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Explore your experimental designs and theories before you exploit them!

Published online by Cambridge University Press:  05 February 2024

Marina Dubova*
Affiliation:
Cognitive Science Program, Indiana University Bloomington, IN, USA mdubova@iu.edu https://www.mdubova.com/
Sabina J. Sloman
Affiliation:
Department of Computer Science, University of Manchester, Manchester, UK sabina.sloman@manchester.ac.uk
Ben Andrew
Affiliation:
Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA benjamin_andrew@brown.edu matthew_nassar@brown.edu musslick@brown.edu www.smusslick.com Carney Institute for Brain Science, Brown University, Providence, RI, USA
Matthew R. Nassar
Affiliation:
Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA benjamin_andrew@brown.edu matthew_nassar@brown.edu musslick@brown.edu www.smusslick.com Carney Institute for Brain Science, Brown University, Providence, RI, USA
Sebastian Musslick
Affiliation:
Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA benjamin_andrew@brown.edu matthew_nassar@brown.edu musslick@brown.edu www.smusslick.com Carney Institute for Brain Science, Brown University, Providence, RI, USA Institute of Cognitive Science, Osnabrück University, Osnabrück, Germany
*
*Corresponding author.

Abstract

In many areas of the social and behavioral sciences, the nature of the experiments and theories that best capture the underlying constructs are themselves areas of active inquiry. Integrative experiment design risks being prematurely exploitative, hindering exploration of experimental paradigms and of diverse theoretical accounts for target phenomena.

Type
Open Peer Commentary
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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References

Awad, E., Dsouza, S., Bonnefon, J. F., Shariff, A., & Rahwan, I. (2020). Crowdsourcing moral machines. Communications of the ACM, 63(3), 4855.CrossRefGoogle Scholar
Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., … Rahwan, I. (2018). The moral machine experiment. Nature, 563(7729), 5964.CrossRefGoogle ScholarPubMed
Baribault, B., Donkin, C., Little, D. R., Trueblood, J. S., Oravecz, Z., Van Ravenzwaaij, D., … Vandekerckhove, J. (2018). Metastudies for robust tests of theory. Proceedings of the National Academy of Sciences of the United States of America, 115(11), 26072612.CrossRefGoogle ScholarPubMed
Bustamante, L. A., Oshinowo, T., Lee, J. R., Tong, E., Burton, A. R., Shenhav, A. S., … Daw, N. D. (2022). Effort foraging task reveals positive correlation between individual differences in the cost of cognitive and physical effort in humans and relationship to self-reported motivation and affect. bioRxiv, 2022-11.Google Scholar
Chang, H. (2012). Is water H2O?: Evidence, realism and pluralism (Vol. 293). Springer Science & Business Media.CrossRefGoogle Scholar
Dubova, M., & Goldstone, R. L. (2023). Carving joints into nature: reengineering scientific concepts in light of concept-laden evidence. Trends in Cognitive Sciences, 27(7), 656670.CrossRefGoogle ScholarPubMed
Dubova, M., Moskvichev, A., & Zollman, K. (2022). Against theory-motivated experimentation in science. MetaArXiv. June, 24.Google Scholar
Koch, I., Poljac, E., Müller, H., & Kiesel, A. (2018). Cognitive structure, flexibility, and plasticity in human multitasking – An integrative review of dual-task and task-switching research. Psychological Bulletin, 144(6), 557.CrossRefGoogle ScholarPubMed
Kool, W., McGuire, J. T., Rosen, Z. B., & Botvinick, M. M. (2010). Decision making and the avoidance of cognitive demand. Journal of Experimental Psychology: General, 139(4), 665.CrossRefGoogle ScholarPubMed
Krakauer, J. W., Ghazanfar, A. A., Gomez-Marin, A., MacIver, M. A., & Poeppel, D. (2017). Neuroscience needs behavior: Correcting a reductionist bias. Neuron, 93(3), 480490.CrossRefGoogle ScholarPubMed
Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information. W.H. Freeman.Google Scholar
Massimi, M. (2022). Perspectival realism. Oxford University Press.CrossRefGoogle Scholar
Medin, D. L., & Bang, M. (2014). Who's asking?: Native science, western science, and science education. MIT Press.CrossRefGoogle Scholar
Olshausen, B. A., & Field, D. J. (2005). How close are we to understanding V1?. Neural Computation, 17(8), 16651699.CrossRefGoogle ScholarPubMed
Peterson, J. C., Bourgin, D. D., Agrawal, M., Reichman, D., & Griffiths, T. L. (2021). Using large-scale experiments and machine learning to discover theories of human decision-making. Science (New York, N.Y.), 372(6547), 12091214.CrossRefGoogle ScholarPubMed
Schrimpf, M., Kubilius, J., Lee, M. J., Murty, N. A. R., Ajemian, R., & DiCarlo, J. J. (2020). Integrative benchmarking to advance neurally mechanistic models of human intelligence. Neuron, 108(3), 413423.CrossRefGoogle ScholarPubMed
Shenhav, A., Musslick, S., Lieder, F., Kool, W., Griffiths, T. L., Cohen, J. D., & Botvinick, M. M. (2017). Toward a rational and mechanistic account of mental effort. Annual Review of Neuroscience, 40, 99124.CrossRefGoogle Scholar
Sloman, S. J., Oppenheimer, D. M., Broomell, S. B., & Shalizi, C. R. (2022). Characterizing the robustness of Bayesian adaptive experimental designs to active learning bias. arXiv preprint arXiv:2205.13698.Google Scholar
Westbrook, A., & Braver, T. S. (2015). Cognitive effort: A neuroeconomic approach. Cognitive, Affective, & Behavioral Neuroscience, 15, 395415.CrossRefGoogle ScholarPubMed
Zhaoping, L. (2014). Understanding vision: Theory, models, and data. Oxford University Press.CrossRefGoogle Scholar