Frank Krueger

Generative Artificial Intelligence (GenAI) systems can improve productivity, yet their long-term implications for human cognition remain contested. This article develops an interdisciplinary framework for explaining how different patterns in the distribution and support of cognitive operations between humans and GenAI may shape cognitive engagement and cognitive outcomes over time. The framework draws upon a Neuro-Information-Systems (NeuroIS)-informed approach by integrating cognitive, behavioral, self-report, and neurophysiological perspectives into a multilevel account of how GenAI use shapes human cognition. We argue that cognitive engagement—the depth, persistence, and strategic quality of human processing during AI-supported tasks—is a proposed central mechanism linking the distribution and support of cognitive operations between humans and GenAI to cognitive outcomes. Direct effects on outcomes may coexist with engagement-mediated effects on outcomes. The cognitive consequences depend particularly on whether cognitively consequential operations related to understanding, learning, critical evaluation, and independent judgment continue to involve meaningful human engagement. Specifically, the framework integrates five elements: (1) antecedent conditions (user, task, and GenAI/system characteristics); (2) regulatory processes (metacognition, trust calibration, and motivational/goal regulation); (3) human–AI cognitive work configuration and cognitive engagement (i.e., how cognitive operations are distributed between the user and the AI system and how cognitively engaged the user remains); (4) short- and intermediate-term outcomes (performance, understanding, retention, and agency); and (5) long-term outcomes of cognitive sustainability and cognitive dependence. The article derives design principles for engagement-preserving AI and contributes to an interdisciplinary research agenda for examining these relationships through convergent behavioral, self-report, neurophysiological, and longitudinal evidence.

As robots enter homes, workplaces, and health care settings, sustaining trust during social interaction becomes a central challenge for human-robot interaction. However, relatively little is known about how humans integrate signals across the brain, hormones, mind, and behavior when robots violate expectations or display social expressiveness. Addressing this gap, we examined how robot performance (congruent versus erroneous) and expressiveness (animated versus stationary) shape multilevel human responses during face-to-face decision-making with an embodied humanoid robot. Participants engaged with the robot in person while neural activity was monitored using functional near-infrared spectroscopy, alongside salivary oxytocin assays, self-reported trust, and behavioral influence measures. Robot errors, implemented as cooperative norm violations, reliably reduced trust and influence, establishing performance reliability as the foundation of trust. Expressiveness amplified these effects: Animated robots elicited stronger prefrontal engagement and cross-level neural-hormonal coupling. Elevated oxytocin was most strongly linked to reduced trust during expressive robot errors, alongside diminished behavioral influence. This pattern is consistent with a context-sensitive vigilance response, indicating that oxytocin in human-robot interaction may heighten sensitivity to norm violations rather than reliably promote bonding. Validation analyses provided small, directionally consistent support for this pattern under counterbalanced order and improved temporal separation. Together, these findings establish a multilevel framework for studying trust in human-robot interaction and reveal a critical design trade-off: Expressive design enhances engagement but can make robot errors disproportionately damaging to trust. These insights identify a biologically grounded boundary condition for oxytocin’s role in social interaction and inform the design of socially effective and trustworthy robots.

Why do uninvolved bystanders punish norm violations less harshly than victims? This longstanding puzzle lies at the heart of theories of fairness and cooperation. Prior accounts suggest that bystanders are less sensitive to unfairness than victims when reacting to fairness norm violations. Across three experiments, we tested this assumption by directly comparing third-party punishment (TPP) and second-party punishment (SPP). In Experiment 1 (N = 234), both fairness-related factors (harm, intention) and interest-related factors (cost) shaped punishment, yet bystanders consistently punished less than victims. Experiment 2 (N = 105) showed that bystanders and victims perceived unfairness similarly, but third parties punished less, decided faster, and displayed reduced decision conflict. Through computational modeling, Experiment 3 (N = 139) revealed that interest-driven motives—self-interest consideration and cost-ratio-based inequality disregarding—accounted for the punishment gap, whereas fairness-driven motives played a secondary role. Moreover, interest-driven motives shaped the initial bias against intervention, prompting bystanders to reach rejection decisions faster. Together, these findings challenge fairness-centered explanations and demonstrate that third parties intervene less, not because they fail to recognize unfairness, but because they are more sensitive to the personal costs of sanctioning. This conclusion refines motivational theories of norm enforcement and highlights how institutional mechanisms that lower the costs of intervention may promote stronger collective action to uphold social norms.

In the evolving landscape of technology, robots have emerged as social companions, prompting an investigation into social bonding between humans and robots. While human-animal interactions are well-studied, human-robot interactions (HRI) remain comparatively underexplored. Ethorobotics, a field of social robotic engineering based on ecology and ethology, suggests designing companion robots modeled on animal companions, which are simpler to emulate than humans. However, it is unclear whether these robots can match the social companionship provided by their original models. This study examined social bonding between humans and AIBOs, dog-inspired companion robots, compared to real dogs. Nineteen female participants engaged in 12 affiliative interactions with dogs and AIBOs across two counter-balanced, one-month bonding phases. Social bonding was assessed through urinary oxytocin (OXT) level change over an interaction, self-reported attachment using an adapted version of the Lexington Attachment to Pets Scale, and social companionship evaluations administering the Robot-Dog Questionnaire. To examine OXT level changes and self-reported attachment by comparing the two social companions, we conducted mixed-effects model analyses and planned follow-up comparisons. Frequency comparison, binary logistic regression, and thematic analysis were performed to analyze social companionship evaluations. Results revealed significant differences between dogs and AIBOs in fostering social bonds. OXT level change increased during interactions with dogs but decreased with AIBOs. Participants reported stronger attachment to dogs and rated them as better social companions. These findings highlight the current limitations of AIBOs in fostering social bonding immediately compared to dogs. Our study contributes to the growing HRI research by demonstrating an existing gap between AIBOs and dogs as social companions. It highlights the need for further investigation to understand the complexities of social bonding with companion robots, which is essential to implement successful applications for social robots in diverse domains such as the elderly and health care, education, and entertainment.