Dataset Information

Risk-sensitivity in Bayesian sensorimotor integration.

ABSTRACT: Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decision-makers are, however, risk-neutral in the sense that they weigh all possibilities based on prior expectation and sensory evidence when they choose the action with highest expected value. In contrast, risk-sensitive decision-makers are sensitive to model uncertainty and bias their decision-making processes when they do inference over unobserved variables. In particular, they allow deviations from their probabilistic model in cases where this model makes imprecise predictions. Here we test for risk-sensitivity in a sensorimotor integration task where subjects exhibit Bayesian information integration when they infer the position of a target from noisy sensory feedback. When introducing a cost associated with subjects' response, we found that subjects exhibited a characteristic bias towards low cost responses when their uncertainty was high. This result is in accordance with risk-sensitive decision-making processes that allow for deviations from Bayes optimal decision-making in the face of uncertainty. Our results suggest that both Bayesian integration and risk-sensitivity are important factors to understand sensorimotor integration in a quantitative fashion.

SUBMITTER: Grau-Moya J

PROVIDER: S-EPMC3459842 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Risk-sensitivity in Bayesian sensorimotor integration.

Grau-Moya Jordi J Ortega Pedro A PA Braun Daniel A DA

PLoS computational biology 20120927 9

Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decision-makers are, however, risk-neutral in the sense that they weigh all possibilities based on prior expectation and sensory evidence when they choose the action with highest expected value. In contrast, risk-sensitive decision-makers are sensitive to model uncertainty and bias their decision-making processes when they do in ...[more]

PMID: 23028294

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Project description:The brain uses information from different sensory systems to guide motor behavior, and aging is associated with simultaneous decline in the quality of sensory information provided to the brain and deterioration in motor control. Correlations between age-dependent decline in sensory anatomical structures and behavior have been demonstrated in many sensorimotor systems, and it has recently been suggested that a Bayesian framework could explain these relationships. Here we show that age-dependent changes in a human sensorimotor reflex, the vestibuloocular reflex, are explained by a Bayesian optimal adaptation in the brain occurring in response to death of motion-sensing hair cells. Specifically, we found that the temporal dynamics of the reflex as a function of age emerge from ( r = 0.93, P < 0.001) a Kalman filter model that determines the optimal behavioral output when the sensory signal-to-noise characteristics are degraded by death of the transducers. These findings demonstrate that the aging brain is capable of generating the ideal and statistically optimal behavioral response when provided with deteriorating sensory information. While the Bayesian framework has been shown to be a general neural principle for multimodal sensory integration and dynamic sensory estimation, these findings provide evidence of longitudinal Bayesian processing over the human life span. These results illuminate how the aging brain strives to optimize motor behavior when faced with deterioration in the peripheral and central nervous systems and have implications in the field of vestibular and balance disorders, as they will likely provide guidance for physical therapy and for prosthetic aids that aim to reduce falls in the elderly. NEW & NOTEWORTHY We showed that age-dependent changes in the vestibuloocular reflex are explained by a Bayesian optimal adaptation in the brain that occurs in response to age-dependent sensory anatomical changes. This demonstrates that the brain can longitudinally respond to age-related sensory loss in an ideal and statistically optimal way. This has implications for understanding and treating vestibular disorders caused by aging and provides insight into the structure-function relationship during aging.

Dataset Information

Risk-sensitivity in Bayesian sensorimotor integration.

Publications

Risk-sensitivity in Bayesian sensorimotor integration.

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