Research Themes


Social Cognitive Neuroscience Studies of Achievement

Funded by: CUNY, NIH

Additional Collaborators: Geraldine Downey (Columbia), Tory Higgins (Columbia)

Summary: How can students best achieve even under challenging circumstances? This research program examines how students’ motivation for achievement and other relevant personality attributes influence the types of strategies they rely on when faced with academic difficulty. In particular, we look at how these strategies influence both neural (brain) and behavioral responses to feedback in challenging academic tasks. Neural measures are made with EEG (Electroencephalography), a non-invasive way of measuring how neurons signal to each other in the human brain. The results of these studies inform interventions that can help students to achieve at their fullest potential. This study is open to all Baruch students. Please go to the Participate! page to learn more about becoming a research participant or research assistant.


Decision Making in Social Networks

Funded by: U.S. Army Research Laboratory Network Science Collaborative Technology Alliance (ARL NS-CTA)

Primary Collaborators: Sibel Adali (Rensselaer Polytechnic Institute, Lead), John O’Donovan (University of California Santa Barbara), Jin-Hee Cho (ARL), Jonathan Bakdash (ARL), Kevin Chan (ARL), Scott Rager (Raytheon BBN Technologies)

Summary: Nowadays, individuals have access to vast amounts of information almost instantaneously through the internet. Often, they find themselves relying on the posted opinions of others they may or may not know personally to make decisions. In this heavily networked world, what cues are used to decide if a source should be trusted or not? What are the conditions under which this information will be used to shape behavior and when will it be rejected? We are working within a computer-science based framework that focuses on the interplay between cognitive trust and distributed decision making in composite networks, with an emphasis on diverse network properties, including perceptual features of the network nodes, qualities of the information content shared by those nodes (i.e., competence and credibility), and the flow of information between them (i.e., reliability and latency). Please go to the Participate! page to learn more about becoming a research participant or research assistant.