In simple judgment tasks, it is generally assumed that thinking for longer leads to more accurate judgments, providing better benefits as suggested by the speed-accuracy tradeoff framework. However, human cognitive resources are limited, and longer thinking induces cognitive costs such as subjective workload. Therefore, a total benefit should be considered under the tradeoff between thinking benefits (i.e., improving accuracy) and thinking costs (i.e., increasing cognitive load) as suggested by the resource rationality framework. We examined this issue using computer simulations and behavioral experiments. Our simulations showed that, if a thinking cost was introduced based on resource-rational approaches, there was an optimal length of time for maximizing a total benefit and the total benefit gradually decreased there. In addition, our experiments demonstrated that judgment accuracy did not always improve even if participants were provided a longer thinking time; conversely, longer thinking time was likely to increase their subjective workload. These results are consistent with resource rationality rather than speed-accuracy tradeoff. The importance of considering cognitive load is suggested to further understand human intelligence in the context of a speed-accuracy tradeoff.

We propose a method to use anchor-biased numerical estimates to enhance the wisdom of the crowd (i.e., aggregating individual estimates becomes more accurate). We predicted that better wisdom of crowds could be achieved by combining estimates affected by two sufficiently different anchors. We conducted a behavioral experiment on anchoring effects and computer simulations of the wisdom of crowds. The behavioral experiment revealed that anchor-biased estimates did not always produce larger errors but produced more accurate or less accurate estimates depending on the relationship between the anchor and true values. We also found that even in the absence of anchoring effects, the estimates were biased. The results of computer simulations revealed that robust and better wisdom of crowds could be achieved using the proposed method. Contrastingly, aggregating estimates that were not biased by anchors did not always lead to better wisdom of crowds.

The extent to which people utilize advice from others differs depending on whether the source of the advice is an algorithm or a human. However, no unifying evidence can be used for advice design. Moreover, the use of advice given at intervals (e.g., 70–90%) has not been fully studied. This study proposed a three-step model of the cognitive process of the use of advice with intervals and conducted a simulation and four behavioral experiments. These experiments showed that differences in advice sources affected the cognitive process in which judges decide whether to update their initial judgment based on the advice; this cognitive process was influenced by the relative weight between their initial judgment and the advice interval. These results suggested that for judges to adjust their judgments, designing advice itself is insufficient and advice must be designed according to the relationship between the advice and judge’s judgments.

It is generally difficult for people to make rational and accurate judgments under their limited cognitive resources. In this study, we propose an intervention to easily improve people’s judgmental accuracy with less workload by waiting for a short time at the beginning of a task. By using a simple binary choice task, we found that when a short (1s) waiting time was inserted, participants showed higher accuracy than when no waiting time was inserted, and they felt less mental workload than when a longer (2.5s) waiting time was inserted. To examine the underlying implicit cognitive processes, we applied mouse tracking approaches during choice behaviors. We found that the inserted time enhanced participants’ change of mind (i.e., they amended their initial wrong judgments). These results suggest that making people wait for only 1s will serve as a simple, effective, and resource-rational intervention to boost people’s accuracy of judgments. Because of its simplicity, we believe that this intervention has potential to be applied in various fields.