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  1. The Role of Attention in Category Representation.Mengcun Gao, Brandon M. Turner & Vladimir M. Sloutsky - 2024 - Cognitive Science 48 (4):e13438.
    Numerous studies have found that selective attention affects category learning. However, previous research did not distinguish between the contribution of focusing and filtering components of selective attention. This study addresses this issue by examining how components of selective attention affect category representation. Participants first learned a rule‐plus‐similarity category structure, and then were presented with category priming followed by categorization and recognition tests. Additionally, to evaluate the involvement of focusing and filtering, we fit models with different attentional mechanisms to the data. (...)
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  • Dimensions of Speech Perception: Semantic Associations in the Affective Lexicon.Lee H. Wurm - 1996 - Cognition and Emotion 10 (4):409-424.
  • On the Interaction of Theory and Data in Concept Learning.Edward J. Wisniewski & Douglas L. Medin - 1994 - Cognitive Science 18 (2):221-281.
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  • Using Wittgenstein’s family resemblance principle to learn exemplars.Sunil Vadera, Andres Rodriguez, Enrique Succar & Jia Wu - 2008 - Foundations of Science 13 (1):67-74.
    The introduction of the notion of family resemblance represented a major shift in Wittgenstein’s thoughts on the meaning of words, moving away from a belief that words were well defined, to a view that words denoted less well defined categories of meaning. This paper presents the use of the notion of family resemblance in the area of machine learning as an example of the benefits that can accrue from adopting the kind of paradigm shift taken by Wittgenstein. The paper presents (...)
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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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  • Alternative strategies of categorization.Edward E. Smith, Andrea L. Patalano & John Jonides - 1998 - Cognition 65 (2-3):167-196.
  • Category coherence and category-based property induction.Bob Rehder & Reid Hastie - 2004 - Cognition 91 (2):113-153.
  • One or two dimensions in spontaneous classification: A simplicity approach.Emmanuel M. Pothos & James Close - 2008 - Cognition 107 (2):581-602.
  • A simplicity principle in unsupervised human categorization.Emmanuel M. Pothos & Nick Chater - 2002 - Cognitive Science 26 (3):303-343.
    We address the problem of predicting how people will spontaneously divide into groups a set of novel items. This is a process akin to perceptual organization. We therefore employ the simplicity principle from perceptual organization to propose a simplicity model of unconstrained spontaneous grouping. The simplicity model predicts that people would prefer the categories for a set of novel items that provide the simplest encoding of these items. Classification predictions are derived from the model without information either about the number (...)
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  • Rule-plus-exception model of classification learning.Robert M. Nosofsky, Thomas J. Palmeri & Stephen C. McKinley - 1994 - Psychological Review 101 (1):53-79.
  • Cultural preferences for formal versus intuitive reasoning.Ara Norenzayan, Edward E. Smith, Beom Jun Kim & Richard E. Nisbett - 2002 - Cognitive Science 26 (5):653-684.
    The authors examined cultural preferences for formal versus intuitive reasoning among East Asian (Chinese and Korean), Asian American, and European American university students. We investigated categorization (Studies 1 and 2), conceptual structure (Study 3), and deductive reasoning (Studies 3 and 4). In each study a cognitive conflict was activated between formal and intuitive strategies of reasoning. European Americans, more than Chinese and Koreans, set aside intuition in favor of formal reasoning. Conversely, Chinese and Koreans relied on intuitive strategies more than (...)
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  • The essence of mentalistic agents.Shaun Nichols - 2017 - Synthese 194 (3):809-825.
    Over the last several decades, there has been a wealth of illuminating work on processes implicated in social cognition. Much less has been done in articulating how we learn the contours of particular concepts deployed in social cognition, like the concept MENTALISTIC AGENT. Recent developments in learning theory afford new tools for approaching these questions. In this article, I describe some rudimentary ways in which learning theoretic considerations can illuminate philosophically important aspects of the MENTALISTIC AGENT concept. I maintain that (...)
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  • Do Americans Have a Preference for Rule‐Based Classification?Gregory L. Murphy, David A. Bosch & ShinWoo Kim - 2017 - Cognitive Science:2026-2052.
    Six experiments investigated variables predicted to influence subjects’ tendency to classify items by a single property instead of overall similarity, following the paradigm of Norenzayan et al., who found that European Americans tended to give more “logical” rule-based responses. However, in five experiments with Mechanical Turk subjects and undergraduates at an American university, we found a consistent preference for similarity-based responding. A sixth experiment with Korean undergraduates revealed an effect of instructions, also reported by Norenzayan et al., in which classification (...)
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  • From implicit skills to explicit knowledge: a bottom‐up model of skill learning.Edward Merrillb & Todd Petersonb - 2001 - Cognitive Science 25 (2):203-244.
    This paper presents a skill learning model CLARION. Different from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line reactive learning. It adopts a two-level dual-representation framework (Sun, 1995), with a combination of localist (...)
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  • SUSTAIN: A Network Model of Category Learning.Bradley C. Love, Douglas L. Medin & Todd M. Gureckis - 2004 - Psychological Review 111 (2):309-332.
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  • Context and Perceptual Salience Influence the Formation of Novel Stereotypes via Cumulative Cultural Evolution.Jacqui Hutchison, Sheila J. Cunningham, Gillian Slessor, James Urquhart, Kenny Smith & Douglas Martin - 2018 - Cognitive Science 42 (S1):186-212.
    We use a transmission chain method to establish how context and category salience influence the formation of novel stereotypes through cumulative cultural evolution. We created novel alien targets by combining features from three category dimensions—color, movement, and shape—thereby creating social targets that were individually unique but that also shared category membership with other aliens (e.g., two aliens might be the same color and shape but move differently). At the start of the transmission chains each alien was randomly assigned attributes that (...)
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  • Exemplar similarity and rule application.Ulrike Hahn, Mercè Prat-Sala, Emmanuel M. Pothos & Duncan P. Brumby - 2010 - Cognition 114 (1):1-18.
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  • Graded similarity in free categorization.John P. Clapper - 2019 - Cognition 190 (C):1-19.
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  • Alignability-based free categorization.John P. Clapper - 2017 - Cognition 162:87-102.
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  • Selective and distributed attention in human and pigeon category learning.Leyre Castro, Olivera Savic, Victor Navarro, Vladimir M. Sloutsky & Edward A. Wasserman - 2020 - Cognition 204 (C):104350.
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  • The Oxford Handbook of Causal Reasoning.Michael Waldmann (ed.) - 2017 - Oxford, England: Oxford University Press.
    Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...)
  • A Two‐Stage Model of Category Construction.Woo-Kyoung Ahn & Douglas L. Medin - 1992 - Cognitive Science 16 (1):81-121.
    The current consensus is that most natural categories are not organized around strict definitions (a list of singly necessary and jointly sufficient features) but rather according to a family resemblance (FR) principle: Objects belong to the same category because they are similar to each other and dissimilar to objects in contrast categories. A number of computational models of category construction have been developed to provide an account of how and why people create FR categories (Anderson, 1990; Fisher, 1987). Surprisingly, however, (...)
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