Smart segmentation supports transfer learning
Year: 2024
Type: preprint
Abstract: Speeded learning based on previous experience, referred to as transfer learning, is thought to depend upon identification of a stable task structure. However, many common tasks contain a hierarchical or nested stable structure, in which subtasks vary in behavioral relevance. How we identify a nested stable task structure and the extent to which transfer learning is linked to task segmentation is currently unknown. We examined learning of a fixed sequence of tasks in which some subtasks acted as distractors and other subtasks determined the outcome of successful goal-oriented navigation. The relationship between subsequent memory, as a marker of task segmentation, and reaction time during learning, together with computational modeling of learning and segmentation, revealed that participants who demonstrated transfer learning adopted a smart task segmentation strategy including separation of distracting subtasks.
Institutions: University of Washington, University of California, Berkeley
Language: English
Topic: Memory and Neural Mechanisms
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4401976882