In his paper, MacKay reviews his Node Structure theory of error detection, but precedes it with a critical discussion of the Perceptual Loop theory of self-monitoring proposed in Levelt . The present commentary is concerned with this latter critique and shows that there are more than casual problems with MacKay's argumentation.

An exploration is given of neural network features now being uncovered in cortical processing which begins to go a little way to help bridge the ''Explanatory Gap'' between phenomenal consciousness and correlated brain activity. A survey of properties suggested as being possessed by phenomenal consciousness leads to a set of criteria to be required of the correlated neural activity. Various neural styles of processing are reviewed and those fitting the criteria are selected for further analysis. One particular processing style, in (...) which semiautonomous and long-lasting cortical activity ''bubbles'' are created by input, is selected as being the most appropriate. Further experimental criteria are used to help narrow the possible neural styles involved. This leads to a class of neural models underpinning phenomenal consciousness and to a related set of testable predictions. (shrink)

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)