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

Journal article

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.

Authors: Petitet, P; Attaallah, B; Manohar, SG; Husain, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Nature Human Behaviour
• Volume: 5
• Pages: 935-946
• Publication date: 2021-05-27
• Acceptance date: 2021-04-14
• DOI: 10.1038/s41562-021-01116-6
• EISSN: 2397-3374
• Language: English
• Keywords: FFR