Computational optimization of associative learning experiments
Fig 5
Design evaluation in Scenario 3: Selection between RW and RWPH models.
Three designs—reference reversal learning design (REF), design optimized under a vague prior (VP-OPT), and design optimized under a point prior (PP-OPT)—are evaluated under the ground truth model being either the RW(V), RWPH(V), RWPH(α), or RWPH(V + α). (A) Model selection accuracy (mean and the Clopper-Pearson binomial 95% CI). Horizontal guideline indicates chance level. Darker bars summarize results across ground truth models. Inset shows the confusion matrix between the ground truth model (rows) and the selected model (columns). (B) Values of the design variables in the two stages of the experiment: cue probabilities P(CS) and joint cue-outcome probabilities P(CS, US). (C) Comparison of fitted RW(V) and RWPH(V + α) model responses obtained under different designs (rows) and these two models as assumed ground truth (columns). Inset labels give the average difference in BIC (±SEM) between the fit of the true model and the alternative model (more negative values indicate stronger evidence in favor of the true model). Note: the model responses are nearly identical when the RW(V) model is true, because this model is a special case of the alternative RWPH(V + α) model.