The DOI matches its Crossref record; 97 of the 97 references it cites were found in a registry or an index.
Ten simple rules for the computational modeling of behavioral data
Open accessAbstract
Computational modeling of behavior has revolutionized psychology and neuroscience. By fitting models to experimental data we can probe the algorithms underlying behavior, find neural correlates of computational variables and better understand the effects of drugs, illness and interventions. But with great power comes great responsibility. Here, we offer ten simple rules to ensure that computational modeling is used with care and yields meaningful insights. In particular, we present a beginner-friendly, pragmatic and details-oriented introduction on how to relate models to data. What, exactly, can a model tell us about the mind? To answer this, we apply our rules to the simplest modeling techniques most accessible to beginning modelers and illustrate them with examples and code available online. However, most rules apply to more advanced techniques. Our hope is that by following our guidelines, researchers will avoid many pitfalls and unleash the power of computational modeling on their own data.
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TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry
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Metadata
- Authors
- Wilson, R. C., & Collins, A. G.
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2019-11-26
- Volume
- 8
- ISSN
- 2050-084X (ISSN-L)
- OpenAlex
- W2989963295
- Cited by
- 710
- References
- 97
- FWCI
- 35.02
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- creativecommons.org/licenses/by/4.0/ (vor)
- creativecommons.org/licenses/by/4.0/ (am)
- creativecommons.org/licenses/by/4.0/ (tdm)
- Funders
- National Institute on Aging (R56 AG061888)
- National Science Foundation (1640885)
- National Institute of Mental Health (R01 MH118279)
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
