Journal
FRONTIERS IN PSYCHOLOGY
Volume 3, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fpsyg.2012.00228
Keywords
multi-context environment; contextual ambiguity; representation learning; contextual cue; change detection
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Funding
- Div Of Information & Intelligent Systems [1150028] Funding Source: National Science Foundation
- EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT [R01HD037082, R01HD067250] Funding Source: NIH RePORTER
- NICHD NIH HHS [R01 HD067250, R01 HD037082] Funding Source: Medline
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Learning an accurate representation of the environment is a difficult task for both animals and humans, because the causal structures of the environment are unobservable and must be inferred from the observable input. In this article, we argue that this difficulty is further increased by the multi-context nature of realistic learning environments. When the environment undergoes a change in context without explicit cueing, the learner must detect the change and employ a new causal model to predict upcoming observations correctly. We discuss the problems and strategies that a rational learner might adopt and existing findings that support such strategies. We advocate hierarchical models as an optimal structure for retaining causal models learned in past contexts, thereby avoiding relearning familiar contexts in the future.
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