Mapping multivariate measures of brain response onto stimulus information during emotional face classification

The relationship between feature processing and visual classification in the brain has been explored through a combination of reverse correlation methods (i.e.“Bubbles” [22]) and electrophysiological measurements (EEG) taken during a facial emotion categorization task [63]. However, in the absence of any specific model of the brain response measurements, this and other [60] attempts to parametrically relate stimulus properties to measurements of brain activation are difficult to interpret. In this thesis I consider a blind data–driven model of brain response. Statistically independent model parameters are found to minimize the expectation of an objective likelihood function over time [55], and a novel combination of methods is proposed for separating the signal from the noise. The model’s estimated signal parameters are then objectively rated by their ability to explain the subject’s performance during a facial emotion classification task, and also by their ability to explain the stimulus features, as revealed in a Bubbles experiment.

Level dc:type.qualificationlevel

PhD

Grantor dc:publisher.institution

University of Glasgow

Year dc:date.issued

2010

Author dc:creator

• Garrod, Oliver G.B.

• BF Psychology

Language dc:language

en