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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 .

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.