Unveiling Parallel Pathways: Bridging the Gap  between Structure and Reinforcement Learning: Utilizing the structure of the tasks and employing reinforcement learning strategies.

This study aimed to investigate the link between reinforcement learning and structure learning. Reinforcement learning is a framework where humans learn to make decisions by interacting with their environment, receiving rewards or punishments based on their actions, with the goal of maximizing cumulative reward over time. While structure learning is the cognitive process by which individuals acquire and internalize the underlying organizational principles or structures of information. It enables individuals to perceive patterns, rules, and relationships, allowing for effective organization and comprehension of knowledge. In the context of the environment, structure learning involves the representation and understanding of stimulus or action-outcome associations within one's surroundings. By recognizing and learning the environmental structure, individuals can better navigate, anticipate, and respond to stimuli, optimizing their interactions and adapting their behaviours accordingly....Show moreThis study aimed to investigate the link between reinforcement learning and structure learning. Reinforcement learning is a framework where humans learn to make decisions by interacting with their environment, receiving rewards or punishments based on their actions, with the goal of maximizing cumulative reward over time. While structure learning is the cognitive process by which individuals acquire and internalize the underlying organizational principles or structures of information. It enables individuals to perceive patterns, rules, and relationships, allowing for effective organization and comprehension of knowledge. In the context of the environment, structure learning involves the representation and understanding of stimulus or action-outcome associations within one's surroundings. By recognizing and learning the environmental structure, individuals can better navigate, anticipate, and respond to stimuli, optimizing their interactions and adapting their behaviours accordingly. The link could be shown by the presence of a cognitive module, between reinforcement and structure learning. The cognitive module refers to the mental processes involved in acquiring, processing, and using information. Participants completed three tasks, as they are a good representation of reinforcement learning and structure learning, as well as, learning and decision-making, and have been shown to be reliably linked to e.g., specific neural correlates. The tasks are the two-stage bandit task, the weather prediction task, and the credit assignment task, Participants performed above chance in all three tasks. Interestingly, we found significant correlations between central (performance) metrics between tasks. The main analysis using Pearson correlation revealed significant correlations between the credit assignment task and the two-stage bandit task, as well as between the credit assignment task and the weather prediction task. There was only a marginal correlation between the weather prediction task and the two-stage bandit task, which disappeared after controlling for shared variance with the credit assignment task using partial correlation. The findings indicate that reinforcement learning and structure learning share common variances and behavioural metrics, such as reaction time, accuracy, performance, and outcome, suggesting a link between the two forms of learning and supporting the presence of a shared cognitive module underlying these processes. This has implications that structure learning seems to be a promising link between different learning tasks, highlighting its importance for understanding learning and decision-making across different contexts and across individuals.Show less

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Unveiling Parallel Pathways: Bridging the Gap between Structure and Reinforcement Learning: Utilizing the structure of the tasks and employing reinforcement learning strategies.