Experiment Reconstruction Reduces Fixation on Surface Details of Explanations
Pages 578 - 582
Abstract
Misunderstandings of science affect many lives. Novices commonly misunderstand explanations by overly relying on surface details instead of evaluating underlying logic. Prior work has found adding a patina of neuroscience leads readers towards positively assessing explanations. How might we help people better understand science explanations? A between-subjects experiment tested whether asking readers to reconstruct experiments leads them to focus more on underlying logic. Participants relied less on irrelevant surface details when reconstructing experiments. However, this did not impact their subsequent assessment of explanations. Our results suggest that reconstruction is a useful strategy for understanding explanations but is not readily transferred towards evaluating explanations.
References
[1]
Adrian Bangerter and Chip Heath. 2004. The Mozart Effect: Tracking the Evolution of a Scientific Legend. British Journal of Social Psychology 43, 4 (2004), 605--623.
[2]
Benjamin S. Bloom. 1956. Taxonomy of Educational Objectives. Vol. 1: Cognitive Domain. New York: McKay (1956), 20--24.
[3]
Michelene TH Chi. 2009. Active-Constructive-Interactive: A Conceptual Framework for Differentiating Learning Activities. Topics in cognitive science 1, 1 (2009), 73--105.
[4]
Michelene TH Chi, Robert Glaser, and Ernest Rees. 1981. Expertise in Problem Solving. Technical Report. PITTSBURGH UNIV PA LEARNING RESEARCH AND DEVELOPMENT CENTER.
[5]
James C. Kaufman. 2009. Beyond Big and Little : The Four C Model of Creativity.
[6]
Frances H. Rauscher, Gordon L. Shaw, and Catherine N. Ky. 1993. Music and Spatial Task Performance. Nature 365, 6447 (1993), 611.
[7]
Steven M. Smith and Steven E. Blankenship. 1991. Incubation and the Persistence of Fixation in Problem Solving. The American journal of psychology (1991), 61--87.
[8]
Deena Skolnick Weisberg, Frank C. Keil, Joshua Goodstein, Elizabeth Rawson, and Jeremy R. Gray. 2008. The Seductive Allure of Neuroscience Explanations. Journal of cognitive neuroscience 20, 3 (2008), 470--477.
Index Terms
- Experiment Reconstruction Reduces Fixation on Surface Details of Explanations
Recommendations
Counterfactual explanations for misclassified images: How human and machine explanations differ
AbstractCounterfactual explanations have emerged as a popular solution for the eXplainable AI (XAI) problem of elucidating the predictions of black-box deep-learning systems because people easily understand them, they apply across different problem ...
Highlights- A novel user-centric methodology where users create counterfactual explanations.
- Human-generated counterfactuals differ significantly from machine-generated ones.
- A novel interpretation of this difference using the notion of “...
Complexity results for explanations in the structural-model approach
We analyze the computational complexity of Halpern and Pearl's (causal) explanations in the structural-model approach, which are based on their notions of weak and actual cause. In particular, we give a precise picture of the complexity of deciding ...
Counterfactual explanations for misclassified images: how human and machine explanations differ (abstract reprint)
AAAI'24/IAAI'24/EAAI'24: Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial IntelligenceCounterfactual explanations have emerged as a popular solution for the eXplainable AI (XAI) problem of elucidating the predictions of black-box deep-learning systems because people easily understand them, they apply across different problem domains and ...
Comments
Information & Contributors
Information
Published In
June 2019
745 pages
ISBN:9781450359177
DOI:10.1145/3325480
- General Chairs:
- Steven Dow,
- Mary Lou Maher,
- Program Chairs:
- Andruid Kerne,
- Celine Latulipe
Copyright © 2019 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 June 2019
Check for updates
Author Tags
Qualifiers
- Poster
Funding Sources
Conference
C&C '19
Sponsor:
Acceptance Rates
C&C '19 Paper Acceptance Rate 30 of 101 submissions, 30%;
Overall Acceptance Rate 108 of 371 submissions, 29%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 267Total Downloads
- Downloads (Last 12 months)61
- Downloads (Last 6 weeks)11
Reflects downloads up to 09 Jan 2025
Other Metrics
Citations
Cited By
View allView Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in