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

Autor: Petitet, P, Attaallah, B, Manohar, SG, Husain, M
Rok vydání: 2021
Předmět:
Male
Social Psychology
Computer science
Cost-Benefit Analysis
Decision Making
Information Seeking Behavior
Theoretical models
Experimental and Cognitive Psychology
03 medical and health sciences
Behavioral Neuroscience
Cognition
0302 clinical medicine
PsyArXiv|Social and Behavioral Sciences|Cognitive Psychology|Judgment and Decision Making
Humans
030304 developmental biology
Structure (mathematical logic)
0303 health sciences
Cost–benefit analysis
Uncertainty
Sampling (statistics)
Cognitive effort
Models
Theoretical
bepress|Social and Behavioral Sciences|Psychology|Cognitive Psychology
PsyArXiv|Social and Behavioral Sciences
Risk analysis (engineering)
Sufficient time
bepress|Social and Behavioral Sciences
PsyArXiv|Social and Behavioral Sciences|Cognitive Psychology
Female
030217 neurology & neurosurgery
Zdroj: Nature Human Behaviour. 5:935-946
ISSN: 2397-3374
DOI: 10.1038/s41562-021-01116-6
Popis: Humans often seek information to minimise the pervasive effect of uncertainty on decisions. Current theories explain how much knowledge people should gather prior to 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 a novel paradigm, we unveil a speed-efficiency trade-off whereby more informative samples actually 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 hidden cost of cognitive effort which, when incorporated into theoretical models, provides a better account of peoples behaviour and also predicts self-reported fatigue accumulated during active sampling. By measuring metacognitive accuracy and uncertainty-reward preferences on a static, passive version of the task, we further validate the theoretical constructs captured by our model. Overall, the results show that 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.
Databáze: OpenAIRE
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