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Impact of feature saliency on visual category learning

People have to sort numerous objects into a large number of meaningful categories while operating in varying contexts. This requires identifying the visual features that best predict the ‘essence’ of objects (e.g., edibility), rather than categorizing objects based on the most salient features in a...

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Autor principal: Hammer, Rubi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4404734/
https://www.ncbi.nlm.nih.gov/pubmed/25954220
http://dx.doi.org/10.3389/fpsyg.2015.00451
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author Hammer, Rubi
author_facet Hammer, Rubi
author_sort Hammer, Rubi
collection PubMed
description People have to sort numerous objects into a large number of meaningful categories while operating in varying contexts. This requires identifying the visual features that best predict the ‘essence’ of objects (e.g., edibility), rather than categorizing objects based on the most salient features in a given context. To gain this capacity, visual category learning (VCL) relies on multiple cognitive processes. These may include unsupervised statistical learning, that requires observing multiple objects for learning the statistics of their features. Other learning processes enable incorporating different sources of supervisory information, alongside the visual features of the categorized objects, from which the categorical relations between few objects can be deduced. These deductions enable inferring that objects from the same category may differ from one another in some high-saliency feature dimensions, whereas lower-saliency feature dimensions can best differentiate objects from distinct categories. Here I illustrate how feature saliency affects VCL, by also discussing kinds of supervisory information enabling reflective categorization. Arguably, principles debated here are often being ignored in categorization studies.
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spelling pubmed-44047342015-05-07 Impact of feature saliency on visual category learning Hammer, Rubi Front Psychol Psychology People have to sort numerous objects into a large number of meaningful categories while operating in varying contexts. This requires identifying the visual features that best predict the ‘essence’ of objects (e.g., edibility), rather than categorizing objects based on the most salient features in a given context. To gain this capacity, visual category learning (VCL) relies on multiple cognitive processes. These may include unsupervised statistical learning, that requires observing multiple objects for learning the statistics of their features. Other learning processes enable incorporating different sources of supervisory information, alongside the visual features of the categorized objects, from which the categorical relations between few objects can be deduced. These deductions enable inferring that objects from the same category may differ from one another in some high-saliency feature dimensions, whereas lower-saliency feature dimensions can best differentiate objects from distinct categories. Here I illustrate how feature saliency affects VCL, by also discussing kinds of supervisory information enabling reflective categorization. Arguably, principles debated here are often being ignored in categorization studies. Frontiers Media S.A. 2015-04-21 /pmc/articles/PMC4404734/ /pubmed/25954220 http://dx.doi.org/10.3389/fpsyg.2015.00451 Text en Copyright © 2015 Hammer. http://creativecommons.org/licenses/by/4.0/ 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.
spellingShingle Psychology
Hammer, Rubi
Impact of feature saliency on visual category learning
title Impact of feature saliency on visual category learning
title_full Impact of feature saliency on visual category learning
title_fullStr Impact of feature saliency on visual category learning
title_full_unstemmed Impact of feature saliency on visual category learning
title_short Impact of feature saliency on visual category learning
title_sort impact of feature saliency on visual category learning
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4404734/
https://www.ncbi.nlm.nih.gov/pubmed/25954220
http://dx.doi.org/10.3389/fpsyg.2015.00451
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