Alexander S. Rich

psiTurk: An open-source framework for conducting replicable behavioral experiments online

Online data collection has begun to revolutionize the behavioral sciences. However, conducting carefully controlled behavioral experiments online introduces a number of new of technical and scientific challenges. The project described in this paper, psiTurk, is an open-source platform which helps researchers develop experiment designs which can be conducted over the Internet. The tool primarily interfaces with Amazon’s Mechanical Turk, a popular crowd-sourcing labor market. This paper describes the basic architecture of the system and introduces new users to the overall goals. psiTurk aims to reduce the technical hurdles for researchers developing online experiments while improving the transparency and collaborative nature of the behavioral sciences.

Exploratory Choice Reflects the Future Value of Information.

The tension between exploration and exploitation is a primary challenge in decision making under uncertainty. Optimal models of choice prescribe that individuals resolve this tension by evaluating how information gained from their choices will improve future choices. However, research in behavioral economics and psychology has yielded conflicting evidence about whether people consider the future during exploratory choice, particularly in complex, uncertain environments. Adding to the empirical evidence on this issue, we examine exploratory decision making in a novel approach-avoid paradigm. In the first set of experiments we find that people parametrically increase their exploration when the expected number of future encounters with a prospect is larger. In the second we demonstrate that when the number of future encounters is unknown, as is often the case in everyday life, people are sensitive to the relative frequency of future encounters with a prospect. Our experiments show that people adaptively utilize information about the future when deciding to explore, a tendency that may shape decisions across several real-world domains.

The limits of learning: Exploration, generalization, and the development of learning traps.

Learning usually improves the accuracy of beliefs through the accumulation of experience. But are there limits to learning that prevent us from accurately understanding our world? In this article we investigate the concept of a “learning trap”—the formation of a stable false belief even with extensive experience. Our review highlights how these traps develop through the interaction of learning and decision making in unknown environments. We further document a particularly pernicious learning trap driven by selective attention, a mechanism often assumed to facilitate learning in complex environments. Using computer simulation, we demonstrate the key attributes of the agent and environment that lead to this new type of learning trap. Then, in a series of experiments we present evidence that people robustly fall into this trap, even in the presence of various interventions predicted to meliorate it. These results highlight a fundamental limit to learning and adaptive behavior that impacts individuals, organizations, animals, and machines.