Decision thresholds : cognitive limitations in sequential probabilistic decision making

Psychologists have long appreciated that many real-world decisions require a balance of expediency and accuracy in gathering evidence. Often the best decisions are made when there is some lingering uncertainty. But how much? Depending on the situation, choosing the right amount of evidence can often be a fine line between making a rash decision and being indecisive. Psychologists have been reluctant to pursue studying peoples' abilities to judge the correct "threshold" for probabilistic decisions. There are two reasons for this: First, the question of a decision threshold, or "when should I stop gathering evidence?" is confounded by the larger issue of how subjects choose and integrate that evidence. Subjects may have a decision threshold that is consistently sub-optimal with respect to a model that does not consider cognitive constraints. However, subjects may actually be choosing the right amount of information given their own cognitive limitations. Second, it has been shown that people often use specific heuristics in making probabilistic decisions. In this case, defining a decision threshold would be largely dependent on the heuristic and task, thus preventing a study of decision thresholds that is widely applicable. The research presented here addresses both of these concerns. I defined a task where the ideal decision threshold is clearly defined, requiring some evidence, but not an exhaustive search. Furthermore, this threshold can be precisely manipulated by changes in the reward structure. Although it is possible to use a "sufficing" or sub-optimal heuristic, subjects are given a significant financial incentive to fully integrate as much evidence as possible. Lastly, and most importantly, a general model of people's cognitive limitations is applied to the traditional normative model. This enhancement allows a more refined study of humans' ability to place their decision threshold according to environmental conditions.