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 g...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2015-04-01
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| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2015.00451/full |
| _version_ | 1818136497648304128 |
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| author | Rubi eHammer |
| author_facet | Rubi eHammer |
| author_sort | Rubi eHammer |
| collection | DOAJ |
| 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 in this manuscript are often being ignored in categorization studies. |
| first_indexed | 2024-12-11T09:41:21Z |
| format | Article |
| id | doaj.art-b395b7a6935448fcb4311de37c32fdcc |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-12-11T09:41:21Z |
| publishDate | 2015-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-b395b7a6935448fcb4311de37c32fdcc2022-12-22T01:12:40ZengFrontiers Media S.A.Frontiers in Psychology1664-10782015-04-01610.3389/fpsyg.2015.00451133325Impact of Feature Saliency on Visual Category LearningRubi eHammer0Northwestern UniversityPeople 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 in this manuscript are often being ignored in categorization studies.http://journal.frontiersin.org/Journal/10.3389/fpsyg.2015.00451/fullVisual Perceptionvisual attentioncategory learningunsupervised learningsupervised learningvisual expertise |
| spellingShingle | Rubi eHammer Impact of Feature Saliency on Visual Category Learning Frontiers in Psychology Visual Perception visual attention category learning unsupervised learning supervised learning visual expertise |
| 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 | Visual Perception visual attention category learning unsupervised learning supervised learning visual expertise |
| url | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2015.00451/full |
| work_keys_str_mv | AT rubiehammer impactoffeaturesaliencyonvisualcategorylearning |