= Anne G. E. Collins
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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>.<Ten simple rules for the computational modeling of behavioral data by Robert C. Wilson and Anne G. E. Collins>[Introduction and model-development rules]{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.<Ten simple rules for the computational modeling of behavioral data by Robert C. Wilson and Anne G. E. Collins>[Simulation rule; parameter-recovery section]{ref}
Institutional research profile: https://vcresearch.berkeley.edu/faculty/anne-collins .
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