Positive social feedback alters emotional ratings and reward valuation of neutral faces

Evaluation of facial and vocal emotional cues is vital in social interactions but can be highly influenced by characteristics of the observer, such as sex, age, and symptoms of affective disorders. Our evaluations of others' emotional expressions are likely to change as we get to know them and anticipate how they are likely to behave. However, the role of associative learning in the evaluation of social cues remains poorly understood. In this study, we investigated whether emotional ratings (valence and arousal) and reward valuation (liking and wanting measures) of neutral facial expressions can be altered through associative learning. We also examined whether emotional ratings and reward valuation varied with symptoms of anxiety and depression, disorders known to impair socio-affective functioning. Participants (N = 324) were young adults, ranging in scores across dimensions of depression and anxiety symptoms: general distress (common to depression and anxiety), anhedonia-apprehension (more specific to depression), and fears (more specific to anxiety). They rated neutral faces and completed a probabilistic learning task that paired images of neutral faces with positive or negative social feedback. Results demonstrated that pairing neutral faces with positive social feedback increased ratings of arousal, valence, and reward valuation (both liking and wanting). Pairing neutral faces with negative feedback reduced valence ratings and reduced wanting, but did not impact arousal ratings or liking. Symptoms of general distress were associated with negative bias in valence ratings, symptoms of anhedonia-apprehension were associated with reduced wanting, and symptoms of fears were associated with altered accuracy over trials. Notably, the association between general distress and negative bias was reduced following the associative learning task. This suggests that disrupted evaluation of social cues can be improved through brief training.