Decisions in motion: vestibular contributions to saccadic target selection

Rincon-Gonzalez, Liliana; Selen, Luc P. J.; Halfwerk, K.; Koppen, Mathieu; Medendorp, W. Pieter

doi:10.1152/jn.01071.2015

Cited by 15 publications

(19 citation statements)

References 71 publications

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“…To dissociate between these hypotheses, we recently examined saccadic decision making while subjects were sinusoidally translated by a whole-body motion platform (Rincon-Gonzalez et al, 2016). During the motion, two visual targets were presented asynchronously but equidistantly on either side of the body stationary fixation point.…”

Section: Decisions In Motionmentioning

confidence: 99%

Vestibular contributions to high-level sensorimotor functions

Medendorp

Selen

2017

Neuropsychologia

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The vestibular system, which detects motion and orientation of the head in space, is known to be important in controlling gaze to stabilize vision, to ensure postural stability and to provide our sense of self-motion. While the brain's computations underlying these functions are extensively studied, the role of the vestibular system in higher level sensorimotor functions is less clear. This review covers new research on the vestibular influence on perceptual judgments, motor decisions, and the ability to learn multiple motor actions. Guided by concepts such as optimization, inference, estimation and control, we focus on how the brain determines causal relationships between memorized and visual representations in the updating of visual space, and how vestibular, visual and efferent motor information are integrated in the estimation of body motion. We also discuss evidence that these computations involve multiple coordinate representations, some of which can be probed in parietal cortex using neuronal oscillations derived from EEG. In addition, we describe work on decision making during self-motion, showing a clear modulation of bottom-up acceleration signals on decisions in the saccadic system. Finally, we consider the importance of vestibular signals as contextual cues in motor learning and recall. Taken together, these results emphasize the impact of vestibular information on high-level sensorimotor functions, and identify future directions for theoretical, behavioral, and neurophysiological investigations.

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Section: Decisions In Motionmentioning

confidence: 99%

Vestibular contributions to high-level sensorimotor functions

Medendorp

Selen

2017

Neuropsychologia

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“…The adaptive procedure converged with the model proposed in earlier work (Stocker & Simoncelli, 2006), whereas the random sampling method was often inconclusive about the underlying noise model. Second, we tested the algorithm on dissociating two models of saccadic target selection under whole body acceleration (Rincon-Gonzalez et al, 2016). Based on the original experimental data it is hard to dissociate between an acceleration-dependent or acceleration-independent target selection model at the individual subject level.…”

Section: Introductionmentioning

confidence: 99%

Bayesian adaptive stimulus selection for dissociating models of psychophysical data

et al. 2018

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Comparing models facilitates testing different hypotheses regarding the computational basis of perception and action. Effective model comparison requires stimuli for which models make different predictions. Typically, experiments use a predetermined set of stimuli or sample stimuli randomly. Both methods have limitations; a predetermined set may not contain stimuli that dissociate the models, whereas random sampling may be inefficient. To overcome these limitations, we expanded the psi-algorithm (Kontsevich & Tyler, 1999) from estimating the parameters of a psychometric curve to distinguishing models. To test our algorithm, we applied it to two distinct problems. First, we investigated dissociating sensory noise models. We simulated ideal observers with different noise models performing a two-alternative forced-choice task. Stimuli were selected randomly or using our algorithm. We found using our algorithm improved the accuracy of model comparison. We also validated the algorithm in subjects by inferring which noise model underlies speed perception. Our algorithm converged quickly to the model previously proposed (Stocker & Simoncelli, 2006), whereas if stimuli were selected randomly, model probabilities separated slower and sometimes supported alternative models. Second, we applied our algorithm to a different problem-comparing models of target selection under body acceleration. Previous work found target choice preference is modulated by whole body acceleration (Rincon-Gonzalez et al., 2016). However, the effect is subtle, making model comparison difficult. We show that selecting stimuli adaptively could have led to stronger conclusions in model comparison. We conclude that our technique is more efficient and more reliable than current methods of stimulus selection for dissociating models.

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“…The adaptive procedure converged to the model proposed in earlier work (Stocker & Simoncelli, 2006) whereas the random sampling method was often inconclusive about the underlying noise model. Second, we tested the algorithm on dissociating two models of saccadic target selection under whole body acceleration (Rincon-Gonzalez et al, 2016). Based on the original experimental data it is hard to dissociate between an acceleration-dependent or acceleration-independent target selection model at the individual subject level.…”

Section: Introductionmentioning

confidence: 99%

“…To ensure our algorithm is broadly applicable, it is important to validate it in multiple settings. Here, as an additional application, we consider comparing models of saccadic target selection during self-motion (Rincon-Gonzalez et al, 2016), a study recently performed in our lab. This example allows us to investigate how much benefit our algorithm provides when the models being compared are highly non-linear and the signal-to-noise ratio in the data is low.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian adaptive stimulus selection for dissociating models of psychophysical data

Cooke¹,

Selen²,

Beers

et al. 2017

Preprint

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15Comparing models facilitates testing different hypotheses regarding the computational basis of percep-16 tion and action. Effective model comparison requires stimuli for which models make different predictions. 17Typically, experiments use a predetermined set of stimuli or sample stimuli randomly. Both methods 18 have limitations; a predetermined set may not contain stimuli that dissociate the models whereas random 19 sampling may be inefficient. To overcome these limitations, we expanded the psi-algorithm (Kontsevich 20 & Tyler, 1999) from estimating the parameters of a psychometric curve to distinguishing models. To 21 test our algorithm, we applied it to two distinct problems. First, we investigated dissociating sensory 22 noise models. We simulated ideal observers with different noise models performing a 2-afc task. Stimuli 23 were selected randomly or using our algorithm. We found using our algorithm improved the accuracy 24 of model comparison. We also validated the algorithm in subjects by inferring which noise model un- in model comparison. We conclude that our technique is more efficient and more reliable than current 32 methods of stimuli selection for dissociating models. 33 Data Availability 34All data and code will be posted on our institutional repository system following acceptance. In the 35 meantime feel free to contact the authors if you would like any of the code.

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Decisions in motion: vestibular contributions to saccadic target selection

Cited by 15 publications

References 71 publications

Vestibular contributions to high-level sensorimotor functions

Vestibular contributions to high-level sensorimotor functions

Bayesian adaptive stimulus selection for dissociating models of psychophysical data

Bayesian adaptive stimulus selection for dissociating models of psychophysical data

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