The goal of neurocognitive word production research is twofold: to understand how the processes represented in functional models of word production are implemented in the brain and to improve functional models by testing their predictions at the brain level. In the first decade of neurocognitive word production research the predominant approach was brain mapping. Researchers investigated the regional cerebral brain activation correlated with word production tasks, such as picture naming and word generation, compared to more or less low-level control tasks. As is the case in most new research fields, the aim of this approach was not so much to test specific hypotheses but to gain a first insight into the behavior of the system under investigation, in this case the neural system supporting word production. This research yielded a wealth of data about which brain regions respond to tasks that were considered word production tasks, such as picture naming and verb or noun generation. Other tasks that were not typically used to study word production nonetheless involve word production components, such as word and pseudoword reading. Indefrey and Levelt (2000
, see also Indefrey, 2007
) conducted comprehensive meta-analyses of word production studies that had used the mapping approach. They first analyzed the tasks with respect to lead-in processes preceding word production and core word production processes as assumed by psycholinguistic models of word production. For the identification of candidate brain regions subserving these components they then followed a simple (some may say “simplistic”) heuristic principle: “If, for a given processing component, there are subserving brain regions, then these regions should be found active in all experimental tasks sharing the processing component, whatever other processing components these tasks may comprise. In addition, the region(s) should not be active in experimental tasks that do not share the component
.” (Indefrey and Levelt, 2000
). These analyses yielded a set of candidate areas corresponding to certain word production components but, of course, the validity of the identification of any of these areas depends on the validity of the underlying task analysis. Thus, essentially, these analyses generated a set of hypotheses that needed confirmation (or falsification) from independent data. A first kind of hypothesis-testing was performed in Indefrey and Levelt (2004
), combining the resulting spatial information on potential neural correlates of component processes of word production with an independent estimate of the time course of word production components provided by behavioral and electrophysiological studies. If, they reasoned for example, the left posterior superior temporal gyrus (STG) was involved in word form retrieval then the time course of its activation in picture naming should fall in the interval of 250–330
ms after picture onset suggested by chronometric studies for word form retrieval. Data from the few available magnetoencephalographic (MEG) studies on picture naming that provided both spatial and temporal information confirmed the proposed assignment of component processes to brain areas in that they were largely compatible with the predicted time windows of activation.
The resulting spatiotemporal model of word production does not only predict time windows of activation but also modulatory effects of psycholinguistic variables on the activation of specific brain regions at a specific time. In recent years, neurocognitive word production research has seen a major change toward a hypothesis-testing approach. This approach is characterized by the design of experimental variables modulating single component processes of word production and testing for predicted effects on spatial or temporal neurocognitive signatures of these components. This change has been accompanied by an impressive broadening of the spectrum of measurement and analysis techniques. Both in functional magnetic resonance imaging (fMRI) and in electroencephalography (EEG) methods have been developed that allow for overt speaking during experiments (for fMRI see, e.g., de Zubicaray et al., 2001
; Grabowski et al., 2006
; Christoffels et al., 2007b
; Heim et al., 2009b
; Hocking et al., 2009
; for EEG see, e.g., Christoffels et al., 2007a
; Koester and Schiller, 2008
; Costa et al., 2009
; Strijkers et al., 2010
). Overt pronunciation provides on-line voice onset time and error data and, hence, some confirmation that a targeted psycholinguistic effect was indeed present in a neurocognitive experiment, thus increasing the likelihood that an observed hemodynamic or electrophysiological effect is indeed due to the same variable that causes an effect in the corresponding psycholinguistic experiment. On-line behavioral data can, furthermore, be used as predictors for the analysis of the neuroimaging data.
Secondly, the number of studies that used techniques that provide both spatial and temporal neurocognitive data increased over the last years. In addition to MEG studies (e.g., Sörös et al., 2003
; Hultén et al., 2009
) and the use of intracranial electrophysiology in neurosurgical patients (Sahin et al., 2009
; Edwards et al., 2010
) the main development has been the use of transcranial magnetic stimulation (TMS) as a tool for temporarily stimulating or interfering with neuronal activity in specific brain regions at a specific time (Schuhmann et al., 2009
; Acheson et al., 2011
). Similar to electrocortical stimulation and lesion-symptom mapping, TMS has the potential to provide evidence as to the functional necessity of a targeted brain area. This evidence is thus complementary to fMRI or positron emission tomography (PET) data that inform about the involvement of brain areas in cognitive processes but not their necessity.
A third recent development in fMRI research on word production is the use of repetition suppression or adaptation paradigms (e.g., Graves et al., 2008
; Peeva et al., 2010
). In the standard fMRI approach a neuronal population involved in a certain cognitive process (e.g., lexical word form retrieval in word production) is identified by subtracting the brain activation of a control condition that does not (or to a smaller extent) contain that cognitive process (for example by using the production of pseudowords that are not lexically stored). In many cases, however, finding the right control condition is extremely difficult, because in addition to the process of interest there are other unavoidable differences between the active condition and the control condition (pseudowords also differ from words in that they have no meaning). The repetition suppression paradigm, by contrast, exploits the fact that the activation of just that neuronal population that is involved in the process of interest tends become smaller the more often that process is repeated. Experimenters can use repetition suppression to target neuronal populations subserving very specific cognitive components. The study of Peeva et al. (2010
) is a nice example of this approach. In one condition, they repeated bisyllabic pseudowords (e.g., fublo, blofu, fublo…) consisting of two constant syllables. In another condition they kept the repetition of phonemes constant but varied syllable structure (lofub, fublo, lofub …). As a result the activation of neuronal populations interested in the specific syllables “fu” and “blo” is suppressed over time in the first but not the second condition. In their study the left ventral premotor cortex showed this behavior so it could be concluded that this region contains neurons representing complete syllables.
Finally new analysis techniques for measuring anatomical connections (diffusion tensor imaging, DTI) and modeling the interaction between brain areas (structural equation modeling, SEM; dynamic causal modeling, DCM, independent component analysis, ICA) have begun to be applied to word production (Saur et al., 2008
; Tourville et al., 2008
; Eickhoff et al., 2009
; Heim et al., 2009a
; van de Ven et al., 2009
). To understand how the processes represented in functional models of word production are implemented in the brain, such approaches - together with methods providing combined spatial and temporal information - are needed to test theoretical assumptions about directions of information flow and interactions between processing components.
In the next two sections I will briefly recapitulate the cascade of processing components involved in word production and their estimated time windows. In subsequent sections I will then discuss the neural correlates of each processing component as presented in Indefrey and Levelt, 2004
, henceforth I&L) and the more recent evidence about the neural implementation of these components.