Date of Degree

9-2020

Document Type

Thesis

Degree Name

M.S.

Program

Cognitive Neuroscience

Advisor

Tatiana Emmanouil

Subject Categories

Cognition and Perception | Cognitive Neuroscience

Keywords

Ensemble perception, Symmetry perception

Abstract

A growing body of research has demonstrated the ability of subjects to extract representative statistics from visual ensembles, images of similar but distinct groups of objects, without conscious effort or attention focused on individual members of the ensemble. When presented with ensembles, subjects have been able to accurately report the mean, range, and even distribution of various features in the ensemble. Research into ensemble perception, as it has become known, has divided mainly into studies of either low-level features, such as orientation, motion, and color, which are processed in early visual areas, at least for individual objects, or high-level features, such as facial expression or life-likeness, processed in later, higher areas. Much less attention has been paid to mid-level features, which play roles in object and scene perception. The present study addresses this imbalance by investigating ensemble perception of symmetry, one such mid-level feature, neither processed in early visual areas, nor an abstract construct or an element in social cognition, as most high-level features are. Subjects were presented with displays of twelve objects of which either 25%, 50%, 75%, or 100% were vertically symmetrical while the remainder were vertically asymmetrical for 1000 ms and asked to indicate the proportion of symmetrical objects. Subjects were found to be significantly better than random at identifying the proportion of symmetry. This serves to confirm findings that symmetry perception is an automatic process that does not require focused attention and to demonstrate that information about the average symmetry of a group of objects is available at a glance, just as information about the color, orientation, and size is.

This work is embargoed and will be available for download on Tuesday, March 30, 2021

Graduate Center users:
To read this work, log in to your GC ILL account and place a thesis request.

Non-GC Users:
See the GC’s lending policies to learn more.

Share

COinS