Format

Send to

Choose Destination
See comment in PubMed Commons below
Cognition. 2000 Nov 16;77(2):97-132.

Detection of errors during speech production: a review of speech monitoring models.

Author information

  • 1Psychological Laboratory, Helmholtz Institute, Utrecht University, Heidelberglaan 2, 3584 CS, Utrecht, The Netherlands. a.postma@fss.uu.nl

Abstract

In this paper three theories of speech monitoring are evaluated. The perception-based approach proposes that the same mechanism employed in understanding other-produced language, the speech comprehension system, is also used to monitor one's own speech production. A conceptual, an inner, and an auditory loop convey information to a central, conscious monitor which scrutinizes the adequacy of the ongoing speech flow. In this model, only the end-products in the speech production sequences, the preverbal (propositional) message, the phonetic plan, and the auditory results, are verified. The production-based account assumes multiple local, autonomous monitoring devices, which can look inside formulation components. Moreover, these devices might be tuned to various signals from the actual speech motor execution, e.g. efferent, tactile, and proprioceptive feedback. Finally, node structure theory views error detection as a natural outflow of the activation patterns in the node system for speech production. Errors result in prolonged activation of uncommitted nodes, which in turn may incite error awareness. The approaches differ on the points of consciousness, volition and control, the number of monitoring channels, and their speed, flexibility, and capacity, and whether they can account for concurrent language comprehension disorders. From the empirical evidence presently available, it is argued for a central perception-based monitor, potentially augmented with a few automatic, production-based error detection devices.

PMID:
10986364
[PubMed - indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Elsevier Science
    Loading ...
    Support Center