Back

Shared neural codes for eye-gaze and valence

Pryluk, R.; Shohat, Y.; Morozov, A.; Friedman, D.; Taub, A. H.; Paz, R.

2019-08-15 neuroscience
10.1101/736462 bioRxiv
Show abstract

The eye-gaze of others is a prominent social cue in primates and crucial for communication1-7, and atypical processing occurs in several conditions as autism-spectrum-disorder (ASD)1,9-14. The neural mechanisms that underlie eye-gaze remain vague, and it is still debated if these computations developed in dedicated neural circuits or shared with non-social elements. In many species, eye-gaze signals a threat and elicits anxiety, yet can also serve as a predictor for the outcome of the encounter: negative or positive2,4,8. Here, we hypothesized and find that neural codes overlap between eye-gaze and valence. Monkeys participated in a modified version of the human-intruder-test8,15 that includes direct and averted eye-gaze and interleaved with blocks of aversive and appetitive conditioning16,17. We find that single-neurons in the amygdala encode gaze18, whereas neurons in the anterior-cingulate-cortex encode the social context19,20 but not gaze. We identify a shared amygdala circuitry where neural responses to averted and direct gaze parallel the responses to appetitive and aversive value, correspondingly. Importantly, we distinguish two shared coding mechanisms: a shared-intensity scheme that is used for gaze and the unconditioned-stimulus, and a shared-activity scheme that is used for gaze and the conditioned-stimulus. The shared-intensity points to overlap in circuitry, whereas the shared-activity requires also correlated activity. Our results demonstrate that eye-gaze is coded as a signal of valence, yet also as the expected value of the interaction. The findings may suggest new insights into the mechanisms that underlie the malfunction of eye-gaze in ASD and the comorbidity with impaired social skills and anxiety.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.