Multiple Choice Neurodynamical Model of the Uncertain Option Task

The uncertain option task has been recently adopted to investigate the neural systems underlying the decision confidence. Latterly single neurons activity has been recorded in lateral intraparietal cortex of monkeys performing an uncertain option task, where the subject is allowed to opt for a small but sure reward instead of making a risky perceptual decision. We propose a multiple choice model implemented in a discrete attractors network. This model is able to reproduce both behavioral and neurophysiological experimental data and therefore provides support to the numerous perspectives that interpret the uncertain option task as a sensory-motor association. The model explains the behavioral and neural data recorded in monkeys as the result of the multistable attractor landscape and produces several testable predictions. One of these predictions may help distinguish our model from a recently proposed continuous attractor model.Author Summary: Recently many studies began to investigate the brain signature of complex cognitive functions such as decision confidence, the feeling of certainty/uncertainty associated with a decision. To this aim, the uncertain option task has been widely adopted in order to assess the confidence in animals. In this study we present a model, detailed at the neuron and synapse level, able to account for the behavior of animals in this task. In addition our model is able to reproduce the neural dynamics found in monkeys brain during this task. However our model is only equipped with a simple multiple choice mechanism and has no mechanism devoted to calculate the confidence. Therefore our study support the idea that the uncertain option task can be solved without relying on confidence assessment (metacognition). The model is based on the idea that the neural dynamics fluctuates around stable equilibrium points (attractors) and associates the landscape of these attractors with the behavior of the monkeys. Finally, our model makes several predictions that could be easily tested in a new experiment. One of these predictions may help distinguish our model from a different one that has been recently proposed.