It has been shown that in certain situations losses exert a stronger effect on behavior than respective gains, and this has been commonly explained by the argument that losses are given more weight in people's decisions than respective gains. However, although much is understood about the effect of losses on cognitive processes and behavior, 2 major inconsistencies remain. First, recent empirical evidence fails to demonstrate that people avoid incentive structures that carry equivalent gains and losses. Second, findings in experience-based decision tasks indicate that following losses, increased arousal is observed simultaneously with no behavioral loss aversion. To account for these findings, we developed an attention-allocation model as a comprehensive framework for the effect of losses. According to this model losses increase on-task attention, thereby enhancing the sensitivity to the reinforcement structure. In the current article we examine whether this model can account for a broad range of empirical phenomena involving losses. We show that as predicted by the attentional model, asymmetric effects of losses on behavior emerge where gains and losses are presented separately but not concurrently. Yet, even in the absence of loss aversion, losses have distinct effects on performance, arousal, frontal cortical activation, and behavioral consistency. The attentional model of losses thus explains some of the main inconsistencies in previous studies of the effect of losses.

The current study examines the psychological factors shaping AI adoption, focusing on anxiety, motivation, and dependency. It identifies two dimensions of AI anxiety: anticipatory anxiety, driven by fears of future disruptions, and annihilation anxiety, reflecting existential concerns about human identity and autonomy. We demonstrate a U-shaped relationship between AI anxiety and usage, where moderate engagement reduces anxiety, and high or low levels increase it. Perceived utility, interest, and attainment significantly correlate with AI engagement, while frequent AI usage is linked to high dependency but not to anxiety. These findings highlight the dual role of psychological factors in hindering and alleviating AI usage. This study enriches the understanding of emotional and motivational drivers in AI adoption and highlights the importance of balanced implementation strategies to foster sustainable and effective AI integration while mitigating the risks of over-reliance.

Robots are predicted to serve in environments in which human honesty is important, such as the workplace, schools, and public institutions. Can the presence of a robot facilitate honest behavior? In this paper, we describe an experimental study evaluating the effects of robot social presence on people's honesty. Participants completed a perceptual task, which is structured so as to allow them to earn more money by not complying with the experiment instructions. We compare three conditions between subjects: Completing the task alone in a room; completing it with a non-monitoring human present; and completing it with a non-monitoring robot present. The robot is a new expressive social head capable of 4-DoF head movement and screen-based eye animation, specifically designed and built for this research. It was designed to convey social presence, but not monitoring. We find that people cheat in all three conditions, but cheat equally less when there is a human or a robot in the room, compared to when they are alone. We did not find differences in the perceived authority of the human and the robot, but did find that people felt significantly less guilty after cheating in the presence of a robot as compared to a human. This has implications for the use of robots in monitoring and supervising tasks in environments in which honesty is key.