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
Journal of Intelligence jintelligence	3.4	4.7	2013	33.9 Days	CHF 2600	Submit
Social Sciences socsci	1.7	3.1	2012	33.1 Days	CHF 1800	Submit

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Published Papers (8 papers)

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All Behavioral Sciences EJIHPE Social Sciences J. Intell.

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25 pages, 6141 KB  

Open AccessArticle

Coding Alone? AI-Assisted Software Work and the Decoupling of Productivity from Public Knowledge-Infrastructure Participation

by Tianhe Jiang

J. Intell. 2026, 14(5), 89; https://doi.org/10.3390/jintelligence14050089 (registering DOI) - 20 May 2026

Abstract

Complex knowledge work depends on individual output and on public exchanges that document problems, evaluate contributions, route expertise, and preserve reusable knowledge. Software work makes this infrastructure unusually visible through GitHub issues, reviews, comments, mentions, and cross-project ties. As generative AI coding tools [...] Read more.

Complex knowledge work depends on individual output and on public exchanges that document problems, evaluate contributions, route expertise, and preserve reusable knowledge. Software work makes this infrastructure unusually visible through GitHub issues, reviews, comments, mentions, and cross-project ties. As generative AI coding tools become private, on-demand sources of task support, it is unclear whether productive output remains tightly coupled with participation in this GitHub-visible public knowledge infrastructure. This study examines that question using a balanced panel of approximately 38,000 freelance developers on GitHub observed quarterly from 2019 to 2025 (approximately 1,080,000 person-quarter observations), estimating within-person changes in the association between a Productivity Index and a Social Connectivity Index. Two-way fixed effects models estimate a substantively large weakening after mid-2022 (−0.138 SD, about 44 percent of the pre-AI slope), and the pattern remains stable across alternative operationalizations, model specifications, and sample definitions. A survey-linked subsample (n = 237) provides individual-level triangulation: the weakening aligns with developers’ self-reported AI adoption dates, and heavier AI users exhibit larger decoupling. Decomposition by exchange function is selective: public exchanges with more direct private AI support pathways (information seeking, troubleshooting, preliminary evaluation) weaken more than exchanges anchored in contextual judgment and new-tie formation. This study documents a large-scale behavioral decoupling between productive output and visible GitHub-based public knowledge-infrastructure participation in a real-world problem-solving setting. The pattern is consistent with cognitive offloading as one micro-level pathway, while direct process evidence is left to future work. Full article

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

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26 pages, 3871 KB  

Open AccessArticle

Who Accommodates Whom? Bidirectional Linguistic Accommodation and Progressive Interpersonal Convergence in Human–AI Conversations

by Pengbo Chen, Huining Guan and Eui Jun Jeong

Behav. Sci. 2026, 16(5), 720; https://doi.org/10.3390/bs16050720 (registering DOI) - 7 May 2026

Viewed by 218

Abstract

Linguistic accommodation during human–AI interaction has been measured in only one direction at a time, leaving the relative magnitude of each side and the trajectory of within-conversation change unresolved. A symmetric within-versus-between conversation dissociation design applied to 1319 multi-turn English GPT-4o conversations from [...] Read more.

Linguistic accommodation during human–AI interaction has been measured in only one direction at a time, leaving the relative magnitude of each side and the trajectory of within-conversation change unresolved. A symmetric within-versus-between conversation dissociation design applied to 1319 multi-turn English GPT-4o conversations from WildChat measures both user-side and model-side function word adaptation within the same data, revealing two distinct temporal dynamics. The model’s adaptation is front-loaded, with strong initial accommodation at the first turn followed by stabilization, while users converge gradually across subsequent turns on interpersonal pronoun dimensions with no progressive change in topic-related categories. In 500 Switchboard human–human conversations, per-conversation similarity slopes are significantly negative ($p=0.022$), though the multilevel interaction is marginal ($p=0.055$). Because the pronoun dimensions on which users converge are the primary linguistic markers through which personality traits manifest in natural language use, this progressive convergence may represent a linguistic indicator of shifts in communicative self-presentation during extended human–AI conversation. Full article

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

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22 pages, 894 KB  

Open AccessArticle

A Moderated Mediation Model of AI-Driven Identity Threats and Employee Cyberloafing: The Role of AI-Inclusive Identity

by Alqa Ashraf, Qingfei Min and Aleena Ashraf

Eur. J. Investig. Health Psychol. Educ. 2026, 16(4), 52; https://doi.org/10.3390/ejihpe16040052 - 1 Apr 2026

Viewed by 1493

Abstract

This study intended to examine how human–AI collaboration-based identity threat appraisals in the form of the loss of autonomy and loss of skill trigger a professional identity that fosters cyberloafing. Based on social identity theory, this study applied a three-wave survey design with [...] Read more.

This study intended to examine how human–AI collaboration-based identity threat appraisals in the form of the loss of autonomy and loss of skill trigger a professional identity that fosters cyberloafing. Based on social identity theory, this study applied a three-wave survey design with 507 employees. The proposed research model was tested using partial least squares structural equation modeling (PLS-SEM) with SmartPLS 4, which enabled the assessment of both measurement and structural models. The perceived loss of skill and loss of autonomy are positively associated with professional identity threat, which mediates their relationships with cyberloafing. AI-inclusive identity weakens these associations for the loss of autonomy, suggesting that employees with strong AI-inclusive identity exhibit weaker professional identity threat. When integrating AI, organizations should mitigate appraisals of the loss of autonomy and loss of skill through participatory design, role redesign, and communication that emphasizes unique human contributions. Supporting healthy AI–human identity integration may reduce counterproductive behaviors such as cyberloafing. By positioning identity threat appraisals as human–AI collaboration-driven antecedents of professional identity threat and cyberloafing, this study extends social identity theory to human–AI contexts. It further demonstrates that over-identification with AI may heighten professional identity threats by diminishing the value of uniquely human contributions. Full article

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

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23 pages, 276 KB  

Open AccessArticle

Idols as My Cyber Lovers: A Behavioral Research on the Figurational Relationship Between Fans and AI-Customized Virtual Idols

by Xin Wang and Yaxin Zhang

Soc. Sci. 2026, 15(4), 225; https://doi.org/10.3390/socsci15040225 - 1 Apr 2026

Viewed by 1133

Abstract

Unlike conventional virtual idols like Hatsune Miku, which rely on pre-set voice libraries and stage scripts, AI-customized virtual idols achieve real-time interaction through generative artificial intelligence, continuously iterating their personality traits, language style, and even value expression along with fan and user interactions. [...] Read more.

Unlike conventional virtual idols like Hatsune Miku, which rely on pre-set voice libraries and stage scripts, AI-customized virtual idols achieve real-time interaction through generative artificial intelligence, continuously iterating their personality traits, language style, and even value expression along with fan and user interactions. AI-customized virtual idols, as pre-defined cultural commodities in the digital age, tend to focus on static, functional interpretations and have not yet fully entered the dynamic construction process as “subjects in the process of generation.” This study, based on a deep mediation perspective, employs a research method combining app roaming and semi-structured interviews to focus on the sociological examination of young fan groups’ use of AI tools to customize virtual idol companionship. It explores the reciprocal relationship between fan groups and customized virtual idols. The study finds that the AI-customized idols fan group constitutes a typical “actor group,” and its interaction practices are essentially a “fluid interaction” of human–machine intimacy. Young fan groups mainly interact with AI-customized virtual idols based on materiality, cognition, visibility, and emotional frames, thereby generating rich meaning production and symbolic imagination during the usage process. Fan groups and AI-customized virtual idols have developed different relationship paths, including mutual attachment, returning to normalcy, seeking substitutes, or direct withdrawal, revealing the inherent contradictions and tensions in digital intimacy, as well as the self-adjustment strategies of individuals under the mediation of technology. This process presents a “human-machine-idol” triadic relationship framework, becoming a new paradigm for intimacy in the digital age. Full article

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

18 pages, 820 KB  

Open AccessArticle

Employees’ Trust in AI and Innovative Behavior: A JD-R Model Perspective

by Chao Liu, Qichen Liao and Junting Lu

Behav. Sci. 2026, 16(3), 425; https://doi.org/10.3390/bs16030425 - 16 Mar 2026

Viewed by 1033

Abstract

With the rapid advancement of technology, whether to cultivate employees’ trust in artificial intelligence (AI) has emerged as a practical issue that managers must address to drive innovation. In this study, we explore how employees’ trust in AI affects their innovative behavior drawing [...] Read more.

With the rapid advancement of technology, whether to cultivate employees’ trust in artificial intelligence (AI) has emerged as a practical issue that managers must address to drive innovation. In this study, we explore how employees’ trust in AI affects their innovative behavior drawing on Job Demands-Resources (JD-R) theory with job autonomy and concentration of work-related flow as parallel mediators, and job complexity as a boundary condition. Using two-wave survey (with a two-week interval) data from 254 participants and structural equation modeling, we find that employees’ trust in AI positively relates to innovative behavior and this relationship is fully mediated by job autonomy and concentration of work-related flow. Furthermore, job complexity negatively moderates the trust in AI-mediator links and weakens the indirect effect on innovation. Based on the findings that enrich the literature on trust in AI and extend its boundary conditions, this study advises managers to cultivate employees’ trust in AI, leverage the resource-gaining and demand-enabling pathways, and adopt differentiated strategies tailored to job complexity to maximize innovation-enhancing effects of trust in AI. Full article

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

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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 584

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

Cited by 2 | Viewed by 716

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 1118

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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