Exploring individual differences in the impact of cognitive constraints on prosocial decision-making via intrinsic...

Prosocial decisions in daily life are often influenced by cognitive constraints, such as time pressure and cognitive load, which can impact how we process information and make decisions that benefit others. Understanding how these constraints interact with our brain's intrinsic connectivity patterns and contribute to individual differences is crucial. This study investigates the neural mechanisms underlying the effects of cognitive constraints on prosocial decision-making. We developed a resting-state functional connectivity (rsFC) network model using machine learning regression to predict how cognitive constraints influence prosocial choices, while accounting for individual variability through intersubject representational similarity analysis (IS-RSA). Our findings reveal that the rsFC network-including regions involved in affective processing (insula, INS; amygdala, AMYG), empathy (temporo-parietal junction, TPJ; medial cingulate gyrus, MCG), and valuation (ventral striatum, VS; ventral prefrontal cortex, vmPFC)-predicts the impact of cognitive constraints on decision-making. Notably, rsFC between MCG and TPJ and bilateral TPJ connectivity showed intersubject variability that aligned with behavioral responses. These findings elucidate how cognitive constraints shape prosocial decision-making at the neural level, uncovering individual variability that advances theoretical understanding and offers practical implications for fostering prosociality in cognitively demanding contexts.