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Anne G. E. Collins studies learning, decision making and executive function using behavioural experiments and computational models. With Robert C. Wilson, she coauthored a 2019 methods paper explaining how to build, simulate, fit and test models of behavior.^[ref]

An illustrative reinforcement learning rule updates a prediction $Q$ after outcome $r$ by $Q_{\mathrm{new}}=Q+\alpha (r-Q)$, where $\alpha$ is a learning rate. Starting at $Q=0.5$ with $r=1$, $\alpha =0.2$ gives $Q_{\mathrm{new}}=0.6$, whereas $\alpha =0.8$ gives $0.9$. This arithmetic shows what a model parameter does; it does not establish that an experimental participant uses that exact rule.

The joint paper emphasizes testing models through computer simulation and parameter recovery, so a fitted parameter is interpreted in light of what the task can actually identify.^[ref]

Institutional research profile: vcresearch.berkeley.edu/faculty/anne-collins .

A computational model of cognition is an explicit set of mathematical or algorithmic rules connecting inputs and previous experience to predictions about behaviour. Its variables can represent internal quantities, while its predictions concern observable choices, reaction times or other measurements. This makes a proposed cognitive mechanism precise enough to simulate and test.^[ref]

A simple reinforcement learning example updates a predicted value $Q_t$ after a reward $r_t$:The learning rate $\alpha$ controls the contribution of the reward prediction error $r_t-Q_t$. With $Q_0=0$, constant reward $r_t=1$ and $\alpha =1/2$, successive values are $1/2$, $3/4$ and $7/8$. Equivalently, $Q_t=1-2^{-t}$. These are model predictions for constructed inputs, not measured human responses.^[ref]

Predicting choices additionally requires a rule linking values to actions. Parameter estimation can fit such a model, but a good fit alone does not establish that its internal variables are uniquely recoverable. Parameter recovery tests this by simulating data from known parameters and checking whether the fitting procedure retrieves them.^[ref] This distinguishes a useful formal account from a merely suggestive verbal description.

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]

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 $20/100=0.20$.

The reverse question has a different denominator. Among the $85$ datasets for which B was selected, $65$ were generated by B, giving $65/85\simeq 0.765$. That calculation uses the example's balanced simulation design. It is not interchangeable with the $0.65$ recovery rate for B-generated datasets. Different generating-model frequencies can change the reverse proportion even if the conditional selection behavior stays fixed.^[ref]

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]

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]

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]

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.