To Trust or Not to Trust? A Simulation-Based Experimental Paradigm [supporting datasets]
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Summary
This document is a data repository record for the study "To Trust or Not to Trust? A Simulation-based Experimental Paradigm," funded by the U.S. Department of Transportation and preserved by the SAFER-SIM University Transportation Center. The research addresses the critical challenge of human acceptance and appropriate utilization of automated driving systems. While automated vehicles are expected to enhance traffic safety and flow, these benefits are compromised if users do not accept the technology or utilize it appropriately. The core problem identified is that trust in automation is a dynamic construct comprising two components: initial or dispositional trust, which is shaped before experiencing system performance, and history-based trust, which evolves with user experience. Inappropriate trust levels, whether over-trust or under-trust, negatively impact the effectiveness of the technology. The study employs a simulator-based experimental paradigm to investigate the factors affecting both initial and history-based trust. The methodology involves a review of the history of research on human trust in automation and existing trust models, followed by the design of a simulation experiment to address gaps in the literature. The experimental design includes the administration of multiple questionnaires to participants, including pre-study perception questionnaires, mid-study perception questionnaires (two rounds), post-study perception questionnaires, and a final trust survey. The dataset supports this experimental framework by providing raw data collected during the study. The dataset consists of two primary collections. The first, "ToTrust_CSV.zip," contains seven CSV files detailing participant responses from various stages of the experiment, including demographics, pre-study perceptions, two mid-study perception assessments, post-study perceptions, and a final trust survey from a pilot round. The second collection, "ToTrust_Data.zip," contains 300 .plt files, which likely represent telemetry or simulation data from 80 participants, with file names indicating subject numbers and drive conditions. The data was collected between January and February 2018. The authors are Michael Knodler Jr., Eleni Christofa, Foroogh Hajiseyedjavadi, Francis Tainter, and Nicholas Campbell, all affiliated with the University of Massachusetts Amherst. The significance of this work lies in its contribution to understanding drivers’ trust in automated vehicles, which is essential for enhancing human-automation interaction models. By distinguishing between dispositional and history-based trust, the study aims to provide insights into how user expectations and actual system performance interact to shape trust. The availability of this replication data allows for further analysis and validation of the experimental paradigm, supporting the broader goal of developing automated driving systems that users can trust appropriately, thereby maximizing safety and traffic flow benefits.
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (8 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 97 | 2026-08-22 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.8-27b-gittensor | summ-v5 | 99 | 2026-08-22 |
| tag | success | vector_similarity | — | — | 24 | 2026-08-11 |
| verify | success | — | — | — | 3 | 2026-08-08 |
Summary generated by qwen3.8-27b-gittensor on 2026-08-22; verification: pending re-verification.
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- Empirical Findings: self report data
- Methodological Resource: tool software, dataset resource