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The curse of expertise: The effects of expertise and debiasing methods on prediction of novice performance.
Abstract
Experts are often called on to predict the performance of novices, but cognitive heuristics may interfere with experts' ability to capitalize on their superior knowledge hi predicting novice task performance. In Study 1, experts, intermediate users, and novices predicted the time it would take novices to complete a complex task. In Study 2, expertise was experimentally manipulated. In both studies, those with more expertise were worse predictors of novice performance times and were resistant to debiasing techniques intended to reduce underestimation. Findings from these studies suggest that experts may have a cognitive handicap that leads to underestimating the difficulty novices face and that those with an intermediate level of expertise may be more accurate in predicting novices' performance. Organizations often rely on experts to predict the performance of novices. Project managers predict team members' times to complete new work assignments. Marketers and designers predict consumers' abilities to master the use of new products. Teachers predict students' efforts to complete homework and finish exams. The predictions of experts influence the allocation of time and resources to projects, decisions about product design, and the pacing of students' lessons. Predictive errors can have significant negative consequences: employee alienation, failure to meet project deadlines, consumer dissatisfaction, and student frustration or boredom. This study draws on the literature in cognitive heuristics to explore whether experts, as compared with those with less expertise and novices themselves, are more or less accurate in predicting the perfor
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“experts tend to forget how they learned what they know, underestimate the work involved in a given task, and thus sometimes struggle to teach novices.”
From Large language models and the problem of rhetorical debt (2025) · cites this paper as Hinds 1999
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Metadata
- Authors
- Hinds, P. J.
- Affiliations
- Stanford University (US)
- Corresponding author
- Hinds, Pamela J.
- ORCID
- Hinds, Pamela J.
- Venue
- Journal of Experimental Psychology: Applied
- Published
- 1999-06
- Volume
- 5
- Issue
- 2
- Pages
- 205-221
- ISSN
- 1076-898X (ISSN-L)
- 1939-2192
- OpenAlex
- W2011381597
- Cited by
- 397
- References
- 38
- Citations by year
- 2026: 15 · 2025: 11 · 2024: 12 · 2023: 9 · 2022: 14 · 2021: 26 · 2020: 38 · 2019: 12 · 2018: 19 · 2017: 18 · 2016: 13 · 2015: 13 · 2014: 12 · 2013: 14 · 2012: 12
- FWCI
- 10.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Experimental Psychology Applied (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- expertise, cognitive heuristics, bias mitigation, underestimation bias, expert opinion, task performance prediction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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