Back

Task-uninformative visual stimuli improve auditory spatial discrimination: an elusive effect with an ambiguous contribution of relative reliability

Cappelloni, M. S. S.; Shivkumar, S.; Haefner, R. M.; Maddox, R. K.

2022-08-25 neuroscience
10.1101/2022.08.24.505112 bioRxiv
Show abstract

The brain combines information from multiple sensory modalities to interpret the environment. These processes, collectively known as multisensory integration, have been modeled as Bayesian causal inference, which proposes that perception involves the combination of information from different sensory modalities based on their reliability and their likelihood of stemming from the same causes in the outside world. Bayesian causal inference has explained a variety of multisensory effects in simple tasks but is largely untested in complex sensory scenes where multisensory integration can provide the most benefit. Recently, we presented data challenging the ideal Bayesian model from a new auditory spatial discrimination task in which spatially aligned visual stimuli improve performance despite providing no information about the correct response. Here, we tested the hypothesis that, despite deviating from the ideal observer, the influence of task-uninformative stimuli was still dependent on the reliabilities of auditory and visual cues. We reasoned shorter stimulus durations should lead to less reliable auditory spatial encoding, and hence stronger effects of more reliable visual cues, which are easily localized even at short durations. While our results replicated the effect from our previous study across a wide range of stimulus durations, we did not find a significant increase in effect size with shorter stimuli, leaving our principal question not fully answered.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.