Damian A. Stanley
Blodgett Hall Room 206
Blodgett Hall Room 206
Ph.D., New York University (2005)
B.A., Oberlin College (1997)
Predicting other peoples’ beliefs, desires, and intentions is a primary function of human cognition and is essential to thrive in our complex social world. To do this efficiently and successfully, we must form lasting representations of individuals and social groups based on information we receive through personal and vicarious experience. When we know very little about another person, we often rely on previously stored social group (e.g. race, gender) associations or biases to predict their behavior. As we learn about a person, we rely less on our social biases for prediction and more on our own experience with the individual in question. My research focuses on developing a rigorous computational account of the neurocognitive mechanisms through which we learn about other people, make social predictions, and are influenced by social biases. To achieve this, I employ a multidisciplinary approach, integrating a wide range of techniques from cognitive neuroscience, neuroeconomics, computational modeling of learning and decision-making, social and clinical psychology.
There are multiple benefits to the computational approach. First, it enables paradigms that more closely resemble real-world situations in which the representation of another person’s character includes uncertainty and evolves over time as we incorporate novel information. Second, it enables the use of rigorous model-based fMRI techniques (in which computational models of a cognitive process are correlated against fMRI data) that provide insight into “how” a process is implemented rather than merely where it is located. Model-based fMRI also makes it possible to detect specific component neural computations that may combine to produce social learning updates (e.g. the modulation of surprise at unexpected outcomes by social biases), and that cannot be estimated from behavioral data alone. Finally, this approach enables one to leverage neural data to arbitrate between different putative models that give rise to social learning behavior, providing concrete evidence for how these processes unfold in the brain5
My research program at Adelphi has three foci:
1) Social bias formation, updating, and utilization: Social biases are not stable constructs, rather they continuously evolve with experience and are deployed in a context dependent manner. One focus of my research in this area is to develop behavioral, fMRI, and E.E.G. (a new direction) protocols for assessing and modeling the dynamic nature of implicit social biases. Research questions include: What specific neural computations underlie implicit social bias formation and updating? Are distinct brain regions involved in the formation and storage of social, compared to nonsocial, implicit biases? A second component of this research uses models of decision-making to characterize how, and in what contexts, implicit bias information is integrated into social decisions. At what point in the decision process do implicit biases exert their influence over decisions and to what extent is this influence sensitive to intervention (e.g. contextual, instructional, etc.). Additionally, in and ongoing collaboration with Dr. Ralph Adolphs at Caltech, I am continuing to work with clinical populations (e.g. individuals with ASD, lesion patients, etc.) to elucidate how these processes can be disrupted in abnormal individuals and demonstrate causal relationships between neural mechanism and the behavioral expression of implicit social bias.
2) Learning about and representing other people: This line of research focuses on developing a systems-level model of the computations performed by, and the functional interactions between, individual nodes (i.e., brain regions) of social processing networks during learning about other individuals, predicting their behavior, and making social decisions. Questions include: What specific computations are performed by brain regions subserving ‘mentalizing’ (e.g. temporal parietal junction, anterior temporal lobe, precuneus, medial prefrontal cortex, etc.)? How does functional connectivity between brain regions vary as a function of psychological process (e.g. prediction versus updating)? How does context influence functional connectivity, feedback sensitivity, or prediction? To what extent are social learning systems dedicated to social, versus general, learning functions? Which specific computations are disrupted in social disorders such as ASD and can we identify interventions that restore social learning and decision-making?
3) Integrating group and individual social representations. During both social learning and decision-making, there is an interplay between group-level and individual-level information. For example, social group biases may determine what feedback we process (e.g. ingroup/outgroup biases), and how we integrate social group knowledge with knowledge about a given individual when making social decisions. This third line of research aims to leverage the products of aims 1 and 2 (above) to characterize and quantify this integration. Questions include: How do implicit social biases influence updating as we learn about a new individual? Where in the brain are implicit social biases integrated with novel observations about a given individual? How does feedback about an individual result in updating of implicit social biases about their social group?
Biological Bases Of Behavior
Doctoral Thesis Supervision I
Doctoral Thesis Supervision II
Psychological Research Lab
Psychological Research I
Psychological Research II
Psychological Statistics Lab
Thesis Research Eeg Of Social Learning
Learning and Decision Making
Stanley, D., Ferneyhough, E., & Phelps, E. (2009). Neural perspectives on emotion: Impact on perception, attention and memory. In the Handbook of Neuroscience for the Behavioral Sciences, Ed. Berntson, G.G. & Cacciopo, J.T., John Wiley and Sons
Stanley, D. (2016). Getting to know you: specific neural computations for learning about people. Social Cognitive and Affective Neuroscience, 11(4):525-536. doi:10.1093/scan/nsv145