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      Inductive generalization with familiar categories: developmental changes in children's reliance on perceptual similarity and kind information

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      Frontiers in Psychology
      Frontiers Media S.A.
      categorization, induction, reasoning, children, cognitive development

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          Abstract

          Inductive generalization is ubiquitous in human cognition; however, the factors underpinning this ability early in development remain contested. The present study was designed to (1) test the predictions of the naïve theory and a similarity-based account and (2) examine the mechanism by which labels promote induction. In Experiment 1, 3- to 5-year-old children made inferences about highly familiar categories. The results were not fully consistent with either theoretical account. In contrast to the predictions of the naïve theory approach, the youngest children in the study did not ignore perceptually compelling lures in favor of category-match items; in contrast to the predictions of the similarity-based account, no group of participants favored perceptually compelling lures in the presence of dissimilar-looking category-match items. In Experiment 2 we investigated the mechanisms by which labels promote induction by examining the influence of different label types, namely category labels (e.g., the target and category-match both labeled as bird) and descriptor labels (e.g., the target and the perceptual lure both labeled as brown) on induction performance. In contrast to the predictions of the naïve theory approach, descriptor labels but not category labels affected induction in 3-year-old children. Consistent with the predictions of the similarity-based account, descriptor labels affected the performance of children in all age groups included in the study. The implications of these findings for the developmental account of induction are discussed.

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          Most cited references60

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          Categories and induction in young children.

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            The discovery of structural form.

            Algorithms for finding structure in data have become increasingly important both as tools for scientific data analysis and as models of human learning, yet they suffer from a critical limitation. Scientists discover qualitatively new forms of structure in observed data: For instance, Linnaeus recognized the hierarchical organization of biological species, and Mendeleev recognized the periodic structure of the chemical elements. Analogous insights play a pivotal role in cognitive development: Children discover that object category labels can be organized into hierarchies, friendship networks are organized into cliques, and comparative relations (e.g., "bigger than" or "better than") respect a transitive order. Standard algorithms, however, can only learn structures of a single form that must be specified in advance: For instance, algorithms for hierarchical clustering create tree structures, whereas algorithms for dimensionality-reduction create low-dimensional spaces. Here, we present a computational model that learns structures of many different forms and that discovers which form is best for a given dataset. The model makes probabilistic inferences over a space of graph grammars representing trees, linear orders, multidimensional spaces, rings, dominance hierarchies, cliques, and other forms and successfully discovers the underlying structure of a variety of physical, biological, and social domains. Our approach brings structure learning methods closer to human abilities and may lead to a deeper computational understanding of cognitive development.
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              Category-based induction.

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                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                07 July 2015
                2015
                : 6
                : 897
                Affiliations
                Department of Psychology, Carnegie Mellon University Pittsburgh, PA, USA
                Author notes

                Edited by: Vladimir Sloutsky, The Ohio State University, USA

                Reviewed by: Andy Wills, Plymouth University, UK; Haley Vlach, University of Wisconsin-Madison, USA

                *Correspondence: Karrie E. Godwin, Department of Psychology, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA kegodwin@ 123456andrew.cmu.edu

                This article was submitted to Cognition, a section of the journal Frontiers in Psychology

                Article
                10.3389/fpsyg.2015.00897
                4493371
                c8ab6f75-659a-49e1-aae3-05d3ef4415af
                Copyright © 2015 Godwin and Fisher.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 19 December 2014
                : 15 June 2015
                Page count
                Figures: 3, Tables: 4, Equations: 0, References: 63, Pages: 15, Words: 12216
                Funding
                Funded by: Carnegie Mellon University by the Department of Education
                Award ID: R305B090023
                Funded by: Understanding Human Cognition – Scholar Award
                Award ID: 220020401
                Categories
                Psychology
                Original Research

                Clinical Psychology & Psychiatry
                categorization,induction,reasoning,children,cognitive development
                Clinical Psychology & Psychiatry
                categorization, induction, reasoning, children, cognitive development

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