Modeling the Perception of Audiovisual Distance: Bayesian Causal Inference and Other Models

Abstract

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.

Auditory Distance Control Using a Variable-Directivity Loudspeaker

“…Therefore we conclude that in the laboratory environment, in which visual cues do not comply with auditory cues, the former play a minor role. This agrees with findings in [29,30] showing that sensory interactions, e.g., vision vs. audition, include a weighting process where the most reliable cue contributes the most to the multi-sensory percept. Figure 14 shows the results for the ASW rating task of the second experiment.…”

Asymmetric visual capture of virtual sound sources in the distance dimension

“…Imprecision of auditory distance perception relative to visual distance perception. Elegant modeling work by Mendonça et al (2016) clearly demonstrates that visual capture in distance is explainable based on the relative imprecision of auditory distance perception relative to visual distance perception. This concept is a fundamental aspect of other probabilistic models of multisensory integration which result in more weight being given to perceptual modalities that produce more precise estimates of a given perceptual quantity ( Ernst and Banks, 2002 ; Alais and Burr, 2004 ).…”

“… Mendonça et al (2016) applied this type of probabilistic explanation to visual capture in the distance dimension by evaluating a number of different probabilistic models. Although in general, this approach can explain situations both where visual capture in distance is ( Gardner, 1968 , 1969 ; Mershon et al, 1980 ) and is not observed ( Zahorik, 2001 ; Calcagno et al, 2012 ), the models evaluated by Mendonça et al (2016) do not incorporate a fundamental aspect of perceived auditory space: that perceived distance is non-linearly related to physical distance. Best evidence suggests that perceived distance is instead logarithmically related to physical sound source distance (see Zahorik et al, 2005 for a meta-analysis of the auditory distance perception literature).…”

Distance in audio for VR

“…[18: 417] Such probabilistic perspectives have gained popularity in recent years as notions of sensory dominance fade. The Maximum Likelihood Estimation (MLE) model which assumes that the perceptual weighing process is statistically optimal, is proving particularly popular [33].…”