Ten simple rules for the computational modeling of behavioral data
By Robert C. Wilson, A. G. Collins. eLife, 2019
564 citations recorded by the source corpus.
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.
Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, General Decision Sciences
View Paper: https://www.semanticscholar.org/paper/91b9d3ab7532ea24ae70cd726355f25235b1fe8b
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Citations in the corpus
- How Computational Modeling Can Force Theory Building in Psychological Science (2020, background): “…men-tees that modeling is neither extremely complex nor requires extra skills beyond those we already expect that they master, for example, programming, experimental design, literature review, and statistical-analysis techniques (e.g., Epstein, 2008; Wills et al., 2017; Wilson & Collins, 2019).”