The Size-Weight Illusion is not anti-Bayesian after all: a unifying Bayesian account

Peters, Megan A. K.; Ji, Wei; Shams, Ladan

doi:10.7717/peerj.2124

Cited by 60 publications

(99 citation statements)

References 30 publications

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“…(The astute reader will note that here we have elided the known bias in volume estimation previously used to model the SWI, i.e. that (Peters, Ma & Shams, 2016) , because in the MWI the two objects' v…”

Section: Computational Modelmentioning

confidence: 99%

“…that R 1 was a priori highly probable (Peters, Ma & Shams, 2016) . However, in the MWI the appearance of different materials leads to a strong a priori probability for R 3 , i.e.…”

Section: Computational Modelmentioning

confidence: 99%

“…The computational model presented here is a direct application of the model previously developed to explain the SWI (Peters, Ma & Shams, 2016) , but with parameters and settings modified to accommodate the circumstances of the MWI instead. Below we describe the logic of the model, and the modifications that were made to convert the model's predictions from mapping the SWI to mapping the MWI.…”

Section: Computational Modelmentioning

confidence: 99%

“…This is because the incoming sensory information about the objects' weight relationship (i.e., the two items actually have the same mass) is too incongruent with the expected weight relationship if they do in fact have the same density (i.e., that the smaller item should be lighter); instead, the alternative hypothesis that the smaller item is denser (Balzer, Peters & Soatto, 2013; Peters, Balzer & Shams, 2015 is determined to be a posteriori more probable, leading to its selection and the ultimate percept that the smaller item feels heavier. Using a series of behavioral experiments, we demonstrated this optimal computational framework well describes human perception (Peters, Ma & Shams, 2016) .…”

mentioning

confidence: 99%

“…Taking inspiration from previous literature on competitive priors in vision (Yuille & Bülthoff, 1996; Knill, 2003, 2007 , we suggested that incoming haptic sensory information from two equalmass but differentsized objects will arbitrate among competing density hypotheses, ultimately resulting in the illusory percept that the smaller item feels heavier than the larger one (Peters, Ma & Shams, 2016) . This is because the incoming sensory information about the objects' weight relationship (i.e., the two items actually have the same mass) is too incongruent with the expected weight relationship if they do in fact have the same density (i.e., that the smaller item should be lighter); instead, the alternative hypothesis that the smaller item is denser (Balzer, Peters & Soatto, 2013; Peters, Balzer & Shams, 2015 is determined to be a posteriori more probable, leading to its selection and the ultimate percept that the smaller item feels heavier.…”

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confidence: 99%

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The material-weight illusion is a Bayes-optimal percept under competing density priors

Peters

Zhang

Shams

2018

Preprint

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The material-weight illusion (MWI) is one example in a class of weight perception illusions that seem to defy principled explanation. In this illusion, when an observer lifts two objects of the same size and mass, but that appear to be made of different materials, the denserlooking (e.g., metal-look) object is perceived as lighter than the less-dense-looking (e.g., polystyrene-look) object. Like the size-weight illusion (SWI), this perceptual illusion occurs in the opposite direction of predictions from an optimal Bayesian inference process, which predicts that the denser-looking object should be perceived as heavier, not lighter. The presence of this class of illusions challenges the often-tacit assumption that Bayesian inference holds universal explanatory power to describe human perception across (nearly) all domains: If an entire class of perceptual illusions cannot be captured by the Bayesian framework, how could it be argued that human perception truly follows optimal inference? However, we recently showed that the SWI can be explained by an optimal hierarchical Bayesian causal inference process (Peters, Ma & Shams, 2016) in which the observer uses haptic information to arbitrate among competing hypotheses about objects' possible density relationship. Here we extend the model to demonstrate that it can readily explain the MWI as well. That hierarchical Bayesian inference can explain both illusions strongly suggests that even puzzling percepts arise from optimal inference processes. AbstractThe materialweight illusion (MWI) is one example in a class of weight perception illusions that seem to defy principled explanation. In this illusion, when an observer lifts two objects of the same size and mass, but that appear to be made of different materials, the denserlooking (e.g., metallook) object is perceived as lighter than the lessdenselooking (e.g., polystyrenelook) object. Like the sizeweight illusion (SWI), this perceptual illusion occurs in the opposite direction of predictions from an optimal Bayesian inference process, which predicts that the denserlooking object should be perceived as heavier , not lighter. The presence of this class of illusions challenges the oftentacit assumption that Bayesian inference holds universal explanatory power to describe human perception across (nearly) all domains: If an entire class of perceptual illusions cannot be captured by the Bayesian framework, how could it be argued that human perception truly follows optimal inference? However, we recently showed that the SWI can be explained by an optimal hierarchical Bayesian causal inference process (Peters, Ma & Shams, 2016) in which the observer uses haptic information to arbitrate among competing hypotheses about objects' possible density relationship. Here we extend the model to demonstrate that it can readily explain the MWI as well. That hierarchical Bayesian inference can explain both illusions strongly suggests that even puzzling percepts arise from optimal inference processes.

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Section: Computational Modelmentioning

confidence: 99%

“…that R 1 was a priori highly probable (Peters, Ma & Shams, 2016) . However, in the MWI the appearance of different materials leads to a strong a priori probability for R 3 , i.e.…”

Section: Computational Modelmentioning

confidence: 99%

Section: Computational Modelmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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The material-weight illusion is a Bayes-optimal percept under competing density priors

Peters

Zhang

Shams

2018

Preprint

Self Cite

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Bayesian modeling of the mind: From norms to neurons

Rescorla

2020

WIRES Cognitive Science

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Bayesian decision theory is a mathematical framework that models reasoning and decision-making under uncertain conditions. The past few decades have witnessed an explosion of Bayesian modeling within cognitive science. Bayesian models are explanatorily successful for an array of psychological domains. This article gives an opinionated survey of foundational issues raised by Bayesian cognitive science, focusing primarily on Bayesian modeling of perception and motor control. Issues discussed include the normative basis of Bayesian decision theory; explanatory achievements of Bayesian cognitive science; intractability of Bayesian computation; realist versus instrumentalist interpretation of Bayesian models; and neural implementation of Bayesian inference.

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