Browse/search for people

Publication - Professor Jeffrey Bowers

    Parallel Distributed Processing theory in the age of deep networks

    Citation

    Bowers, J, 2017, ‘Parallel Distributed Processing theory in the age of deep networks’. Trends in Cognitive Sciences, vol 21., pp. 950-961

    Abstract

    Parallel Distributed Processing (PDP) models in psychology are the precursors of deep networks used in computer science. However, only PDP models are associated with two core psychological claims, namely, that all knowledge is coded in a distributed format, and cognition is mediated by non-symbolic computations. These claims have long been debated within cognitive science, and recent work with deep networks speaks to this debate. Specifically, single-unit recordings show that deep networks learn units that respond selectively to meaningful categories, and researchers are finding that deep networks need to be supplemented with symbolic systems in order to perform some tasks. Given the close links between PDP and deep networks, it is surprising that research with deep networks is challenging PDP theory.

    Full details in the University publications repository