Aug 2026· Human Factors· pp.
187208261477486
· 0 citations· 59 references
Medicine
TL;DR
High cognitive load elevated both subjective trust and behavioral reliance on AI, and cognitive constraints rebalance dual trust pathways-weakening analytic evaluation while amplifying heuristic outcome reliance-by revealing how cognitive constraints rebalance dual trust pathways.
Abstract
ObjectiveThis study examined whether cognitive load produces selective effects on different trust updating pathways in AI-assisted decision making.BackgroundAlthough cognitive load affects trust in automation, its influence on the mechanisms of trial-by-trial trust updating remains unclear.MethodsA dual-task paradigm embedded in a mining exploration task manipulated cognitive load while capturing dynamic trust calibration. Guided by a dual-pathway framework, we operationalized process-based (analytical evaluation of AI recommendation correctness) and outcome-based (heuristic reliance on task outcomes) trust updating pathways. Trust dynamics and behavioral reliance were examined using linear mixed-effects models.ResultsCognitive load shifted the relative influence of the two trust updating pathways. Process-based updating was attenuated under high cognitive load, indicating reduced sensitivity to AI recommendation correctness during trust updating. Outcome-based information gained greater influence under high load, amplifying outcome-driven bias regardless of recommendation correctness. Asymmetric trust updating was evident overall, although the influence of cognitive load on this asymmetry depended on task outcomes. Overall, high cognitive load elevated both subjective trust and behavioral reliance on AI.ConclusionCognitive load shapes trust calibration through mechanism-level reconfiguration rather than global impairment. By revealing how cognitive constraints rebalance dual trust pathways-weakening analytic evaluation while amplifying heuristic outcome reliance-this study advances theoretical understanding of dynamic trust in human-AI collaboration.ApplicationThe results provide practical guidance for the design of AI systems in high-stakes settings, highlighting the need to support analytic trust updating and mitigate over-reliance under cognitive strain.
Artificial intelligence (AI)-based decision support systems are increasingly shaping organizational decision-making by influencing how humans engage in cognitive tasks. While prior research has largely emphasized the performance benefits of AI adoption, less attention has been given to its association with human judgment. Drawing on Automation Bias theory and Human–AI collaboration research, this study examines the relationships among trust in AI-based decision support systems (AI-DSS), reliance intention, judgment attenuation—defined here as a perceived reduction in independent evaluative effort—and accountability pressure. Survey data from 400 organizational employees were analyzed using covariance-based structural equation modeling. The results show that trust in AI-DSS is positively associated with users’ reliance intention, which in turn is associated with greater self-reported judgment attenuation. Trust also shows a direct association with judgment attenuation, indicating that AI use co-occurs with both behavioral and cognitive correlates of reduced independent evaluation. Furthermore, the positive association between trust and judgment attenuation is weaker under conditions of high accountability pressure, a pattern consistent with, though not a direct test of, greater cognitive engagement. Because judgment attenuation is measured by self-report at a single time point without a performance criterion, these findings should be interpreted as evidence of perceived rather than behaviorally verified erosion of judgment, and the cross-sectional design precludes strong causal claims. This study extends Automation Bias theory to contemporary Human–AI collaboration by showing that trust in AI is associated with both the facilitation and the perceived constraint of organizational decision-making, and it highlights accountability as a candidate governance mechanism for preserving self-reported human judgment in AI-assisted environments, pending behavioral validation.
Youngkeun Choi· Human Systems Management· 0 citations
A model develops and validates a model to examine how interface clarity, cognitive-technical alignment, algorithmic reliability, and decision explainability collectively influence behavioral intent to adopt AI-based decision support tools, and offers strong empirical support for applying CFT in AI adoption contexts.
A. Waqar, Khaled A. Alrasheed, Azlan Shah Ali et al.· Acta Psychologica· 1 citation
Aim/Purpose: This study addresses a limitation of existing AI-supported learning research: its reliance on linear models that fail to capture the complex interplay among technological, cognitive, and behavioral factors in shaping learning innovation performance.
Background: To overcome this limitation, the study proposes a multi-path direct model that simultaneously examines the roles of AI usage, algorithmic trust, cognitive load management, engagement, and adaptability in a unified framework.
Methodology: The study adopts a quantitative approach using Partial Least Squares Structural Equation Modeling. Data were collected from undergraduate engineering students with experience in AI-supported learning environments, and the analysis followed measurement and structural model evaluation procedures.
Contribution: This study contributes to the literature by advancing a multi-path direct perspective that captures interdependent relationships among key constructs, redefines the roles of engagement and algorithmic trust, and positions self-regulated learning adaptability as the central mechanism driving innovation outcomes.
Findings: The findings show that AI learning companion usage significantly strengthens algorithmic trust and learning adaptability, while cognitive load management enhances both engagement and adaptability. Self-regulated learning adaptability emerges as the strongest predictor of learning innovation performance. In contrast, engagement and algorithmic trust do not directly influence innovation outcomes, indicating that deeper cognitive transformation is required.
Recommendations for Practitioners: Educators should emphasize adaptive learning strategies and critical use of AI tools while designing learning environments that support cognitive regulation rather than focusing solely on engagement.
Recommendation for Researchers: Researchers should adopt integrative, non-linear models to capture the complexity of AI-supported learning better and to investigate indirect mechanisms among key variables further.
Impact on Society: The study provides insights for developing more effective AI-enhanced learning systems that foster innovation, critical thinking, and adaptability in increasingly complex digital environments.
Future Research: Future studies should extend this model across disciplines and cultural contexts, apply longitudinal designs, and incorporate objective performance measures to strengthen causal understanding.
M. Husin· Journal of Information Techn...· 0 citations
As artificial intelligence increasingly serves as an active teammate in complex work environments, understanding how AI behavior influences trust and team performance is essential. This study examined the effects of AI transparency and cooperation in a three-agent (Human-Human-AI) Human–Automation Teaming task based on a cooperative disease treatment simulation. 146 participants (73 teams) were assigned to one of four conditions in a 2 (Cooperation: High vs. Low) × 2 (Transparency: High vs. Low) between-subjects design. Subjective trust, behavioral reliance, team efficiency, and win rate were assessed using surveys, gameplay behaviors, and system logs. Neither subjective trust, behavioral reliance, nor win rate differed across conditions. However, after controlling for agent processing time, teams interacting with a highly transparent agent completed turns significantly faster than teams with a low-transparency agent. These findings suggest transparency may improve coordination efficiency without affecting trust or team effectiveness in newly formed Human–AI teams.
Spencer S. Onstot, Eric T. Greenlee, Gregory J. Funke et al.· Proceedings of the Human Fac...· 0 citations
The proliferation of artificial intelligence (AI) in complex decision environments has intensified scholarly and practical interest in how humans calibrate trust when collaborating with intelligent systems. This paper presents a cross-domain empirical investigation of human-AI collaborative decision-making across climate intelligence and organisational governance contexts. Drawing on a mixed-methods synthesis of 68 primary studies, original survey data from 200 industry professionals, and 30 semi-structured interviews, we examine trust calibration dynamics, performance outcomes, and implementation barriers. Our findings reveal a systematic trust calibration paradox: while 75% of organisations reported efficiency gains from AI integration, only 45% perceived their systems as transparent and explainable, and 38% identified persistent algorithmic bias. Effect sizes varied substantially across applications, reaching g = 1.65 for extreme-weather detection, with considerable between-study heterogeneity. Critically, we find that trust exhibited a non-linear association with AI autonomy, with higher observed trust at intermediate than at maximal levels of delegation; turning regions varied by decision context and should not be interpreted as universal autonomy thresholds. These findings advance human-intelligent systems integration theory by demonstrating that effective collaboration depends less on algorithmic performance alone and more on calibrated trust, institutional capacity, and governance frameworks that sustain human agency within hybrid decision architectures.
Elizabeth I. Igbodor, I. Mordi, N. B. Umoru et al.· Human-Intelligent Systems In...· 0 citations
Artificial intelligence recommendations must do more than reproduce a consumer’s established preferences: they must also facilitate worthwhile discovery without undermining confidence in the platform. Drawing on the stimulus–organism–response framework, this study examines recommendation accuracy and recommendation novelty as two distinct system cues, perceived value and trust as parallel cognitive mechanisms, and online purchase intention as the behavioral response. The model was tested with 493 complete survey responses and 27 reflective indicators using partial least squares structural equation modeling in SmartPLS 4. Recommendation accuracy and novelty were positively related to both perceived value and trust. Perceived value, in turn, had a positive relationship with purchase intention. Trust also had a positive but comparatively weak relationship with purchase intention; this path met the conventional percentile-bootstrap criterion, although its bias-corrected confidence interval included zero. The specific indirect effects through perceived value were supported for both accuracy and novelty, whereas the corresponding trust-based indirect effects were not supported under conservative inference. The model explained 7.5% of perceived value, 3.4% of trust, and 4.0% of purchase intention, indicating that recommendation quality is a meaningful but limited part of the wider purchase decision. The findings distinguish relevance from discovery and show that the value pathway is more dependable than the trust pathway in translating AI recommendation quality into purchase intention.
Bích Đào Phạm Thị· International Journal of Adv...· 0 citations
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