skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Development of a Real-Time Trust/Distrust Metric Using Interactive Hybrid Cognitive Task Analysis
While there is increased interest in how trust spreads in Human Autonomy Teams (HATs), most trust measurements are subjective and do not examine real-time changes in trust. To develop a trust metric that consists of objective variables influenced by trust/distrust manipulations, we conducted an Interactive hybrid Cognitive Task Analysis (IhCTA) for a Remotely Piloted Aerial System (RPAS) HAT. The IhCTA adapted parts of the hybrid Cognitive Task Analysis (hCTA) framework. In this paper, we present the four steps of the IhCTA approach, including 1) generating a scenario task overview, 2) generating teammate-specific event flow diagrams, 3) identifying interactions and interdependencies impacted by trust/distrust manipulations, and 4) processing RPAS variables based on the IhCTA to create a metric. We demonstrate the application of the metric through a case study that examines how the influence of specific interactions on team state changes before and after the spread of distrust.  more » « less
Award ID(s):
2019805
PAR ID:
10499795
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Date Published:
Journal Name:
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Volume:
67
Issue:
1
ISSN:
1071-1813
Page Range / eLocation ID:
2128 to 2136
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, Nature Communications)
    Abstract Success in many real-world tasks depends on our ability to dynamically track hidden states of the world. We hypothesized that neural populations estimate these states by processing sensory history through recurrent interactions which reflect the internal model of the world. To test this, we recorded brain activity in posterior parietal cortex (PPC) of monkeys navigating by optic flow to a hidden target location within a virtual environment, without explicit position cues. In addition to sequential neural dynamics and strong interneuronal interactions, we found that the hidden state - monkey’s displacement from the goal - was encoded in single neurons, and could be dynamically decoded from population activity. The decoded estimates predicted navigation performance on individual trials. Task manipulations that perturbed the world model induced substantial changes in neural interactions, and modified the neural representation of the hidden state, while representations of sensory and motor variables remained stable. The findings were recapitulated by a task-optimized recurrent neural network model, suggesting that task demands shape the neural interactions in PPC, leading them to embody a world model that consolidates information and tracks task-relevant hidden states. 
    more » « less
  2. ; (, Catalyst: Feminism, Theory, Technoscience)
    null (Ed.)
    In this paper we theorize trust as emerging in different material/infrastructural and epistemic realities as part of our multidisciplinary collaboration about water, called Neighborhood Environments as Socio-Techno-Bio Systems: Water Quality, Public Trust, and Health in Mexico City (NESTSMX)”. This collaboration, led by feminist anthropologists, brings together anthropology, environmental-public health, and environmental engineering researchers to analyze how neighborhoods as “socio-techno-bio systems” shape how people trust or distrust water. Our project follows the infrastructures and social structures that move water in and out of neighborhoods, households, and bodies making them trust it more or less. At the same time our multi-disciplinary research team inhabits different epistemic research environments that creates tensions about how we make knowledge and what counts as data. Trust and distrust then shapes how we constitute both our object of inquiry and how we know it. 
    more » « less
  3. ; ; ; ; (, Computers and Education: Artificial Intelligence)
    Generative AI (GenAI) has brought opportunities and challenges for higher education as it integrates into teaching and learning environments. As instructors navigate this new landscape, understanding their engagement with and attitudes toward GenAI is crucial. We surveyed 178 instructors from a single U.S. university to examine their current practices, perceptions, trust, and distrust of GenAI in higher education in March 2024. While most surveyed instructors reported moderate to high familiarity with GenAI-related concepts, their actual use of GenAI tools for direct instructional tasks remained limited. Our quantitative results show that trust and distrust in GenAI are related yet distinct; high trust does not necessarily imply low distrust, and vice versa. We also found significant differences in surveyed instructors' familiarity with GenAI across different trust and distrust groups. Our qualitative results show nuanced manifestations of trust and distrust among surveyed instructors and various approaches to support calibrated trust in GenAI. We discuss practical implications focused on (dis)trust calibration among instructors. 
    more » « less
  4. ; ; ; (, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)
    Development of responsive automation necessitates a framework for studying human-automation interactions in a broad range of operating conditions. This study uses a novel experiment design involving multiple binary perturbations in different stimuli to elicit measurable changes in cognitive factors that affect human-decision making during conditionally-automated (SAE Level 3) driving: trust in automation, mental workload, self-confidence, and risk perception. To infer changes in these factors, psychophysiological metrics such as heart rate variability and galvanic skin response, behavioral metrics such as eye gaze and reliance on automation, and self-reports were collected. Findings from statistical tests revealed significant changes, particularly in psychophysiological and behavioral metrics, for some treatments. However, other treatments did not elicit a significant change, highlighting the complexities of a between-subject experiment design with variations in multiple independent variables. Findings also underscore the importance of collecting heterogeneous human data to infer changes in cognitive factors during interactions with automation. 
    more » « less
  5. ; ; ; ; (, Proceedings of CHI 2020: Human Factors in Computing)
    A novel eye-tracked measure of pupil diameter oscillation is derived as an indicator of cognitive load. The new metric, termed the Low/High Index of Pupillary Activity (LHIPA), is able to discriminate cognitive load (vis-à-vis task difficulty) in several experiments where the Index of Pupillary Activity fails to do so. Rationale for the LHIPA is tied to the functioning of the human autonomic nervous system yielding a hybrid measure based on the ratio of Low/High frequencies of pupil oscillation. The paper’s contribution is twofold. First, full documentation is provided for the calculation of the LHIPA. As with the IPA, it is possible for researchers to apply this metric to their own experiments where a measure of cognitive load is of interest. Second, robustness of the LHIPA is shown in analysis of three experiments, a restrictive fixed-gaze number counting task, a less restrictive fixed-gaze n-back task, and an applied eye-typing task. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email