Data from: Modeling the perception of audiovisual distance: Bayesian causal inference and other models
Mendonça, Catarina; Mandelli, Pietro; Pulkki, Ville (2017), Data from: Modeling the perception of audiovisual distance: Bayesian causal inference and other models, Dryad, Dataset, https://doi.org/10.5061/dryad.r5gg0
Studies of audiovisual perception of distance are rare. Here, visual and auditory cue interactions in distance are tested against several multisensory models, including a modified causal inference model. In this causal inference model predictions of estimate distributions are included. In our study, the audiovisual perception of distance was overall better explained by Bayesian causal inference than by other traditional models, such as sensory dominance and mandatory integration, and no interaction. Causal inference resolved with probability matching yielded the best fit to the data. Finally, we propose that sensory weights can also be estimated from causal inference. The analysis of the sensory weights allows us to obtain windows within which there is an interaction between the audiovisual stimuli. We find that the visual stimulus always contributes by more than 80% to the perception of visual distance. The visual stimulus also contributes by more than 50% to the perception of auditory distance, but only within a mobile window of interaction, which ranges from 1 to 4 m.