Proceedings of the Annual Meeting of the Cognitive Science Society

Most work on attention (in terms of both psychophysical experiments and computational modeling) involves selection instatic scenes. And even when dynamic displays are used, performance is still typically characterized with only a singlevariable (such as the number of items correctly tracked in Multiple Object Tracking; MOT). But the allocation of attentionin daily life (e.g. during foraging, navigation, or play) involves both objective performance and subjective effort, and canvary dramatically from moment to moment. Here we attempt to capture this sort of rich temporal ebb and flow of attentionin a novel and generalizable adaptive computation architecture. In this architecture, computing resources are dynamicallyallocated to perform partial belief updates over both objects (in space) and moments (in time) flexibly and according totask demands. During MOT this framework is able to explain both objective tracking performance and the subjective senseof trial-by-trial effort.