bluuebunny/crossref_metadata_2025 huggingface dataset:
Encoding Shape and Spatial Relations: The Role of Receptive Field Size in Coordinating Complementary Representations
Authors: Jacobs Robert A. , Kosslyn Stephen M.
Published: August 2005

An effective functional architecture facilitates interactions among subsystems that are often used together. Computer simulations showed that differences in receptive field sizes can promote such organization. When input was filtered through relatively small nonoverlapping receptive fields, artificial neural net‐works learned to categorize shapes relatively quickly; in contrast, when input was filtered through relatively large overlapping receptive fields, networks learned to encode specific shape exemplars or metric spatial relations relatively quickly. Moreover, when the receptive field sizes were allowed to adapt during learning, networks developed smaller receptive fields when they were trained to categorize shapes or spatial relations, and developed larger receptive fields when they were trained to encode specific exemplars or metric distances. In addition, when pairs of networks were constrained to use input from the same type of receptive fields, networks learned a task faster when they were paired with networks that were trained to perform a compatible type of task. Finally, using a novel modular architecture, networks were not preassigned a task, but rather competed to perform the different tasks. Networks with small nonover‐lapping receptive fields tended to win the competition for categorical tasks whereas networks with large overlapping receptive fields tended to win the competition for exemplar/metric tasks.
Constraints on Analogical Mapping: A Comparison of Three Models
Authors: Keane Mark T. , Ledgeway Tim , Duff Stuart
Published: August 2005

Three theories of analogy have been proposed that are supported by computational models and data from experiments on human analogical abilities. In this article we show how these theories can be unified within a common metatheoretical framework that distinguishes among levels of informational, behavioral, and hardware constraints. This framework clarifies the distinctions among three computational models in the literature: the Analogical Constraint Mapping Engine (ACME), the Structure‐Mapping Engine (SME), and the Incremental Analogy Machine (IAM). We then go on to develop a methodology for the comparative testing of these models. In two different manipulations of an analogical mapping task we compare the results of computational experiments with these models against the results of psychological experiments. In the first experiment we show that increasing the number of similar elements in two analogical domains decreases the response time taken to reach the correct mapping for an analogy problem. In the second psychological experiment we find that the order in which the elements of the two domains are presented has significant facilitative effects on the ease of analogical mapping. Of the three models, only IAM embodies behavioral constraints and predicts both of these results. Finally, the immediate implications of these results for analogy research are discussed, along with the wider implications the research has for cognitive science methodology.
Eliciting Self‐Explanations Improves Understanding
Authors: Chi Michelene T.H. , De Leeuw Nicholas , Chiu Mei‐Hung , Lavancher Christian
Published: August 2005

Learning involves the integration of new information into existing knowledge. Generating explanations to oneself (self‐explaining) facilitates that integration process. Previously, self‐explanation has been shown to improve the acquisition of problem‐solving skills when studying worked‐out examples. This study extends that finding, showing that self‐explanation can also be facilitative when it is explicitly promoted, in the context of learning declarative knowledge from an expository text. Without any extensive training, 14 eighth‐grade students were merely asked to self‐explain after reading each line of a passage on the human circulatory system. Ten students in the control group read the same text twice, but were not prompted to self‐explain. All of the students were tested for their circulatory system knowledge before and after reading the text. The prompted group had a greater gain from the pretest to the posttest. Moreover, prompted students who generated a large number of self‐explanations (the high explainers) learned with greater understanding than low explainers. Understanding was assessed by answering very complex questions and inducing the function of a component when it was only implicitly stated. Understanding was further captured by a mental model analysis of the self‐explanation protocols. High explainers all achieved the correct mental model of the circulatory system, whereas many of the unprompted students as well as the low explainers did not. Three processing characteristics of self‐explaining are considered as reasons for the gains in deeper understanding.
For the Right Reasons: The FORR Architecture for Learning in a Skill Domain
Authors: Epstein Susan L.
Published: August 2005

The theme of this article is that knowledge acquisition can be managed as a transition from general expertise to specific expertise. FORR is a general architecture for learning and problem solving that models expertise at a set of related problem classes. This architecture postulates initial brood domain knowledge, and gradually specializes it to simulate expertise in individual problem classes. FORR is based upon a realistic portrayal of the nature of human expertise and its application. Rather than restrict learning to a single method or a single kind of knowledge, the architecture pragmatically requires multiple, disagreeing heuristic agents to collaborate on decisions. A FORR‐based program learns both from its apprenticeship to an external expert model and from practice in its domain. An implementation for game playing is described that raises interesting issues about the organization and modification of conflicting expertise, and the role that experience plays in such learning. FORR's principal strengths are its smooth integration of multiple expertise, its ability to learn many ways, its tolerance for human and machine error, its graceful degradation, its transparency, and its support for a developmental paradigm.
On Distinguishing Epistemic from Pragmatic Action
Authors: Kirsh David , Maglio Paul
Published: August 2005

We present data and argument to show that in Tetris—a real‐time, interactive video game—certain cognitive and perceptual problems are more quickly, easily, and reliably solved by performing actions in the world than by performing computational actions in the head alone. We have found that some of the translations and rotations made by players of this video game are best understood as actions that use the world to improve cognition. These actions are not used to implement a plan, or to implement a reaction; they are used to change the world in order to simplify the problem‐solving task. Thus, we distinguish pragmatic actions—actions performed to bring one physically closer to a goal—from epistemic actions—actions performed to uncover informatioan that is hidden or hard to compute mentally.

To illustrate the need for epistemic actions, we first develop a standard information‐processing model of Tetris cognition and show that it cannot explain performance data from human players of the game—even when we relax the assumption of fully sequential processing. Standard models disregard many actions taken by players because they appear unmotivated or superfluous. However, we show that such actions are actually far from superfluous; they play a valuable role in improving human performance. We argue that traditional accounts are limited because they regard action as having a single function: to change the world. By recognizing a second function of action—an epistemic function—we can explain many of the actions that a traditional model cannot. Although our argument is supported by numerous examples specifically from Tetris, we outline how the new category of epistemic action can be incorporated into theories of action more generally.
The Manipulation of Images to Handle Indeterminacy in Spatial Reasoning
Authors: Ioerger Thomas R.
Published: August 2005

In this article, we propose a novel method for using images to do spatial reasoning. The task we consider is to answer questions about descriptions involving spatial prepositional phrases. Psychological experiments suggest that imagery is an appropriate cognitive model of how people solve such problems when descriptions are determinate. However, natural language discourse often contains indeterminate descriptions, and imagery has been criticized for its apparent inability to represent incomplete information necessary for making inferences in such situations.

We propose a new image‐based method of reasoning, called ISR for indeterminacy in spatial reasoning, which dynamically constructs and inspects multiple images to reason about spatial prepositional phrases. The consideration of more than one consistent image, as a model of the description, increases the accuracy of reasoning. However, because it is intractable to consider every possible consistent image, ISR uses several heuristics to generate only the most relevant images, admitting some inaccuracy. Thus, ISR exploits the computational trade‐off between efficiency and accuracy, and we show empirically in a representative domain that this trade‐off is effective.

The ISR method demonstrates that, with the help of specialized procedures, imagery can be more accurate for reasoning about spatially indeterminate descriptions. However, as we observe in attempting to extend ISR to a scaled‐up domain, the procedural encoding of knowledge hinders the maintenance of an effective computational trade‐off. We conclude that imagery is distinct from axiomatic/deductive approaches only in having heuristic knowledge about space for making approximations.
A Symbolic Model of the Nonconscious Acquisition of Information
Authors: Ling Charles X. , Marinov Marin
Published: August 2005

This article presents counter evidence against Smolensky's theory that human intuitive/nonconscious congnitive processes can only be accurately explained in terms of subsymbolic computations carried out in artificial neural networks. We presentsymboliclearning models of two well‐studied, complicated cognitive tasks involving nonconscious acquisition of information: learning production rules and artificial finite state grammars. Our results demonstrate that intuitive learning does not imply subsymbolic computation, and that the already well‐established, perceived correlation between “conscious” and “symbolic” on the one hand, and between “nonconscious” and “subsymbolic” on the other, does not exist.
Prototypes, Location, and Associative Networks (PLAN): Towards a Unified Theory of Cognitive Mapping
Authors: Chown Eric , Kaplan Stephen , Kortenkamp David
Published: August 2005

An integrated representation of large‐scale space, or cognitive map, colled PLAN, is presented that attempts to address a broader spectrum of issues than has been previously attempted in a single model. Rather than examining way‐finding as a process separate from the rest of cognition, one or the fundamental goals of this work is to examine how the wayfinding process is integrated into general cognition. One result of this approach is that the model is “heads‐up,” or scene‐based, because it takes advantage of the properties of the human visual system and, particularly, the visual system's split into two pathways. The emphasis on the human location or “where” system is new to cognitive mapping and is port of an attempt to synthesize prototype theory, associative networks and location together in a connectionist system. Not all of PLAN is new, however. Many of its parts have analogues in one or another preexisting theory. What makes PLAN unique is integrating the various components into a coherent whole, and the capacity of this resulting system to speak to a wide range of constraints. Our approach emphasizes adaptiveness; thus, our focus on such issues as ease of use and efficiency of learning. The result is a model that has a stronger relationship both to the environment, and to the ways that humans interact with it, compared with previous models. The resulting model is examined in some detail and compared to other systems.
Reduced Memory Representations for Music
Authors: Large Edward W. , Palmėr Caroline , Pollack Jordan B.
Published: August 2005

We address the problem of musical variation (identification of different musical sequences as variations) and its implications for mental representations of music. According to reductionist theories, listeners judge the structural importance of musical events while forming mental representations. These judgments may result from the production of reduced memory representations that retain only the musical gist. In a study of improvised music performance, pianists produced variations on melodies. Analyses of the musical events retained across variations provided support for the reductionist account of structural importance. A neural network trained to produce reduced memory representations for the same melodies represented structurally important events more efficiently than others. Agreement among the musicians' improvisations, the network model, and music‐theoretic predictions suggest that perceived constancy across musical variation is a natural result of a reductionist mechanism for producing memory representations.
A Cognitive Theory of Graphical and Linguistic Reasoning: Logic and Implementation
Authors: Stenning Keith , Oberlander Jon
Published: August 2005

We discuss external and internal graphical and linguistic representational systems. We argue that a cognitive theory of peoples' reasoning performance must account for (a) the logical equivalence of inferences expressed in graphical and linguistic form, and (b) the implementational differences that affect facility of inference. Our theory proposes that graphical representation limit abstraction and thereby aid “processibility”. We discuss the ideas of specificity and abstraction, and their cognitive relevance. Empirical support both comes from tasks which involve the manipulation of external graphics and tasks that do not. For the former, we take Euler's (1772) circles, provide a novel computational reconstruction, show how it captures abstractions, and contrast it with earlier construals and with Johnson‐Laird's (1983) mental models representations. We demonstrate equivalence of the graphical Euler system, and the nongraphical mental models system. For tasks not involving manipulation of external graphics, we discuss text comprehension, and the mental performance of syllogisms. By positing an internal system with the same specificity as Euler's circles, we cover the mental models data, and generate new empirical predictions. Finally, we consider how the architecture of working memory explains why such specific representations are relatively easy to store.
MAC/FAC: A Model of Similarity‐Based Retrieval
Authors: Forbus Kenneth D. , Gentner Dedre , Law Keith
Published: August 2005

We present a model of similarity‐based retrieval that attempts to capture three seemingly contradictory psychological phenomena: (a) structural commonalities are weighed more heavily than surface commonalities in similarity judgments for items in working memory; (b) in retrieval, superficial similarity is more important than structural similarity; and yet (c) purely structural (analogical) remindings e sometimes experienced. Our model, MAC/FAC, explains these phenomena in terms of a two‐stage process. The first stage uses a computationally cheap, non‐structural matcher to filter candidate long‐term memory items. It uses content vectors, a redundant encoding of structured representations whose dot product estimates how well the corresponding structural representations will match. The second stage uses SME (structure‐mapping engine) to compute structural matches on the handful of items found by the first stage. We show the utility of the MAC/FAC model through a series of computational experiments: (a) We demonstrate that MAC/FAC can model patterns of access found in psychological data; (b) we argue via sensitivity analyses that these simulation results rely on the theory; and (c) we compare the performance of MAC/FAC with ARCS, an alternate model of similarity‐based retrieval, and demonstrate that MAC/FAC explains the data better than ARCS. Finally, we discuss limitations and possible extensions of the model, relationships with other recent retrieval models, and place MAC/FAC in the context of other recent work on the nature of similarity.
The Alleged Priority of Literal Interpretation
Authors: Récanati François
Published: August 2005

In this article, I argue against a widely accepted model of utterance interpretation, namely the LS model (literality‐based serial model), according to which the literal interpretation of an utterance (the proposition literally expressed by that utterance) must be computed before nonliteral interpretations can be entertained. Alleged arguments in favor of this model are shown to be fallacious, counter‐examples are provided, and alternative models are sketched.
Computational Interpretations of the Gricean Maxims in the Generation of Referring Expressions
Authors: Dale Robert , Reiter Ehud
Published: August 2005

We examine the problem of generating definite noun phrases that are appropriate referring expressions; that is, noun phrases that (a) successfully identify the intended referent to the hearer whilst (b) not conveying to him or her any false conversational implicatures (Grice, 1975). We review several possible computational interpretations of the conversational implicature maxims, with different computational costs, and argue that the simplest may be the best, because it seems to be closest to what human speakers do. We describe our recommended algorithm in detail, along with a specification of the resources a host system must provide in order to make use of the algorithm, and an implementation used in the natural language generation component of the IDAS system.
How a Cockpit Remembers Its Speeds
Authors: Hutchins Edwin
Published: August 2005

Cognitive science normally takes the individual agent as its unit of analysis. In many human endeavors, however, the outcomes of interest are not determined entirely by the information processing properties of individuals. Nor can they be inferred from the properties of the individual agents, alone, no matter how detailed the knowledge of the properties of those individuals may be. In commercial aviation, for example, the successful completion of a flight is produced by a system that typically includes two or more pilots interacting with each other and with a suite of technological devices. This article presents a theoretical framework that takes a distributed, socio‐technical system rather than an individual mind as its primary unit of analysis. This framework is explicitly cognitive in that it is concerned with how information is represented and how representations are transformed and propagated in the performance of tasks. An analysis of a memory task in the cockpit of a commercial airliner shows how the cognitive properties of such distributed systems can differ radically from the cognitive properties of the individuals who inhabit them.
Learning to Troubleshoot: Multistrategy Learning of Diagnostic Knowledge for a Real‐World Problem‐Solving Task
Authors: Ram Ashwin , Narayanan S. , Cox Michael T.
Published: August 2005

This article presents a computational model of the learning of diagnostic knowledge, based on observations of human operators engaged in real‐world troubleshooting tasks. We present a model of problem solving and learning in which the reasoner introspects about its own performance on the problem‐solving task, identifies what it needs to learn to improve its performance, formulates learning goals to acquire the required knowledge, and pursues its learning goals using multiple learning strategies. The model is implemented in a computer system which provides a case study based on observations of troubleshooting operators and protocol analysis of the data gathered in the test area of an operational electronics manufacturing plant. The model not only addresses issues in human learning, but, in addition, is computationally justified as a uniform, extensible framework for multistrategy learning.
The Nature and Origin of Rational Errors in Arithmetic Thinking: Induction from Examples and Prior Knowledge
Authors: Ben‐Zeev Talia
Published: August 2005

Students systematically and deliberately apply rule‐based but erroneous algorithms to solving unfamiliar arithmetic problems. These algorithms result in erroneous solutions termed rational errors. Computationally, students' erroneous algorithms can be represented by perturbations or bugs in otherwise correct arithmetic algorithms (Brown & VanLehn, 1980; Langley & Ohilson, 1984; VanLehn, 1983, 1986, 1990; Young S O'Sheo, 1981). Bugs are useful for describing how rational errors occur but bugs are not sufficient for explaining their origin. A possible explanation for this is that rational errors are the result of incorrect induction from examples. This prediction is termed the “induction hypothesis” (VanLehn, 1986). The purpose of the present study was to: (a) expand on post formulations of the induction hypothesis, and (b) use a new methodology to test the induction hypothesis more carefully than has been done previously. The first step involved teaching participants a new number system called NewAbacus, a written modification of the abacus system. The second step consisted of dividing them into different groups, where each individual received an example of only one port of the NewAbacus addition algorithm. During the third and final step, participants were instructed to solve both familiar and unfamiliar types of addition problems in NewAbacus. The induction hypothesis was supported by using both empirical and computational investigations.
A Source of Bayesian Priors
Authors: Osherson Daniel , Smith Edward E. , Shafir Eldar , Gualtierotti Antoine , Biolsi Kevin
Published: August 2005

Establishing reasonable, prior distributions remains a significant obstacle for the construction of probabilistic expert systems. Human assessment of chance is often relied upon for this purpose, but this has the drawback of being inconsistent with axioms of probability. This article advances a method for extracting a coherent distribution of probability from human judgment. The method is based on a psychological model of probabilistic reasoning, followed by a correction phase using linear programming.
A Connectionist Model of Phonological Representation in Speech Perception
Authors: Gaskell M. Gareth , Hare Mary , Marslen‐Wilson William D.
Published: August 2005

A number of recent studies have examined the effects of phonological variation on the perception of speech. These studies show that both the lexical representations of words and the mechanisms of lexical access are organized so that natural, systematic variation is tolerated by the perceptual system, while a general intolerance of random deviation is maintained. Lexical abstraction distinguishes between phonetic features that form the invariant core of a word and those that are susceptible to variation. Phonological inference relies on the context of surface changes to retrieve the underlying phonological form. In this article we present a model of these processes in speech perception, based on connectionist learning techniques. A simple recurrent network was trained on the mapping from the variant surface form of speech to the underlying form. Once trained, the network exhibited features of both abstraction and inference in its processing of normal speech, and predicted that similar behavior will be found in the perception of nonsense words. This prediction was confirmed in subsequent research (Gaskell & Marslen‐Wilson, 1994).
Sense Generation: A “Quasi‐Classical” Approach to Concepts and Concept Combination
Authors: Franks Bradley
Published: August 2005

This article presents a detailed formal approach to concepts and concept combination. Sense generation is a competence‐level theory that attempts to respect constraints from the various cognitive sciences, and postulates “quasi‐classical” conceptual structures where attributes receive only one value (but are defeasible and so do not represent necessary and sufficient conditions on category membership) and where classification is binary (but explicitly context‐sensitive). It is also argued that any general theory of concepts must account for “privative” combinations (e.g., stone lion, fake gun, apparent friend) as extreme test‐cases of representational and classificatory flexibility. The approach presented therefore provides a treatment of these combinations. The approach differentiates between the “lexical concept” (the stable information represented in a mental lexicon) which acts as a base from which the various “senses” (flexible contents associated with words and phrases in context, and used in classification) are “generated.” Generation allows nonmonotonicity, so that in different circumstances, different attributes may be defeated or modified. Classification is treated as relative to the perspective adopted, so that a classification acceptable from one perspective may be unacceptable from another, without contradiction. The result is a view that assumes bottom‐up priority in concept combination, where the range of senses generated by bottom‐up rules of combination is tempered by pragmatic‐communicative constraints on classification. An account of the representational and classification behavior of privative combinations is outlined, and the article concludes with a discussion of some of the implications of the approach.
Program Structure and Design
Authors: Rist Robert S.
Published: August 2005

Most models of computer programming explain the programmer's behaviour by a single design strategy. This article presents a cognitive architecture that uses cue‐based search to model multiple design strategies including procedural, functional, means‐end or focal, and opportunistic design. The model has been implemented in an artificial intelligence (AI) system that generates Pascal programs from English specifications.

Knowledge is represented as nodes that reside in internal or external memory, where a node encodes an action that may range from a line of code to a routine in size. A program is built by linking nodes through a search cue of the form . The cue is broadcast to memory, and any matching node is returned; the cue provides a question to ask, and the return provides the answer. A cue on the newly linked node is then selected as a new focus, and the search process repeated. Each design strategy defines a specific node visiting order that traverses the program structure through its links.
Correlations Between Input and Output Units in Neural Networks
Authors: Cook Norman D.
Published: August 2005

Correlation analyses of recent back‐propagation neural networks show that network results are due to imbalances in stimulus input. Conclusions concerning the effects of receptive field size, hemispheric specialization, and other issues of relevance to psychology cannot therefore be drawn until the dominating effects of low‐level correlations are removed. Statistical techniques for evaluating the stimulus materials for neural networks are introduced.
Neural Network Models as Evidence for Different Types of Visual Representations
Authors: Kosslyn Stephen M. , Chabris Christopher F. , Baker David P.
Published: August 2005

Cook (1995) criticizes the work of Jacobs and Kosslyn (1994) on spatial relations, shape representations, and receptive fields in neural network models on the grounds that first‐order correlations between input and output unit activities can explain the results. We reply briefly to Cook's arguments here (and in Kosslyn, Chabris, Marsolek, Jacobs & Koenig, 1995) and discuss how new simulations can confirm the importance of receptive field size as a crucial variable in the encoding of categorical and coordinate spatial relations and the corresponding shape representations; such simulations would testify to the computational distinction between the different types of representations.
Generating Facial Expressions for Speech
Authors: Pelachaud Catherine , Badler Norman I. , Steedman Mark
Published: August 2005

This article reports results from a program that produces high‐quality animation of facial expressions and head movements as automatically as possible in conjunction with meaning‐based speech synthesis, including spoken intonation. The goal of the research is as much to test and define our theories of the formal semantics for such gestures, as to produce convincing animation. Towards this end, we have produced a high‐level programming language for three‐dimensional (3‐D) animation of facial expressions. We have been concerned primarily with expressions conveying information correlated with the intonation of the voice: This includes the differences of timing, pitch, and emphasis that are related to such semantic distinctions of discourse as “focus,”“topic,” and “comment,”“theme” and “rheme,” or “given” and “new” information. We are also interested in the relation of affect or emotion to facial expression. Until now, systems have not embodied such rule‐governed translation from spoken utterance meaning to facial expressions. Our system embodies rules that describe and coordinate these relations: intonation/information, intonation/affect, and facial expressions/affect. A meaning representation includes discourse information: What is contrastive/background information in the given context, and what is the “topic” or “theme” of the discourse? The system maps the meaning representation into how accents and their placement are chosen, how they are conveyed over facial expression, and how speech and facial expressions are coordinated. This determines a sequence of functional groups: lip shapes, conversational signals, punctuators, regulators, and manipulators. Our algorithms then impose synchrony, create coarticulation effects, and determine affectual signals, eye and head movements. The lowest level representation is the Facial Action Coding System (FACS), which makes the generation system portable to other facial models.
Subsymbolic Case‐Role Analysis of Sentences with Embedded Clauses
Authors: Miikkulainen Risto
Published: August 2005

A distributed neural network model called SPEC for processing sentences with recursive relative clauses is described. The model is based on separating the tasks of segmenting the input word sequence into clauses, forming the case‐role representations, and keeping track of the recursive embeddings into different modules. The system needs to be trained only with the basic sentence constructs, and it generalizes not only to new instances of familiar relative clause structures but to novel structures as well. SPEC exhibits plausible memory degradation as the depth of the center embeddings increases, its memory is primed by earlier constituents, and its performance is aided by semantic constraints between the constituents. The ability to process structure is largely due to a central executive network that monitors and controls the execution of the entire system. This way, in contrast to earlier subsymbolic systems, parsing is modeled as a controlled high‐level process rather than one based on automatic reflex responses.
The Impact of Goal Specificity on Strategy Use and the Acquisition of Problem Structure
Authors: Vollmeyer Regina , Burns Bruce D. , Holyoak Keith J.
Published: August 2005

Theories of skill acquisition have made radically different predictions about the role of general problem‐solving methods in acquiring rules that promote effective transfer to new problems. Under one view, methods that focus on reaching specific goals, such as means‐ends analysis, are assumed to provide the basis for efficient knowledge compilation (Anderson, 1987), whereas under an alternative view such methods are believed to disrupt rule induction (Sweller, 1988). We suggest that the role of general methods in learning varies with both the specificity of the problem solver's goal and the systematicity of the strategies used for testing hypotheses about rules. In the absence of a specific goal people are more likely to use a rule‐induction learning strategy, whereas provision of a specific goal fosters use of difference reduction, which tends to be a non‐rule‐induction strategy. We performed two experiments to investigate the impact of goal specificity and systematicity of rule‐induction strategies in learning and transfer within a complex dynamic system. The results of Experiment 1 indicated that during free exploration of a problem space, greater learning occurred if participants adopted more systematic strategies for rule induction, and that participants come to favor such strategies. Experiment 2 revealed that participants who were provided with a specific goal performed well on the initial problem but were impaired on a transfer test using a similar problem with a different goal. Instruction on a systematic rule‐induction strategy facilitated solution for both the initial and transfer problems, but participants' use of this strategy declined if they had a specific goal. Our results support Sweller's (1988) proposal that general problemsolving methods applied to a specific goal foster acquisition of knowledge about an isolated solution path but do not provide an effective way of learning the overall structure of a problem space. We interpret these results in terms of dualspace theories of search through problem space.
On the Validity of Simulating Stagewise Development by Means of PDP Networks: Application of Catastrophe Analysis and an Experimental Test of Rule‐Like Network Performance
Authors: Raijmakers Maartje E.J. , von Koten Sylvester , Molenaar Peter C.M.
Published: August 2005

This article addresses the ability of Parallel Distributed Processing (PDP) networks to generate stagewise cognitive development in accordance with Piaget's theory of cognitive epigenesis. We carried out a replication study of the simulation experiments by McClelland (1989) and McClelland and Jenkins (1991) in which a PDP network learns to solve balance scale problems. In objective tests motivated from catastrophe theory, a mathematical theory of transitions in epigenetical systems, no evidence for stage transitions in network performance was found. It is concluded that PDP networks lack the ability to recover the positive outcomes of analogous catastrophe analyses of real cognitive developmental data. In an attempt to further characterize the learning behaviour of PDP networks, we carried out a second simulation study using the discrimination‐shift paradigm. The results thus obtained indicate that PDP learning is compatible with the learning of stimulus‐response relationships, not with the acquisition of mediating rules such as conceived in (neo‐)Piagetian theory. In closing, we speculate about the feasibility of simulating stagewise development with alternative network architectures.
A Probabilistic Model of Lexical and Syntactic Access and Disambiguation
Authors: Jurafsky Daniel
Published: August 2005

The problems of access—retrieving linguistic structure from some mental grammar —and disambiguation—choosing among these structures to correctly parse ambiguous linguistic input—are fundamental to language understanding. The literature abounds with psychological results on lexical access, the access of idioms, syntactic rule access, parsing preferences, syntactic disambiguation, and the processing of garden‐path sentences. Unfortunately, it has been difficult to combine models which account for these results to build a general, uniform model of access and disambiguation at the lexical, idiomatic, and syntactic levels. For example, psycholinguistic theories of lexical access and idiom access and parsing theories of syntactic rule access have almost no commonality in methodology or coverage of psycholinguistic data. This article presents a single probabilistic algorithm which models both the access and disambiguation of linguistic knowledge. The algorithm is based on a parallel parser which ranks constructions for access, and interpretations for disambiguation, by their conditional probability. Low‐ranked constructions and interpretations are pruned through beam‐search; this pruning accounts, among other things, for the garden‐path effect. I show that this motivated probabilistic treatment accounts for a wide variety of psycholinguistic results, arguing for a more uniform representation of linguistic knowledge and for the use of probabilistically‐enriched grammars and interpreters as models of human knowledge of and processing of language.
Episodic Learner Modeling
Authors: Weber Gerhard
Published: August 2005

Modeling the learner is a central aspect of intelligent tutoring systems and knowledge‐based help systems that support learners in complex problem‐solving domains. In this article, the episodic learner model ELM is introduced as a hybrid system that analyses novices' solutions to programming tasks based on both rule‐based and case‐based reasoning. ELM behaves like to a human tutor. Initially, ELM is able to analyze problem solutions based only on its domain knowledge. With increasing knowledge about a particular learner captured in a dynamic episodic case base, it adapts to the learner's individual problem‐solving behavior. Two simulation studies were performed to validate the system. The first study shows that the system can learn which rules are applied successfully to diagnose code produced by programmers and that using this information reduces the computational effort of diagnoses. Using information from the episodic learner model additionally speeds up the diagnostic process. The second study shows that ELM is able to predict individual solutions. Finally, correspondences and differences to related systems are discussed.
Social Context in HCl: A New Framework for Mental Models, Cooperation, and Communication
Authors: Mantovani Giuseppe
Published: August 2005

This article considers current research in computer‐supported cooperative work (CSCW), computer‐mediated communication (CMC), and distributed artificial intelligence (DAl). These areas need an articulated model of social contexts to bridge the persisting gap between social and technological dimensions in computer system design and use.

A conceptual model of context is presented to account for both cooperation‐conflict and communication‐negotiation processes. The model conceives of contexts as including not only physical objects and other people but also social norms which influence both individuals and organizations. It assumes that computer system use occurs in social scenarios in which the features cannot be reduced to any type of input or data in the world that designers and users can process along with other information coming from the current task. The model is built on three levels: from social contexts as normative order (Level 1), to specific and intrinsically complex situations (Level 2), to person‐computer interactions for the performance of particular tasks (Level 3).

The model has three main implications. First, Human‐Computer Interaction (HCl) studies—especially scenario‐based design—may profit from a fresh topdown approach to designers' and users' mental models taking into account normative social processes which have been neglected in previous research. Second, CSCW may realize how deeply discrepant perspectives affect multi‐agent environments and why in real working life negotiation is intertwined with cooperation. Designers may use this insight to design systems allowing more place for negotiation among actors. Third, we should dismiss the view that CMC lacks adequate social cues and fosters impulsive behavior. Cognitive processes such as categorization, stereotype construction, and social identification can make electronic environments even more strongly sensible to social norms than face‐to‐face communication. Context, according to our model, is not restricted to the physical copresence of other people but consists mainly of processes providing situations with socially recognizable meaning.
Multiattribute Decision Making in Context: A Dynamic Neural Network Methodology
Authors: Leven Samuel J. , Levine Daniel S.
Published: August 2005

A theoretical structure for multiattribute decision making is presented, based on a dynamical system for interactions in a neural network incorporating affective and rational variables. This enables modeling of problems that elude two prevailing economic decision theories: subjective expected utility theory and prospect theory. The network is unlike some that fit economic data by choosing optimal weights or coefficients within a predetermined mathematical framework. Rather, the framework itself is based on principles used elsewhere to model many other cognitive and behavioral data, in a manner approximating how humans perform behavioral functions. Different, interconnected modules within the network encode (a) attributes of objects among which choices are made, (b) object categories, (c) and goals of the decision maker. An example is utilized to simulate the actual consumer choice between old and new versions of Coca‐Cola. Potential applications are also discussed to market decisions involving negotiations between participants, such as international petroleum traders.
Learning Consistent, Interactive, and Meaningful Task‐Action Mappings: A Computational Model
Authors: Howes Andrew , Young Richard M.
Published: August 2005

Within the field of human‐computer interaction, the study of the interaction between people and computers has revealed many phenomena. For example, highly interactive devices, such as the Apple Macintosh, are often easier to learn and use than keyboard‐based devices such as Unix. Similarly, consistent interfaces are easier to learn and use than inconsistent ones. This article describes an integrated cognitive model designed to exhibit a range of these phenomena while learning task‐action mappings: action sequences for achieving simple goals, such as opening a file in a word processor. The model, called TAL, is of a user who is familiar with the basic operations of a keyboard and mouse, but unfamiliar with the particular menu structures, words, and actions required to use the device. The model is constructed in Soar and employs a single set of architectural mechanisms. It exhibits behavior that captures human preference for consistent, interactive, and meaningful task‐action mappings.
Text Integration and Mathematical Connections: A Computer Model of Arithmetic Word Problem Solving
Authors: LeBlanc Mark D. , Weber‐Russell Sylvia
Published: August 2005

Understanding arithmetic word problems involves a complex interaction of text comprehension and mathematical processes. This article presents a computer simulation designed to capture the working memory demands required in “bottomup” comprehension of arithmetic word problems. The simulation's sentence‐level parser and text integration component reflect the importance of processing the problem from its original natural language presentation. Children's probability of solution was analyzed in exploratory regression analyses as a function of the simulation's sentence‐level and text integration processes. Working memory variables measuring the combined effects of concepts to remember and text integration inferences account for a significant proportion of variance in children's solution probabilities across the first four grade levels (K‐3). Consistent with previous results from others, which highlighted the significance of small changes in problem wording, the simulation offers a process‐oriented perspective as to why natural language presentation constrains the comprehension of mathematical relationships.
Primitive Auditory Segregation Based on Oscillatory Correlation
Authors: Wang DeLiang
Published: August 2005

Auditory scene analysis is critical for complex auditory processing. We study auditory segregation from the neural network perspective, and develop a framework for primitive auditory scene analysis. The architecture is a laterally coupled two‐dimensional network of relaxation oscillators with a global inhibitor. One dimension represents time and another one represents frequency. We show that this architecture, plus systematic delay lines, can in real time group auditory features into a stream by phase synchrony and segregate different streams by desynchronization. The network demonstrates a set of psychological phenomena regarding primitive auditory scene analysis, including dependency on frequency proximity and the rate of presentation, sequential capturing, and competition among different perceptual organizations. We offer a neurocomputational theory—shifting synchronization theory—for explaining how auditory segregation might be achieved in the brain, and the psychological phenomenon of stream segregation. Possible extensions of the model are discussed.
Shuttling Between Depictive Models and Abstract Rules: Induction and Fallback
Authors: Schwartz Daniel L. , Black John B.
Published: August 2005

A productive way to think about imagistic mental models of physical systems is as though they were sources of quasi‐empirical evidence. People depict or imagine events at those points in time when they would experiment with the world if possible. Moreover, just as they would do when observing the world, people induce patterns of behavior from the results depicted in their imaginations. These resulting patterns of behavior can then be cast into symbolic rules to simplify thinking about future problems and to reveal higher order relationships. Using simple gear problems, three experiments explored the occasions of use for, and the inductive transitions between, depictive models and number‐based rules. The first two experiments used the convergent evidence of problem‐solving latencies, hand motions, referential language and error data to document the initial use of a model, the induction of rules from the modeling results, and the fallback to a model when a rule fails. The third experiment explored the intermediate representations that facilitate the induction of rules from depictive models. The strengths and weaknesses of depictive modeling and more analytic systems of reasoning are delineated to motivate the reasons for these transitions.
Accounting for Graded Performance within a Discrete Search Framework
Authors: Miller Craig S. , Laird John E.
Published: August 2005

This article presents a process account of some typicality effects and related similarity‐dependent accuracy and response time phenomena that arise in the context of supervised concept acquisition. We describe Symbolic Concept Acquisition (SCA), a computational system that acquires and activates category prediction rules. In contrast to gradient representations, SCA performs by probing for prediction rules in a series of discrete steps. For learning new rules, it acquires general rules but then incrementally learns more specific ones. In describing SCA, we emphasize its functionality in terms of accuracy and efficiency and motivate its design within the set of symbolic mechanisms and memory structures defined by the Soar architecture (Laird, Newell & Rosenbloom, 1987). For replicating human behavior, we first show how SCA exhibits some typicality effects in the course of learning responding faster and more accurately to more typical test examples. Then, using data from human experiments, we evaluate SCA's qualitative predictions on accuracy and response time on individual dataset instances. We show how SCA's predictions correlate with human data across three experimental conditions concerning the effect of instruction on learning strategy.