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The human ability to flexibly discover alternatives without fixating on a known solution supports a variety of human creative activities. Previous research has shown that people who discover an alternative procedure relax their attentional bias to information regarding the known solutions just prior to the discovery. This study examined whether the difference in the distribution of attention between the finders and non-finders of the alternative procedure is observed from the phase of solving the problem using the trained procedure. We evaluated the characteristics of the finders' distribution of attention in situations where problem solving using a trained procedure was successful. This aspect has been little examined in previous research. Our study obtained empirical evidence for the fact that, compared to non-finders, finders pay more attention to information unrelated to the trained procedure acquired through knowledge and experience, even time when using a trained procedure. We also confirmed that this difference does not exist from the beginning of the task, but emerges during repeated use of familiar procedures. These findings indicate that in order to find an alternative procedure, one should not only divert attention from a familiar procedure just before the discovery but also pay a certain amount of attention to information unrelated to the familiar procedure even when the familiar procedure is functioning well.

It is important to reveal how humans evaluate an explanation of the recent development of explainable artificial intelligence. So, what makes people feel that one explanation is more likely than another? In the present study, we examine how explanatory virtues affect the process of estimating subjective posterior probability. Through systematically manipulating two virtues, Simplicity—the number of causes used to explain effects—and Scope—the number of effects predicted by causes—in three different conditions, we clarified two points in Experiment 1: (i) that Scope's effect is greater than Simplicity's; and (ii) that these virtues affect the outcome independently. In Experiment 2, we found that instruction about the explanatory structure increased the impact of both virtues' effects but especially that of Simplicity. These results suggest that Scope predominantly affects the estimation of subjective posterior probability, but that, if perspective on the explanatory structure is provided, Simplicity can also affect probability estimation.

This paper presents a cognitive model that simulates an adaptation process to automation in a time-critical task. The paper uses a simple tracking task (which represents vehicle operation) to reveal how the reliance on automation changes as the success probabilities of the automatic and manual mode vary. The model was developed by using a cognitive architecture, ACT-R (Adaptive Control of Thought-Rational). We also introduce two methods of reinforcement learning: the summation of rewards over time and a gating mechanism. The model performs this task through productions that manage perception and motor control. The utility values of these productions are updated based on rewards in every perception-action cycle. A run of this model simulated the overall trends of the behavioral data such as the performance (tracking accuracy), the auto use ratio, and the number of switches between the two modes, suggesting some validity of the assumptions made in our model. This work shows how combining different paradigms of cognitive modeling can lead to practical representations and solutions to automation and trust in automation.