TY - JOUR
UR - https://doi.org/10.7287/peerj.preprints.26825v1
DO - 10.7287/peerj.preprints.26825v1
TI - The material-weight illusion is a Bayes-optimal percept under competing density priors
AU - Peters,Megan A K
AU - Zhang,Ling-Qi
AU - Shams,Ladan
DA - 2018/04/04
PY - 2018
KW - material-weight illusion
KW - visuohaptic perception
KW - size-weight illusion
KW - Bayesian hierarchical causal inference
KW - heaviness perception
AB -
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 objectsâ€™ 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.
VL - 6
SP - e26825v1
T2 - PeerJ Preprints
JO - PeerJ Preprints
J2 - PeerJ Preprints
SN - 2167-9843
ER -