Description: Previous work in my lab has demonstrated that the low-level structural regularities of visual scenes likely form the basis for rapid scene categorization performance (as opposed to their conceptual attributes); a notion that, until now, has remained largely theoretical. In order to advance this line of research, the current project will investigate how different stages of early visual processes (assessed via visual evoked potentials, or VEPs) interact as a function of information that competes for a centralized categorical representation via time-varying multivariate pattern classification analysis. If time permits, this project will involve testing for a causal link between those neural interactions and human categorization performance through high-definition transcranial direct current stimulation (tDCS).
The results of this project will shed light on the interactive role of early VEP signals involved in the recognition of different scene categories (measured behaviorally) by directly modulating those interactions through brain stimulation. It is likely that results from this project will be submitted for publication in a peer-reviewed journal.
Student research fellows will be involved in designing, programming, and executing behavioral experiments that will be coupled to a tDCS-EEG system. Thus, with this project, students will gain experience working with cutting edge brain stimulation techniques coupled to a state-of-the-art EEG system. Further, student research fellows will learn how to program experiments from the ground up and build models that best accout for the data. Note that computer programming skills are not necessary for this position, but any previous experience in computer programming will greatly facilitate the training phase for this position.
Required: NEUR 170 or PSYC 275.
Ideal: COSC 101 & MATH 214