Integration of audiovisual spatial signals is not consistent with maximum likelihood estimation

被引:37
|
作者
Meijer, David [1 ]
Veselic, Sebastijan [1 ]
Calafiore, Carmelo [1 ]
Noppeney, Uta [1 ]
机构
[1] Univ Birmingham, Computat Neurosci & Cognit Robot Ctr, Computat Cognit Neuroimaging Lab, Birmingham, W Midlands, England
基金
欧洲研究理事会;
关键词
Multisensory perception; Optimality; Maximum likelihood estimation; Multisensory integration; Spatial ventriloquism; PSYCHOMETRIC FUNCTION; VISION; TESTS; CUES;
D O I
10.1016/j.cortex.2019.03.026
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
Multisensory perception is regarded as one of the most prominent examples where human behaviour conforms to the computational principles of maximum likelihood estimation (MLE). In particular, observers are thought to integrate auditory and visual spatial cues weighted in proportion to their relative sensory reliabilities into the most reliable and unbiased percept consistent with MLE. Yet, evidence to date has been inconsistent. The current pre-registered, large-scale (N = 36) replication study investigated the extent to which human behaviour for audiovisual localization is in line with maximum likelihood estimation. The acquired psychophysics data show that while observers were able to reduce their multisensory variance relative to the unisensory variances in accordance with MLE, they weighed the visual signals significantly stronger than predicted by MLE. Simulations show that this dissociation can be explained by a greater sensitivity of standard estimation procedures to detect deviations from MLE predictions for sensory weights than for audiovisual variances. Our results therefore suggest that observers did not integrate audiovisual spatial signals weighted exactly in proportion to their relative reliabilities for localization. These small deviations from the predictions of maximum likelihood estimation may be explained by observers' uncertainty about the world's causal structure as accounted for by Bayesian causal inference. (C) 2019 The Authors. Published by Elsevier Ltd.
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页码:74 / 88
页数:15
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