Decoding Affect: A Computational Model for Affective Adaptation and Construction

Affective adaptation and construction are fundamental psychological processes shaped by multiple interacting factors, including reference points, stimulus inherent value, attention, negativity bias, adaptation rate, and time. While decades of research have underscored the importance of these factors, a unified theoretical framework that quantifies their complex interactions—and their cumulative impact on affective responses—remains lacking. To address this gap, we propose ARIA (Attention–Reference Interaction Adaptation), a computational framework that describes how the brain dynamically generates affective responses to changing environments. ARIA posits that negativity bias, together with the inherent values of both the current stimulus and relevant reference points, jointly determine attention allocation and adaptation rate. Additionally, the attention-capturing capacity of the current stimulus regulates the adaptation rate. As a result, affective responses are constructed based on current external stimuli, internal psychological reference points formed by past experiences, and the cognitive processes that mediate the interaction between them. By integrating multiple psychological mechanisms (reference-dependent evaluation, value-driven attention allocation, and negativity bias) into a single theoretical framework, ARIA offers a unified explanation for both classic affective phenomena (e.g., exponential decay of affect and affective after-reactions) and anomalous affective phenomena that existing theories have struggled to account for (e.g., environmental improvements do not necessarily induce pleasure). Simulations based on ARIA reveal the adaptive and dynamic nature of affective responses, providing theoretical guidance for fields such as built-environment management, user-experience design, behavioral economics, affective computing, and emotion regulation.