This paper introduces a special issue of Cognitive Science initiated on the 25th anniversary of the publication of Parallel Distributed Processing (PDP), a two-volume work that introduced the use of neural network models as vehicles for understanding cognition. The collection surveys the core commitments of the PDP framework, the key issues the framework has addressed, and the debates the framework has spawned, and presents viewpoints on the current status of these issues. The articles focus on both historical roots and contemporary (...) developments in learning, optimality theory, perception, memory, language, conceptual knowledge, cognitive control, and consciousness. Here we consider the approach more generally, reviewing the original motivations, the resulting framework, and the central tenets of the underlying theory. We then evaluate the impact of PDP both on the field at large and within specific subdomains of cognitive science and consider the current role of PDP models within the broader landscape of contemporary theoretical frameworks in cognitive science. Looking to the future, we consider the implications for cognitive science of the recent success of machine learning systems called “deep networks”—systems that build on key ideas presented in the PDP volumes. (shrink)

Consciousness and experience, William G. Lycan. Cambridge, MA: MIT Press, 1996. ISBN 0–262–12197–2 (hc)Mind as motion: explorations in the dynamics of cognition, Robert Port & Timothy Van Gelder (Eds). Cambridge, MA: MIT/Bradford, 1995. ISBN 0–262–16150–8Perception, Kathleen Akins (Ed.). New York: Oxford University Press, 1996. ISBN 0–19–508461 (hbk); 0–19–508462–4 (pbk)Context and consciousness, B. Nardi (Ed.). Cambridge, MA: MIT Press, 1996. ISBN: 0–262–14058–6Catching ourselves in the act, Horst Hendriks‐Jansen. Cambridge, MA: MIT Press, 1996. ISBN 0–262–08246–2The search for mind. A new foundation for (...) cognitive science, Sean O'Nuallain. Norwood, NJ: Ablex, 1995Mindblindness: an essay on autism and theory of mind, Simon Baron‐Cohen. Cambridge, MA: MIT Press, 1995. ISBN 0–262–02384–9. (shrink)

Although debates continue, studies of cognition in infancy suggest that knowledge begins to emerge early in life and constitutes part of humans' innate endowment. Early-developing knowledge appears to be both domain-specific and task-specific, it appears to capture fundamental constraints on ecologically important classes of entities in the child's environment, and it appears to remain central to the commonsense knowledge systems of adults.

Connectionist models provide a promising alternative to the traditional computational approach that has for several decades dominated cognitive science and artificial intelligence, although the nature of connectionist models and their relation to symbol processing remains controversial. Connectionist models can be characterized by three general computational features: distinct layers of interconnected units, recursive rules for updating the strengths of the connections during learning, and “simple” homogeneous computing elements. Using just these three features one can construct surprisingly elegant and powerful models of (...) memory, perception, motor control, categorization, and reasoning. What makes the connectionist approach unique is not its variety of representational possibilities or its departure from explicit rule-based models, or even its preoccupation with the brain metaphor. Rather, it is that connectionist models can be used to explore systematically the complex interaction between learning and representation, as we try to demonstrate through the analysis of several large networks. (shrink)