A BayesianModel ofMultisensory Integration in Peripersonal Space
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2023-08-28
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en
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Abstract
The environment is filled with a plethora of multisensory stimuli. In order to process these signals correctly,
your brain must deduce the origin. For example, if you want to swat a fly off your arm, you can use the
visual and tactile information to deduce where the target is. This multisensory integration is sensitive to
spatial and temporal discrepancies. In order for visual and tactile information to be integrated into a single
representation, the two cues have to happen shortly after each other and the visual cue has to be located
near the location of touch, in peripersonal space (i.e. the space around a body part). When the two cues are
in spatio-temporal register, there is a higher chance they are caused by the same source. The exact process of
inferring the causal structure of multisensory stimuli is unknown, though evidence suggests multisensory
integration follows the principles of Bayesian inference. This project will attempt to answer the question:
‘To what extent can the integration of multisensory stimuli in peripersonal space be approximated using
Bayesian multisensory visual-tactile integration?’ In this project, I built a Bayesian model of multisensory
integration to recover the underlying parameters and approximate such an integration. Additionally, I
designed a localisation task to measure multisensory integration within peripersonal space. The majority of
participants integrated the visual and tactile stimuli throughout the extent of peripersonal space. They did so
following the precision-control principle in order to reduce uncertainty. A minority of participants ignored
the visual stimulus and just localised the touch. The recovered visual variances were higher than measured,
suggesting that participants used attentional mechanisms, such as precision control, to make them noisier.
The proposedmodels were a good fit to the behavioral data. This suggests that spatial localisation within
peripersonal space follows the principles of Bayesian integration. As the spatial localisation was mostly
multisensory, we infer that Bayesian inference can be used to approximate visual-tactile localisation in
peripersonal space.
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Faculteit der Sociale Wetenschappen