AUTHOR=Sun Qian , Wang Si-Yu , Sun Meng-Ying , You Fan-Huan , Ran Ping , Sun Qi 

TITLE=Effects of attention on the asymmetric serial dependences between form and motion patterns and their computational processes

JOURNAL=Frontiers in Psychology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1505031

DOI=10.3389/fpsyg.2025.1505031

ISSN=1664-1078

ABSTRACT=Recent studies have revealed that serial dependences are asymmetric in the estimation of the focus of expansion (FoE) in the global static form and dynamic optic flow displays. In the current study, we conducted two experiments to examine whether and how attention affected the serial dependences between the two displays. The results showed that when all attentional resources are allocated to the FoE estimation task, the serial dependence of the form FoE estimation on the previous flow FoE (SDEflow−form) still existed even as the flow FoE was 40°, while the serial dependence of the flow FoE estimation on the previous form FoE (SDEform−flow) disappeared as the form FoE was beyond 30°. When attentional resources are distributed by other tasks, the SDEflow−form tended to be stronger than the SDEform−flow. Therefore, the SDEflow−form and SDEform−flow are asymmetric regardless of observers' attentional states. Finally, we developed two Bayesian models to address the computational mechanism underlying the attentional effects. Both models proposed that attention modulated the certainty of sensory representations of currently presented features. In addition, the effects of working memory on previously presented features were considered in one model. The results showed that the Bayesian inference model that included working memory predicted participants' performances better than the model without considering working memory. In summary, the current study demonstrated that attention and working memory affected the serial dependences between form and flow displays, and the effects could be quantitatively predicted by Bayesian inference models.