Preparing words in speech production is normally a fast and accurate process. We generate them two or three per second in fluent conversation; and overtly naming a clear picture of an object can easily be initiated within 600 msec after picture onset. The underlying process, however, is exceedingly complex. The theory reviewed in this target article analyzes this process as staged and feedforward. After a first stage of conceptual preparation, word generation proceeds through lexical selection, morphological and phonological encoding, phonetic (...) encoding, and articulation itself. In addition, the speaker exerts some degree of output control, by monitoring of self-produced internal and overt speech. The core of the theory, ranging from lexical selection to the initiation of phonetic encoding, is captured in a computational model, called WEAVER++. Both the theory and the computational model have been developed in interaction with reaction time experiments, particularly in picture naming or related word production paradigms, with the aim of accounting for the real-time processing in normal word production. A comprehensive review of theory, model, and experiments is presented. The model can handle some of the main observations in the domain of speech errors (the major empirical domain for most other theories of lexical access), and the theory opens new ways of approaching the cerebral organization of speech production by way of high-temporal-resolution imaging. (shrink)
The commentaries provide a multitude of perspectives on the theory of lexical access presented in our target article. We respond, on the one hand, to criticisms that concern the embeddings of our model in the larger theoretical frameworks of human performance and of a speaker's multiword sentence and discourse generation. These embeddings, we argue, are either already there or naturally forgeable. On the other hand, we reply to a host of theory-internal issues concerning the abstract properties of our feedforward spreading (...) activation model, which functions without the usual cascading, feedback, and inhibitory connections. These issues also concern the concrete stratification in terms of lexical concepts, syntactic lemmas, and morphophonology. Our response stresses the parsimony of our modeling in the light of its substantial empirical coverage. We elaborate its usefulness for neuroimaging and aphasiology and suggest further cross-linguistic extensions of the model. (shrink)
How can one conceive of the neuronal implementation of the processing model we proposed in our target article? In his commentary (Pulvermüller 1999, reprinted here in this issue), Pulvermüller makes various proposals concerning the underlying neural mechanisms and their potential localizations in the brain. These proposals demonstrate the compatibility of our processing model and current neuroscience. We add further evidence on details of localization based on a recent meta-analysis of neuroimaging studies of word production (Indefrey & Levelt 2000). We also (...) express some minor disagreements with respect to Pulvermüller's interpretation of the “lemma” notion, and concerning his neural modeling of phonological code retrieval. Branigan & Pickering discuss important aspects of syntactic encoding, which was not the topic of the target article. We discuss their well-taken proposal that multiple syntactic frames for a single verb lemma are represented as independent nodes, which can be shared with other verbs, such as accounting for syntactic priming in speech production. We also discuss how, in principle, the alternative multiple-frame-multiple-lemma account can be tested empirically. The available evidence does not seem to support that account. Footnotes1 BBS Note: The original manuscript of this Response article was received on January 14, 2000. (shrink)
A comparison of Merge, a model of comprehension, and WEAVER, a model of production, raises five issues: merging models of comprehension and production necessarily creates feedback; neither model is a comprehensive account of word processing; the models are incomplete in different ways; the models differ in their handling of competition; as opposed to WEAVER, Merge is a model of metalinguistic behavior.
Many authors have recently highlighted the importance of prediction for language comprehension. Pickering & Garrod (P&G) are the first to propose a central role for prediction in language production. This is an intriguing idea, but it is not clear what it means for speakers to predict their own utterances, and how prediction during production can be empirically distinguished from production proper.