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. (shrink)

Previous research described different cognitive processes on how individuals process distributional information. Based on these processes, the current research uncovered a novel phenomenon in distribution perception: the Endpoint Leverage Effect. Subjective endpoints influence distribution estimations not only locally around the endpoint but also influence estimations across the whole value range of the distribution. The influence is largest close to the respective endpoint and decreases in size toward the opposite end of the value range. Three experiments investigate this phenomenon: Experiment 1 (...) provides correlational evidence for the Endpoint Leverage Effect after presenting participants with a numerical distribution. Experiment 2 demonstrates the Endpoint Leverage Effect by manipulating the subjective endpoints of a numerical distribution directly. Experiment 3 generalizes the phenomenon by investigating a general population sample and estimations regarding a real‐world income distribution. In addition, quantitative model analysis examines the cognitive processes underlying the effect. Overall, the novel Endpoint Leverage Effect is found in all three experiments, inspiring further research in a wide area of contexts. (shrink)

Cognitive science has been traditionally organized around the individual as the basic unit of cognition. Despite developments in areas such as communication, human–machine interaction, group behavior, and community organization, the individual-centric approach heavily dominates both cognitive research and its application. A promising direction for cognitive science is the study of augmented intelligence, or the way social and technological systems interact with and extend individual cognition. The cognitive science of augmented intelligence holds promise in helping society tackle major real-world challenges that (...) can only be discovered and solved by teams made of individuals and machines with complementary skills who can productively collaborate with each other. (shrink)

A number of important decision domains, including decisions about hiring, global warming, and weather hazards, are characterized by a global–local incompatibility. These domains involve variables that cannot be observed by a single decision maker (DM) and require the integration of observations from locally available information cues. This paper presents a new bifocal lens model that describes how the structure of the environment can lead to a unique form of overconfidence when generalizing the reliability of the local environment to a global (...) scale. When the local environment does not reliably reflect the global environment, they are incompatible. While local perspectives vary across DMs, global–local incompatibility can be understood using the structure of classical test theory as the difference between (a) perceived estimates of the reliability derived from the local environment and (b) the true reliability of the local environment. I model global–local incompatibility as the difference between the true and estimated reliability when the assumptions of classical test theory are violated. Using a series of case studies and an empirical study, I demonstrate the widespread utility of this framework, and I conclude by discussing implications for cognitive–ecological theory, risk communication, and overconfidence. (shrink)