= Robert C. Wilson
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<Robert C. Wilson> investigates computational accounts of <learning> and <decision making>. His 2019 methods paper with <Anne G. E. Collins> presents practical ways to test whether a behavioural model and its fitting procedure support the intended scientific inference.<Ten simple rules for the computational modeling of behavioral data by Robert C. Wilson and Anne G. E. Collins>[Introduction and rules for simulation, fitting and validation]{ref}
A central check is <parameter recovery>: generate synthetic observations using known settings, then fit the model and compare estimated settings with their known values. <Model recovery> asks a different question: when synthetic data come from one candidate model, does the comparison procedure select that model over its competitors?<Ten simple rules for the computational modeling of behavioral data by Robert C. Wilson and Anne G. E. Collins>[Parameter-recovery section, Box4 and model-recovery section]{ref}
For an invented example, a program given data generated with <learning rate> $\alpha=0.2$ might repeatedly estimate values near $0.8$. That failure could indicate a coding problem, insufficient task information or confounding between parameters. A numerical optimizer returning an answer is therefore not enough to establish a useful psychological measurement. The example illustrates the methodological contribution without assigning an observed human difference to a parameter by name alone.
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