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Cue competition and feature representation in a category learning task: an fMRI study

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dc.contributor.authorJentink, Kade, author
dc.contributor.authorSeger, Carol, advisor
dc.contributor.authorBurzynska, Agnieszka, committee member
dc.contributor.authorRojas, Don, committee member
dc.contributor.authorThomas, Michael, committee member
dc.date.accessioned2023-08-28T10:29:00Z
dc.date.available2023-08-28T10:29:00Z
dc.date.issued2023
dc.description.abstractDuring learning, attention is limited, and therefore selecting what feature(s) to attend to in the environment is important. Sometimes, attention is captured by a cue or feature in such a way that other cues or features are not attended to, known as overshadowing. This process is not entirely understood in category learning, with some studies suggesting that it enhances learning of other features (Murphy et al., 2017), while others suggest that it inhibits (Lau et al., 2020). Furthermore, the location and organization of the neural representations that develop for category features during overshadowing has not been previously examined in this context. The present experiment used representational similarity analyses (RSA), a method for interrogating representational structure (Kriegeskorte et al., 2008), in order to examine where and how features were represented during overshadowing in a category learning task. Participants completed a category learning task in which categories were defined based on two informative features, one binary and one continuous. The binary feature was easier to learn (i.e., more salient), and it was hypothesized that it would overshadow learning of the more difficult continuous feature. This was demonstrated behaviorally: participants learned to categorize when the binary feature was present, then performed at chance when it was removed in a transfer task. Three different hypothetical models were fit to the neural data to determine underlying representational structure: a binary category model, an effector-specific motor model, and a model representing the degree of perceptual similarity for the continuous feature. During initial learning when the primary binary feature was present, the category model fit data from both early visual and object-specific areas of visual cortex, while the motor model fit data from motor-related regions including primary somatomotor cortex and the cerebellum. The perceptual similarity model for the continuous feature did not fit any task data during either Training or Transfer. However, there was a trend for the category model to fit activity in the basal ganglia and lateral occipital complex (LOC) during the Transfer task when the only information available for categorization was the continuous feature. Taken together, these results suggest that, although overshadowing inhibits use of the overshadowed continuous feature as the basis of categorization behavior, it might still contribute to activation of neural representations of category membership.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierJentink_colostate_0053A_17879.pdf
dc.identifier.urihttps://hdl.handle.net/10217/236929
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectcognitive neuroscience
dc.subjectneuroimaging
dc.subjectcue competition
dc.subjectcategory learning
dc.titleCue competition and feature representation in a category learning task: an fMRI study
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplinePsychology
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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