Dynamic Representations in Visual Face Processing
Open Access
- Author:
- Bittner, Jennifer Lee
- Graduate Program:
- Psychology
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- December 16, 2010
- Committee Members:
- Michael J. Wenger, Committee Chair/Co-Chair
Rick O. Gilmore, Committee Chair/Co-Chair
Reginal B. Adams, Committee Member
Frank E. Ritter, Committee Member - Keywords:
- face
vision
learning
dynamic systems
general recognition theory - Abstract:
- Work in facial perception has demonstrated improved levels of performance for identification of an anatomical feature when it is surrounded by a context consistent with that of a studied identity (Tanaka & Farah, 1993; Tanaka & Sengco, 1997). This was originally demonstrated using accuracy measures in three conditions—in isolation, in the original (studied) context, and in an augmented facial context (Tanaka & Farah, 1993; Tanaka & Sengco, 1997)—with these patterns interpreted as reflecting holistic or configural processing or representation (Farah, Wilson, Drain & Tanaka, 1998). The current research examines how learning may be functional in this phenomena, through the use of a dynamic systems model based on the structures of Ashby (1989).The original model of Ashby was adapted to represent configurality (e.g Tanaka & Farah, 1993; Tanaka & Sengco, 1997) by way of dimensional dependencies. Models at the level of channel interactions were created and empirically tested using both the traditional paradigm found in the literature as well as a complete identification task. Results from the experimental studies indicated a lack of dimensional dependencies at all GRT construct levels, conflicting with specific model predictions. A series of additional models were created to examine other possible representations of the system. Additionally, further theoretical exploration consisting of the evaluation and comparison of these models was developed though the search for distinctions in model predictions. Taken together, this work provides the capability for learning representations to be considered within facial perception while providing a solid foundation for future work.