Modeling Decision Making During Social Cognition

Cognitive modeling advances social cognition by formally specifying the processes thatshape how people perceive, interpret, and respond to the self, others, and social contexts.Evidence accumulation models (EAMs) provide one such framework, capturing howinformation is integrated over time until a decision is reached. This chapter focuses onthe drift diffusion model (DDM), which jointly predicts choice and response timedistributions while decomposing these behavioral measures into psychologicallymeaningful parameters. We illustrate the value of this approach through three casestudies: gender categorization, police officers’ decisions to shoot, and gaze detection inclinical populations. Across these examples, the DDM reveals whether social biases arisefrom pre-decisional tendencies or biased evidence accumulation, with results pointingmore often to the latter. At the same time, the work highlights several open challenges,including the specification of the evidence being accumulated, the joint modeling of otherprocess measures such as eye-tracking and neural data, and the leveraging of the model todesign targeted interventions. Together, these studies demonstrate the promise of EAMsfor advancing computational social psychology.