Concept Learning, Perceptual Fluency, and Expert Classification

Bibliographic Details
Main Author: Zeigler, Derek E.
Language:English
Published: Ohio University / OhioLINK 2016
Subjects:
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1468418263
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-ohiou14684182632021-08-03T06:37:21Z Concept Learning, Perceptual Fluency, and Expert Classification Zeigler, Derek E., Cognitive Psychology concept learning categorization perceptual fluency expertise The way in which category specific knowledge is acquired over time has been a longstanding central topic in the cognitive and perceptual sciences. Accordingly, the influence of training and experience on learning has been the focus of much empirical work. This research often involves accounting for the results of concept learning tasks that necessitate classifying category members and non-members. Studies in this area explore questions like the following. Can different concepts be ordered by their relative learning difficulty? Does repeated exposure to a concept result in perceptual expertise and/or expert classification? Is concept acquisition inherently easier for some individuals? The relative difficulty between categories tells us something fundamental about the conceptual system by revealing which relational structures humans are most sensitive. As such, concept learning difficulty orderings for categorical stimuli form an important part of the empirical foundation of concept learning research. However, it is rare that the stability of such orderings is tested over a period of extended learning. Further, this research rarely explores dependent variables beyond classification accuracy that may also indicate relative learning difficulty. Accordingly, this investigation explores the relationship between accuracy and response times (RTs) when practice is gained over multiple category learning sessions. Of particular interest is the extent to which the relative learning difficulty between categories remains stable over sessions of learning. Of additional interest are measures of perceptual fluency (classification RTs) that might reflect category difficulty. Learning difficulty orderings in terms of classification RTs provide an alternative to the conventional approach that construes difficulty solely in terms of mean proportion of correct/incorrect responses. In light of recent empirical support for an invariance–based structural account of conceptual representations (Vigo, 2011a; 2013; 2014), the acquired data is interpreted in the context of generalized invariance structure theory (GIST; Vigo, 2013, 2014) in order to reveal how task experience influences the way concepts are learned and represented over time. 2016-09-23 English text Ohio University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1468418263 http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1468418263 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Cognitive Psychology
concept learning
categorization
perceptual fluency
expertise
spellingShingle Cognitive Psychology
concept learning
categorization
perceptual fluency
expertise
Zeigler, Derek E.,
Concept Learning, Perceptual Fluency, and Expert Classification
author Zeigler, Derek E.,
author_facet Zeigler, Derek E.,
author_sort Zeigler, Derek E.,
title Concept Learning, Perceptual Fluency, and Expert Classification
title_short Concept Learning, Perceptual Fluency, and Expert Classification
title_full Concept Learning, Perceptual Fluency, and Expert Classification
title_fullStr Concept Learning, Perceptual Fluency, and Expert Classification
title_full_unstemmed Concept Learning, Perceptual Fluency, and Expert Classification
title_sort concept learning, perceptual fluency, and expert classification
publisher Ohio University / OhioLINK
publishDate 2016
url http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1468418263
work_keys_str_mv AT zeiglerdereke conceptlearningperceptualfluencyandexpertclassification
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