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  1.  9
    Visual and Affective Multimodal Models of Word Meaning in Language and Mind.Simon De Deyne, Danielle J. Navarro, Guillem Collell & Andrew Perfors - 2021 - Cognitive Science 45 (1):e12922.
    One of the main limitations of natural language‐based approaches to meaning is that they do not incorporate multimodal representations the way humans do. In this study, we evaluate how well different kinds of models account for people's representations of both concrete and abstract concepts. The models we compare include unimodal distributional linguistic models as well as multimodal models which combine linguistic with perceptual or affective information. There are two types of linguistic models: those based on text corpora and those derived (...)
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  2.  5
    Language Evolution Can Be Shaped by the Structure of the World.Andrew Perfors & Daniel J. Navarro - 2014 - Cognitive Science 38 (4):775-793.
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  3.  14
    When Extremists Win: Cultural Transmission Via Iterated Learning When Populations Are Heterogeneous.Danielle J. Navarro, Andrew Perfors, Arthur Kary, Scott D. Brown & Chris Donkin - 2018 - Cognitive Science 42 (7):2108-2149.
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  4.  6
    Leaping to Conclusions: Why Premise Relevance Affects Argument Strength.Keith J. Ransom, Andrew Perfors & Daniel J. Navarro - 2016 - Cognitive Science 40 (7):1775-1796.
    Everyday reasoning requires more evidence than raw data alone can provide. We explore the idea that people can go beyond this data by reasoning about how the data was sampled. This idea is investigated through an examination of premise non‐monotonicity, in which adding premises to a category‐based argument weakens rather than strengthens it. Relevance theories explain this phenomenon in terms of people's sensitivity to the relationships among premise items. We show that a Bayesian model of category‐based induction taking premise sampling (...)
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  5.  4
    Do Additional Features Help or Hurt Category Learning? The Curse of Dimensionality in Human Learners.Wai Keen Vong, Andrew T. Hendrickson, Danielle J. Navarro & Andrew Perfors - 2019 - Cognitive Science 43 (3).
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  6.  7
    Changing your mind about the data: Updating sampling assumptions in inductive inference.Brett K. Hayes, Joshua Pham, Jaimie Lee, Andrew Perfors, Keith Ransom & Saoirse Connor Desai - 2024 - Cognition 245 (C):105717.
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  7.  12
    Socially Situated Transmission: The Bias to Transmit Negative Information is Moderated by the Social Context.Nicolas Fay, Bradley Walker, Yoshihisa Kashima & Andrew Perfors - 2021 - Cognitive Science 45 (9):e13033.
    Cultural evolutionary theory has identified a range of cognitive biases that guide human social learning. Naturalistic and experimental studies indicate transmission biases favoring negative and positive information. To address these conflicting findings, the present study takes a socially situated view of information transmission, which predicts that bias expression will depend on the social context. We report a large‐scale experiment (N = 425) that manipulated the social context and examined its effect on the transmission of the positive and negative information contained (...)
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  8.  9
    Category Clustering and Morphological Learning.John Mansfield, Carmen Saldana, Peter Hurst, Rachel Nordlinger, Sabine Stoll, Balthasar Bickel & Andrew Perfors - 2022 - Cognitive Science 46 (2):e13107.
    Cognitive Science, Volume 46, Issue 2, February 2022.
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  9.  8
    The Role of Stimulus‐Specific Perceptual Fluency in Statistical Learning.Andrew Perfors & Evan Kidd - 2022 - Cognitive Science 46 (2):e13100.
    Humans have the ability to learn surprisingly complicated statistical information in a variety of modalities and situations, often based on relatively little input. These statistical learning (SL) skills appear to underlie many kinds of learning, but despite their ubiquity, we still do not fully understand precisely what SL is and what individual differences on SL tasks reflect. Here, we present experimental work suggesting that at least some individual differences arise from stimulus-specific variation in perceptual fluency: the ability to rapidly or (...)
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  10.  5
    The Role of Stimulus‐Specific Perceptual Fluency in Statistical Learning.Andrew Perfors & Evan Kidd - 2022 - Cognitive Science 46 (2):e13100.
    Cognitive Science, Volume 46, Issue 2, February 2022.
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