Abstract
Bayesian models of behavior suggest that organisms represent uncertainty associated with sensory variables. However, the neural code of uncertainty remains elusive. A central hypothesis is that uncertainty is encoded in the population activity of cortical neurons in the form of likelihood functions. We tested this hypothesis by simultaneously recording population activity from primate visual cortex during a visual categorization task in which trial-to-trial uncertainty about stimulus orientation was relevant for the decision. We decoded the likelihood function from the trial-to-trial population activity and found that it predicted decisions better than a point estimate of orientation. This remained true when we conditioned on the true orientation, suggesting that internal fluctuations in neural activity drive behaviorally meaningful variations in the likelihood function. Our results establish the role of population-encoded likelihood functions in mediating behavior and provide a neural underpinning for Bayesian models of perception.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 122-129 |
| Number of pages | 8 |
| Journal | Nature neuroscience |
| Volume | 23 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2020 |
Funding
The research was supported by a National Science Foundation Grant (no. IIS-1132009 to W.J.M. and A.S.T.), a DP1 EY023176 Pioneer Grant (to A.S.T.) and grants from the US Department of Health & Human Services, National Institutes of Health, National Eye Institute (nos. F30 EY025510 to E.Y.W., R01 EY026927 to A.S.T. and W.J.M., and T32 EY00252037 and T32 EY07001 to A.S.T.) and National Institute of Mental Health (nos. F30 F30MH088228 to R.J.C.). We thank F. Sinz for helpful discussion and suggestions on the DNN fitting to likelihood functions. We also thank T. Shinn for assistance in the behavioral training of the monkeys and experimental data collection.
ASJC Scopus subject areas
- General Neuroscience