26To minimize the number of errors in speech, and thereby facilitate 27 communication, speech is monitored before articulation. It is, however, unclear at 28 which level during speech production monitoring takes place, and what mechanisms 29 are used to detect and correct errors. The present study investigated whether 30 internal verbal monitoring takes place through the speech perception system, as 31proposed by perception--based theories of speech monitoring, or whether 32 mechanisms independent of perception are applied, as proposed by production--33 based theories of speech monitoring. With the use of fMRI during a tongue twister 34 task we observed that error detection in internal speech during noise--masked overt 35 speech production and error detection in speech perception both recruit the same 36 neural network, which includes pre--supplementary motor area (pre--SMA), dorsal 37 anterior cingulate cortex (dACC), anterior insula (AI), and inferior frontal gyrus (IFG). 38Although production and perception recruit similar areas, as proposed by 39 perception--based accounts, we did not find activation in superior temporal areas 40 (which are typically associated with speech perception) during internal speech 41 monitoring in speech production as hypothesized by these accounts. On the 42 contrary, results are highly compatible with a domain general approach to speech 43 monitoring, by which internal speech monitoring takes place through detection of 44 conflict between response options, which is subsequently resolved by a domain 45 general executive center (e.g., the ACC). 46 47 3

Some language processing theories propose that, just as for other somatic actions, self-monitoring of language production is achieved through internal modeling. The cerebellum is the proposed center of such internal modeling in motor control, and the right cerebellum has been linked to an increasing number of language functions, including predictive processing during comprehension.Relating these findings, we tested whether the right posterior cerebellum has a causal role for self-monitoring of speech errors. Participants received 1Hz repetitive transcranial magnetic stimulation during 15 minutes to lobules Crus I and II in the right hemisphere, and, in counterbalanced orders, to the contralateral area in the left cerebellar hemisphere (control) in order to induce a temporary inactivation of one of these zones. Immediately afterwards, they engaged in a speech production task priming the production of speech errors.Language production was impaired after right compared to left hemisphere stimulation, a finding that provides evidence for a causal role of the cerebellum during language production. We interpreted this role in terms of internal modeling of upcoming speech through a verbal working memory process used to prevent errors.

As all human activities, verbal communication is fraught with errors. It is estimated that humans produce around 16,000 words per day, but the word that is selected for production is not always correct and neither is the articulation always flawless. However, to facilitate communication, it is important to limit the number of errors. This is accomplished via the verbal monitoring mechanism. A body of research over the last century has uncovered a number of properties of the mechanisms at work during verbal monitoring. Over a dozen routes for verbal monitoring have been postulated. However, to date a complete account of verbal monitoring does not exist. In the current paper we first outline the properties of verbal monitoring that have been empirically demonstrated. This is followed by a discussion of current verbal monitoring models: the perceptual loop theory, conflict monitoring, the hierarchical state feedback control model, and the forward model theory. Each of these models is evaluated given empirical findings and theoretical considerations. We then outline lacunae of current theories, which we address with a proposal for a new model of verbal monitoring for production and perception, based on conflict monitoring models. Additionally, this novel model suggests a mechanism of how a detected error leads to a correction. The error resolution mechanism proposed in our new model is then tested in a computational model. Finally, we outline the advances and predictions of the model.