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Human online adaptation to changes in prior probability.

ABSTRACT: Optimal sensory decision-making requires the combination of uncertain sensory signals with prior expectations. The effect of prior probability is often described as a shift in the decision criterion. Can observers track sudden changes in probability? To answer this question, we used a change-point detection paradigm that is frequently used to examine behavior in changing environments. In a pair of orientation-categorization tasks, we investigated the effects of changing probabilities on decision-making. In both tasks, category probability was updated using a sample-and-hold procedure: probability was held constant for a period of time before jumping to another probability state that was randomly selected from a predetermined set of probability states. We developed an ideal Bayesian change-point detection model in which the observer marginalizes over both the current run length (i.e., time since last change) and the current category probability. We compared this model to various alternative models that correspond to different strategies-from approximately Bayesian to simple heuristics-that the observers may have adopted to update their beliefs about probabilities. While a number of models provided decent fits to the data, model comparison favored a model in which probability is estimated following an exponential averaging model with a bias towards equal priors, consistent with a conservative bias, and a flexible variant of the Bayesian change-point detection model with incorrect beliefs. We interpret the former as a simpler, more biologically plausible explanation suggesting that the mechanism underlying change of decision criterion is a combination of on-line estimation of prior probability and a stable, long-term equal-probability prior, thus operating at two very different timescales.

SUBMITTER: Norton EH

PROVIDER: S-EPMC6638982 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

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Human online adaptation to changes in prior probability.

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