Failure to Learn From Failure Is Mitigated by Loss-Framing and Corrective Feedback: A Replication and Test of the Boundary Conditions of the Tune-Out Effect

Research output: Journal contributionsJournal articlesResearchpeer-review

Standard

Failure to Learn From Failure Is Mitigated by Loss-Framing and Corrective Feedback: A Replication and Test of the Boundary Conditions of the Tune-Out Effect. / Keith, Nina; Horvath, Dorothee; Klamar, Alexander et al.
In: Journal of Experimental Psychology: General, Vol. 151, No. 8, 01.08.2022, p. 19-25.

Research output: Journal contributionsJournal articlesResearchpeer-review

Harvard

APA

Vancouver

Bibtex

@article{7ac84dab028b48eb89b0942a9ef4a86d,
title = "Failure to Learn From Failure Is Mitigated by Loss-Framing and Corrective Feedback: A Replication and Test of the Boundary Conditions of the Tune-Out Effect",
abstract = "Do people learn from failure or do they mentally “tune-out” upon failure feedback, which in turn undermines learning? Recent research (Eskreis-Winkler & Fishbach, 2019) has suggested the latter, whereas research in educational and work settings indicates that failure can lead to more learning than can success and error-free performance. We conducted two preregistered experiments to replicate the tune-out effect and to test two potential boundary conditions (N = 520). The tune-out effect fully replicated in those experimental conditions that represented close replications of the original study, underscoring the reliability of the original effect. However, the effect disappeared when the same monetary incentives for participation were expressed in terms of a loss (i.e., losing money for each wrong answer) rather than a gain (i.e., earning money for each correct answer; Experiment 1). The effect also disappeared when additional corrective feedback was given (Experiment 2). It seems that switching from gain to loss framing or giving corrective feedback (vs. no corrective feedback) are substantial and meaningful variations of the original paradigm that constitute boundary conditions of the tune-out effect. These results help explain the conflicting findings on learning from failure and suggest that in many applied settings, tuning out upon failure might not be an option",
keywords = "learning from errors, learning from failure, loss aversion, corrective feedback, Management studies, Business psychology",
author = "Nina Keith and Dorothee Horvath and Alexander Klamar and Michael Frese",
note = "Publisher Copyright: {\textcopyright} 2022. American Psychological Association",
year = "2022",
month = aug,
day = "1",
doi = "10.1037/xge0001170",
language = "English",
volume = "151",
pages = "19--25",
journal = "Journal of Experimental Psychology: General",
issn = "0096-3445",
publisher = "American Psychological Association Inc.",
number = "8",

}

RIS

TY - JOUR

T1 - Failure to Learn From Failure Is Mitigated by Loss-Framing and Corrective Feedback

T2 - A Replication and Test of the Boundary Conditions of the Tune-Out Effect

AU - Keith, Nina

AU - Horvath, Dorothee

AU - Klamar, Alexander

AU - Frese, Michael

N1 - Publisher Copyright: © 2022. American Psychological Association

PY - 2022/8/1

Y1 - 2022/8/1

N2 - Do people learn from failure or do they mentally “tune-out” upon failure feedback, which in turn undermines learning? Recent research (Eskreis-Winkler & Fishbach, 2019) has suggested the latter, whereas research in educational and work settings indicates that failure can lead to more learning than can success and error-free performance. We conducted two preregistered experiments to replicate the tune-out effect and to test two potential boundary conditions (N = 520). The tune-out effect fully replicated in those experimental conditions that represented close replications of the original study, underscoring the reliability of the original effect. However, the effect disappeared when the same monetary incentives for participation were expressed in terms of a loss (i.e., losing money for each wrong answer) rather than a gain (i.e., earning money for each correct answer; Experiment 1). The effect also disappeared when additional corrective feedback was given (Experiment 2). It seems that switching from gain to loss framing or giving corrective feedback (vs. no corrective feedback) are substantial and meaningful variations of the original paradigm that constitute boundary conditions of the tune-out effect. These results help explain the conflicting findings on learning from failure and suggest that in many applied settings, tuning out upon failure might not be an option

AB - Do people learn from failure or do they mentally “tune-out” upon failure feedback, which in turn undermines learning? Recent research (Eskreis-Winkler & Fishbach, 2019) has suggested the latter, whereas research in educational and work settings indicates that failure can lead to more learning than can success and error-free performance. We conducted two preregistered experiments to replicate the tune-out effect and to test two potential boundary conditions (N = 520). The tune-out effect fully replicated in those experimental conditions that represented close replications of the original study, underscoring the reliability of the original effect. However, the effect disappeared when the same monetary incentives for participation were expressed in terms of a loss (i.e., losing money for each wrong answer) rather than a gain (i.e., earning money for each correct answer; Experiment 1). The effect also disappeared when additional corrective feedback was given (Experiment 2). It seems that switching from gain to loss framing or giving corrective feedback (vs. no corrective feedback) are substantial and meaningful variations of the original paradigm that constitute boundary conditions of the tune-out effect. These results help explain the conflicting findings on learning from failure and suggest that in many applied settings, tuning out upon failure might not be an option

KW - learning from errors

KW - learning from failure

KW - loss aversion

KW - corrective feedback

KW - Management studies

KW - Business psychology

UR - http://www.scopus.com/inward/record.url?scp=85136631331&partnerID=8YFLogxK

U2 - 10.1037/xge0001170

DO - 10.1037/xge0001170

M3 - Journal articles

C2 - 35951406

VL - 151

SP - 19

EP - 25

JO - Journal of Experimental Psychology: General

JF - Journal of Experimental Psychology: General

SN - 0096-3445

IS - 8

ER -

DOI

Recently viewed

Publications

  1. An Adaptive and Optimized Switching Observer for Sensorless Control of an Electromagnetic Valve Actuator in Camless Internal Combustion Engines
  2. Image compression based on periodic principal components
  3. Using CNNs to Detect Graphical Representations of Structural Equation Models in IS Papers
  4. Exploring the limits of graph invariant- and spectrum-based discrimination of (sub)structures.
  5. Data based analysis of order processing strategies to support the positioning between conflicting economic and logistic objectives
  6. Linear free vibrations with uncertain initial conditions
  7. Appendix A: Design, implementation, and analysis of the iGOES project
  8. Digging into the roots
  9. Dividing Apples and Pears: Towards a Taxonomy for Agile Transformation
  10. Optimization of 3D laser scanning speed by use of combined variable step
  11. Gain Adaptation in Sliding Mode Control Using Model Predictive Control and Disturbance Compensation with Application to Actuators
  12. Overcoming Multi-legacy Application Challenges through Building Dynamic Capabilities for Low-Code Adoption
  13. Modelling biodegradability based on OECD 301D data for the design of mineralising ionic liquids
  14. Effectiveness of a Web-Based Cognitive Behavioural Intervention for Subthreshold Depression
  15. Switching between reading tasks leads to phase-transitions in reading times in L1 and L2 readers
  16. Challenge-oriented policy making and innovation systems theory: reconsidering systemic instruments
  17. Cross-case knowledge transfer in transformative research: enabling learning in and across sustainability-oriented labs through case reporting
  18. Towards Advanced Learning in Dispatching Rule-Based Scheuling