  The goal of this article is to investigate how human participants allocate
their limited time to decisions with different properties. We report the
results of two behavioral experiments. In each trial of the experiments, the
participant must accumulate noisy information to make a decision. The
participants received positive and negative rewards for their correct and
incorrect decisions, respectively. The stimulus was designed such that
decisions based on more accumulated information were more accurate but took
longer. Therefore, the total outcome that a participant could achieve during
the limited experiments' time depended on her "decision threshold", the amount
of information she needed to make a decision. In the first experiment, two
types of trials were intermixed randomly: hard and easy. Crucially, the hard
trials were associated with smaller positive and negative rewards than the easy
trials. A cue presented at the beginning of each trial would indicate the type
of the upcoming trial. The optimal strategy was to adopt a small decision
threshold for hard trials. The results showed that several of the participants
did not learn this simple strategy. We then investigated how the participants
adjusted their decision threshold based on the feedback they received in each
trial. To this end, we developed and compared 10 computational models for
adjusting the decision threshold. The models differ in their assumptions on the
shape of the decision thresholds and the way the feedback is used to adjust the
decision thresholds. The results of Bayesian model comparison showed that a
model with time-varying thresholds whose parameters are updated by a
reinforcement learning algorithm is the most likely model.
