Data from: Modeling the perception of audiovisual distance: Bayesian causal inference and other models

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.