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Adaptive design optimization (ADO) is a state-of-the-art technique forexperimental design (Cavagnaro, Myung, Pitt, & Kujala, 2010). ADO dynamicallyidentifies stimuli that, in expectation, yield the most information about ahypothetical construct of interest (e.g., parameters of a cognitive model). Tocalculate this expectation, ADO leverages the modeler's existing knowledge,specified in the form of a prior distribution. Informative priors align withthe distribution of the focal construct in the participant population. Thisalignment is assumed by ADO's internal assessment of expected information gain.If the prior is instead misinformative, i.e., does not align with theparticipant population, ADO's estimates of expected information gain could beinaccurate. In many cases, the true distribution that characterizes theparticipant population is unknown, and experimenters rely on heuristics intheir choice of prior and without an understanding of how this choice affectsADO's behavior.  Our work introduces a mathematical framework that facilitates investigationof the consequences of the choice of prior distribution on the efficiency ofexperiments designed using ADO. Through theoretical and empirical results, weshow that, in the context of prior misinformation, measures of expectedinformation gain are distinct from the correctness of the correspondinginference. Through a series of simulation experiments, we show that, in thecase of parameter estimation, ADO nevertheless outperforms other designmethods. Conversely, in the case of model selection, misinformative priors canlead inference to favor the wrong model, and rather than mitigating thispitfall, ADO exacerbates it.

Knowing what to know: Implications of the choice of prior distribution on the behavior of adaptive design optimization