Thesis Defense: Bin Lou
Thesis Defense for Bin Lou
Sponsor: Paul Sajda
Date & Time: Friday, January 30th at 1:00 pm
Location: BME Conference Room
Title: The Time Course of a Perceptual Decision: Linking Neural Correlates of Pre-stimulus Brain State, Decision Formation and Response Evaluation
Perceptual decision making is a cognitive process that involves transforming sensory evidence into a decision and behavioral response through accumulating sensory information over time. Previous research has identified some temporally distinct components during the decision process; however, not all aspects of a perceptual decision are characterized by the post-stimulus activity. Using single-trial analysis with temporal localization techniques, we are able to identify a cascade of cognitive events associated with perceptual decision making, including what happens outside the period of evidence accumulation. The goal of this dissertation is to elucidate the association between neural correlates of these cognitive events. We design a set of experimental paradigms based on visual discrimination of scrambled face, car and house images and analyze EEG evoked potentials and oscillations using advanced machine learning and statistical analysis approaches. We first exploit the correlation between pre-stimulus attention and oscillatory activity and investigate such covariation within the context of behaviorally-latent fluctuations in task-relevant post-stimulus neural activity. We find that early perceptual representations, rather than temporally later neural correlates of the perceptual decision, are modulated by pre-stimulus brain state. Secondly, we demonstrate that the visual salience of stimulus image, being a surrogate for the decision difficulty, differentially modulates exogenous and endogenous oscillations at different times during decision making. This may reflect underlying information processing flow and allocation of attentional resources during the visual discrimination task. Finally, to study the effect of visual salience and value information of stimulus on feedback processing, we propose a model that can estimate expected reward and reward prediction error on a single-trial basis by integrating value information with perceptual decision evidence characterized by single-trial decoding of EEG. Taken together, these results provide a complete temporal characterization of perceptual decision making that includes the pre-stimulus brain state, the evidence accumulation during decisions and the post-feedback response evaluation.