These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Natural statistics of head roll: implications for Bayesian inference in spatial orientation.
    Author: Willemsen SCMJ, Oostwoud Wijdenes L, van Beers RJ, Koppen M, Medendorp WP.
    Journal: J Neurophysiol; 2022 Dec 01; 128(6):1409-1420. PubMed ID: 36321734.
    Abstract:
    We previously proposed a Bayesian model of multisensory integration in spatial orientation (Clemens IAH, de Vrijer M, Selen LPJ, van Gisbergen JAM, Medendorp WP. J Neurosci 31: 5365-5377, 2011). Using a Gaussian prior, centered on an upright head orientation, this model could explain various perceptual observations in roll-tilted participants, such as the subjective visual vertical, the subjective body tilt (Clemens IAH, de Vrijer M, Selen LPJ, van Gisbergen JAM, Medendorp WP. J Neurosci 31: 5365-5377, 2011), the rod-and-frame effect (Alberts BBGT, de Brouwer AJ, Selen LPJ, Medendorp WP. eNeuro 3: ENEURO.0093-16.2016, 2016), as well as their clinical (Alberts BBGT, Selen LPJ, Verhagen WIM, Medendorp WP. Physiol Rep 3: e12385, 2015) and age-related deficits (Alberts BBGT, Selen LPJ, Medendorp WP. J Neurophysiol 121: 1279-1288, 2019). Because it is generally assumed that the prior reflects an accumulated history of previous head orientations, and recent work on natural head motion suggests non-Gaussian statistics, we examined how the model would perform with a non-Gaussian prior. In the present study, we first experimentally generalized the previous observations in showing that also the natural statistics of head orientation are characterized by long tails, best quantified as a t-location-scale distribution. Next, we compared the performance of the Bayesian model and various model variants using such a t-distributed prior to the original model with the Gaussian prior on their accounts of previously published data of the subjective visual vertical and subjective body tilt tasks. All of these variants performed substantially worse than the original model, suggesting a special value of the Gaussian prior. We provide computational and neurophysiological reasons for the implementation of such a prior, in terms of its associated precision-accuracy trade-off in vertical perception across the tilt range.NEW & NOTEWORTHY It has been argued that the brain uses Bayesian computations to process multiple sensory cues in vertical perception, including a prior centered on upright head orientation which is usually taken to be Gaussian. Here, we show that non-Gaussian prior distributions, although more akin to the statistics of head orientation during natural activities, provide a much worse explanation of such perceptual observations than a Gaussian prior.
    [Abstract] [Full Text] [Related] [New Search]