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)

The acquisition of English noun and verb morphology is modeled using a single-system connectionist network. The network is trained to produce the plurals and past tense forms of a large corpus of monosyllabic English nouns and verbs. The developmental trajectory of network performance is analyzed in detail and is shown to mimic a number of important features of the acquisition of English noun and verb morphology in young children. These include an initial error-free period of performance on both nouns and (...) verbs followed by a period of intermittent over-regularization of irregular nouns and verbs. Errors in the model show evidence of phonological conditioning and frequency effects. Furthermore, the network demonstrates a strong tendency to regularize denominal verbs and deverbal nouns and masters the principles of voicing assimilation. Despite their incorporation into a single-system network, nouns and verbs exhibit some important differences in their profiles of acquisition. Most importantly, noun inflections are acquired earlier than verb inflections. The simulations generate several empirical predictions that can be used to evaluate further the suitability of this type of cognitive architecture in the domain of inflectional morphology. (shrink)

How do English‐speaking children inflect nouns for plurality and verbs for the past tense? We assess theoretical answers to this question by considering errors of omission, which occur when children produce a stem in place of its inflected counterpart (e.g., saying “dress” to refer to 5 dresses). A total of 307 children (aged 3;11–9;9) participated in 3 inflection studies. In Study 1, we show that errors of omission occur until the age of 7 and are more likely with both sibilant (...) regular nouns (e.g., dress) and irregular nouns (e.g., man) than regular nouns (e.g., dog). Sibilant nouns are more likely to be inflected if they are high frequency. In Studies 2 and 3, we show that similar effects apply to the inflection of verbs and that there is an advantage for “regular‐like” irregulars whose inflected form, but not stem form, ends in d/t. The results imply that (a) stems and inflected forms compete for production and (b) children generalize both product‐oriented and source‐oriented schemas when learning about inflectional morphology. (shrink)