Leveraging behavioral variability to identify core computations in human decision-making – VARICOMP

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 choices. 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, most such variability actually
arises from approximations in core computations leading to these decisions. Thus, ignoring this
variability might lead to mis-interpreting the key computational determinants of decision errors. Instead,
we will leverage computational variability as a source of information about the mechanisms which drive
behavior. Based on this principle, we will investigate each component of the decision-making process,
starting from how the nervous system processes noisy and/or ambiguous sensory stimuli to extract
decision-relevant evidence, over the format of the evidence that is subsequently accumulated, to the
variability in evidence accumulation itself.