Work

A Bayesian inference model can predict the effects of attention on the serial dependence in heading estimation from optic flow

Year: 2024

Type: article

Abstract: It has been demonstrated that observers can accurately estimate their self-motion direction (i.e., heading) from optic flow, which can be affected by attention. However, it remains unclear how attention affects the serial dependence in the estimation. In the current study, participants conducted two experiments. The results showed that the estimation accuracy decreased when attentional resources allocated to the heading estimation task were reduced. Additionally, the estimates of currently presented headings were biased toward the headings of previously seen headings, showing serial dependence. Especially, this effect decreased (increased) when the attentional resources allocated to the previously (currently) seen headings were reduced. Furthermore, importantly, we developed a Bayesian inference model, which incorporated attention-modulated likelihoods and qualitatively predicted changes in the estimation accuracy and serial dependence. In summary, the current study shows that attention affects the serial dependence in heading estimation from optic flow and reveals the Bayesian computational mechanism behind the heading estimation.

Source: Journal of Vision

DOI: 10.1167/jov.24.9.11

Authors: Qi Sun, Si-Yu Wang, Lin-Zhe Zhan, Fan-Huan You, Qian Sun

Institution: Zhejiang Normal University

Language: English

Topic: Visual perception and processing mechanisms

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $1,850

Funder: National Natural Science Foundation of China