Integration of social cues and individual experiences during instrumental avoidance learning

Heterogeneity in strategy use during arbitration between experiential and observational learning

To navigate our complex social world, it is crucial to deploy multiple learning strategies, such as learning from directly experiencing action outcomes or from observing other people's behavior. Despite the prevalence of experiential and observational learning in humans and other social animals, it remains unclear how people favor one strategy over the other depending on the environment, and how individuals vary in their strategy use. Here, we describe an arbitration mechanism in which the prediction errors associated with each learning strategy influence their weight over behavior. We designed an online behavioral task to test our computational model, and found that while a substantial proportion of participants relied on the proposed arbitration mechanism, there was some meaningful heterogeneity in how people solved this task. Four other groups were identified: those who used a fixed mixture between the two strategies, those who relied on a single strategy and non-learners with irrelevant strategies. Furthermore, groups were found to differ on key behavioral signatures, and on transdiagnostic symptom dimensions, in particular autism traits and anxiety. Together, these results demonstrate how large heterogeneous datasets and computational methods can be leveraged to better characterize individual differences.

A boost in self-esteem after positive social evaluation predicts social and non-social learning

Fluctuations in self-esteem resulting from social acceptance and rejection could guide social behaviour by putting us in a state that is more or less open to social experiences. However, it remains unclear whether social acceptance and rejection may shape learning from social information depending on individual differences in self-esteem changes. Here we used a social feedback paradigm to manipulate social acceptance and rejection in a between-subjects design. Subsequently, we administered a behavioural task that enables the assessment of how well individuals learn on the basis of own experiences versus social information. Participants receiving positive (N = 43) versus negative (N = 44) social evaluation demonstrated an increase in subjective self-esteem. Importantly, the effect of the social evaluation on social learning was moderated by self-esteem changes. Specifically, an increase in self-esteem, as induced by positive evaluation, was associated with increased learning from social, but decreased learning from individual information. A decrease in self-esteem in response to negative evaluation was associated with decreased learning from individual information. These data suggest that increases in self-esteem in response to positive evaluation can induce a shift in the inclination to use social versus non-social information and may open one up to constructive learning from others.

The face value of feedback: facial behaviour is shaped by goals and punishments during interaction with dynamic faces

Responding appropriately to others' facial expressions is key to successful social functioning. Despite the large body of work on face perception and spontaneous responses to static faces, little is known about responses to faces in dynamic, naturalistic situations, and no study has investigated how goal directed responses to faces are influenced by learning during dyadic interactions. To experimentally model such situations, we developed a novel method based on online integration of electromyography signals from the participants' face (corrugator supercilii and zygomaticus major) during facial expression exchange with dynamic faces displaying happy and angry facial expressions. Fifty-eight participants learned by trial-and-error to avoid receiving aversive stimulation by either reciprocate (congruently) or respond opposite (incongruently) to the expression of the target face. Our results validated our method, showing that participants learned to optimize their facial behaviour, and replicated earlier findings of faster and more accurate responses in congruent versus incongruent conditions. Moreover, participants performed better on trials when confronted with smiling, when compared with frowning, faces, suggesting it might be easier to adapt facial responses to positively associated expressions. Finally, we applied drift diffusion and reinforcement learning models to provide a mechanistic explanation for our findings which helped clarifying the underlying decision-making processes of our experimental manipulation. Our results introduce a new method to study learning and decision-making in facial expression exchange, in which there is a need to gradually adapt facial expression selection to both social and non-social reinforcements.