks can be trained with the CHL rule to approximate discrete and continuous probability distributions of various types.
Problem‐Solving Restructuration: Elimination of Implicit Constraints
Authors: Richard Jean‐François , Poitrenaud Sébastien , Tijus Charles
Published: August 2005

A general model of problem‐solving processes based on misconception elimination is presented to simulate both impasses and solving processes. The model operates on goal‐related rules and a set of constraint rules in the form of “if (state or goal), do not (Action)” for the explicit constraints in the instructions and the implicit constraints that come from misconceptions of legal moves. When impasses occur, a constraint elimination mechanism is applied. Because successive eliminations of implicit constraints enlarge the problem space and have an effect on planning, the model integrates “plan‐based” and “constraint‐based” approaches to problem‐solving behavior.

Simulating individual protocols of Tower of Hanoi situations shows that the model, which has a proper set of constraints, predicts a single move with no alternative on about 61% of the movements and that protocols are quite successfully simulated movement by movement. Finally, it is shown that many features of previous models are embedded in the constraint elimination model.
Flexible Strategy Use in Young Children's Tic‐Tac‐Toe
Authors: Crowley Kevin , Siegler Robert S.
Published: August 2005

In domains with multiple competing goals, people face a basic challenge: How to make their strategy use flexible enough to deal with shifting circumstances without losing track of their overall objectives. This article examines how young children meet this challenge in one such domain, tic‐tac‐toe. Experiment 1 provides an overviews of development in the area; it indicates that children's tic‐tac‐toe strategies are rule based and that new rules are added one at a time. Experiment 2 demonstrates that even young children flexibly tailor their strategy use to meet shifting circumstances. Experiment 3 indicates that these adaptations are accomplished through a process of goal‐based resource allocation, whereby children focus their cognitive resources on applying rules most consistent with their current primary goal. A computer simulation specifies how this process works and demonstrates its sufficiency for producing behavior much like that of the children. The findings are discussed as part of a broader framework of mechanisms for generating problem‐solving approaches.
The Phylogeny of Rationality
Authors: Pollock John L.
Published: August 2005

A rational agent has beliefs reflecting the state of its environment, and likes or dislikes Its situation. When it finds the world not entirely to Its liking, it tries to change that. We can, accordingly, evaluate a system of cognition in terms of its probable success in bringing about situations that are to the agent's liking. In doing this we are viewing practical reasoning from “the design stance.” It is argued that a considerable amount of the structure of rationality can be elicited as providing the only apporant solutions to various logical and feasibility problems that arise in the course of trying to design a rational agent that satisfies this design specification.
Machine Interpretation of Emotion: Design of a Memory‐Based Expert System for Interpreting Facial Expressions in Terms of Signaled Emotions
Authors: Kearney Garrett D. , McKenzie Sati
Published: August 2005

As a first step in involving user emotion in human‐computer interaction, a memory‐based expert system (JANUS; Kearney, 1991) was designed to interpret facial expression in terms of the signaled emotion. Anticipating that a VDU‐mounted camera will eventually supply face parameters automatically, JANUS now accepts manually made measurements on a digitized full‐face photograph and returns emotion labels used by college students. An intermediate representation in terms of face actions (e.g., mouth open) is also used. Production rules convert the geometry into these. A dynamic memory (Kolodner, 1984; Schank, 1982) interprets the face actions in terms of emotion labels. The memory is dynamic in the sense that new emotion labels can be learned with experience. A prototype system has been implemented on a Sun 2/120 system using POPLOG. Validation studies on the prototype suggest that the interpretations achieved are generally consistent with those of college students without formal instruction in emotion signals.
Connectionist Models and Linguistic Theory: Investigations of Stress Systems in Language
Authors: Gupta Prahlad , Touretzky David S.
Published: August 2005

We question the widespread assumption that linguistic theory should guide the formulation of mechanistic accounts of human language processing. We develop a pseudo‐linguistic theory for the domain of linguistic stress, based on observation of the learning behavior of a perceptron exposed to a variety of stress patterns. There are significant similarities between our analysis of perception stress learning and metrical phonology, the linguistic theory of human stress. Both approaches attempt to identify salient characteristics of the stress systems under examination without reference to the workings of the underlying processor. Our theory and computer simulations exhibit some strikingly suggestive correspondences with metrical theory. We show, however, that our high‐level pseudo‐linguistic account bears no causal relation to processing in the perception, and provides little insight into the nature of this processing. Because of the persuasive similarities between the nature of our theory and linguistic theorizing, we suggest that linguistic theory may be in much the same position. Contrary to the usual assumption, it may not provide useful guidance in attempts to identify processing mechanisms underlying human language.
TSUNAMI: Simultaneous Understanding, Answering, and Memory Interaction for Questions
Authors: Robertson Scott P.
Published: August 2005

Question processing involves parsing, memory retrieval, question categorization, initiation of appropriate answer‐retrieval heuristics, answer formulation, and output. Computational and psychological models have traditionally treated these processes as separate, sequential, independent, and in pursuit of a single answer type at a time. Here this view is challenged and the implications of a theory in which question processes operate simultaneously on multiple question interpretations are explored. A highly interactive model is described in which an expectation‐driven parser generates multiple question candidates, including partially‐specified candidates. Question candidates act as constraints for a matcher which activates memory items. An answer retrieval process examines question candidates and the active portions of memory in an attempt to generate answer candidates. Answer candidates are examined by an output process that derives the final answer. These processes run simultaneously and interact. Three experiments on human question answering are also described which provide evidence that working memory load during question reading is affected by processes related to answer retrieval.
Representations in Distributed Cognitive Tasks
Authors: Zhang Jiaje , Norman Donald A.
Published: August 2005

In this article we propose a theoretical framework of distributed representations and a methodology of representational analysis for the study of distributed cognitive tasks—tasks that require the processing of information distributed across the internal mind and the external environment. The basic principle of distributed representations Is that the representational system of a distributed cognitive task is a set of internal and external representations, which together represent the abstract structure of the task. The basic strategy of representational analysis is to decompose the representation of a hierarchical task into its component levels so that the representational properties at each level can be independently examined. The theoretical framework and the methodology are used to analyze the hierarchical structure of the Tower of Hanoi problem. Based on this analysis, four experiments are designed to examine the representational properties of the Tower of Hanoi. Finally, the nature of external representations is discussed.
Mental Models of the Day/Night Cycle
Authors: Vosniadou Stella , Brewer William F.
Published: August 2005

This article presents the results of an experiment which investigated elementary school children's explanations of the day/night cycle. First, third, and fifth grade children were asked to explain certain phenomena, such as the disappearance of the sun during the night, the disappearance of stars during the day, the apparent movement of the moon, and the alteration of day and night. The results showed that the majority of the children in our sample used in a consistent fashion a small number of relatively well‐defined mental models of the earth, the sun, and the moon to explain the day/night cycle. These mental models of the day/night cycle were empirically accurate, logically consistent and revealed some sensitivity on the part of the children to issues of simplicity of explanation. The younger children formed Initial mental models which provided explanations of the day/night cycle based on everyday experience (e.g., the sun goes down behind mountains, clouds cover up the sun). The older children constructed synthetic mental models (e.g., the sun and the moon revolve around the stationary earth every 24 hours; the earth rotates in an up/down direction and the sun and moon are fixed on opposite sides) which represented attempts to synthesize the culturally accepted view with aspects of their Initial models. A few of the older children appeared to have constructed a mental model of the day/night cycle similar to the scientific one. A theoretical framework is outlined which explains the formation of initial, synthetic, and scientific models of the day/night cycle in terms of the reinterpretation of a hierarchy of constraints, some of which are present early in the child's life, and others which emerge later out of the structure of the acquired knowledge.
Simulation Models of the Influence of Learning Mode and Training Variance on Category Learning
Authors: Elio Renée , Lin Kui
Published: August 2005

This article uses simulation as an empirical method for identifying process models of strategy effects in a category‐learning task. A general set of learning assumptions defined a symbolic learning framework in which alternative simulation models were defined and tested. The goal was to identify process models that could account for previously reported data on the interaction between how a learner encounters category variance across a series of training samples and whether the task instructions suggested an active, hypothesis‐testing approach, or a more passive learning mode. Descriptive characterizations of active and passive learning were mapped into complementary settings of parameters operating with the general learning framework. Alternative models, defined by different configurations of these parameters, were evaluated on their goodness of fit to the observed data. The signature differences between models that best fit the passive learning data and models that best fit the active learning data concerned a delayed versus immediate learning parameter and a degree‐of‐match parameter that determined which patterns were retrieved to make category decisions. A functional account of these parameters is given by considering the learning task as a search process and the role of these parameters in localizing the impact of learning mechanisms in certain areas of the search space. Issues related to simulation as an empirical method for identifying candidate process models are discussed.
On the Interaction of Theory and Data in Concept Learning
Authors: Wisniewski Edward J. , Medin Douglas L.
Published: August 2005

Standard models of concept learning generally focus on deriving statistical properties of a category based on data (i.e., category members and the features that describe them) but fail to give appropriate weight to the contact between people's intuitive theories and these data. Two experiments explored the role of people's prior knowledge or intuitive theories on category learning by manipulating the labels associated with the category. Learning differed dramatically when categories of children's drawings were meaningfully labeled (e.g., “done by creative children”) compared to when they were labeled in a neutral manner. When categories are meaningfully labeled, people bring intuitive theories to the learning context. Learning then involves a process in which people search for evidence in the data that supports abstract features or hypotheses that have been activated by the intuitive theories. In contrast, when categories are labeled in a neutral manner, people search for simple features that distinguish one category from another. Importantly, the final study suggests that learning involves an interaction of people's intuitive theories with data, in which theories and data mutually influence each other. The results strongly suggest that straight‐forward, relatively modular ways of incorporating prior knowledge into models of category learning are inadequate. More telling, the results suggest that standard models may have fundamental limitations. We outline a speculative model of learning in which the interaction of theory and data is tightly coupled. The article concludes by comparing the results to recent artificial intelligence systems that use prior knowledge during learning.
Explaining Emotions
Authors: O'Rorke Paul , Ortony Andrew
Published: August 2005

Emotions and cognition are inextricably intertwined. Feelings influence thoughts and actions, which in turn can give rise to new emotional reactions. We claim that people infer emotional states in others using commonsense psychological theories of the interactions among emotions, cognition, and action. We present a situation calculus theory of emotion elicitation representing knowledge underlying commonsense causal reasoning involving emotions, and show how the theory can be used to construct explanations for emotional states. The method for constructing explanations is based on the notion of abduction. This method has been implemented in a computer program called AbMaL. The results of computational experiments using AbMaL to construct explanations of examples based on cases taken from a diary study of emotions indicate that the abductive approach to explanatory reasoning about emotions offers significant advantages. We found that the majority of the diary study examples cannot be explained using deduction alone, but they can be explained by making abjuctive inferences. These inferences provide useful information relevant to emotional states.
Orientation Invariance and Geometric Primitives in Shape Recognition
Authors: Farah Martha J. , Rochlin Robin , Klein Karen L.
Published: August 2005

Although it is generally assumed that vision is orientation invariant, that is, that shapes can be recognized regardless of viewing angle, there is little evidence that speaks directly to this issue, and what evidence there is fails to support orientation invariance. We propose an explanation for the previous results in terms of the kinds of shape primitives used by the visual system in achieving orientation invariance: Whereas contours are used at stages of vision that ore not orientation invariant, surfaces and/or volumes are used at stages of vision that are orientation invariant. The stimuli in previously reported studies were wire forms, which can represented only in terms of contour. In four experiments, testing both short‐term and long‐term memory for shape, we replicated the previous failures of orientation invariance using wire forms, but found relatively good or perfect orientation invariance with equivalently shaped surfaces.