bluuebunny/crossref_metadata_2025 huggingface dataset:
Instruction and Practice in Learning to use a Device
Authors: Bibby Peter A. , Payne Stephen J.
Published: August 2005

We explore the extent to which Anderson's (1987) theory of knowledge compilation can account for the relationship between instructions and practice in learning to use a simple device. Bibby and Payne (1993) reported experimental support for knowledge compilation in this domain. This article replicates the finding of a performance cross‐over between instruction type and task type that disappears with practice on the tasks. The research is extended by using verbal protocols to model the strategies of novice and more experienced individuals. Production system models of these strategies suggest that knowledge compilation only provides an adequate account of practice for one of two Instruction groups. To model the strategy shifts for the second group, we employ a “procedure modification heuristic” (after Neches, 1987), which relies on access to a declarative model of the configuration of the device. This suggests that instructionally derived declarative knowledge may have a subtle ongoing effect on the changes in procedural knowledge with practice.
Primate Social Intelligence
Authors: Worden Robert P.
Published: August 2005

A computational theory of primate social intelligence is proposed in which primates represent social situations internally by discrete symbol structures, called scripts. Three well‐defined computational operations on scripts are sufficient to support social learning, planning, and prediction. This gives a formal, predictive model with which to analyse how primate social knowledge is acquired, as well as how it is used.

The theory is compared with primate data, such as Cheney and Seyfarth's observations of vervet monkeys. It gives simple, understandable script‐based analyses of many observed phenomena—such as the recognition and use of kin relations, learning of alarm calls, habituation to calls, knowledge of rank, tactical deception, and attachment behaviour.

I argue that a tight, concise theory of social cognition, such as script theory, is needed to explain the rapid learning and social guile seen in primates. It also has the benefits of simplicity and testability. The extension of scripts to incorporate a primate theory of mind is described in a subsequent paper.
Learning from Worked‐Out Examples: A Study on Individual Differences
Authors: Renkl Alexander
Published: August 2005

The goal of this study was to Investigate individual differences in learning from worked‐out examples with respect to the quality of self‐explanations. Restrictions of former studies (e.g., lacking control of time‐on‐task) were avoided and additional research questions (e.g., reliability and dimensionality of self‐explanation characteristics) were addressed. An investigation with 36 university freshmen students of education working in individual sessions was conducted. The domain was probability calculation. Prior knowledge and the quality of self‐explanations (protocols of the individuals' thinking aloud) were assessed as predictors of learning. A post‐test was employed to measure the learning gains as the dependent variable. The following main results were obtained. Most self‐explanation characteristics could be regarded as relatively stable person characteristics. The individual differences in the quality of self‐explanations were, however, found to be multidimensional. Most Important, even when controlling for time‐on‐task (quantitative aspect), learning gains could be substantially predicted by qualitative differences of self‐explanation characteristics. In particular, successful learners tended to employ more prlnciplebased explanations, more explication of operator‐goal combinations, and more anticipative reasoning. In addition, there were two types of effective learners, labeled anticipative reasoners and principle‐based explainers.
A Working Memory Model of a Common Procedural Error
Authors: Byrne Michael D. , Bovair Susan
Published: August 2005

Systematic errors In performance are an important aspect of human behavior that have not received adequate explanation. One such systematic error is termed postcompletion error; a typical example is leaving one's card In the automatic teller after withdrawing cash. This type of error seems to occur when people have an extra step to perform in a procedure after the main goal has been satisfied. The fact that people frequently make this type of error, but do not make this error every time, may best be explained by considering the working memory load at the time the step is to be performed: The error is made when the load on working memory is high, but will not be made when the load is low. A model of performance In the task was constructed using Just and Carpenter's (1992) CAPS that predicted that high working memory load should be associated with postcompletion errors. Two experiments confirmed that such errors can be produced in a laboratory as well as a naturalistic setting, and that the conditions under which the CAPS model makes the error are consistent with the conditions under which the errors occur in the laboratory.
Quasi‐Indexicals and Knowledge Reports
Authors: Rapaport William J. , Shapiro Stuart C. , Wiebe Janyce M.
Published: August 2005

We present a computational analysis of de re, de dicto, and de se belief and knowledge reports. Our analysis solves a problem first observed by Hector‐Neri Castaneda, namely, that the simple rule ‘(A knows that P) implies P’ apparently does not hold if P contains a quasi‐indexical. We present a single rule, in the context of a knowledge‐representation and reasoning system, that holds for all P, including those containing quasi‐indexicals. In so doing, we explore the difference between reasoning in a public communication language and in a knowledge‐representation language, we demonstrate the importance of representing proper names explicitly, and we provide support for the necessity of considering sentences in the context of extended discourse (e.g., written narrative) in order to fully capture certain features of their semantics.
Collaborative Discovery in a Scientific Domain
Authors: Okada Takeshi , Simon Herbert A.
Published: August 2005

This study compares Pairs of subjects with Single subjects in a task of discovering scientific laws with the aid of experiments. Subjects solved a molecular genetics task in a computer micro‐world (Dunbar, 1993). Pairs were more successful in discovery than Singles and participated more actively in explanatory activities (i.e., entertaining hypotheses and considering alternative ideas and justifications). Explanatory activities were effective for discovery only when the subjects also conducted crucial experiments. Explanatory activities were facilitated when paired subjects made requests of each other for explanation and focused on them. The study extends from individual to collaborative discovery activities the importance to the discovery process of setting goals to find hypotheses and evidence (Dunbar, 1993) and to construct explanations of phenomena and processes encountered in examples (Chi, Bassok, Lewis, & Glaser, 1989).
Children's Thoughts on the Origin of Species: A Study of Explanatory Coherence
Authors: Samarapungavan Ala , Wiers Reinout W.
Published: August 2005

This paper presents the results of a study which examined children's ideas about speciation. Two groups of elementary school students, 9‐year‐olds and 12‐year‐olds, were interviewed using a semi‐structured questionnaire. The results indicate that several children explain the phenomena of speciation in terms of consistent explanatory frameworks that strongly resemble either early Greek or renaissance variants of Essentialist theories in biology. The core beliefs of such frameworks constrain the types of solutions that are generated for a variety of biological problems.
The Nature of External Representations in Problem Solving
Authors: Zhang Jiajie
Published: August 2005

This article proposes a theoretical framework for external representation based problem solving. The Tic‐Tac‐Toe and its isomorphs are used to illustrate the procedures of the framework as a methodology and test the predictions of the framework as a functional model. Experimental results show that the behavior in the Tic‐Tac‐Toe is determined by the directly available information in external and internal representations in terms of perceptual and cognitive biases, regardless of whether the biases are consistent with, inconsistent with, or irrelevant to the task. It is shown that external representations are not merely inputs and stimuli to the internal mind and that they have much more important functions than mere memory aids. A representational determinism is suggested—the form of a representation determines what information can be perceived, what processes can be activated, and what structures can be discovered from the specific representation.
The Texture Lexicon: Understanding the Categorization of Visual Texture Terms and Their Relationship to Texture Images
Authors: Bhushan Nalini , Rao A. Ravishankar , Lohse Gerald L.
Published: August 2005

In this paper we present the results of two experiments. The first is on the categorization of texture words in the English language. The goal was to determine whether there is a common basis for subjects' groupings of words related to visual texture, and if so, to identify the underlying dimensions used to categorize those words.

Eleven major clusters were identified through hierarchical cluster analysis, ranging from “random” to “repetitive”. These clusters remained intact in a multidimensional scaling solution. The stress for a three‐dimensional solution obtained through multidimensional scaling was 0.18, meaning that 82% of the variance in the data is explained through the use of three dimensions. It appears that the major dimensions of texture descriptors are repetitive versus nonrepetitive; linearly oriented versus circularly oriented; and simple versus complex.

In the second experiment we measured the strength of association between texture words and texture images. The goal was to determine whether there is any systematic correspondence between the domains of texture words and texture images. Pearson's coefficient of contingency, a measure of the strength of association, was found to be 0.63 for words corresponding to given images and 0.56 for images corresponding to given words. Thus the texture categories in the verbal space and those in the visual space are strongly tied.

In sum, our two experiments show (a) that despite the tremendous variety in the words we have to describe textures, there is an underlying structure to the lexical space which can be derived from the experimental data; and (b) that the association between a category of words and a category of images was strongest when both categories represent the same underlying property. This suggests that subjects' organizations of texture terms are systematically tied to their organization of texture images.
Productivity and Schematicity in Metaphors
Authors: Clausner Timothy C. , Croft William
Published: August 2005

The theory of metaphor proposed by Lakoff and Johnson (1980a, 1980b) and Lakoff (1993) involves a mapping of conceptual structure from one semantic domain to another. We investigate properties of these conceptual domain mappings by comparing them to morphological derivational relations. Schematicity and productivity are properties that Bybee (1985) and Langacker (1987) propose for characterizing morphological derivational relations, which we apply to our analysis of metaphor. Metaphors are argued to vary in their degree of semantic schematicity: Domain relations function as generalizations over specific metaphorical expressions. We also demonstrate three points on a continuum of productivity: Conventional metaphors are highly productive, metaphorically motivated transparent idioms are semiproductive, and opaque idioms are unproductive. Each point is compared with an example of morphological productivity having a corresponding conceptual organization. The results demonstrate that semantic productivity can be characterized in the same way as morphological productivity, suggesting that form and meaning are organized by the same principles.
Children's Productivity in the English Past Tense: The Role of Frequency, Phonology, and Neighborhood Structure
Authors: Marchman Virginia A.
Published: August 2005

The productive use of English past tense morphology in school‐aged children (N = 74; 3 years, 8 months to 13 years, 5 months) is explored using an elicited production task. Errors represented 20% of the responses overall. Virtually all of the children demonstrated productivity with regular (e.g., good) and irregular patterns (zero‐marking, e.g., sit → sit; vowel‐change, e.g., ride → rid). Overall frequency of errors decreased with age, yet the tendency for certain types of irregularizations increased in the older groups. Analyses across items indicated that all error types were predicted by combinations of item frequency, phonological characteristics of stems and past tense forms, and aspects of phonological past tense “neighborhoods”. Contrary to “hybrid” or dual‐mechanism models incorporating a phonologically‐insensitive default mechanism (e.g., Prasada & Pinker, 1993), these results suggest that children's productivity with regular and irregular patterns is consistent with a phonologically‐based constraint satisfaction system similar to that implemented in connectionist models (Daugherty & Seidenberg, 1992; Plunkett & Marchman, 1991, 1993).
CaMeRa: A Computational Model of Multiple Representations
Authors: Tabachneck‐Schijf Hermina J.M. , Leonardo Anthony M. , Simon Herbert A.
Published: August 2005

This research aims to clarify, by constructing and testing a computer simulation, the use of multiple representations in problem solving, focusing on their role in visual reasoning. The model is motivated by extensive experimental evidence in the literature for the features it incorporates, but this article focuses on the system's structure. We illustrate the model's behavior by simulating the cognitive and perceptual processes of an economics expert as he teaches some well‐learned economics principles while drawing a graph on a blackboard. Data in the experimental literature and concurrent verbal protocols were used to guide construction of a linked production system and parallel network, CaMeRa (Computation with Multiple Representations), that employs a “Mind's Eye” representation for pictorial information, consisting of a bitmap and associated node‐link structures. Propositional list structures are used to represent verbal information and reasoning. Small individual pieces from the different representations are linked on a sequential and temporary basis to form a reasoning and inferencing chain, using visually encoded information recalled to the Mind's Eye from long‐term memory and from cues recognized on an external display. CaMeRa, like the expert, uses the diagrammatic and verbal representations to complement one another, thus exploiting the unique advantages of each.
The Correlational Structure of Natural Images and the Calibration of Spatial Representations
Authors: Baddeley Roland
Published: August 2005

Physiologists have long proposed that correlated input activity is important in normal sensory development. Here it is postulated that the visual system is sensitive to the correlation in image intensity across the visual field, and that these correlations are used to help calibrate spatial representations. Since measurements made near to each other in the visual field are more correlated than measurements made at a distance, the degree of correlation can be used as an estimate of the distance between two measurements and can therefore be used to calibrate a roughly organized spatial representation. We therefore explored the hypothesis that low level spatial representations are calibrated using a signal based on image intensity correlation. If the visual system uses input statistics to calibrate its spatial representation, then any distortions and anisotropies in these input statistics should be mirrored by distortions in the representation of space. To test the psychological implications of this hypothesis, a collection of 81 images of open and urban landscapes were used to estimate the degree of correlation between image intensity measurement pairs as a function of both distance and orientation. Doing this we show that a system that used the statistics measured to calibrate its representation would show: (1) a horizontal‐vertical illusion;(2) the magnitude of this illusion would depend on the amount of open and urban landscapes in the environment;(3) there would be a nontrivial relationship between line orientation and judged length. Analogues of all these distortions and regularities can be found in the psychophysical literature on distance estimation. This gives strength to the proposal that spatial representations are calibrated using input statistics.
Constraints on Analogical Inference
Authors: Markman Arthur B.
Published: August 2005

The ability to reason by analogy is particularly important because it permits the extension of knowledge of a target domain by virtue of its similarity to a base domain via a process of analogical inference. The general procedure for analogical inference involves copying structure from the base to the target in which missing information is generated, and substitutions are made for items for which analogical correspondences have already been found. A pure copying with substitution and generation process is too profligate to be useful, and so constraints must be placed on what information is to be carried over. In this paper, the importance of systematicity as a constraint on inference is explored in four studies in which subjects find correspondences between domains and also make inferences. This work suggests that people prefer to make inferences of information connected to systematic correspondences between domains. A second important theme of this paper is that violations of one‐to‐one mapping can lead to inconsistent object substitutions in inference. The data reveal no such inconsistent substitutions in people's inferences, suggesting that they do respect one‐to‐one mapping in analogical inference. These findings are discussed relative to four prominent computational models of analogical mapping and inference.
Belief Change as Propositional Update
Authors: Elio Renée , Pelletier Francis Jeffry
Published: August 2005

This study examines the problem of belief revision, defined as deciding which of several initially accepted sentences to disbelieve, when new information presents a logical inconsistency with the initial set. In the first three experiments, the initial sentence set included a conditional sentence, a non‐conditional (ground) sentence, and an inferred conclusion drawn from the first two. The new information contradicted the inferred conclusion. Results indicated that conditional sentences were more readily abandoned than ground sentences, even when either choice would lead to a consistent belief state, and that this preference was more pronounced when problems used natural language cover stories rather than symbols. The pattern of belief revision choices differed depending on whether the contradicted conclusion from the initial belief set had been a modus ponens or modus tollens inference. Two additional experiments examined alternative model‐theoretic definitions of minimal change to a belief state, using problems that contained multiple models of the initial belief state and of the new information that provided the contradiction. The results indicated that people did not follow any of four formal definitions of minimal change on these problems. The new information and the contradiction it offered was not, for example, used to select a particular model of the initial belief state as a way of reconciling the contradiction. The preferred revision was to retain only those initial sentences that had the same, unambiguous truth value within and across both the initial and new information sets. The study and results are presented in the context of certain logic‐based formalizations of belief revision, syntactic and model‐theoretic representations of belief states, and performance models of human deduction. Principles by which some types of sentences might be more “entrenched” than others in the face of contradiction are also discussed from the perspective of induction and theory revision.
The Dynamical Challenge
Authors: Clark Andy
Published: August 2005

Recent studies such as Thelen and Smith (1994), Kelso, (1995), Van Gelder, (1995), Beer, (1995), and others have presented a forceful case for a dynamical systems approach to understanding cognition and adaptive behavior. These studies call into question some foundational assumptions concerning the nature of cognitive scientific explanation and (in particular) the role of notions such as internal representation and computation. These are exciting and important challenges. But they must be handled with care. It is all too easy, in this debate, to lose sight of the explanatorily important issues and to talk at cross‐purposes, courtesy of the (surprisingly) various ways in which different theorists often conceive the key terms. The primary goal of the present paper is thus a modest one: to begin to clarify just what is at issue and to highlight some of the most central and pressing concerns. In so doing, we may hope to develop a constructive framework for future debate. In addition, I try to open up a space of intermediate options—ways in which dynamical and representational/computational understandings may sometimes afford complementary (rather than competing) perspectives on adaptive success.
Coherence as Constraint Satisfaction
Authors: Thagard Paul , Verbeurgt Karsten
Published: August 2005

This paper provides a computational characterization of coherence that applies to a wide range of philosophical problems and psychological phenomena. Maximizing coherence is a matter of maximizing satisfaction of a set of positive and negative constraints. After comparing five algorithms for maximizing coherence, we show how our characterization of coherence overcomes traditional philosophical objections about circularity and truth.
A Model Theory of Modal Reasoning
Authors: Bell Victoria A. , Johnson‐Laird P. N.
Published: August 2005

This paper presents a new theory of modal reasoning, i.e. reasoning about what may or may not be the case, and what must or must not be the case. It postulates that individuals construct models of the premises in which they make explicit only what is true. A conclusion is possible if it holds in at least one model, whereas it is necessary if it holds in all the models. The theory makes three predictions, which are corroborated experimentally. First, conclusions correspond to the true, but not the false, components of possibilities. Second, there is a key interaction: it is easier to infer that a situation is possible as opposed to impossible, whereas it is easier to infer that a situation is not necessary as opposed to necessary. Third, individuals make systematic errors of omission and of commission. We contrast the theory with theories based on formal rules.
Pragmatic Action
Authors: Alterman Richard , Zito‐Wolf Roland , Carpenter Tamitha
Published: August 2005

This paper begins with a discussion of two features of the everyday task environment. First, the everyday task environment is designed, and an important part of the design is the provision of explicit information to guide the individual in the adaptation of his activity. Second, some task environments are semi‐permanent. These two features of the task environment reveal some important characteristics in the psychology of the individual. When novelty occurs, expansion in the range of behavior of the individual is guided by a process of internalization of information provided in the task environment by another. Because of the semi‐permanence of home task environments, there is pay‐off in organizing behavior in terms of the particulars of those environments. The body of this paper examines these ideas from the perspective of FLOABN. FLOABN is a computational cognitive model of an individual acquiring skill at using household and office devices.
The Growth of Multidisciplinarity in the Cognitive Science Society
Authors: Schunn Christian D. , Crowley Kevin , Okada Takeshi
Published: August 2005

In a case study of the growth of cognitive science, we analyzed the activities of the Cognitive Science Society with a particular emphasis on the multidisciplinary nature of the field. Analyses of departmental affiliations, training back‐grounds, research methodology, and paper citations suggest that the journal Cognitive Science and the Annual Meeting of the Cognitive Science Society are dominated by cognitive psychology and computer science, rather than being an equal division among the constituent disciplines of cognitive science. However, at many levels, a growing percentage of work was found to involve a conjunction of multiple disciplines, such that approximately 30–50% of recent work in the Cognitive Science Society is multidisciplinary. In a questionnaire study of cognitive scientists involved in collaborative research, multidisciplinarity was found to shape the research process and affect the factors associated with successful research.
Turing's Analysis of Computation and Theories of Cognitive Architecture
Authors: Wells A. J.
Published: August 2005

Turing's analysis of computation is a fundamental part of the background of cognitive science. In this paper it is argued that a re‐interpretation of Turing's work is required to underpin theorizing about cognitive architecture. It is claimed that the symbol systems view of the mind, which is the conventional way of understanding how Turing's work impacts on cognitive science, is deeply flawed. There is an alternative interpretation that is more faithful to Turing's original insights, avoids the criticisms made of the symbol systems approach and is compatible with the growing interest in agent‐environment interaction. It is argued that this interpretation should form the basis for theories of cognitive architecture.
Representations and Cognitive Explanations: Assessing the Dynamicist's Challenge in Cognitive Science
Authors: Bechtel William
Published: August 2005

Advocates of dynamical systems theory (DST) sometimes employ revolutionary rhetoric. In an attempt to clarify how DST models differ from others in cognitive science, I focus on two issues raised by DST: the role for representations in mental models and the conception of explanation invoked. Two features of representations are their role in standing‐in for features external to the system and their format. DST advocates sometimes claim to have repudiated the need for stand‐ins in DST models, but I argue that they are mistaken. Nonetheless, DST does offer new ideas as to the format of representations employed in cognitive systems. With respect to explanation, I argue that some DST models are better seen as conforming to the covering‐law conception of explanation than to the mechanistic conception of explanation implicit in most cognitive science research. But even here, I argue, DST models are a valuable complement to more mechanistic cognitive explanations.
Reading Abraham Lincoln: An Expert/Expert Study in the Interpretation of Historical Texts
Authors: Wineburg Sam
Published: August 2005

This study explored how historians with different background knowledge read a series of primary source documents. Two university‐based historians thought aloud as they read documents about Abraham Lincoln and the question of slavery, with the broad goal of understanding Lincoln's views on race. The first historian brought detailed content knowledge to the documents; the second historian was familiar with some of the themes in the documents but quickly became confused in the details. After much cognitive flailing, the second historian was able to piece together an interpretative structure that brought him by the task's end to where his more knowledgeable colleague began. Data analysis focused on how, lacking detailed content knowledge, this historian was able to regain his intellectual footing, work through confusion, and resist the urge to simplify. Implications of this work for cognitive analyses in history and education are discussed.
Analogy Events: How Examples are Used During Problem Solving
Authors: VanLehn Kurt
Published: August 2005

In attempting to fit a model of analogical problem solving to protocol data of students solving physics problems, several unexpected observations were made. Analogies between examples and exercises (a form of case‐based reasoning) consisted of two distinct types of events. During an initialization event, the solver retrieved an example, set up a mapping between it and the problem, and decided whether the example was useful. During a transfer event, the solver inferred something about the problem's solution. Many different types of initialization and transfer events were observed. Poor solvers tended to follow the example verbatim, copying each solution line over to the problem. Good solvers tried to solve the problem themselves, but referred to the example when they got stuck, or wanted to check a step, or wanted to avoid a detailed calculation. Rather than learn from analogies, both Good and Poor solvers tended to repeat analogies at subsequent similar situations. A revised version of the model is proposed (but not yet implemented) that appears to be consistent with all the findings observed in this and other studies of the same subjects.
The Representation and Processing of Coreference in Discourse
Authors: Gordon Peter C. , Hendrick Randall
Published: August 2005

A model is presented that addresses both the distribution and comprehension of different forms of referring expressions in language. This model is expressed in a formalism (Kamp & Reyle, 1993) that uses interpretive rules to map syntactic representations onto representations of discourse. Basic interpretive rules are developed for names, pronouns, definite descriptions, and quantified descriptions. These rules are triggered by syntactic input and interact dynamically with representations of discourse to establish reference and coreference. This interaction determines the ease with which coreference can be established for different linguistic forms given the existing discourse context. The performance of the model approximates that observed in studies of intuitive judgments of grammaticality and studies using online measures of language comprehension. The model uses the same basic interpretive mechanisms for coreference within and between sentences, thereby linking the domain traditionally studied by generative linguists to domains that have been of concern primarily to psychologists and computational linguists.
Distributional Information: A Powerful Cue for Acquiring Syntactic Categories
Authors: Redington Martin , Chater Nick , Finch Steven
Published: August 2005

Many theorists have dismissed a priori the idea that distributional information could play a significant role in syntactic category acquisition. We demonstrate empirically that such information provides a powerful cue to syntactic category membership, which can be exploited by a variety of simple, psychologically plausible mechanisms. We present a range of results using a large corpus of child‐directed speech and explore their psychological implications. While our results show that a considerable amount of information concerning the syntactic categories can be obtained from distributional information alone, we stress that many other sources of information may also be potential contributors to the identification of syntactic classes.
On the Role of Mathematics in Explaining the Material World: Mental Models for Proportional Reasoning
Authors: Schwartz Daniel L. , Moore Joyce L.
Published: August 2005

Contemporary psychological research that studies how people apply mathematics has largely viewed mathematics as a computational tool for deriving an answer. The tacit assumption has been that people first understand a situation, and then choose which computations to apply. We examine an alternative assumption that mathematics can also serve as a tool that helps one to construct an understanding of a situation in the first place. Three studies were conducted with 6th‐grade children in the context of proportional situations because early proportional reasoning is a premier example of where mathematics may provide new understanding of the world. The children predicted whether two differently‐sized glasses of orange juice would taste the same when they were filled from a single carton of juice made from concentrate and water. To examine the relative contributions and interactions of situational and mathematical knowledge, we manipulated the formal features of the problem display (e.g., diagram vs. photograph) and the numerical complexity (e.g., divisibility) of the containers and the ingredient ratios. When the problem was presented as a diagram with complex numbers, or “realistically” with easy numbers, the children predicted the glasses would taste different because one glass had more juice than the other. But, when the problem was presented realistically with complex numbers, the children predicted the glasses would taste the same on the basis of empirical knowledge (e.g., “Juice can't change by itself”). And finally, when the problem was presented as a diagram with easy numbers, the children predicted the glasses would taste the same on the basis of proportional relations. These complex interactions illuminate how mathematical and empirical knowledge can jointly constrain the construction of a new understanding of the world. We propose that mathematics helped in the case of successful proportional reasoning because it made a complex empirical situation cognitively tractable, and thereby helped the children construct mental models of that situation. We sketch one aspect of the mental models that are constructed in the domain of quantity—a preference for specificity—that helps explain the current findings.
Pertinence Generation in Radiological Diagnosis: Spreading Activation and the Nature of Expertise
Authors: Raufaste Eric , Eyrolle Hélène , Mariné Claudette
Published: August 2005

An empirical study of human expert reasoning processes is presented. Its purpose is to test a model of how a human expert's cognitive system learns to detect, and does detect, pertinent data and hypotheses. This process is called pertinence generation. The model is based on the phenomenon of spreading activation within semantic networks. Twenty‐two radiologists were asked to produce diagnoses from two very difficult X‐ray films. As the model predicted, pertinence increased with experience and with semantic network integration. However, the experts whose daily work involved explicit reasoning were able, in addition, to go beyond and to generate more pertinence. The results suggest that two qualitatively different kinds of expertise, basic and super, should be distinguished. A reinterpretation of the results of Lesgold et al. (1988) is proposed, suggesting that apparent nonmonotonicities in performance are not representative of common radiological expertise acquisition but result from the inclusion of basic and super expertise on the same curve.
From Pixels to People: A Model of Familiar Face Recognition
Authors: Burton A.Mike , Bruce Vicki , Hancock P.J.B.
Published: August 2005

Research in face recognition has largely been divided between those projects concerned with front‐end image processing and those projects concerned with memory for familiar people. These perceptual and cognitive programmes of research have proceeded in parallel, with only limited mutual influence. In this paper we present a model of human face recognition which combines both a perceptual and a cognitive component. The perceptual front‐end is based on principal components analysis of face images, and the cognitive back‐end is based on a simple interactive activation and competition architecture. We demonstrate that this model has a much wider predictive range than either perceptual or cognitive models alone, and we show that this type of combination is necessary in order to analyse some important effects in human face recognition. In sum, the model takes varying images of “known” faces and delivers information about these people.
Encoding Categorical and Coordinate Spatial Relations Without Input‐Output Correlations: New Simulation Models
Authors: Baker David P. , Chabris Christopher F. , Kosslyn Stephen M.
Published: August 2005

Cook (1995) criticized Kosslyn, Chabris, Marsolek & Koenig's (1992) network simulation models of spatial relations encoding in part because the absolute position of a stimulus in the input array was correlated with its spatial relation to a landmark; thus, on at least some trials, the networks did not need to compute spatial relations. The network models reported here include larger input arrays, which allow stimuli to appear in a large range of locations with an equal probability of being above or below a “bar,” thus eliminating the confound present in earlier models. The results confirm the original hypothesis that as the size of the network's receptive fields increases, performance on a coordinate spatial relations task (which requires computing precise, metric distance) will be relatively better than on a categorical spatial relations task (which requires computing above/below relative to a landmark).
Mapping across Domains Without Feedback: A Neural Network Model of Transfer of Implicit Knowledge
Authors: Dienes Zoltán , Altmann Gerry T.M. , Gao Shi‐Ji
Published: August 2005

This paper shows how a neural network can model the way people who have acquired knowledge of an artificial grammar in one perceptual domain (e.g., sequences of tones differing in pitch) can apply the knowledge to a quite different perceptual domain (e.g., sequences of letters). It is shown that a version of the Simple Recurrent Network (SRN) can transfer its knowledge of artificial grammars across domains without feedback. The performance of the model is sensitive to at least some of the same variables that affect subjects' performance—for example, the model is responsive to both the grammaticality of test sequences and their similarity to training sequences, to the cover task used during training, and to whether training is on bigrams or larger sequences.
Attention Metaphors: How Metaphors Guide the Cognitive Psychology of Attention
Authors: Fernandez‐Duque Diego , Johnson Mark L.
Published: August 2005

The concept of attention is defined by multiple inconsistent metaphors that scientists use to identify relevant phenomena, frame hypotheses, construct experiments, and interpret data. (1) The Filter metaphor shapes debates about partial vs. complete filtering, early vs. late selection, and information filtering vs. enhancement. (2) The Spotlight metaphor raises the issue of space‐ vs. object‐based selection, and it guides research on the size, shape, and movement of the attentional focus. (3) The Spotlight‐in‐the‐Brain metaphor is frequently used to interpret imaging studies of attention. (4) The debate between supramodal and pre‐motor theories of attention replays the dichotomy between the Spotlight and the Vision metaphors of attention. Our analysis reveals the central role of metaphor in scientific theory and research on attention, exposes hidden assumptions behind various research strategies, and shows the need for flexibility in the use of current metaphors.
Comprehension‐Based Skill Acquisition
Authors: Doane Stephanie M. , Sohn Young Woo , McNamara Danielle S. , Adams David
Published: August 2005

We present a comprehension‐based computational model of UNIX user skill acquisition and performance in a training context (UNICOM). The work extends a comprehension‐based theory of planning to account for skill acquisition and learning. Individual models of 22 UNIX users were constructed and used to simulate user performance on successive command production problems in a training context. Comparisons of model and the human empirical data result in a high degree of agreement, validating the ability of UNICOM to predict user response to training.
Accommodating Surprise in Taxonomic Tasks: The Role of Expertise
Authors: Alberdi Eugenio , Sleeman Derek H. , Korpi Meg
Published: August 2005

This paper reports a psychological study of human categorization that looked at the procedures used by expert scientists when dealing with puzzling items. Five professional botanists were asked to specify a category from a set of positive and negative instances. The target category in the study was defined by a feature that was unusual, hence situations of uncertainty and puzzlement were generated. Subjects were asked to think aloud while solving the tasks, and their verbal reports were analyzed. A number of problem solving strategies were identified, and subsequently integrated in a model of knowledge‐guided inductive categorization. Our model proposes that expert knowledge influences the subjects' reasoning in more complex ways than suggested by earlier investigations of scientific reasoning. As in previous studies, domain knowledge influenced our subjects' hypothesis generation and testing; but, additionally, it played a central role when subjects revised their hypotheses.
Computational Evidence for the Subitizing Phenomenon as an Emergent Property of the Human Cognitive Architecture
Authors: Peterson Scott A. , Simon Tony J.
Published: August 2005

A computational modeling approach was used to test one possible explanation for the limited capacity of the subitizing phenomenon. Most existing models of this phenomenon associate the subitizing span with an assumed structural limitation of the human information processing system. In contrast, we show how this limit might emerge as the combinatorics of the space of enumeration problems interacts with the human cognitive architecture in the context of an enumeration task. Subitizing‐like behavior was generated in two different models of enumeration, one based on the ACT‐R cognitive architecture and the other based on the principles of parallel distributed processing (PDP). Our results provide good qualitative fits to results obtained in a variety of empirical studies.
A Local Connectionist Account of Consonant Harmony in Child Language
Authors: Berg Thomas , Schade Ulrich
Published: August 2005

This study represents an attempt to sketch a processing model of phonological development in children acquiring their first language. The investigation is framed within a local connectionist network in which activation spreads between levels and inhibition within levels. Three ways are focused on in which an emergent processing system may diverge from a fully developed one—hypoactivation (too little activation due to underdeveloped links), hyperactivation (too much activation due to overloaded links) and impaired self‐inhibition. With a view to determining how children's productions relate to these three “error mechanisms,” one widespread phonological process, consonant harmony, is subjected to examination. Hypoactivation is found to capture the great majority of harmonic patterns. Hyperactivation and impaired self‐inhibition play a more marginal role and are only needed to account for exceptional cases of harmony. The feasibility of the psycholinguistic model is demonstrated with the help of computer simulations that were run with noise on both fully developed and developing networks. The model makes a number of predictions including the claim that difficult sounds are acquired in coda positions first.
A Neuronal Basis for the Fan Effect
Authors: Goetz Philip , Walters Deborah
Published: August 2005

The fan effect says that “activation” spreading from a concept is divided among the concepts it spreads to. Because this activation is not a physical entity, but an abstraction of unknown lower‐level processes, the spreading activation model has predictive but not explanatory power. We provide one explanation of the fan effect by showing that distributed neuronal memory networks (specifically, Hopfield networks) reproduce four qualitative aspects of the fan effect: faster recognition of sentences containing lower‐fan words, faster recognition of sentences when more cues are provided, faster acceptance of studied sentences than rejection of probes, and faster recognition of sentences studied more frequently. These are all a natural result of the dynamics of distributed associative memory.
Getting to Best: Efficiency versus Optimality in Negotiation
Authors: Hyder Elaine B. , Prietula Michael J. , Weingart Laurie R.
Published: August 2005

Negotiation between two individuals is a common task that typically involves two goals: maximize individual outcomes and obtain an agreement. However, research on the simplest negotiation tasks demonstrates that although naive subjects can be induced to improve their performance, they are often no more likely to achieve fully optimal solutions. The present study tested the prediction that a decrease in a particular type of argumentative behavior, substantiation, would result in an increase in optimal agreements. As substantiation behaviors depend primarily on supplied content of the negotiation task, it was also predicted that substantiation behavior would be reduced by curtailing the content. A 2 × 2 experimental design was employed, where both negotiation tactics (list of tactics present versus absent) and negotiation task content (high versus low) were varied to determine the processes leading beyond solution improvement to solution optimality. Sixty‐one dyads engaged in a two‐party, four‐issue negotiation task. All negotiations were videotaped and analyzed. Although the list of negotiation tactics resulted in improved performance, only the content manipulation resulted in a significant increase in dyads achieving optimal solutions. Analyses of the coded protocols indicated that the key difference in achieving optimality was a reduction in persistent substantiation‐related operators (substantiation, along with single‐issue preferences and procedures) and an increase in a complex macro‐operator, multi‐issue offers that reduced the problem space, facilitating the search for optimality.
The Nature and Processing of Errors in Interactive Behavior
Authors: Gray Wayne D.
Published: August 2005

Understanding the nature of errors in a simple, rule‐based task—programming a VCR—required analyzing the interactions among human cognition, the artifact, and the task. This analysis was guided by least‐effort principles and yielded a control structure that combined a rule hierarchy task‐to‐device with display‐based difference‐reduction. A model based on this analysis was used to trace action protocols collected from participants as they programmed a simulated VCR. Trials that ended without success (the show was not correctly programmed) were interrogated to yield insights regarding problems in acquiring the control structure. For successful trials (the show was correctly programmed), steps that the model would make were categorized as matches to the model; steps that the model would not make were violations of the model. The model was able to trace the vast majority of correct keystrokes and yielded a business‐as‐usual account of the detection and correction of errors. Violations of the model fell into one of two fundamental categories. The model provided insights into certain subcategories of errors; whereas, regularities within other subcategories of error suggested limitations to the model. Although errors were rare when compared to the total number of correct actions, they were important. Errors were made on 4% of the keypresses that, if not detected, would have prevented two‐thirds of the shows from being successfully recorded. A misprogrammed show is a minor annoyance to the user. However, devices with the approximate complexity of a VCR are ubiquitous and have found their way into emergency rooms, airplane cockpits, power plants, and so on. Errors of ignorance may be reduced by training; however, errors in the routine performance of skilled users can only be reduced by design.