Computational optimization of associative learning experiments
Fig 4
Design evaluation in Scenario 2: Selection between RW and KRW models.
Three designs—reference backward blocking 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 or the KRW model. (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. (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 model responses obtained under different designs (rows) and different ground truth models (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).