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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 denser-looking (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.
In this project, we investigate whether the puzzling "material-weight illusion" (MWI) might be a product of optimal Bayesian inference. In the MWI, two objects that have the same size and mass are presented to an observer, with one appearing to be made out of a denser material (e.g., metal) and the other a less-dense material (e.g., polystyrene). When the observer lifts the objects, the denser-looking one feels lighter, which appears to oppose the predictions of typical Bayesian inference. Here we applied a previously-described model that can account for the related "size-weight illusion" (SWI; Peters et al., 2016, PeerJ) to the MWI. In the model, the perceptual system arbitrates among competing density priors via hierarchical Bayesian inference. We show through simulation that the MWI can be explained by the same optimal inference framework that can explain the SWI, unifying these puzzling illusions with the broader literature describing human perception as Bayes-optimal.