Contributions of optostatic and optokinetic cues to the perception of vertical

Niehof, N.; Perdreau, Florian; Koppen, Mathieu; Medendorp, W. Pieter

doi:10.1152/jn.00740.2018

Cited by 8 publications

(10 citation statements)

References 45 publications

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“…We simultaneously fit the three free parameters to the data using non-linear least squares regression. Optimal parameter values were obtained through maximum likelihood estimation using the Matlab routine 'fmincon' (Clemens et al, 2011;McGuire and Sabes, 2009;Niehof et al, 2019). All modelling was done with the combined data from both localization tasks.…”

Section: Modelling Perceptual Variabilitymentioning

confidence: 99%

A neural surveyor to map touch on the body

Miller

Fabio

Beers

et al. 2020

Preprint

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Perhaps the most recognizable sensory map in all of neuroscience is the somatosensory homunculus. Though it seems straightforward, this simple representation belies the complex link between an activation in somatosensory Area 3b and the associated touch location on the body. Any isolated activation is spatially ambiguous without a neural decoder that can read its position within the entire map, though how this is computed by neural networks is unknown.We propose that somatosensory cortex implements multilateration, a common computation used by surveying and GPS systems to localize objects. Specifically, to decode touch location on the body, the somatosensory system estimates the relative distance between the afferent input and the body's joints. We show that a simple feedforward neural network which captures the receptive field properties of somatosensory cortex implements a Bayes-optimal multilateral decoder via a combination of bell-shaped (Area 3b) and sigmoidal (Areas 1/2) tuning curves. Simulations demonstrated that this decoder produced a unique pattern of localization variability between two joints that was not produced by other known neural decoders. Finally, we identify this neural signature of multilateration in actual psychophysical experiments, suggesting that it is a candidate computational mechanism underlying tactile localization. take place in the frontal and parietal cortices (Burnod et al., 1999;Crawford et al., 2004;Medendorp et al., 2005;Pesaran et al., 2006).Equally crucial to localizing objects in the environment is localizing objects on the personal space of the body. Despite over 180 years of research on the sense of touch (Weber, 1834), the computations underlying tactile localization remain largely unknown. Recent accounts have suggested that tactile localization requires two computational steps Medina and Coslett, 2010). First, afferent input must be localized within a topographic map in

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Section: Modelling Perceptual Variabilitymentioning

confidence: 99%

A neural surveyor to map touch on the body

Miller

Fabio

Beers

et al. 2020

Preprint

Self Cite

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“…Note that tilt signals have been identified in the navigation system (Angelaki et al, 2020), but these cells were not tested during tilt/translation discrimination protocols, thus it is unknown whether they convey a net tilt signal. Although tilt perception is driven by a 3D internal model in humans (Clark & Graybiel, 1966; Merfeld et al, 2001; Niehof et al, 2019a, 2019b; Vingerhoets et al, 2007), whether tilt-selective cells exist outside the NU remains unknown.…”

Section: Discussionmentioning

confidence: 99%

“…Although tilt perception is driven by a 3D internal model in humans (Clark & Graybiel, 1966;Merfeld et al, 2001;Niehof et al, 2019aNiehof et al, , 2019bVingerhoets et al, 2007), whether tilt-selective cells exist outside the NU remains unknown.…”

Section: Neuronal and Behavioural Outputs Of The Numentioning

confidence: 99%

Two functionally distinct Purkinje cell populations implement an internal model within a single olivo-cerebellar loop

Laurens

2021

Preprint

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Olivo-cerebellar loops, where anatomical patches of the cerebellar cortex and inferior olive project one onto the other, form an anatomical unit of cerebellar computation. Here, we investigated how successive computational steps map onto olivo-cerebellar loops. Lobules IX-X of the cerebellar vermis, i.e. the nodulus and uvula, implement an internal model of the inner ear's graviceptor, the otolith organs. We have previously identified two populations of Purkinje cells that participate in this computation: Tilt-selective cells transform egocentric rotation signals into allocentric tilt velocity signals, to track head motion relative to gravity, and translation-selective cells encode otolith prediction error. Here we show that, despite very distinct simple spike response properties, both types of Purkinje cells emit complex spikes that are proportional to sensory prediction error. This indicates that both cell populations comprise a single olivo-cerebellar loop, in which only translation-selective cells project to the inferior olive. We propose a neural network model where sensory prediction errors computed by translation-selective cells are used as a teaching signal for both populations, and demonstrate that this network can learn to implement an internal model of the otoliths.

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“…More impoverished visual scenes, lacking clear panoramic cues, also affect the percept of vertical ( Ebenholtz and Callan, 1980 ; Li and Matin, 2005a ). For example, the perceived orientation of an earth-vertical line is biased when surrounded by a tilted squared frame ( Witkin and Asch, 1948 ; Alberts et al, 2016 ; Niehof et al, 2019 ), an effect known as the rod-and-frame effect (RFE; Witkin and Asch, 1948 ). The magnitude of this RFE cyclically changes as the frame rotates across a 90° range ( Wenderoth, 1973 ; Alberts et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Panoramic Uncertainty in Vertical Perception

Stapel

Medendorp

2021

Front. Integr. Neurosci.

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Judgments of the orientation of a visual line with respect to earth vertical are affected by panoramic visual cues. This is illustrated by the rod-and-frame effect (RFE), the finding that the perceived orientation of a luminous rod is biased by the orientation of a surrounding squared frame. In this study, we tested how the uncertainty of frame orientation affects the RFE by asking upright or tilted participants to psychometrically judge the orientation of a briefly flashed rod contained within either a circular frame, a squared frame, or either of two intermediate frame forms, called squircles, presented in various orientations. Results showed a cyclical modulation of frame-induced bias across the range of the square and squircular frame orientations. The magnitude of this bias increased with increasing squaredness of the frame, as if the more unequivocal the orientation cues of the frame, the larger the reliance on them for rod orientation judgments. These findings are explained with a Bayesian optimal integration model in which participants flexibly weigh visual panoramic cues, depending on their orientation reliability, and non-visual cues in the perception of vertical.

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Contributions of optostatic and optokinetic cues to the perception of vertical

Cited by 8 publications

References 45 publications

A neural surveyor to map touch on the body

A neural surveyor to map touch on the body

Two functionally distinct Purkinje cell populations implement an internal model within a single olivo-cerebellar loop

Panoramic Uncertainty in Vertical Perception

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