Facial electromyography reveals dissociable affective responses in social and non-social cooperation

Belief Updating during Social Interactions: Neural Dynamics and Causal Role of Dorsomedial Prefrontal Cortex

In competitive interactions, humans have to flexibly update their beliefs about another person's intentions in order to adjust their own choice strategy, such as when believing that the other may exploit their cooperativeness. Here we investigate both the neural dynamics and the causal neural substrate of belief updating processes in humans. We used an adapted prisoner's dilemma game in which participants explicitly predicted the coplayer's actions, which allowed us to quantify the prediction error between expected and actual behavior. First, in an EEG experiment, we found a stronger medial frontal negativity (MFN) for negative than positive prediction errors, suggesting that this medial frontal ERP component may encode unexpected defection of the coplayer. The MFN also predicted subsequent belief updating after negative prediction errors. In a second experiment, we used transcranial magnetic stimulation (TMS) to investigate whether the dorsomedial prefrontal cortex (dmPFC) causally implements belief updating after unexpected outcomes. Our results show that dmPFC TMS impaired belief updating and strategic behavioral adjustments after negative prediction errors. Taken together, our findings reveal the time course of the use of prediction errors in social decisions and suggest that the dmPFC plays a crucial role in updating mental representations of others' intentions.

Measuring emotions during learning: lack of coherence between automated facial emotion recognition and emotional experience

Measuring emotions non-intrusively via affective computing provides a promising source of information for adaptive learning and intelligent tutoring systems. Using non-intrusive, simultaneous measures of emotions, such systems could steadily adapt to students emotional states. One drawback, however, is the lack of evidence on how such modern measures of emotions relate to traditional self-reports. The aim of this study was to compare a prominent area of affective computing, facial emotion recognition, to students’ self-reports of interest, boredom, and valence. We analyzed different types of aggregation of the simultaneous facial emotion recognition estimates and compared them to self-reports after reading a text. Analyses of 103 students revealed no relationship between the aggregated facial emotion recognition estimates of the software FaceReader and self-reports. Irrespective of different types of aggregation of the facial emotion recognition estimates, neither the epistemic emotions (i.e., boredom and interest), nor the estimates of valence predicted the respective self-report measure. We conclude that assumptions on the subjective experience of emotions cannot necessarily be transferred to other emotional components, such as estimated by affective computing. We advise to wait for more comprehensive evidence on the predictive validity of facial emotion recognition for learning before relying on it in educational practice.