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Bernstein-Gruppe Components of cognition: small networks to flexible rules: Context-dependent associative learning
Bogdan Vlasenko, Martin Schafföner, Stefan Glüge
The overarching questions to be addressed by this project are as follows:
  • Is the learning of context-conditional associations by human observers influenced by, or even predicated on, consistent temporal ordering of environmental events? In other words, can the context-dependence of human associative learning be understood in terms of a temporalorderdependence?
  • How does temporal-order-dependent learning compare to abstract learning algorithms (e.g.,support-vector machines, dynamic adaptation of neural nets) for detecting patterns and regularities in high-dimensional data streams?
  • Is temporal-order-dependent learning suited as a general solution to complex learning problems? How does it perform on diverse problems such as those described in section 7.3 (i.e., learning to recognize prosodic signals in speech or emotional markers in facial expression)?


Context-dependent associative learning

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