Cognitive flexibility and decision-making in a model of conditional visuomotor associations

Arbitrary visuomotor associations map a stimulus onto a particular response upon reinforcing rewards. Changes in the associations between stimuli and responses require the neural networks to discard the already learned mappings and build new ones. This is a key aspect of cognitive flexibility. In addition, learning within this experimental paradigm requires a trial-and-error exploration strategy of the available stimulus-response associations. A system performing this task must be able to both build up mappings for stimulus-response associations and at the same time perform non-deterministically to explore the behavioural space until it reaches certainty. We hypothesize an integrate-and-fire network model that accounts for the neurophysiological data of a conditional visuomotor association task and is able to show non-deterministic behaviour. We aim at identifying multistable attractor regimes in the network dynamics, which intrinsically enable the system to make errors and thereby to perform trial-and-error exploration. Our model combines cognitive flexibility with multistable attractors in neurodynamical systems, believed to be the basis of decision-making. If multistable attractors support the exploration of the behavioural space, then our model predicts that the brain should respond stochastically with correct or incorrect activity to visuomotor associations until it has reached certainty. This should be visible not only in the corresponding activity in the premotor area, but also in activity corresponding to other associations or even other stimuli in the prefrontal cortex.