Abstract
Humans can adopt optimal discounting strategy under real-time constraints
Highlights
In the limited amount of time available before nighttime, winter, or retirement, we need to make a large number of choices to maximize our total reward gain
Critical to these choices are the shape and the steepness of the reward values, which monotonically decrease as a function of the delay: the rewards are said to be discounted as a function of the delays (Figure 1A)
Researchers in artificial intelligence favor exponential discounting in uncertain environments, e.g., [4,14,15], all behavioral studies that have directly compared the two types of discounting in animals or humans have concluded that hyperbolic discounting better fits delayed reward choice data than does exponential discounting, e.g., [6,7,8,16,17,18]
Summary
In the limited amount of time available before nighttime, winter, or retirement, we need to make a large number of choices to maximize our total reward gain. As given by Equation 4, is the reward rate, it maximizes the total gain in situations of constant delays at each trial (with no reward loss and with an exact estimate of the time of future reward delivery). Does hyperbolic discounting maximize the total gain in foraging-like situations, that is, in situations of repeated forced choices with varying delays to the rewards, constant ITI, and limited total time? The discount rate should be carefully adjusted to maximize total gain in task situations of repeated forced choices with varying delays to the rewards and limited total time [14,15]. Subjects were compensated by the total reward earned at the end of the experiment
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