  In a recent study, we reported the results of a new decision making paradigm
in which the participants were asked to balance between their speed and
accuracy to maximize the total reward they achieve during the experiment. The
results of computational modeling provided strong evidence suggesting that the
participants used time-varying decision boundaries. Previous theoretical
studies of the optimal speed-accuracy trade-off suggested that the participants
may learn to use these time-varying boundaries to maximize their average reward
rate. The results in our experiment, however, showed that the participants used
such boundaries even at the beginning of the experiment and without any prior
experience in the task. In this paper, we hypothesize that these boundaries are
the results of using some heuristic rules to make decisions in the task. To
formulate decision making by these heuristic rules as a computational
framework, we use the fuzzy logic theory. Based on this theory, we propose a
new computational framework for decision making in evidence accumulation tasks.
In this framework, there is no explicit decision boundary. Instead, the
subject's desire to stop accumulating evidence and responding at each moment
within a trial and for a given value of the accumulated evidence, is determined
by a set of fuzzy "IF-TEHN rules". We then use the back-propagation method to
derive an algorithm for fitting the fuzzy model to each participant's data. We
then investigate how the difference in the participants' performance in the
experiment is reflected in the difference in the parameters of the fitted model
