Although artificial intelligence systems can now outperform humans in a variety of domains, they still lag behind in the ability to arrive at good solutions to problems using limited resources. Recent proposals have suggested that the key to this cognitive efficiency is intelligent selection of the situations in which computational resources are spent. We tested this hypothesis in the domain of complex planning by analysing how humans managed time available for thinking in over 12 million online chess matches. We found that players spent more time thinking in board positions where planning was more beneficial. This effect was greater in stronger players, and additionally strengthened by considering only the information available to the player at the time of choice. Finally, we found the qualitative shape of this relationship could be captured by rationally trading off the benefits of improving board position with the costs of spending time. This provides evidence that human efficiency is supported by intelligent selection of when to apply computation.

A ubiquitous feature of human decision making under risk is that individuals differ from each other, as well as from normativity, in how they incorporate reward and probability information. One possible explanation for these deviations is a desire to reduce the number of potential outcomes considered during choice evaluation. Although multiple behavioral models can be invoked involving selective consideration of choice outcomes, whether differences in these tendencies underlie behavioral differences in sensitivity to reward and probability information is unknown. Here we consider neural evidence where we exploit magnetoencephalography (MEG) to decode the actual choice outcomes participants consider when they decide between a gamble and a safe outcome. We show that variability in tendencies of individual participants to reinstate neural outcome representations, based on either their probability or reward, explains variability in the extent to which their choices reflect consideration of probability and reward information. In keeping with this we also show that participants who are higher in behavioral impulsivity fail to preferentially reinstate outcomes with higher probability. Our results suggest that neural differences in the degree to which outcomes are considered shape risk taking strategy, both in decision making tasks, as well as in real life.