Computational optimization of associative learning experiments

With computational biology striving to provide more accurate theoretical accounts of biological systems, use of increasingly complex computational models seems inevitable. However, this trend engenders a challenge of optimal experimental design: due to the flexibility of complex models, it is difficult to intuitively design experiments that will efficiently expose differences between candidate models or allow accurate estimation of their parameters. This challenge is well exemplified in associative learning research. Associative learning theory has a rich tradition of computational modeling, resulting in a growing space of increasingly complex models, which in turn renders manual design of informative experiments difficult. Here we propose a novel method for computational optimization of associative learning experiments. We first formalize associative learning experiments using a low number of tunable design variables, to make optimization tractable. Next, we combine simulation-based Bayesian experimental design with Bayesian optimization to arrive at a flexible method of tuning design variables. Finally, we validate the proposed method through extensive simulations covering both the objectives of accurate parameter estimation and model selection. The validation results show that computationally optimized experimental designs have the potential to substantially improve upon manual designs drawn from the literature, even when prior information guiding the optimization is scarce. Computational optimization of experiments may help address recent concerns over reproducibility by increasing the expected utility of studies, and it may even incentivize practices such as study pre-registration, since optimization requires a pre-specified analysis plan. Moreover, design optimization has the potential not only to improve basic research in domains such as associative learning, but also to play an important role in translational research. For example, design of behavioral and physiological diagnostic tests in the nascent field of computational psychiatry could benefit from an optimization-based approach, similar to the one presented here.Author summary: To capture complex biological systems, computational biology harnesses accordingly complex models. The flexibility of such models allows them to better explain real-world data; however, this flexibility also creates a challenge in designing informative experiments. Because flexible models can, by definition, fit a variety of experimental outcomes, it is difficult to intuitively design experiments that will expose differences between such models, or allow their parameters to be estimated with accuracy. This challenge of experimental design is apparent in research on associative learning, where the tradition of modeling has produced a growing space of increasingly complex theories. Here, we propose to use computational optimization methods to design associative learning experiments. We first formalize associative learning experiments, making their optimization possible, and then we describe a Bayesian, simulation-based method of finding optimized experiments. In several simulated scenarios, we demonstrate that optimized experimental designs can substantially improve upon the utility of often-used canonical designs. Moreover, a similar approach could also be used in translational research; e.g., in the nascent field of computational psychiatry, designs of behavioral and physiological diagnostic tests could be computationally optimized.