Abstract
Visual inputs are often highly structured, and statistical regularities of these signals can be used to guide future visuomotor associations and thus optimize behavior. Through a recurrent neural network (RNN) model, human psychophysics, and electroencephalography (EEG), we probed the neural mechanisms for processing probabilistic structures of visual signals to guide behavior. We first constructed and trained a biophysically constrained RNN model to perform a series of probabilistic visual discrimination tasks similar to paradigms designed for humans. Specifically, the training environment was probabilistic such that one stimulus was more probable than the others. We showed that both humans and RNNs successfully learned the stimulus probability and integrated this knowledge into their decisions and task strategy in a new environment. Performance of both humans and RNNs varied with the degree to which the stimulus probability of the new environment matched the formed expectation. In both cases, this expectation effect was more prominent when the strength of sensory evidence was low, suggesting that like humans, the RNNs placed more emphasis on prior expectation (top-down signals) when the available sensory information (bottom-up signals) was limited, thereby optimizing task performance. By dissecting the trained RNNs, we demonstrated how competitive inhibition and recurrent excitation form the basis for neural circuitry optimized to perform probabilistic visual processing.
Funding: NIMH (F30MH115605-01A1 to R.K.); NEI R01EY025872-10 (J.T.S.); NIBIB R01EB026899-01 (T.J.S.); NINDS R01NS104368 (T.J.S.); and Mission funding from the U.S. Army Research Laboratory (N.R.)