Optimizing associative learning experiments
Understanding associative learning - the ability to acquire knowledge about contingencies between stimuli, responses, and outcomes - is crucial in explaining how animals adapt to their environments. Moreover, the theory of associative learning also provides a rationale for clinical treatments, such as exposure therapy for phobias. The study of associative learning has been significantly advanced through reliance on formal psychological modeling. However, the rich history of modeling, and the resulting abundance of models, lead to challenges in designing informative experiments. With a growing space of increasingly flexible candidate models, it is difficult to manually design experiments which efficiently discriminate between them. Here we propose to address this challenge through formal optimization of experimental designs. We first consider the structure of classical conditioning experimental designs and propose low-dimensional formalizations amenable to optimization. Next, we combine simulation-based evaluation of design utility with Bayesian optimization to efficiently search the experiment space for utility-maximizing designs. Lastly, we describe several simulated scenarios which show that optimized designs can substantially outperform canonical manual designs, whether the goal is model comparison or parameter estimation. Based on these results, we sketch out possible future avenues for optimal experimental design in associative learning, and cognitive science more broadly.