Visual Recency Bias is Explained by a Mixture Model of Internal Representations

Overview

Journal J Vis

Specialty Ophthalmology

Date 2018 Jul 5

PMID 29971347

Citations 17

Authors

Kristjan Kalm

Dennis Norris

Affiliations

Soon will be listed here.

Abstract

Human bias towards more recent events is a common and well-studied phenomenon. Recent studies in visual perception have shown that this recency bias persists even when past events contain no information about the future. Reasons for this suboptimal behavior are not well understood and the internal model that leads people to exhibit recency bias is unknown. Here we use a well-known orientation estimation task to frame the human recency bias in terms of incremental Bayesian inference. We show that the only Bayesian model capable of explaining the recency bias relies on a weighted mixture of past states. Furthermore, we suggest that this mixture model is a consequence of participants' failure to infer a model for data in visual short-term memory, and reflects the nature of the internal representations used in the task.

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