Title: Mid-Frontal Cognitive-Control Signals: a Hub for Social, Emotional, and Motivational Processing?
Date: 15 January, 1-2 pm
Venue: AS4/02-08 (Psychology Department Meeting Room)
Recently, cognitive neuroscientists have identified a family of neural signals that involve in realization of the need for cognitive-control. These so-called mid-frontal cognitive-control signals are enhanced, for instance, to 1) a NoGo stimulus that signals individuals to inhibit their response in a Go/NoGo task, or to 2) an important feedback that informs individuals about their performance. These mid-frontal cognitive-control signals can be quantified through ERPs (e.g., the N2 and Feedback-Related Negativity, FRN), EEG oscillations (e.g., the Frontal-Midline Theta, FMT) or fMRI (e.g., BOLD activity in the ACC). Nonetheless, it is less clear 1) how social, emotional and motivational factors modulate these cognitive-control signals, and more importantly, 2) how changes in these signals relate to decision-making. In a series of experiments, I will demonstrate, firstly, that social (e.g., cultural values), emotional (e.g., emotional-temperaments and stimulus’ valence) and motivational (e.g., monetary rewards) factors modulate these cognitive-control signals. Secondly, I will further demonstrate that these signals, in turn, predict decision-making, including 1) trial-by-trial deception behaviors as well as 2) individual-differences in delay-discounting. Hence, I argue that mid-frontal cognitive-control signals can be considered a hub that integrates social, emotional, motivational information, and ultimately influences decision-making.
About the Speaker:
Narun “Non” Pornpattananangkul is a postdoctoral research fellow in Dr. Rongjun Yu’s Decision Lab. He completed his Ph.D. in Brain, Behavior and Cognition at Northwestern University, USA, in 2015 where he conducted cognitive neuroscience research on cognitive-control and reward-processing. His current research at NUS is on economic decision-making, using several techniques such as EEG, fMRI, concurrent EEG-fMRI, intracranial EEG, hormone and computational modeling.