| Abstrakt: |
In reversal learning tasks, the behavior of humans and animals is often assumed to be uniform within single experimental sessions to facilitate data analysis and model fitting. However, behavior of agents can display substantial variability in single experimental sessions, as they execute different blocks of trials with different transition dynamics. Here, we observed that in a deterministic reversal learning task, mice display noisy and sub-optimal choice transitions even at the expert stages of learning. We investigated two sources of the sub-optimality in the behavior. First, we found that mice exhibit a high lapse rate during task execution, as they reverted to unrewarded directions after choice transitions. Second, we unexpectedly found that a majority of mice did not execute a uniform strategy, but rather mixed between several behavioral modes with different transition dynamics. We quantified the use of such mixtures with a state-space model, block Hidden Markov Model (block HMM), to dissociate the mixtures of dynamic choice transitions in individual blocks of trials. Additionally, we found that blockHMM transition modes in rodent behavior can be accounted for by two different types of behavioral algorithms, model-free or inference-based learning, that might be used to solve the task. Combining these approaches, we found that mice used a mixture of both exploratory, model-free strategies and deterministic, inference-based behavior in the task, explaining their overall noisy choice sequences. Together, our combined computational approach highlights intrinsic sources of noise in rodent reversal learning behavior and provides a richer description of behavior than conventional techniques, while uncovering the hidden states that underlie the block-by-block transitions. Author summary: Humans and animals can use diverse decision-making strategies to maximize rewards in uncertain environments, but previous studies have not investigated the use of multiple strategies that involve distinct latent switching dynamics in reward-guided behavior. Here, using a reversal learning task, we showed that mice displayed a much more variable behavior than would be expected from a uniform strategy, suggesting that they mix between multiple behavioral modes in the task. We develop a computational method to dissociate these learning modes from behavioral data, addressing the challenges faced by current analytical methods when agents mix between different strategies. We found that the use of multiple strategies is a key feature of rodent behavior even in the expert stages of learning, and applied our tools to quantify the highly diverse strategies used by individual mice in the task. We further mapped these behavioral modes to two types of underlying algorithms, model-free Q-learning and inference-based behavior. These rich descriptions of underlying latent states form the basis of detecting abnormal patterns of behavior in reward-guided decision-making. [ABSTRACT FROM AUTHOR] |
