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

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.