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Description
Learning and transfer were investigated for a categorical structure in which relevant stimulus information could be mapped without loss from one modality to another. The category space was composed of three non-overlapping, linearly-separable categories. Each stimulus was composed of a sequence of on-off events that varied in duration and number

Learning and transfer were investigated for a categorical structure in which relevant stimulus information could be mapped without loss from one modality to another. The category space was composed of three non-overlapping, linearly-separable categories. Each stimulus was composed of a sequence of on-off events that varied in duration and number of sub-events (complexity). Categories were learned visually, haptically, or auditorily, and transferred to the same or an alternate modality. The transfer set contained old, new, and prototype stimuli, and subjects made both classification and recognition judgments. The results showed an early learning advantage in the visual modality, with transfer performance varying among the conditions in both classification and recognition. In general, classification accuracy was highest for the category prototype, with false recognition of the category prototype higher in the cross-modality conditions. The results are discussed in terms of current theories in modality transfer, and shed preliminary light on categorical transfer of temporal stimuli.
ContributorsFerguson, Ryan (Author) / Homa, Donald (Thesis advisor) / Goldinger, Stephen (Committee member) / Glenberg, Arthur (Committee member) / Arizona State University (Publisher)
Created2011
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Description
A category is a set of entities associated by specific characteristics (features). These features can have different relations between one another, including correlations and causal connections. The purpose of this study was to examine how the relations between features would affect the inference of unknown features of new entities from

A category is a set of entities associated by specific characteristics (features). These features can have different relations between one another, including correlations and causal connections. The purpose of this study was to examine how the relations between features would affect the inference of unknown features of new entities from a given set of features. Categories and their relations were learned in a Learning Phase, whereas features were inferred in Transfer and Selection Phases. Correct inference of feature was enhanced by correlation between the features given and the features inferred. It is less clear whether causal connections further enhanced correct inference of features over and above the effect of the correlation. Future research of this topic may benefit from utilizing more difficult tasks, repeating instructions, or manipulating the participants' understanding of the relation in ways other than administration of instructions.
ContributorsDoty, Andrew Emerson (Author) / Homa, Donald (Thesis director) / Presson, Clark (Committee member) / Goldinger, Stephen (Committee member) / Barrett, The Honors College (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor) / Department of Psychology (Contributor)
Created2013-05