Impact of Feature Saliency on Visual Category Learning

Frontiers in Psychology

Rubi eHammer¹ 

[1] Northwestern University;

关键词: Visual Perception;    visual attention;    category learning;    unsupervised learning;    supervised learning;    visual expertise;   

DOI  :  10.3389/fpsyg.2015.00451

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown