Biological / Cognitive Neuroscience Seminar
University of California
Berkeley, California
Friday, Sept. 21, 2001
Scott Makeig, Institute for Neural Computation, UCSD & The Salk Institute, La Jolla CA
Self in Experience: Top-down dynamics in EEG and fMRI
An important part of our experience of self is our "top-down" experience of "myself" as interacting with events and/or with past impressions, experiences we might recount, for example, by saying, "All of a sudden, ..." or, "I looked to see if ..." or, "I searched for ..." or, "I looked again at ..." or, "I noticed ..." or else, "It occurred to me ...," "I wondered whether...," "I decided that ...," "I realized that ..." or, "I imagined ... ." We tend to associate such experiences with moments. Linked together they form our "personal" stream of conscious impressions -- equally as (or even more than) events in the external world. They are the stuff that "interesting" first-person accounts are largely made up of. They form a large part, at least, of our waking (and dreaming) experience of "myself."
Beginning in the early 1960s, cognitive neuroscience researchers believed that neural processes accompanying at least some such events could be captured by averaged event-related potentials (ERPs) - for example, the P300 ("Aha!"), CNV ("Rrrrready-Go!") or (later) the N400 ("??") and ERN ("Oops!"). In the last few years, similar hopes have arisen relative to averaged event-related BOLD signal responses. Unfortunately, the average of a set of response waveforms does not capture the richness of dynamics in event-related data, unless a particular neuroscientific model is accepted (explicitly or implicitly). Historically, scientific neuroscience began with observing activation of hierarchically structured peripheral sensorimotor pathways. A similar model of (passive) perception in cortex -- in which external stimuli successively activated pools of cortical neurons tuned, first, to specific and, next, to broader features of a presented stimulus -- was used at first to model ERP dynamics. The most obvious possible ERP correlates of brief periods of information processing within functionally distinct cortical areas were the successive positive and negative peaks contained in single-channel averaged ERP waveforms.
It is now clear, however, that this model is insufficient for characterizing event-related EEG dynamics and for modeling their relationship to neural processes supporting cognitive behavior and experience. Currently more sufficient models of EEG dynamics are beginning to emerge, at the same time as new neurophysiologic evidence linking distributed oscillatory burst events in extra-cellular fields to top-down behavior and experience. I will show evidence from event-related EEG experiments of the insufficiency of ERP averaging, and will give an example of more adequate modeling of event-related EEG dynamics during self-error recognition. Finally, I will discuss the prevalence of top-down events in BOLD data, and prospects for concurrent EEG/fMRI analysis of top-down brain dynamics.