Decisions based on uncertain perceptual evidence are an ubiquitous component of everyday behavior. Much research has focused on the computational and neural basis of how our nervous system accumulates this uncertain evidence to make efficient decisions. While extremely successful to explain average behavior, variability around this average has either been mostly ignored or attributed to sensory noise or stochastic action selection. As we have recently shown, however, a large fraction of behavioral variability actually arises from approximations in core computations leading to these decisions. This is a critical finding, as mental disease, such as schizophrenia or OCD, is known to involve impairments in handling uncertain information. Thus, misattributing the locus of behavioral variability leads to misinterpreting the key computational determinants of decision errors. This, in turn, might lead to misidentifying the decision-making computations altered in mental disease. We will avoid this pitfall by leveraging behavioral variability to investigate the computational and neural mechanisms which drive human behavior under uncertainty. Based on this principle, we will investigate each component of the decision-making process, starting from how the central nervous system processes noisy and/or ambiguous sensory signals to extract decision-relevant evidence, over the format of the evidence that is subsequently accumulated, to the variability in evidence accumulation itself. We will do so through a combination of computational modeling, and behavioral and MEG experiments in _hEl.althy_ Mental disease, such as schizophrenia or OCD, is known to involve impairments in decision-making under uncertainty. While such impairments have been mostly tested in the context of noisy sensory percepts, we will use an original taxonomy for decision uncertainty that considers ambiguity as an additional source. The handling of noise and ambiguity might rely on different computational and neural mechanisms, suggesting that their dissociation might ultimately provide important insights for understanding mental disease.Public Health Relevance
