The role of a mental model in learning to operate a device
Open accessAbstract
This report presents three studies concerned with learning how to operate a simple control panel device, and how this learning is affected by understanding a device model that describes the internal mechanism of the device. The first experiment compared two groups, one of which learned a set of operating procedures for the device by rote, and the other learned the device model before receiving the identical procedure training. The model group learned the procedures faster, retained them more accurately, executed them faster, and simplified inefficient procedures far more often, than the rote group. The second study demonstrated that the model group is able to infer the procedures much more easily than the rote group, which would lead to more rapid learning and better recall performance. The third study showed that the important content of the device model was the specific configuration of components and controls, and not the motivational aspects, component descriptions, or general principles. This specific information is what is logically required to infer the procedures. Thus, the benefits of having a device model depend on whether it supports direct and simple inference of the exact steps required to operate the device.
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The use of appropriate mental models can help people learn, remember and execute procedures faster.
“While incorrect mental models can lead to difficulties in problem solving, the use of appropriate mental models can help people learn, remember and execute procedures faster (Kieras and Bovair, 1984).”
From Understanding and Resolving Failures in Human-Robot Interaction: Literature Review and Model Development (2018) · cites this paper as Kieras and Bovair, 1984
Incorrect mental models can lead to difficulties in problem solving.
“While incorrect mental models can lead to difficulties in problem solving, the use of appropriate mental models can help people learn, remember and execute procedures faster (Kieras and Bovair, 1984).”
From Understanding and Resolving Failures in Human-Robot Interaction: Literature Review and Model Development (2018) · cites this paper as Kieras and Bovair, 1984
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References 9 · Cited by 340
Research works printed but not linked: 5
- [1]1. Alexander, J. H. (1982). Computer text-editing: The development of a cognitive skill. Unpub- lished doctoral dissertation, Department of Psychology, University of Colorado, Boulder.
- [3]3. Foss, D. J., Smith, P. L., & Rosson, M. B. (1982, November). The novice at the terminal: Variables affecting undersfanding and performance. Paper presented at the Psycho- nomic Society Meeting, Minneapolis. 15516709, 1984, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1207/s15516709cog0803_3, Wiley Online Library on [17/08/2026]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
- [4]4. Gentner, D., & Gentner, D. R. (1983). Flowing waters or teeming crowds: Mental models of electricity. In D. Gentner & A. L. Stevens (Eds.), Menfal mode/s. Hillsdale, NJ: Erlbaum.
- [5]5. Gentner, D., & Stevens, A. L. (Eds.). (1983). Menfal models. Hillsdale, NJ: Erlbaum.
- [17]17. Williams, M. D.. Hollan, J. D., & Stevens, A. L. (1983). Human reasoning about a simple physical system. In D. Gentner & A. L. Stevens (Eds.), Menial models. Hillsdale, NJ: Erlbaum.
- Authors
- Kieras, D. E., & Bovair, S.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Kieras, David E.
- ORCID
- Kieras, David E.
- Venue
- Cognitive Science
- Publisher
- Wiley
- Published
- 1984-07
- Volume
- 8
- Issue
- 3
- Pages
- 255-273
- ISSN
- 0364-0213 (ISSN-L)
- 1551-6709
- OpenAlex
- W2251129220
- Semantic Scholar
- 2bb0900a46b68f3a36175830959a17194d6f64db
- Cited by
- 340
- References
- 9
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 2 · 2023: 4 · 2022: 11 · 2021: 19 · 2020: 10 · 2019: 10 · 2018: 11 · 2017: 6 · 2016: 8 · 2015: 7 · 2014: 7 · 2013: 13 · 2012: 10
- FWCI
- 11.03
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cognitive Science (publishedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Funders
- Office of Naval Research
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Physical Sciences › Engineering › Automotive Engineering › Spatial Cognition and Navigation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Keywords
- mental models, device models, procedural learning, rote learning, inference, retention, learning efficiency, component configuration
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