Error monitoring in sequence learning

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Actions followed by positive outcomes are more likely to be repeated, while those followed by negative outcomes are less likely to recur. This suggests that behavior must be assessed based on its consequences, with the detection of errors being crucial for reinforcement learning. Humans continuously monitor their performance to identify and correct mistakes, enabling them to adapt to changing environments and learn new behaviors. Extensive studies on reinforcement learning in animals have highlighted the importance of the mesencephalic dopamine system. When an expectancy mismatch occurs during reinforcement learning, resulting in an event perceived as "worse than expected," there is a phasic decrease in dopaminergic activity. This reduction in midbrain dopamine is believed to trigger the error-related negativity (ERN) or error negativity (Ne), a negative deflection in the response-locked event-related potential (ERP) that appears after an error is made. The reinforcement learning hypothesis posits that the ERN/Ne serves as a feedback signal for learning. This work utilized ERPs to investigate whether error detection and its implications for learning require an intention to learn or can occur unconsciously. It also explored whether errors that go unnoticed can act as internal feedback signals for learning, as well as how these error monitoring processes are influenced by aging and the role of expectancies in learning.