Substitution Sensitivity and the Bat-and-Ball Problem: A Direct Replication of De Neys et al. (2013)
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Keywords

Bat-and-ball problem
Attribute substitution
Dual process theory
Replication study

How to Cite

Machalani, A., Gallant, A., Orha, V., Ostbo, N., & Hehman, E. (2020). Substitution Sensitivity and the Bat-and-Ball Problem: A Direct Replication of De Neys et al. (2013). McGill Science Undergraduate Research Journal, 15(1), 46–51. https://doi.org/10.26443/msurj.v15i1.9

Abstract

Background: Cognitive misers are no happy fools. Earlier findings (1) came to this conclusion by assessing people’s sensitivity to attribute substitution, which they defined as the situation that occurs when we are confronted with a problem that demands greater cognitive effort, for which we rely on automatic and intuitive processes that substitute the complex situation for an easier one.

Methods: Through the exploration of the “bat-and-ball” problem, (2) De Neys, Rossi, and Houdé  (1) found that participants were indeed sensitive to the substitution bias. Specifically, participants who incorrectly answered the question that gave rise to the substitution bias were significantly less confident in their answer relative to their answer on a control problem that did not give rise to the substitution. Using the same methods, we conducted a direct replication study on a sample of 264 undergraduate psychology students.

Results and Conclusion: Our results suggest that we successfully replicated the original conclusions; participants who answered by substituting the difficult question for an easier one significantly (p<.0001) decreased their confidence ratings on the version of the problem that gave rise to the substitution bias, relative to the problem that did not.

Limitations: Though there may have been limitations, it seems that we are sensitive to attribute substitution.

https://doi.org/10.26443/msurj.v15i1.9
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Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright (c) 2020 Alexandra Machalani, Amanda Gallant, Victoria Orha, Nora Ostbo, Eric Hehman

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