The computational cost of active information sampling before decision-making under uncertainty

Pierre Petitet (), Bahaaeddin Attaallah, Sanjay G. Manohar and Masud Husain ()
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Pierre Petitet: University of Oxford
Bahaaeddin Attaallah: University of Oxford
Sanjay G. Manohar: University of Oxford
Masud Husain: University of Oxford

Nature Human Behaviour, 2021, vol. 5, issue 7, 935-946

Abstract: Abstract Humans often seek information to minimize the pervasive effect of uncertainty on decisions. Current theories explain how much knowledge people should gather before a decision, based on the cost–benefit structure of the problem at hand. Here, we demonstrate that this framework omits a crucial agent-related factor: the cognitive effort expended while collecting information. Using an active sampling model, we unveil a speed–efficiency trade-off whereby more informative samples take longer to find. Crucially, under sufficient time pressure, humans can break this trade-off, sampling both faster and more efficiently. Computational modelling demonstrates the existence of a cost of cognitive effort which, when incorporated into theoretical models, provides a better account of people’s behaviour and also relates to self-reported fatigue accumulated during active sampling. Thus, the way people seek knowledge to guide their decisions is shaped not only by task-related costs and benefits, but also crucially by the quantifiable computational costs incurred.

Date: 2021
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DOI: 10.1038/s41562-021-01116-6

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