- Oxytocin enhances learning of other-oriented rank relationships and increases ventromedial prefrontal cortex activity during training.
- At test, oxytocin shifts the self versus other social memory balance, improving inference accuracy for networks in which participants were not embedded.
- Oxytocin recruits amygdala responses during social memory retrieval and overall modulates neurocomputational mechanisms depending on reference frame.
Proc Natl Acad Sci U S A. 2026 Jun 23;123(25):e2606871123. doi: 10.1073/pnas.2606871123. Epub 2026 Jun 15.
ABSTRACT
Indirect evidence from preclinical and neuropharmacological studies suggests that oxytocin may attenuate social dominance relationships and modulate social memory. In humans, oxytocin may also modulate self- versus other-oriented reward learning and in-group/out-group decisions differently. Although the neural bases of learning social hierarchy by observation have been identified for linear hierarchies (a1 < a2 < an), the brain mechanisms engaged in learning more complex rank relationships (i.e. <, =, >) in social networks remain unknown. Here, we investigated the modulatory role of oxytocin on the neurocomputational mechanisms engaged when learning ranks in social networks and when making transitive inferences based on social memory retrieval. Participants learned rank relationships between members of two social networks, one in which they were embedded and the other not. During a subsequent test phase, they inferred rank relationships between pairs of members not encountered before. During training, oxytocin (vs. placebo) improved choice accuracy regarding rank relationship between pairs of members for other-oriented network and increased activity of the ventromedial prefrontal cortex. During the test phase, oxytocin modulated the balance between self/other social memory, boosting performance and amygdala responses for memory retrieval concerning social networks in which participants were not embedded. These findings demonstrate that oxytocin modulates the brain computations needed to learn and retrieve rank relationships in social networks in a manner that depends upon reference-frame.
PMID:42296342 | DOI:10.1073/pnas.2606871123
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