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Late Bayesian inference in mental transformations
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DOI：10.1038/s41467-018-06726-9.png)
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Abstract
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Many skills rely on performing noisy mental computations on noisy sensory measurements. Bayesian models suggest that humans compensate for measurement noise and reduce behavioral variability by biasing perception toward prior expectations. Whether a similar strategy is employed to compensate for noise in downstream mental and sensorimotor computations is not known. We tested humans in a battery of tasks and found that tasks which involved more complex mental transformations resulted in increased bias, suggesting that humans are able to mitigate the effect of noise in both sensorimotor and mental transformations. These results indicate that humans delay inference in order to account for both measurement noise and noise in downstream computations.
Keywords:
AUDITORY SIGNALS
INTEGRATION
PERCEPTION
MOVEMENT
REPRESENTATIONS
UNCERTAINTY
INFORMATION
TARGETS
ERRORS
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