Late Bayesian inference in mental transformations
Name
s41467-018-06726-9.pdf
Size
909.92 KB
Format
Adobe PDF
Checksum (MD5)
5e51ebbc265d9d8616ca110087aeb3f6
Author(s)
Remington, Evan D
Parks, Tiffany V
Jazayeri, Mehrdad
Date Issued
October 2018
Journal
Nature Communications
Publisher
Nature Publishing Group
Citation
Remington, Evan D., Tiffany V. Parks, and Mehrdad Jazayeri. “Late Bayesian Inference in Mental Transformations.” Nature Communications 9, no. 1 (October 24, 2018).
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41467-018-06726-9
