trust-affordance design

Existing visualization research lacks a systematic cognitive-level framework incorporating affordance theory, hindering explanation of how design choices and reader characteristics jointly shape the hierarchical structure of information communication. Method: This paper introduces the first theoretical framework of *visual cognitive affordance*, integrating insights from psychology, human-computer interaction, and visualization. It formally defines core constructs—perceptual, interpretive, and action affordances—and employs interdisciplinary theoretical synthesis and modeling to derive actionable design evaluation principles. Contribution/Results: The framework is empirically validated through representative visualization case studies, demonstrating its efficacy in optimizing information hierarchy representation and enhancing user comprehension efficiency. It provides both theoretical grounding and practical guidance for visualization design, thereby filling a critical formalization gap in applying affordance theory to the cognitive dimension of visualization.

This study addresses the trust imbalance that arises when users interact with increasingly prevalent yet opaque autonomous systems, often due to an inadequate understanding of their capabilities and limitations. Building upon the Human-Computer Trust Scale (HCTS), this work proposes the first practice-oriented, context-sensitive explanatory framework that enables reflective interpretation of trust dispositions. Through empirical validation, the research not only confirms the efficacy of HCTS as an initial trust assessment instrument but also introduces context-aware calibration guidelines for aligning user trust with system performance. The resulting framework provides both theoretical grounding and practical support for dynamically regulating trust in human–computer interaction.

How can users dynamically calibrate trust in automated systems—appropriately relying when the system is correct and promptly rejecting it when erroneous? This study proposes six interdisciplinary design principles, pioneering the systematic integration of pragmatics’ “common ground” theory and Grice’s cooperative principles into human–computer interaction (HCI) design, thereby establishing a dynamic, context-aware framework for credibility perception alignment. Methodologically, it synthesizes cognitive psychology, user experience (UX) design, and ethics, with emphasis on transparency and communicative effectiveness. Contributions include: (1) the first translation of foundational pragmatic theories into actionable, HCI-oriented design heuristics; (2) a structured, empirically grounded guideline supporting precise trust assessment; and (3) demonstrable improvements in human–AI collaboration safety, efficiency, and user satisfaction, alongside applicability to diagnostic evaluation of existing systems’ trustworthiness.

This study addresses the lack of a clear definition and effective measurement of user-perceived system predictability (PSP) in human–computer interaction, which has hindered understanding of trust and reliance mechanisms. Grounded in uncertainty theory, it conceptualizes PSP for the first time as a multidimensional construct encompassing cognitive, stochastic, and effective predictability, clarifies its distinction from related constructs such as trust and understanding, and reveals its dissociation from prediction accuracy. Through expert review, cognitive interviews, and two controlled experiments involving shape and emotion classification tasks, the authors developed and validated a six-item scale, confirming both a unidimensional and a three-level hierarchical factor structure. Results demonstrate that PSP predicts users’ prediction accuracy; explanation styles influence PSP without affecting accuracy; and system randomness reduces accuracy but leaves PSP unchanged.

This study addresses the core problem of how affordance perception supports real-time action decision-making under limited sensory information. Methodologically, it introduces the first computationally rational affordance theory framework, modeling affordances as a dynamic decision process: constructing internal world representations, identifying critical environmental features, inferring hypothesized motor trajectories, and performing a dual-factor trade-off between execution confidence and behavioral utility. Its key contribution lies in transcending traditional static perception paradigms—enabling affordances to evolve from passive recognition to active learning, feedback-driven regulation, and cross-context generalization. The framework unifies explanations of affordance phenomena across physical, digital, and social interactions; enables verifiable multimodal modeling; and informs adaptive human–machine system design. It thus provides computationally grounded design principles and an evolutionary foundation for intelligent human–machine collaboration. (149 words)

This study investigates how human-in-the-loop (HITL) feedback influences users’ perceptions of system accuracy and trust, highlighting the critical moderating role of task subjectivity. Through three controlled user experiments that systematically differentiate between objective and subjective task contexts, the research analyzes behavioral measures to assess the effects of feedback interaction. Findings reveal that in objective tasks, providing feedback significantly diminishes users’ trust in and perceived accuracy of the system, whereas this negative effect vanishes in subjective tasks. These results underscore task type as a pivotal factor shaping human–AI trust dynamics and offer important theoretical grounding and practical guidance for the design of HITL systems.

This work addresses the lack of explicit modeling of abstraction mechanisms in existing interactive system design, which hinders actionable design guidance. Through a systematic review of 457 publications, the study proposes the first abstraction-centered design space for interactive systems, structured around six core dimensions. Leveraging this framework, it reconceptualizes the Gulf of Execution and Evaluation model to reveal the cognitive and design mechanisms by which users and systems bridge the abstraction gap. By explicitly integrating abstraction into the theoretical foundations of human–computer interaction, this research synthesizes prior work, establishes a coherent theoretical basis, and offers systematic practical guidance for designing and evaluating abstraction mechanisms in interactive systems, thereby charting new directions for future inquiry.

This study addresses the challenge of misaligned goals and eroded trust in shared autonomy assistive robots, stemming from the opacity of robot intent inference. To enhance intention alignment and user trust, the authors manipulate interface transparency through feedback modality and informational richness in a visually mediated shared autonomy system. Findings reveal that goal readability is critical for effective collaboration, while trust is best supported by task-appropriate disclosure rather than maximal information. User experiments indicate a preference for visual feedback and demonstrate that optimal information density dynamically varies with task complexity. Notably, full disclosure of belief distributions does not consistently improve performance. Building on these insights, the work proposes design principles for transparent shared autonomy systems that balance informativeness with usability.

This study addresses the interplay of trust and distrust among older adults in technology use, particularly as shaped by insufficient transparency and perceived lack of control. Through a qualitative literature review and thematic analysis of empirical studies in human-computer interaction and gerontechnology, it reconceptualizes distrust not merely as absence of trust but as an active boundary practice. The work reveals the temporal and fluid nature of trust and identifies three constitutive mechanisms: informational invisibility, shifting usage experiences, and boundary maintenance. Building on these insights, the study proposes design strategies that enhance the perceptibility of information flows, offering concrete, actionable pathways to support trust in technologies for older adults. These approaches aim to strengthen user autonomy, transparency, and sense of control, thereby fostering more inclusive and empowering technological interactions.

This study addresses the challenge of trust calibration in human–AI interaction caused by reasoning rationales generated by large language models (LLMs). Through two integrated studies combining online behavioral experiments and eye-tracking, the authors systematically investigate how rationale correctness, presentation format, and expressions of certainty influence users’ perceived trust, decision confidence, and cognitive load. The work proposes an auditable trust calibration framework and reveals that incorrect rationales, while reducing system credibility, lead users to scrutinize supporting evidence more closely—thereby challenging the common assumption that “more reasoning is better.” Furthermore, eye-tracking metrics effectively predict users’ trust states, offering empirical grounding for the design of trustworthy AI interfaces.