Personality and Cognition in Human–AI Interaction

Topic Information

Dear Colleagues,

The goal of this topic is to educate and generate continued interest and enthusiasm in research on personality and cognition in human–AI interaction.

Although extensive research has been conducted to explore the influence of personality on cognition, it remains unclear as to whether these same relationships manifest in human–AI interaction. Examples of human–AI interaction include, but are not limited to, virtual assistants such as Siri, Alexa, Google Assistant, ChatGPT, AI-powered chatbots, and AI companions. Given the rapid development of AI technology, a more comprehensive understanding of how personality may influence cognitive processes in human–AI interaction is essential because it profoundly shapes the conclusions of findings and the focus of future research.

Here, we define personality broadly as individual differences that can distinguish one person from another, including but not limited to personality traits such as the “Big Five”, self-concept, emotions, intelligence, emotional intelligence, and motives. Personality profoundly shapes how individuals interact with the world. It influences various aspects of social engagement, from initial encounters, trust formation, to cooperation and the maintenance of long-term relationships. We are interested in understanding how personality may relate to cognitive processes and functions, from attention, perception, and memory to decision-making, mindsets, and problem-solving.

Therefore, a central issue we aimed to discuss within this topic is the relationship between personality and cognition in human–AI interaction from the perspective of a wide range of individual differences and cognitive processes. Specifically, we invite you to explore the parallels and distinctions in how personality and cognition influence interactions between humans and AI compared to human–human interactions. This juxtaposition will serve to clarify the knowledge gained.

We invite you to contribute a paper to this topic, which we anticipate will be a stimulating exploration. We aim to uncover hidden connections and contrasts between different research approaches, fostering innovative ideas for future work.

Prof. Dr. Virginia Sau Y. Kwan
Dr. Samantha McMichael
Dr. Julia Brailovskaia
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Behavioral Sciences behavsci	2.5	3.1	2011	32 Days	CHF 2200	Submit
European Journal of Investigation in Health, Psychology and Education ejihpe	2.6	5.1	2011	25.8 Days	CHF 1600	Submit
Social Sciences socsci	1.7	3.1	2012	33.1 Days	CHF 1800	Submit
Journal of Intelligence jintelligence	3.4	4.7	2013	33.9 Days	CHF 2600	Submit

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Published Papers (3 papers)

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All Behavioral Sciences EJIHPE Social Sciences J. Intell.

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14 pages, 6344 KB  

Open AccessArticle

From Initial to Situational Automation Trust: The Interplay of Personality, Interpersonal Trust, and Trust Calibration in Young Males

by Menghan Tang, Tianjiao Lu and Xuqun You

Behav. Sci. 2026, 16(2), 176; https://doi.org/10.3390/bs16020176 - 26 Jan 2026

Viewed by 132

Abstract

To understand human–machine interactions, we adopted a framework that distinguishes between stable individual differences (enduring personality/interpersonal traits), initial trust (pre-interaction expectations), and situational trust (dynamic calibration via gaze and behavior). A driving simulator experiment was conducted with 30 male participants to investigate trust [...] Read more.

To understand human–machine interactions, we adopted a framework that distinguishes between stable individual differences (enduring personality/interpersonal traits), initial trust (pre-interaction expectations), and situational trust (dynamic calibration via gaze and behavior). A driving simulator experiment was conducted with 30 male participants to investigate trust calibration across three levels: manual (Level 0), semi-automated (Level 2, requiring monitoring), and fully automated (Level 4, system handles tasks). We combined eye tracking (pupillometry/fixations) with the Eysenck Personality Questionnaire (EPQ) and Interpersonal Trust Scale (ITS). Results indicated that semi-automation yielded a higher hazard detection sensitivity (d′ = 0.81) but induced greater physiological costs (pupil diameter, ηp² = 0.445) compared to manual driving. A mediation analysis confirmed that neuroticism was associated with initial trust specifically through interpersonal trust. Critically, despite lower initial trust, young male individuals with high interpersonal trust exhibited slower reaction times in the semi-automation model (B = 0.60, p = 0.035), revealing a “social complacency” effect where social faith paradoxically predicted lower behavioral readiness. Based on these findings, we propose that situational trust is a multi-layer calibration process involving dissociated attentional and behavioral mechanisms, suggesting that such “wary but complacent” drivers require adaptive HMI interventions. Full article

(This article belongs to the Topic Personality and Cognition in Human–AI Interaction)

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32 pages, 929 KB  

Open AccessArticle

Reflecting the Self: The Mirror Effect of Narcissistic Self-Regulation in Older Adults’ Evaluations of Empathic vs. Cold Socially Assistive Robots

by Avi Besser, Virgil Zeigler-Hill and Keren Mazuz

Behav. Sci. 2026, 16(2), 164; https://doi.org/10.3390/bs16020164 - 23 Jan 2026

Viewed by 250

Abstract

Empathic behavior is increasingly incorporated into socially assistive robots, yet little is known about how older adults’ personality-based self-regulatory processes shape responses to such designs. The present study examined a recognition-based “mirror effect” framework of narcissistic self-regulation, referring to the ways individuals maintain [...] Read more.

Empathic behavior is increasingly incorporated into socially assistive robots, yet little is known about how older adults’ personality-based self-regulatory processes shape responses to such designs. The present study examined a recognition-based “mirror effect” framework of narcissistic self-regulation, referring to the ways individuals maintain a valued self-image through social feedback and acknowledgment. We focused on two core dimensions: narcissistic admiration, characterized by self-promotion and the pursuit of affirmation, and narcissistic rivalry, characterized by defensiveness, antagonism, and sensitivity to threat. Community-dwelling older adults (N = 527; M_age = 72.73) were randomly assigned to view a video of a socially assistive robot interacting in either an empathic or a cold manner. Participants reported their perceived recognition by the robot, defined as the subjective experience of feeling seen, acknowledged, and valued, as well as multiple robot evaluations (anthropomorphism, likability, perceived intelligence, safety, and intention to use). At the mean level, empathic robot behavior increased perceived recognition, anthropomorphism, and likability but did not improve perceived intelligence, safety, or intention to use. Conditional process analyses revealed that narcissistic admiration was positively associated with perceived recognition, which in turn predicted more favorable robot evaluations, regardless of robot behavior. In contrast, narcissistic rivalry showed a behavior-dependent pattern: rivalry was associated with reduced perceived recognition and less favorable evaluations primarily in the empathic condition, whereas this association reversed in the cold condition. Importantly, once perceived recognition and narcissistic traits were accounted for, the cold robot was evaluated as more intelligent, safer, and more desirable to use than the empathic robot. Studying these processes in older adults is theoretically and practically significant, as later life is marked by shifts in social roles, autonomy concerns, and sensitivity to interpersonal evaluation, which may alter how empathic technologies are experienced. Together, the findings identify perceived recognition as a central psychological mechanism linking personality and robot design and suggest that greater robotic empathy is not universally beneficial, particularly for users high in rivalry-related threat sensitivity. Full article

(This article belongs to the Topic Personality and Cognition in Human–AI Interaction)

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21 pages, 526 KB  

Open AccessFeature PaperArticle

Beyond Risk Reduction: Vigilant Trust in Artificial Intelligence Based on Evidence from China

by Wuyao Ding, Yun Wu and Junxiu Wang

Behav. Sci. 2026, 16(1), 95; https://doi.org/10.3390/bs16010095 - 9 Jan 2026

Viewed by 369

Abstract

Public trust in artificial intelligence (AI) is often assumed to promote acceptance by reducing perceived risks. Using a nationally representative survey of 10,294 Chinese adults, this study challenges that assumption and introduces the concept of vigilant trust. We argue that trust in AI [...] Read more.

Public trust in artificial intelligence (AI) is often assumed to promote acceptance by reducing perceived risks. Using a nationally representative survey of 10,294 Chinese adults, this study challenges that assumption and introduces the concept of vigilant trust. We argue that trust in AI does not necessarily diminish risk awareness but can coexist with, and even intensify, attention to potential harms. By examining four dimensions of trust—trusting stance, competence, benevolence, and integrity—we find that all of them consistently enhance perceived benefits, which emerge as the strongest predictor of AI acceptance. However, trust shows differentiated relationships with perceived risks: benevolence reduces risk perception, whereas trusting stance is associated with higher perceptions of both benefits and risks. Perceived risks do not uniformly deter acceptance and, in some contexts, are positively associated with willingness to adopt AI. By moving beyond the conventional view of trust as a risk-reduction mechanism, this study conceptualizes vigilant trust as a mode of engagement in which openness to AI is accompanied by sustained awareness of uncertainty. The findings offer a more nuanced understanding of public acceptance of AI and its implications for governance and communication. Full article

(This article belongs to the Topic Personality and Cognition in Human–AI Interaction)

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