"Electrophysiological correlates of tactile awareness and associated confidence" by Michael Pereira et al. receives Best Poster Award at the 23rd annual conference of the Association for the Scientific Study of Consciousness (ASSC23) in London, Ontario.
The Association for the Scientific Study of Consciousness (ASSC) promotes research "directed toward understanding the nature, function, and underlying mechanisms of consciousness". Its annual conference brings together resaerchers from cognitive science, medicine, neuroscience, philosophy, and other relevant disciplines in the sciences and humanities. At this year's event, Michael Pereira received the Best Poster Award for his work on electrophysiological correlates of tactile awareness. The award was presented by Professor Mel Goodale of Western University, London, ON, Canada.
Title: Electrophysiological correlates of tactile awareness and associated confidence
Authors: Michael Pereira*, Nathan Faivre*, Mi Xue Tan, Wenwen Chang, Fosco Bernasconi, Olaf Blanke
Abstract: One way of studying perceptual consciousness experimentally is to contrast conscious and unconscious processing. In a detection task, we applied weak vibrotactile stimuli to 18 healthy participants while keeping stimulation intensity at the perceptual level using an adaptive procedure. We asked participants to report whether they perceived (hit) the stimuli or not (miss) and how confident they were in their response. A Bayesian mixture model revealed that confidence in hits was lower, and spread over intermediate values, while confidence in correct rejections and misses were higher, with most extreme values observed when participants report confidently perceiving nothing in the absence of stimulus (i.e., correct rejection). Results from concurrent EEG recordings showed that N2 and P3 event-related potentials as well as 8 – 30 Hz time-frequency activity correlated with tactile awareness over the sensorimotor cortices. The same EEG features also encoded the confidence of subjects in their hits. Furthermore, we developped a signal detection theory modelwith EEG features as input. Behavioral data was best fitted when the model relatedsingle-trial estimations of P3 amplitude to variability in the amount of perceptual evidence (d’), but not to decision criterion (c). Conversely, behavioral data was best fitted when relating N2amplitudeor 8-30 Hz time-frequency activity to decision criterion,but not to perceptual evidence. In sum, by contrasting hits and misses while keeping the physical properties of the stimuli fixed, our results suggest that the P3 is a neural correlates of the amount of perceptual evidence while the N2 and 8-30 Hz time-frequency activity are neural correlates of the decision criterion.A similar approach linking EEG features to confidence is now being applied using a Bayesian model of confidence.