To what extent is symbolic processing required for intelligent behavior? Advances in both sub-symbolic deep learning systems and explicitly symbolic probabilistic program induction approaches have recently reinvigorated this long standing question about cognition.

While sub-symbolic approaches have shown impressive results, they still lag far behind human cognition, e.g., in the compositional re-use of learned concepts or generalizing to new contexts. Symbolic systems have successfully addressed some of these shortcomings, but face other unsolved issues relating to feature selection, thorny search spaces and scalability.

This workshop at CogSci 2021 intends to bring together established and newly emerging perspectives on the debate and explore the recently rekindled interest in hybrid architectures.

You can view the full workshop description here.