title: Normative evidence accumulation in unpredictable environments.
abstract: In our dynamic world, decisions about noisy stimuli can require temporal accumulation of evidence to identify steady signals, differentiation to detect unpredictable changes in those signals, or both. Normative models can account for learning in these environments but have not yet been applied to faster decision processes. We present a novel, normative formulation of adaptive learning models that forms decisions by acting as a leaky accumulator with non-absorbing bounds. These dynamics, derived for both discrete and continuous cases, depend on the expected rate of change of the statistics of the evidence and balance signal identification and change detection. We found that, for two different tasks, human subjects learned these expectations, albeit imperfectly, then used them to make decisions in accordance with the normative model. The results represent a unified, empirically supported account of decision-making in unpredictable environments that provides new insights into the expectation-driven dynamics of the underlying neural signals.
authors: Department of Neuroscience, University of Pennsylvania, Philadelphia, United States.
authors: Christopher M
authors: CM
authors: Glaze
authors: Department of Psychology, University of Pennsylvania, Philadelphia, United States.
authors: Joseph W
authors: JW
authors: Kable
authors: Department of Neuroscience, University of Pennsylvania, Philadelphia, United States.
authors: Joshua I
authors: JI
authors: Gold
mesh_terms: D000328:Adult; D003657:Decision Making; D005260:Female; D006801:Humans; D007858:Learning; D008297:Male; D008875:Middle Aged; D008960:Models, Psychological; D011581:Psychological Tests; D055815:Young Adult
keywords: change detection; decision-making; drift-diffusion models; human; neuroscience