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  1. The misunderstood limits of folk science: an illusion of explanatory depth.Leonid Rozenblit & Frank Keil - 2002 - Cognitive Science 26 (5):521-562.
    People feel they understand complex phenomena with far greater precision, coherence, and depth than they really do; they are subject to an illusion—an illusion of explanatory depth. The illusion is far stronger for explanatory knowledge than many other kinds of knowledge, such as that for facts, procedures or narratives. The illusion for explanatory knowledge is most robust where the environment supports real‐time explanations with visible mechanisms. We demonstrate the illusion of depth with explanatory knowledge in Studies 1–6. Then we show (...)
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  • All words are not created equal: Expectations about word length guide infant statistical learning.Jenny R. Saffran & Casey Lew-Williams - 2012 - Cognition 122 (2):241-246.
    Infants have been described as 'statistical learners' capable of extracting structure (such as words) from patterned input (such as language). Here, we investigated whether prior knowledge influences how infants track transitional probabilities in word segmentation tasks. Are infants biased by prior experience when engaging in sequential statistical learning? In a laboratory simulation of learning across time, we exposed 9- and 10-month-old infants to a list of either disyllabic or trisyllabic nonsense words, followed by a pause-free speech stream composed of a (...)
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  • On the nature and scope of featural representations of word meaning.Ken McRae, Virginia R. de Sa & Mark S. Seidenberg - 1997 - Journal of Experimental Psychology 126 (2):99-130.
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  • Relational learning re-examined.Chris Thornton & Andy Clark - 1997 - Behavioral and Brain Sciences 20 (1):83-83.
    We argue that existing learning algorithms are often poorly equipped to solve problems involving a certain type of important and widespread regularity that we call “type-2 regularity.” The solution in these cases is to trade achieved representation against computational search. We investigate several ways in which such a trade-off may be pursued including simple incremental learning, modular connectionism, and the developmental hypothesis of “representational redescription.”.
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  • Two types of thought: Evidence from aphasia.Jules Davidoff - 2005 - Behavioral and Brain Sciences 28 (1):20-21.
    Evidence from aphasia is considered that leads to a distinction between abstract and concrete thought processes and hence for a distinction between rules and similarity. It is argued that perceptual classification is inherently a rule-following procedure and these rules are unable to be followed when a patient has difficulty with name comprehension and retrieval.
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  • Finding categories through words: More nameable features improve category learning.Martin Zettersten & Gary Lupyan - 2020 - Cognition 196 (C):104135.
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  • Against a “mindless” account of perceptual expertise.Amit Chaturvedi - 2019 - Phenomenology and the Cognitive Sciences 18 (3):509-531.
    According to Hubert Dreyfus’s famous claim that expertise is fundamentally “mindless,” experts in any domain perform most effectively when their activity is automatic and unmediated by concepts or cognitive processes like attention and memory. While several scholars have recently challenged the plausibility of Dreyfus’s “mindless” account of expertise for explaining a wide range of expert activities, there has been little consideration of the one form of expertise which might be most amenable to Dreyfus’s account – namely, perceptual expertise. Indeed, Dreyfus’s (...)
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  • Perceived consequences of evolution: College students perceive negative personal and social impact in evolutionary theory.Sarah K. Brem, Michael Ranney & Jennifer Schindel - 2003 - Science Education 87 (2):181-206.
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  • Accommodating Surprise in Taxonomic Tasks: The Role of Expertise.Eugenio Alberdi, Derek H. Sleeman & Meg Korpi - 2000 - Cognitive Science 24 (1):53-91.
    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 (...)
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  • Trading spaces: Computation, representation, and the limits of uninformed learning.Andy Clark & Chris Thornton - 1997 - Behavioral and Brain Sciences 20 (1):57-66.
    Some regularities enjoy only an attenuated existence in a body of training data. These are regularities whose statistical visibility depends on some systematic recoding of the data. The space of possible recodings is, however, infinitely large – it is the space of applicable Turing machines. As a result, mappings that pivot on such attenuated regularities cannot, in general, be found by brute-force search. The class of problems that present such mappings we call the class of “type-2 problems.” Type-1 problems, by (...)
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  • Background beliefs and evidence interpretation.Aidan Feeney, Jonathan StB. T. Evans & John Clibbens - 2000 - Thinking and Reasoning 6 (2):97-124.
    In this paper we argue that it is often adaptive to use one's background beliefs when interpreting information that, from a normative point of view, is incomplete. In both of the experiments reported here participants were presented with an item possessing two features and were asked to judge, in the light of some evidence concerning the features, to which of two categories it was more likely that the item belonged. It was found that when participants received evidence relevant to just (...)
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  • Computational Models of Consciousness: An Evaluation.Ron Sun - 1999 - Journal of Intelligent Systems 9 (5-6):507-568.
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  • Contrastive Constraints Guide Explanation‐Based Category Learning.Seth Chin-Parker & Julie Cantelon - 2017 - Cognitive Science 41 (6):1645-1655.
    This paper provides evidence for a contrastive account of explanation that is motivated by pragmatic theories that recognize the contribution that context makes to the interpretation of a prompt for explanation. This study replicates the primary findings of previous work in explanation-based category learning, extending that work by illustrating the critical role of the context in this type of learning. Participants interacted with items from two categories either by describing the items or explaining their category membership. We manipulated the feature (...)
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  • Causal models and the acquisition of category structure.Michael R. Waldmann, Keith J. Holyoak & Angela Fratianne - 1995 - Journal of Experimental Psychology: General 124 (2):181.
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  • Categorization as causal reasoning⋆.Bob Rehder - 2003 - Cognitive Science 27 (5):709-748.
    A theory of categorization is presented in which knowledge of causal relationships between category features is represented in terms of asymmetric and probabilistic causal mechanisms. According to causal‐model theory, objects are classified as category members to the extent they are likely to have been generated or produced by those mechanisms. The empirical results confirmed that participants rated exemplars good category members to the extent their features manifested the expectations that causal knowledge induces, such as correlations between feature pairs that are (...)
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  • Two dogmas of conceptual empiricism: implications for hybrid models of the structure of knowledge.Frank Keil - 1998 - Cognition 65 (2-3):103-135.
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  • Visual appearance interacts with conceptual knowledge in object recognition.Olivia S. Cheung & Isabel Gauthier - 2014 - Frontiers in Psychology 5.
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  • The role of background knowledge in speeded perceptual categorization.Thomas J. Palmeri & Celina Blalock - 2000 - Cognition 77 (2):B45-B57.
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  • Altering object representations through category learning.Robert L. Goldstone, Yvonne Lippa & Richard M. Shiffrin - 2001 - Cognition 78 (1):27-43.
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  • Explanation and Evidence in Informal Argument.Sarah K. Brem & Lance J. Rips - 2000 - Cognitive Science 24 (4):573-604.
    A substantial body of evidence shows that people tend to rely too heavily on explanations when trying to justify an opinion. Some research suggests these errors may arise from an inability to distinguish between explanations and the evidence that bears upon them. We examine an alternative account, that many people do distinguish between explanations and evidence, but rely more heavily on unsubstantiated explanations when evidence is scarce or absent. We examine the philosophical and psychological distinctions between explanation and evidence, and (...)
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  • The Computational Origin of Representation.Steven T. Piantadosi - 2020 - Minds and Machines 31 (1):1-58.
    Each of our theories of mental representation provides some insight into how the mind works. However, these insights often seem incompatible, as the debates between symbolic, dynamical, emergentist, sub-symbolic, and grounded approaches to cognition attest. Mental representations—whatever they are—must share many features with each of our theories of representation, and yet there are few hypotheses about how a synthesis could be possible. Here, I develop a theory of the underpinnings of symbolic cognition that shows how sub-symbolic dynamics may give rise (...)
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  • Time-scale dynamics and the development of an embodied cognition.Esther Thelen - 1995 - In Tim van Gelder & Robert Port (eds.), Mind As Motion: Explorations in the Dynamics of Cognition. MIT Press. pp. 69--100.
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  • Detecting anomalous features in complex stimuli: The role of structured comparison.Kenneth J. Kurtz & Dedre Gentner - 2013 - Journal of Experimental Psychology: Applied 19 (3):219.
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  • Evidence of coordination as a cure for concept eliminativism.Andrea Scarantino - 2010 - Behavioral and Brain Sciences 33 (2-3):223-224.
    I argue that Machery stacks the deck against hybrid theories of concepts by relying on an unduly restrictive understanding of coordination between concept parts. Once a less restrictive notion of coordination is introduced, the empirical case for hybrid theories of concepts becomes stronger, and the appeal of concept eliminativism weaker.
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  • The Role of Explanation in Discovery and Generalization: Evidence From Category Learning.Joseph J. Williams & Tania Lombrozo - 2010 - Cognitive Science 34 (5):776-806.
    Research in education and cognitive development suggests that explaining plays a key role in learning and generalization: When learners provide explanations—even to themselves—they learn more effectively and generalize more readily to novel situations. This paper proposes and tests a subsumptive constraints account of this effect. Motivated by philosophical theories of explanation, this account predicts that explaining guides learners to interpret what they are learning in terms of unifying patterns or regularities, which promotes the discovery of broad generalizations. Three experiments provide (...)
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  • The development of features in object concepts.Philippe G. Schyns, Robert L. Goldstone & Jean-Pierre Thibaut - 1998 - Behavioral and Brain Sciences 21 (1):1-17.
    According to one productive and influential approach to cognition, categorization, object recognition, and higher level cognitive processes operate on a set of fixed features, which are the output of lower level perceptual processes. In many situations, however, it is the higher level cognitive process being executed that influences the lower level features that are created. Rather than viewing the repertoire of features as being fixed by low-level processes, we present a theory in which people create features to subserve the representation (...)
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  • The rules versus similarity distinction.Emmanuel M. Pothos - 2005 - Behavioral and Brain Sciences 28 (1):1-14.
    The distinction between rules and similarity is central to our understanding of much of cognitive psychology. Two aspects of existing research have motivated the present work. First, in different cognitive psychology areas we typically see different conceptions of rules and similarity; for example, rules in language appear to be of a different kind compared to rules in categorization. Second, rules processes are typically modeled as separate from similarity ones; for example, in a learning experiment, rules and similarity influences would be (...)
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  • The Cultural Mind: Environmental Decision Making and Cultural Modeling Within and Across Populations.Scott Atran, Douglas L. Medin & Norbert O. Ross - 2005 - Psychological Review 112 (4):744-776.
    This paper describes a cross-cultural research project on the relation between how people conceptualize nature and how they act in it. Mental models of nature differ dramatically among and within populations living in the same area and engaged in more or less the same activities. This has novel implications for environmental decision making and management, including dealing with commons problems. Our research also offers a distinct perspective on models of culture, and a unified approach to the study of culture and (...)
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  • A Neurocomputational Approach to Trained and Transitive Relations in Equivalence Classes.Ángel E. Tovar & Gert Westermann - 2017 - Frontiers in Psychology 8.
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  • The time course of implicit and explicit concept learning.Eleni Ziori & Zoltán Dienes - 2012 - Consciousness and Cognition 21 (1):204-216.
    The present experiment investigated the development of implicit and explicit knowledge during concept learning. According to Cleeremans and Jiménez , the content of a representation can be conscious only when the representation is of a sufficiently good quality; on this theory, increasing explicit and decreasing implicit knowledge might be expected with training. The view that implicit knowledge arises from compilation of explicit knowledge makes the opposite prediction. The present research tested these possibilities using subjective measures based on confidence ratings. One (...)
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  • Prior knowledge and subtyping effects in children's category learning.Brett K. Hayes, Katrina Foster & Naomi Gadd - 2003 - Cognition 88 (2):171-199.
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  • Product evolution and the classification of business interest in scientific advances.Steven Payson - 1997 - Knowledge, Technology & Policy 9 (4):3-26.
    Technological change is widely studied in economic discourse, dominated by an “industrial perspective” in which scientific and engineering advances are categorized by, and analyzed in the context of, industrial classifications. The present study compares this perspective with the alternative approach of studying the effects of scientific advances onproducts (goods and services) in the context of the functions those products serve. Using a function-based classification scheme, data on the economic effects of scientific advances are developed from articles appearing in business journals. (...)
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  • Building theory-based concepts: Four-year-olds preferentially seek explanations for features of kinds.Andrei Cimpian & Gina Petro - 2014 - Cognition 131 (2):300-310.
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  • Social Context in HCl: A New Framework for Mental Models, Cooperation, and Communication.Giuseppe Mantovani - 1996 - Cognitive Science 20 (2):237-269.
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