Elucidating the differential impact of extreme-outcomes in perceptual and preferential choice

When making decisions in complex environments we must selectively sample and process information with respect to task demands. Previous studies have shown that this requirement can manifest in the influence that extreme outcomes (i.e. values at the edges of a distribution) have on judgment and choice. We elucidate this influence via a task in which participants are presented, briefly, with an array of numbers and have to make one of two judgments. In 'preferential' judgments where the participants' goal was to choose between a safe, known outcome, and an unknown outcome drawn from the array, extreme-outcomes had a greater influence on choice than mid-range outcomes, especially under shorter time-limits. In 'perceptual' judgments where the participants' goal was to estimate the arrays' average, the influence of the extremes was less pronounced. A novel cognitive process model captures these patterns via a two-step selective-sampling and integration mechanism. Together our results shed light on how task goals modulate sampling from complex environments, show how sampling determines choice, and highlight the conflicting conclusions that arise from applying statistical and cognitive models to data.