This Project investigates the basic computations at work in making simple social decisions, and contrasts them with simple non-social decisions (e.g., ones based on the value of juice or money, rather than the value of other people). It sets the stage for all the others in investigating how social reward is represented and compares to nonsocial reward. An example of a non-social decision is choosing what to drink by pushing one of several buttons on a soda dispensing machine, an example of a social decision is choosing what person to call to go on a date. Here we address these questions: Are there regions in the amygdala and prefrontal cortex that encode stimulus values at the time of choice, and experienced (hedonic) values at the time of outcome, in the social domain (seeing smiling or beautiful faces), as they do in the nonsocial case (getting juice when thirsty)? Are there neurons specialized for valuation of social stimuli, or do the same cells encode value in social and non-social decisions? Does the valuation of different types of social stimuli require specific sub-circuits? And how are individual differences between people reflected in these processes? We will address these questions by carrying out parallel experiments in humans and rhesus monkeys, using the complementary techniques of fMRI and electrophysiological recording in both species, and using a variety of basic social and non-social stimuli. Comparisons will be made across species and across single-unit, local field potential, and BOLD-fMRI data, as well as with data from the other Projects and across individual differences.

Public Health Relevance

Many mental illnesses are associated with the most disabling dysfunction in the social domain. For instance, people with autism are impaired in their social interactions. A major limitation in our understanding of those disorders is that we do not yet understand how social reward signals are processed in the brain, and how they guide behavior This Project constitutes the foundation of that investigation and will have relevance for the ultimate diagnosis, management and treatment of mental illnesses.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Specialized Center (P50)
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Special Emphasis Panel (ZMH1-ERB-S (02))
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California Institute of Technology
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Pastor-Bernier, Alexandre; Plott, Charles R; Schultz, Wolfram (2017) Monkeys choose as if maximizing utility compatible with basic principles of revealed preference theory. Proc Natl Acad Sci U S A 114:E1766-E1775
Wang, Shuo; Adolphs, Ralph (2017) Reduced specificity in emotion judgment in people with autism spectrum disorder. Neuropsychologia 99:286-295
Zhang, Carey Y; Aflalo, Tyson; Revechkis, Boris et al. (2017) Partially Mixed Selectivity in Human Posterior Parietal Association Cortex. Neuron 95:697-708.e4
Spunt, Robert P; Ellsworth, Emily; Adolphs, Ralph (2017) The neural basis of understanding the expression of the emotions in man and animals. Soc Cogn Affect Neurosci 12:95-105
Collette, Sven; Pauli, Wolfgang M; Bossaerts, Peter et al. (2017) Neural computations underlying inverse reinforcement learning in the human brain. Elife 6:
Dubois, Julien; Oya, Hiroyuki; Tyszka, J Michael et al. (2017) Causal mapping of emotion networks in the human brain: Framework and initial findings. Neuropsychologia :
Diederen, Kelly M J; Ziauddeen, Hisham; Vestergaard, Martin D et al. (2017) Dopamine Modulates Adaptive Prediction Error Coding in the Human Midbrain and Striatum. J Neurosci 37:1708-1720
Reber, Justin; Feinstein, Justin S; O'Doherty, John P et al. (2017) Selective impairment of goal-directed decision-making following lesions to the human ventromedial prefrontal cortex. Brain 140:1743-1756
Spunt, Robert P; Adolphs, Ralph (2017) The neuroscience of understanding the emotions of others. Neurosci Lett :
Reed, Chrystal M; Birch, Kurtis G; Kami?ski, Jan et al. (2017) Automatic detection of periods of slow wave sleep based on intracranial depth electrode recordings. J Neurosci Methods 282:1-8

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