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Adaptive design optimization (ADO) is a state-of-the-art technique for experimental design (Cavagnaro et al., 2010). ADO dynamically identifies stimuli that, in expectation, yield the most information about a hypothetical construct of interest (e.g., parameters of a cognitive model). To calculate this expectation, ADO leverages the modeler's existing knowledge, specified in the form of a prior distribution. Informative priors align with the distribution of the focal construct in the participant population. This alignment is assumed by ADO's internal assessment of expected information gain. If the prior is instead misinformative, i.e., does not align with the participant population, ADO's estimates of expected information gain could be inaccurate. In many cases, the true distribution that characterizes the participant population is unknown, and experimenters rely on heuristics in their choice of prior and without an understanding of how this choice affects ADO's behavior. Our work introduces a mathematical framework that facilitates investigation of the consequences of the choice of prior distribution on the efficiency of experiments designed using ADO. Through theoretical and empirical results, we show that, in the context of prior misinformation, measures of expected information gain are distinct from the correctness of the corresponding inference. Through a series of simulation experiments, we show that, in the case of parameter estimation, ADO nevertheless outperforms other design methods. Conversely, in the case of model selection, misinformative priors can lead inference to favor the wrong model, and rather than mitigating this pitfall, ADO exacerbates it. [ABSTRACT FROM AUTHOR]

Approaching issues through the lens of nonnegotiable values increases the perceived intractability of debate (Baron & Spranca in Organizational Behavior and Human Decision Processes, 70, 1–16, 1997), while focusing on the concrete consequences of policies instead results in the moderation of extreme opinions (Fernbach, Rogers, Fox, & Sloman in Psychological Science, 24, 939–946, 2013) and a greater likelihood of conflict resolution (Baron & Leshner in Journal of Experimental Psychology: Applied, 6, 183–194, 2000). Using comments on the popular social media platform Reddit from January 2006 until September 2017, we showed how changes in the framing of same-sex marriage in public discourse relate to changes in public opinion. We used a topic model to show that the contributions of certain protected-values-based topics to the debate (religious arguments and freedom of opinion) increased prior to the emergence of a public consensus in support of same-sex marriage (Gallup, 2017), and declined afterward. In contrast, the discussion of certain consequentialist topics (the impact of politicians' stance and same-sex marriage as a matter of policy) showed the opposite pattern. Our results reinforce the meaningfulness of protected values and consequentialism as relevant dimensions for describing public discourse and highlight the usefulness of unsupervised machine-learning methods in tackling questions about social attitude change. [ABSTRACT FROM AUTHOR]