Predicting user confidence during visual decision making
- Submitting institution
-
University of the West of England, Bristol
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 867445
- Type
- D - Journal article
- DOI
-
10.1145/3185524
- Title of journal
- ACM Transactions on Interactive Intelligent Systems
- Article number
- 10
- First page
- -
- Volume
- 8
- Issue
- 2
- ISSN
- 2160-6455
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2018
- URL
-
https://doi.org/10.1145/3185524
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Originality: Paper reports interdisciplinary project with researchers from Dept Psychology, creating novel representations of gaze-traces from which we could accurately model aspects of cognitive state normally ignored in Machine Learning. Crucially, we do this without the need to normalise for individual user characteristics.
Rigour: Uses widely accepted abstract visual problem solving tasks coupled with range of gaze-trace representations, problem formulations, and learning algorithms to identify approaches with errors of less than +/- on Likert scale.
Significance: The findings of this project were subsequently exploited via the project “Human-centric Active-learning for decision Support in Threat Exploration (DSTLX-1000121349)
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
