Neural representations in mPFC and insula encode individual differences in estimating others’ preferences

In human society, successful social interactions often hinge upon the ability to accurately estimate other's perspectives, a skill that necessitates integrating contextual cues. This study investigates the neural mechanism involved in this capacity through a preference estimation task. In this task, participants were presented with the target's face and asked to predict their preference for a given item. Preference estimation accuracy was assessed by calculating the percentage of correct guesses, where participants' responses matched the target's preferences on a 4-point Likert scale. Our research demonstrates that, based on inter-subject representational similarity analysis (IS-RSA), the multi-voxel patterns in the medial prefrontal cortex (mPFC) and the anterior insula (AI) predict individual differences in preference estimation accuracy. Specifically, the varying behavioral tendencies among participants in inferring others' preferences were mirrored in the multivariate neural representations within these regions, both of which are known for their involvement in individual differences in interoception and context-dependent interpretation of ambiguous facial emotion. These findings suggest that mPFC and AI play pivotal roles in accurately estimating others' preferences based on minimal information and provide insights that transcend the limitations of traditional univariate approaches by employing multivariate pattern analysis.