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DCL Seminar: Sridevi Sarma - Fragility in the Human Decision Making System: When Irrationality Hijacks Logic

Event Type
Decision and Control Laboratory, Coordinated Science Laboratory
CSL Auditorium, Room B02
Oct 5, 2016   3:00 pm  
Sridevi Sarma, Ph.D.
Linda Meccoli
Originating Calendar
CSL Decision and Control Group

Decision and Control Lecture Series

Coordinated Science Laboratory


“Fragility in the Human Decision Making System: When Irrationality

Hijacks Logic”

 Sridevi Sarma, Ph.D.

Johns Hopkins University


Wednesday, October 5, 2016

3:00 p.m. to 4:00 p.m.

CSL Auditorium (B02)



Decision-making links cognition to behavior and is a key driver of human personality, fundamental for survival, and essential for our ability to learn and adapt. It has been well established that humans make logical decisions where they, for example, maximize an expected reward, but this rationality is influenced by internal biases such as emotions. Psychiatric patients who have dysfunctional cognitive and emotional circuitry frequently make irrational decisions. Unfortunately, the function of relevant neural circuits in humans is largely uncharted at fine temporal scales, severely limiting the understanding of changes underlying disruption associated with diseases. In this study, we localize neural populations, circuits, and their temporal patterns on a millisecond scale that are critically involved in human decision-making.

Twelve human subjects, implanted with multiple depth electrodes for clinical purposes, performed a gambling task while we recorded local field potential activity from deep and peripheral brain structures. The gambling task consisted of a game of “high card”, where the subject bets high ($20) or low ($5) on whether her card will be higher than the computer’s card. We posited that each subject’s decision-making system consists of a feedforward model with the playing card as the input and betting behavior as the output (e.g. how she bets and how quickly she bets). The behavior and gambling outcome is a feedback signal to the model, which updates an internal latent state (e.g. internal bias) of the feedforward model. For each subject, we estimated the closed-loop decision making system from betting behavior using maximum likelihood methods.

The models capture the variability amongst subjects and suggest a spectrum of decision-makers that range from irrational to logical. Analysis of the neural data suggest that particular oscillations in populations of neurons of key limbic (amygdala, anterior cingulate cortex) and cognitive structures (orbitofrontal cortex) encode different components of the decision-making system such as the expected reward and risk of reward. These findings provide new insight into how humans link their internal biases (e.g. emotions) with logic to make decisions.



Sridevi Sarma received the B.S. degree in electrical engineering from Cornell University, Ithaca NY, in 1994; and an M.S.  and Ph.D. degrees in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in, Cambridge MA, in 1997 and 2006, respectively. From 2000-2003 she took a leave of absence to start a data analytics company. From 2006--2009, she was a Postdoctoral Fellow in the Brain and Cognitive Sciences Department at the Massachusetts Institute of Technology, Cambridge.  She is now an associate professor in the Institute for Computational Medicine, Department of Biomedical Engineering, at Johns Hopkins University, Baltimore MD. Her research interests include modeling, estimation and control of neural systems using electrical stimulation. She is a recipient of the GE faculty for the future scholarship, a National Science Foundation graduate research fellow, a L'Oreal For Women in Science fellow, the Burroughs Wellcome Fund Careers at the Scientific Interface Award, the Krishna Kumar New Investigator Award from the North American Neuromodulation Society, and a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) and the Whiting School of Engineering Robert B. Pond Excellence in Teaching Award.

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