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.[ref]
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
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?[ref]
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
For an invented example, a program given data generated with learning rate might repeatedly estimate values near . 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.
Articles by others on the same topic
There are currently no matching articles.