Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts

Abstract

Active reinforcement learning enables dynamic prediction and control, where one should not only maximize rewards but also minimize costs such as of inference, decisions, actions, and time. For an embodied agent such as a human, decisions are also shaped by physical aspects of actions. Beyond the effects of reward outcomes on learning processes, to what extent can modeling of behavior in a reinforcement-learning task be complicated by other sources of variance in sequential action choices? What of the effects of action bias (for actions per se) and action hysteresis determined by the history of actions chosen previously? The present study addressed these questions with incremental assembly of models for the sequential choice data from a task with hierarchical structure for additional complexity in learning. With systematic comparison and falsification of computational models, human choices were tested for signatures of parallel modules representing not only an enhanced form of generalized reinforcement learning but also action bias and hysteresis. We found evidence for substantial differences in bias and hysteresis across participants—even comparable in magnitude to the individual differences in learning. Individuals who did not learn well revealed the greatest biases, but those who did learn accurately were also significantly biased. The direction of hysteresis varied among individuals as repetition or, more commonly, alternation biases persisting from multiple previous actions. Considering that these actions were button presses with trivial motor demands, the idiosyncratic forces biasing sequences of action choices were robust enough to suggest ubiquity across individuals and across tasks requiring various actions. In light of how bias and hysteresis function as a heuristic for efficient control that adapts to uncertainty or low motivation by minimizing the cost of effort, these phenomena broaden the consilient theory of a mixture of experts to encompass a mixture of expert and nonexpert controllers of behavior.

Author summary

Reinforcement learning unifies neuroscience and AI with a universal computational framework for motivated behavior. Humans and robots alike are active and embodied agents who physically interact with the world and learn from feedback to guide future actions while weighing costs of time and energy. Initially, the modeling here attempted to identify learning algorithms for an interactive environment structured with patterns in counterfactual information that a human brain could learn to generalize. However, behavioral analysis revealed that a wider scope was necessary to identify individual differences in not only complex learning but also action bias and hysteresis. Sequential choices in the pursuit of rewards were clearly influenced by endogenous action preferences and persistent bias effects from action history causing repetition or alternation of previous actions. By modeling a modular brain as a mixture of expert and nonexpert systems for behavioral control, a distinct profile could be characterized for each individual attempting the experiment. Even for actions as simple as button pressing, effects specific to actions were as substantial as the effects from reward outcomes that decisions were supposed to follow from. Bias and hysteresis are concluded to be ubiquitous and intertwined with processes of active reinforcement learning for efficiency in behavior.