How Do Real Animals Account for the Passage of Time During Associative Learning?

Animals routinely learn to associate environmental stimuli and self-generated actions with their outcomes such as rewards. One of the most popular theoretical models of such learning is the reinforcement learning (RL) framework. The simplest form of RL, model-free RL, is widely applied to explain animal behavior in numerous neuroscientific studies. More complex RL versions assume that animals build and store an explicit model of the world in memory. To apply these approaches to explain animal behavior, typical neuroscientific RL models make implicit assumptions about how real animals represent the passage of time. In this perspective, I explicitly list these assumptions and show that they have several problematic implications. I hope that the explicit discussion of these problems encourages the field to seriously examine the assumptions underlying timing and reinforcement learning.