The Temporal Dynamics of Hypothesis Generation and Maintenance

University Of Oklahoma Norman Campus, Norman OK

Investigators

Abstract

Hypothesis generation is the process by which people think of explanations (e.g., medical diagnoses) to account for patterns of data (e.g., symptoms) observed in the environment. Although hypothesis generation is an important component of many professional domains ranging from generating possible diagnoses in medicine, generating causes of going concern in auditing, and interpretations of satellite imagery in intelligence, it is also quite common in a number of non-professional contexts (e.g., explaining peoples' behavior in social contexts). Although scientists are beginning to understand how hypothesis generation, evaluation, and testing processes operate in discrete time frames, little is known about how these processes unfold when carried out across time. For instance, a physician does not receive the full pattern of data to be explained upfront. Rather, the full pattern of data unfolds over time (e.g., as test results are received). This research develops innovative empirical methodologies and a novel computational model to better understand the temporal dynamics of hypothesis generation. The resulting model will transform a current model of hypothesis generation by incorporating mechanisms from a state-of-the-art model of dynamic human memory processes. The resulting hybrid model is comprehensive and accounts for a wide array of phenomena underlying hypothesis generation and a multitude of processes reliant upon hypothesis generation such as information search and hypothesis evaluation. In service of the theoretical goals of the proposed research, the research team advances a novel empirical methodology that allows one to measure what people are thinking about, at any point in time, by assessing eye movements across carefully constructed visual search arrays. The main advantage of this technique is that it will be less invasive and more sensitive than existing procedures. This methodology is not tied to any one domain or type of task, so the methodology has the potential to support work in a host of domains across not only decision making, but cognitive science more generally. The project has implications for the design of decision support systems. The work reveals situational hazards resulting in biased or impoverished hypothesis generation and fosters new understandings of the underlying memory dynamics contributing to real-world hypothesis generation.