Search

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]

How do people use information from others to solve complex problems? Prior work has addressed this question by placing people in social learning situations where the problems they were asked to solve required varying degrees of exploration. This past work uncovered important interactions between groups' connectivity and the problem's complexity: the advantage of less connected networks over more connected networks increased as exploration was increasingly required for optimally solving the problem at hand. We propose the Social Interpolation Model (SIM), an agent‐based model to explore the cognitive mechanisms that can underlie exploratory behavior in groups. Through results from simulation experiments, we conclude that "exploration" may not be a single cognitive property, but rather the emergent result of three distinct behavioral and cognitive mechanisms, namely, (a) breadth of generalization, (b) quality of prior expectation, and (c) relative valuation of self‐obtained information. We formalize these mechanisms in the SIM, and explore their effects on group dynamics and success at solving different kinds of problems. Our main finding is that broad generalization and high quality of prior expectation facilitate successful search in environments where exploration is important, and hinder successful search in environments where exploitation alone is sufficient. [ABSTRACT FROM AUTHOR]

Previous work has demonstrated that certain speech patterns vary systematically between sociodemographic groups, so that in some cases the way a person speaks is a valid cue to group membership. Our work addresses whether or not participants use these linguistic cues when assessing a speaker's likely political identity. We use a database of speeches by U.S. Congressional representatives to isolate words that are statistically diagnostic of a speaker's party identity. In a series of four studies, we demonstrate that participants' judgments track variation in word usage between the two parties more often than chance, and that this effect persists even when potentially interfering cues such as the meaning of the word are controlled for. Our results are consistent with a body of literature suggesting that humans' language-related judgments reflect the statistical distributions of our environment. [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]