Electronic Theses and Dissertations

Identifier

5990

Date

2017

Date of Award

7-14-2017

Document Type

Thesis

Degree Name

Master of Science

Major

Psychology

Concentration

General Psychology

Committee Chair

Stephanie Marie Huette

Committee Member

Roger J. Kreuz

Committee Member

Gavin M. Bidelman

Abstract

Categorical distinctions for spatial relationships differ across languages. Visual statistics gleaned from the environment in conjunction with the labels present in a native language may be used to form semantic categories. The current study makes use of visual statistical learning to train novel category labels for familiar and novel spatial categories present in 4 different continua using fully and minimally overlapping distributions. Prior categorical perception and spatial categorization research suggest the presence of L1 interference in forming novel semantic categories. Visual statistical learning research suggests the ability to track category statistics across visual scenes. Results showed significantly steeper slopes for familiar continua as compared to novel continua for the respective label distributions indicating the presence of L1 interference. The presence of a shallower slope in the novel continua suggests and interaction of the statistics and L1 interference.

Comments

Data is provided by the student.

Library Comment

dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.

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