Research concerning the impact of positive mood on cognitive performance is inconsistent. We suggest that specific self-efficacy moderates this relationship. The current study proposed that participants in a positive mood with a high level of specific self-efficacy would anticipate mood-maintaining success on a task. Hence, they would be more strongly motivated, and perform better on the task, than individuals in other moods. Conversely, participants in a positive mood with low specific self-efficacy should expect mood-threatening failure. Thus, these individuals should be less motivated and perform more poorly than individuals in other moods. The current study included 139 participants with different levels of specific self-efficacy performing a comprehension task in either a positive or negative mood or a control condition. Results confirmed our hypothesis whereby specific self-efficacy affects cognitive performance but only during a positive mood. These findings support the role of specific self-efficacy in maintaining positive mood by regulating task activity.

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The present study contributes to the research problem of applying social robots in autism diagnosis. There is a common belief that existing diagnostic methods for autistic spectrum disorder are not effective. Advances in Human–Robot Interactions (HRI) provide potential new diagnostic methods based on interactive robots. We investigated deficits in turn-taking in preschool children by observing their interactions with the NAO robot during two games: (Dance with me vs. Touch me). We compared children’s interaction profiles with the robot (five autistic vs. five typically developing young children). Then, to investigate turn-taking deficits, we adopted a rating procedure to indicate differences between both groups of children based on an observational scale. A statistical analysis based on ratings of the children’s interactions with the NAO robot indicated that autistic children presented a deficient level of turn-taking behaviors. Our study provides evidence for the potential of designing and implementing an interactive dyadic game between a child and a social robot that can be used to detect turn-taking deficits based on objective measures. We also discuss our results in the context of existing studies and propose guidelines for a robotic-enabled autism diagnosis system.