Computational optimization of associative learning experiments
Fig 1
The simulation-based method of evaluating and optimizing experiment designs.
Note: light-blue elements denote required user inputs. (A) Dataset simulation process generates experiment realizations (e.g., cue-outcome sequences) and model responses (e.g., predicted physiological or behavioral responses), which together form a simulated dataset. (B) Dataset analysis applies the user-specified analysis procedure to the simulated dataset and produces analysis results (e.g., model evidence or parameter estimates). If the analysis procedure is Bayesian, an additional analysis prior needs to be specified. (C) The calculation of expected design utility requires simulating and analyzing a number of datasets. The user-defined utility function—which can depend directly on the design or on other simulation-specific quantities—provides the utility value for each simulated experiment. The expected design utility is obtained by averaging experiment-wise utilities. (D) Design optimization proceeds in iterations: the optimization algorithm proposes a design from the design space, the design is evaluated under the design prior, and the expected design utility is passed back to the optimizer. If the optimization satisfies the termination criterion (which is one of the user-defined optimization options), the optimizer returns the optimized design.