Abstract
Choice impulsivity is characterized by the choice of immediate, smaller reward options over future, larger reward options, and is often thought to be associated with negative life outcomes. However, some environments make future rewards more uncertain, and in these environments impulsive choices can be beneficial. Here we examined the conditions under which impulsive vs. non-impulsive decision strategies would be advantageous. We used Markov Decision Processes (MDPs) to model three common decision-making tasks: Temporal Discounting, Information Sampling, and an Explore-Exploit task. We manipulated environmental variables to create circumstances where future outcomes were relatively uncertain. We then manipulated the discount factor of an MDP agent, which affects the value of immediate versus future rewards, to model impulsive and non-impulsive behavior. This allowed us to examine the performance of impulsive and non-impulsive agents in more or less predictable environments. In Temporal Discounting, we manipulated the transition probability to delayed rewards and found that the agent with the lower discount factor (i.e. the impulsive agent) collected more average reward than the agent with a higher discount factor (the non-impulsive agent) by selecting immediate reward options when the probability of receiving the future reward was low. In the Information Sampling task, we manipulated the amount of information obtained with each sample. When sampling led to small information gains, the impulsive MDP agent collected more average reward than the non-impulsive agent. Third, in the Explore-Exploit task, we manipulated the substitution rate for novel options. When the substitution rate was high, the impulsive agent again performed better than the non-impulsive agent, as it explored the novel options less and instead exploited options with known reward values. The results of these analyses show that impulsivity can be advantageous in environments that are unexpectedly uncertain.