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Joined 2026-09-21 Message
Incoming links: Model recovery
Model recovery 2026-11-05
Model recovery tests whether a model selection procedure can identify the known model that generated simulated data. Generate observations from each candidate statistical model, fit the candidates using the intended pipeline, and record which candidate is selected. It is a check on the comparison procedure under stated simulation conditions.[ref]
Ten simple rules for the computational modeling of behavioral data by Robert C. Wilson and Anne G. E. Collins. Can you arbitrate between different models?, model-recovery discussion following Eq. (10)
This differs from parameter recovery, which compares estimated parameters with the settings used to generate data within a model. Correctly identifying a model does not automatically imply accurate parameter estimation, and accurate fitting within one model does not establish that a procedure can distinguish it from its competitors.
For an invented example, suppose one hundred simulated datasets come from each of models A and B. The resulting counts areThis confusion matrix estimates selection behavior conditional on the generating model. Its empirical rate of choosing B when A generated is .
The reverse question has a different denominator. Among the datasets for which B was selected, were generated by B, giving . That calculation uses the example's balanced simulation design. It is not interchangeable with the recovery rate for B-generated datasets. Different generating-model frequencies can change the reverse proportion even if the conditional selection behavior stays fixed.[ref]
Ten simple rules for the computational modeling of behavioral data by Robert C. Wilson and Anne G. E. Collins. A note on interpreting the confusion matrix; Appendix 3, reverse inference
Recovery depends on parameter ranges, observation count, noise and the selection criterion. Models with nearly indistinguishable predictions can be hard to recover despite successful numerical optimization. Repeat simulations in the regimes relevant to the planned inference. Finally, recovery among a chosen candidate set does not prove that the real system belongs to that set; checking how well the selected model reproduces relevant observations remains a separate task.[ref]
Ten simple rules for the computational modeling of behavioral data by Robert C. Wilson and Anne G. E. Collins. Be careful with the choice of parameters; The elephant in the room with model comparison
Robert C. Wilson 2026-11-05
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.