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.
“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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Gilbert, D. T., Pinel, E. C., Wilson, T. D., Blumberg, S. J., & Wheatley, T. P. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511808098.018 · Cited by 66 · see citing works
Measuring Crowdsourcing Effort with Error-Time Curves
Cheng, J., Teevan, J. B., & Bernstein, M. S. (2015) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702145 · Cited by 64 · see citing works
“It is difficult for re-questers to accurately estimate task difficulty, as experts cat-egorically underestimate novices’ completion times and dif-ficulty [12, 13].”
BackgroundWhat Can Children Tell Us About Hindsight Bias: A Fundamental Constraint on Perspective–Taking?
Birch, S., & Bernstein, D. M. (2007) · Social Cognition · doi:10.1521/soco.2007.25.1.98 · Cited by 63 · see citing works
“If they fail to fully appreciate their audience’s more naive perspective they will deliver their message at a less than optimal level (e.g., Hinds, 1999).”
BackgroundPeer-based computer-supported knowledge refinement
Cho, K., Chung, T. R., King, W. R., & Schunn, C. D. (2008) · Communications of the ACM · doi:10.1145/1325555.1325571 · Cited by 62 · see citing works
“Hinds (1999) showed that experts overestimated non-”
BackgroundChowdhury, P. D., Haque, C. E., & Driedger, S. M. (2011) · Journal of Risk Research · doi:10.1080/13669877.2011.601319 · Cited by 62 · see citing works
Bias in memory predicts bias in estimation of future task duration
Roy, M. M., & Christenfeld, N. J. S. (2007) · Memory & Cognition · doi:10.3758/bf03193294 · Cited by 59 · see citing works
Bell, H. H., Andrews, D. H., Wulfeck, W. H., Villachica, S. W., Stone, D. L., Clark, R. E., Yates, K. A., Early, S. D., Moulton, K., Mayer, R. E., et al. (2009) · Cited by 57 · see citing works
Dow, S. P., Kulkarni, A. P., Bunge, B., Nguyen, T., Klemmer, S., & Hartmann, B. (2011) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/1979742.1979826 · Cited by 57 · see citing works
“However, experts often underestimate the difficulty novices face in solving tasks [7] or use language or concepts that are beyond the grasp of novices [6].”
BackgroundOstermann, A., Leuders, T., & Nückles, M. (2017) · Journal of Mathematics Teacher Education · doi:10.1007/s10857-017-9369-z · Cited by 56 · see citing works
Timmerman, B. C., Feldon, D. F., Maher, M., Strickland, D. C., & Gilmore, J. A. (2011) · Studies in Higher Education · doi:10.1080/03075079.2011.590971 · Cited by 56 · see citing works
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Probability Judgment across Cultures
Yates, J. F., Lee, J., Sieck, W. R., Choi, I., & Price, P. C. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511808098.017 · Cited by 55 · see citing works
Dunning–Kruger effects in face perception
Zhou, X., & Jenkins, R. (2020) · Cognition · doi:10.1016/j.cognition.2020.104345 · Cited by 53 · see citing works
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Jansen, T., & Møller, J. (2021) · Teaching and Teacher Education · doi:10.1016/j.tate.2021.103616 · Cited by 66 · see citing works
Heuristics and Biases in Application
Fischhoff, B. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511808098.043 · Cited by 50 · see citing works
Clinical versus Actuarial Judgment
Dawes, R. M., Faust, D., & Meehl, P. E. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511808098.042 · Cited by 52 · see citing works
Putting Adjustment Back in the Anchoring and Adjustment Heuristic
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The Effects of Politeness-Related Instruction on Medical Tutoring
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Like Goes with Like: The Role of Representativeness in Erroneous and Pseudo-Scientific Beliefs
Gilovich, T. D., & Savitsky, K. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511808098.036 · Cited by 47 · see citing works
Sherman, S. J., Cialdini, R. B., Schwartzman, D. F., & Reynolds, K. D. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511808098.006 · Cited by 46 · see citing works
Gaikwad, S. S., Morina, D., Ginzberg, A., Mullings, C. A., Goyal, S., Gamage, D., Diemert, C., Burton, M., Zhou, S., Whiting, M. E., et al. (2016) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2984511.2984542 · Cited by 45 · see citing works
“measurable because workers have no incentive to report this number after they complete a task, automatic logs are not accurate [26, 11, 12], and requesters underestimate how long a task will take [20].”
BackgroundThe effect of construal level on predictions of task duration
Kanten, A. B. (2011) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2011.04.005 · Cited by 43 · see citing works
Correcting memory improves accuracy of predicted task duration.
Roy, M. M., Mitten, S. T., & Christenfeld, N. J. S. (2008) · Journal of Experimental Psychology Applied · doi:10.1037/1076-898x.14.3.266 · Cited by 43 · see citing works
Communicative Visualizations as a Learning Problem
Adar, E., & Lee-Robbins, E. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030375 · Cited by 43 · see citing works
“Because the experience of the student may be far removed from what the teacher intended [47,64], assessment helps close the loop [73].”
BackgroundRemarks on Support Theory: Recent Advances and Future Directions
Brenner, L. A., Koehler, D. J., & Rottenstreich, Y. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511808098.029 · Cited by 43 · see citing works
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“This phenomenon has tellingly been coined as an " expert blind spot " (Nathan and Koedinger, 2000) or the " curse of expertise " (Hinds, 1999).”
BackgroundInfluentialKang, H., & Kim, J. (2022) · Applied Sciences · doi:10.3390/app12115614 · Cited by 42 · see citing works
- 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
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