Toward a psychology of real-life decision-making

Understanding human decision-making processes in everyday life is a central, yet rarely addressed, challenge in psychology. Either real-life complexity is reduced by isolating specific aspects of decision-making in highly constrained experimental settings, yielding insights into specific cognitive mechanisms under idealized conditions, or decision-making is studied in real-life contexts, using high-level descriptions of behavior that do not afford fine-grained, process-level insights. Bridging this gap poses a challenge of both measurement and inference. Recent advances in high-resolution tracking technologies provide novel solutions to many measurement challenges but are rarely integrated with formal psychological theory. In this article, we review tracking technologies and statistical tools, proposing a cognitive-computational framework that uses high-resolution spatiotemporal data to investigate the mechanisms of real-life decision-making in a theory-driven manner.