Literature DB >> 27872180

On Learning Natural-Science Categories That Violate the Family-Resemblance Principle.

Robert M Nosofsky1, Craig A Sanders1, Alex Gerdom1, Bruce J Douglas2, Mark A McDaniel3.   

Abstract

The general view in psychological science is that natural categories obey a coherent, family-resemblance principle. In this investigation, we documented an example of an important exception to this principle: Results of a multidimensional-scaling study of igneous, metamorphic, and sedimentary rocks (Experiment 1) suggested that the structure of these categories is disorganized and dispersed. This finding motivated us to explore what might be the optimal procedures for teaching dispersed categories, a goal that is likely critical to science education in general. Subjects in Experiment 2 learned to classify pictures of rocks into compact or dispersed high-level categories. One group learned the categories through focused high-level training, whereas a second group was required to simultaneously learn classifications at a subtype level. Although high-level training led to enhanced performance when the categories were compact, subtype training was better when the categories were dispersed. We provide an interpretation of the results in terms of an exemplar-memory model of category learning.

Entities:  

Keywords:  category learning; computational modeling; open data; similarity

Mesh:

Year:  2016        PMID: 27872180     DOI: 10.1177/0956797616675636

Source DB:  PubMed          Journal:  Psychol Sci        ISSN: 0956-7976


  7 in total

Review 1.  Visual category learning: Navigating the intersection of rules and similarity.

Authors:  Gregory I Hughes; Ayanna K Thomas
Journal:  Psychon Bull Rev       Date:  2021-01-19

2.  Organized simultaneous displays facilitate learning of complex natural science categories.

Authors:  Brian J Meagher; Paulo F Carvalho; Robert L Goldstone; Robert M Nosofsky
Journal:  Psychon Bull Rev       Date:  2017-12

Review 3.  Model-guided search for optimal natural-science-category training exemplars: A work in progress.

Authors:  Robert M Nosofsky; Craig A Sanders; Xiaojin Zhu; Mark A McDaniel
Journal:  Psychon Bull Rev       Date:  2019-02

4.  Transfer of category learning to impoverished contexts.

Authors:  Peter S Whitehead; Amanda Zamary; Elizabeth J Marsh
Journal:  Psychon Bull Rev       Date:  2021-12-16

5.  The structure of prior knowledge enhances memory in experts by reducing interference.

Authors:  Erik A Wing; Ford Burles; Jennifer D Ryan; Asaf Gilboa
Journal:  Proc Natl Acad Sci U S A       Date:  2022-06-23       Impact factor: 12.779

6.  Learning hierarchically organized science categories: simultaneous instruction at the high and subtype levels.

Authors:  Robert M Nosofsky; Colin Slaughter; Mark A McDaniel
Journal:  Cogn Res Princ Implic       Date:  2019-12-19

7.  Relating categorization to set summary statistics perception.

Authors:  Noam Khayat; Shaul Hochstein
Journal:  Atten Percept Psychophys       Date:  2019-11       Impact factor: 2.199

  7 in total

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