. This Project 1, directed by John O'Doherty, is a renewal of Project 2 in our current Conte Center.
It aims to continue our investigation of how we can learn to make decisions by observing the choices of another person. This ability, observational learning, is present behaviorally in several species and likely constitutes the main mechanism for the acquisition of social decision-making skills in humans. However, compared to the systems we now know to mediate direct learning through personal experience, surprisingly little is known about the systems that mediate observational learning. What systems are there? How are they differentially employed depending on the context? How might this vary across individuals? We will address these questions across four Aims that test the engagement of three postulated neural systems for observational learning. Of specific interest is a system that relies on social inference, the focus theme of this Conte Center. This system, which is thought to recruit sectors of medial prefrontal cortex and the temporoparietal junction, mediates a computationally more powerful and flexible form of observational learning that requires imputing hidden states to people to explain their actions: their values, goals, and beliefs. We hypothesize, and will test, that this is the same social inference system that is engaged in standard social neuroscience tasks, such as the ?why/how? task administered to all participants under Core 2 and investigated explicitly in Project 2. This Project 1 also links to an Aim that is described under Project 4, where we will investigate the single-unit correlates of observational learning. The strong links between this Project and several others are reflected in its personnel, which include PIs from other Projects (Mobbs, Andersen, Rutishauser, Hutcherson) and post- docs shared with other Projects. The primary approach of Project 1 uses computational fMRI, which designs fMRI tasks such that regional brain activation can be fit to the parameters in a model of the observational learning process. It will test 50 healthy participants in each of 6 experiments, recruited through Cores 2 and 3, and shared in part with the participants of Projects 2 and 3.
Its Aims will test how attention to specific features of social stimuli engages different systems, how social context matters (e.g., if we are observing a human or a computer), how the reliability of the different systems may influence arbitration amongst them, and to what extent there are individual differences that correlate with psychological assessment scores from Core 3, or results from experiments that shared overlapping subjects in other Projects.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Specialized Center (P50)
Project #
5P50MH094258-10
Application #
10101672
Study Section
Special Emphasis Panel (ZMH1)
Project Start
2012-07-26
Project End
2022-02-28
Budget Start
2021-03-01
Budget End
2022-02-28
Support Year
10
Fiscal Year
2021
Total Cost
Indirect Cost
Name
California Institute of Technology
Department
Type
DUNS #
009584210
City
Pasadena
State
CA
Country
United States
Zip Code
91125
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Lin, Chujun; Adolphs, Ralph; Alvarez, R Michael (2018) Inferring Whether Officials Are Corruptible From Looking at Their Faces. Psychol Sci 29:1807-1823
Dubois, Julien; Galdi, Paola; Han, Yanting et al. (2018) Resting-state functional brain connectivity best predicts the personality dimension of openness to experience. Personal Neurosci 1:
Adolphs, Ralph; Gläscher, Jan; Tranel, Daniel (2018) Searching for the neural causes of criminal behavior. Proc Natl Acad Sci U S A 115:451-452
Wang, Oliver; Lee, Sang Wan; O'Doherty, John et al. (2018) Model-based and model-free pain avoidance learning. Brain Neurosci Adv 2:2398212818772964
Charpentier, Caroline J; O'Doherty, John P (2018) The application of computational models to social neuroscience: promises and pitfalls. Soc Neurosci 13:637-647
Tusche, Anita; Hutcherson, Cendri A (2018) Cognitive regulation alters social and dietary choice by changing attribute representations in domain-general and domain-specific brain circuits. Elife 7:

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