Topic: perceptual decisions

Humans constantly make perceptual decisions on human faces and voices. These regularly come with the challenge of receiving only uncertain sensory evidence, resulting from noisy input and noisy neural processes. Efficiently adapting one’s internal decision system including prior expectations and subsequent metacognitive assessments to these challenges is crucial in everyday life. However, the exact decision mechanisms and whether these represent modifiable states remain unknown in the general population and clinical patients with psychosis. Using data from a laboratory-based sample of healthy

To survive in the uncertain world, humans and animals must pick the right task to perform in each situation and flexibly switch between tasks when they infer that the environment has changed. Yet such flexibility comes at a cost. Task performance decreases when the relevance of the task is uncertain. Here we present multiple lines of evidence from monkey electrophysiology, human psychophysics, and artificial neural networks demonstrating a neuronal mechanism that inflicts a cost of flexibility. In a behavioral paradigm designed to measure and manipulate subjects’ belief about the relevance of

The brain makes decisions by accumulating evidence until there is enough to stop and choose. Neural mechanisms of evidence accumulation are well established in association cortex, but the site and mechanism of termination is unknown. Here, we elucidate a mechanism for termination by neurons in the primate superior colliculus. We recorded simultaneously from neurons in lateral intraparietal cortex (LIP) and the superior colliculus (SC) while monkeys made perceptual decisions, reported by eye-movements. Single-trial analyses revealed distinct dynamics: LIP tracked the accumulation of evidence on

Our brains predict future sensory input based on their current beliefs about the world around us, but interpreting prediction errors can be challenging in a volatile environment because they can be caused by stochastic sensory noise or by outdated predictions. Noisy signals should be integrated with prior beliefs to improve precision, but the two should be segregated when environmental changes render prior beliefs irrelevant. Bayesian inference provides a statistically optimal solution to deal with such problems of causal uncertainty [1]. However, the method quickly becomes memory intensive an