A reinforcement learning model with choice traces for a progressive ratio schedule.

The progressive ratio (PR) lever-press task serves as a benchmark for assessing goal-oriented motivation. However, a well-recognized limitation of the PR task is that only a single data point, known as the breakpoint, is obtained from an entire session as a barometer of motivation. Because the breakpoint is defined as the final ratio of responses achieved in a PR session, variations in choice behavior during the PR task cannot be captured. We addressed this limitation by constructing four reinforcement learning models: a simple Q-learning model, an asymmetric model with two learning rates, a perseverance model with choice traces, and a perseverance model without learning. These models incorporated three behavioral choices: reinforced and non-reinforced lever presses and void magazine nosepokes, because we noticed that male mice performed frequent magazine nosepokes during PR tasks. The best model was the perseverance model, which predicted a gradual reduction in amplitudes of reward prediction errors (RPEs) upon void magazine nosepokes. We confirmed the prediction experimentally with fiber photometry of extracellular dopamine (DA) dynamics in the ventral striatum of male mice using a fluorescent protein (genetically encoded GPCR activation-based DA sensor: GRAB