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The goal of this article is to investigate how human participants allocatetheir limited time to decisions with different properties. We report theresults of two behavioral experiments. In each trial of the experiments, theparticipant must accumulate noisy information to make a decision. Theparticipants received positive and negative rewards for their correct andincorrect decisions, respectively. The stimulus was designed such thatdecisions based on more accumulated information were more accurate but tooklonger. Therefore, the total outcome that a participant could achieve duringthe limited experiments' time depended on her "decision threshold", the amountof information she needed to make a decision. In the first experiment, twotypes of trials were intermixed randomly: hard and easy. Crucially, the hardtrials were associated with smaller positive and negative rewards than the easytrials. A cue presented at the beginning of each trial would indicate the typeof the upcoming trial. The optimal strategy was to adopt a small decisionthreshold for hard trials. The results showed that several of the participantsdid not learn this simple strategy. We then investigated how the participantsadjusted their decision threshold based on the feedback they received in eachtrial. To this end, we developed and compared 10 computational models foradjusting the decision threshold. The models differ in their assumptions on theshape of the decision thresholds and the way the feedback is used to adjust thedecision thresholds. The results of Bayesian model comparison showed that amodel with time-varying thresholds whose parameters are updated by areinforcement learning algorithm is the most likely model.

Learning to Allocate Limited Time to Decisions with Different Expected Outcomes