Abstract
Self-reports remain affective science's only direct measure of subjective affective experiences. Yet, little research has sought to understand the psychological process that transforms subjective experience into self-reports. Here, we propose that by framing these self-reports as dynamic affective decisions, affective scientists may leverage the computational tools of decision-making research, sequential sampling models specifically, to better disentangle affective experience from the noisy decision processes that constitute self-report. We further outline how such an approach could help affective scientists better probe the specific mechanisms that underlie important moderators of affective experience (e.g., contextual differences, individual differences, and emotion regulation) and discuss how adopting this decision-making framework could generate insight into affective processes more broadly and facilitate reciprocal collaborations between affective and decision scientists towards a more comprehensive and integrative psychological science.