Satohiro Tajima: “Optimal policy for value-based decision-making: Why are we so slow to decide between two good options”
CiNet 1F Conference Room
Department of Basic Neuroscience
University of Geneva
For decades now, normative theories of perceptual decisions, and their implementation as drift diffusion models, have driven and significantly improved our understanding of human and animal behaviour and the underlying neural processes. While similar processes seem to govern value-based decisions, we still lack the theoretical understanding of why this ought to be the case. Here, we show that, similar to perceptual decisions, drift diffusion models implement the optimal strategy for value-based decisions. Such optimal decisions require the models’ decision boundaries to collapse over time, and to depend on the a priori knowledge about reward contingencies. Diffusion models only implement the optimal strategy under specific task assumptions, and cease to be optimal once we start relaxing these assumptions, by, for example, using non-linear utility functions. Our findings thus provide the much-needed theory for value-based decisions, explain the apparent similarity to perceptual decisions, and predict conditions under which this similarity should break down.
S Tajima, J Drugowitsch & A Pouget. (2016) Optimal policy for value- based decision-making. Nature Communications, 7:12400.