Commentary on Strijkers and Costa: the cortical dynamics of speaking

Producing a simple word such as “yes” can be the result of a complex chain of cognitive processes, for example, when standing at the altar. Even though it is a short and highly familiar word, it requires remarkable control of our tongue, lips and airflow. The flexibility and efficiency of human verbal communication is unique to our species. The skill of speaking takes years of our childhood to develop, and there is, “in fact, never a steady state” (Levelt, 1995). The same is probably true for the literature on the neuroscience of language production, which has grown rapidly over the last few decades. Indefrey and Levelt (I&L) have taken on the gargantuan task of reviewing, structuring and analysing this literature, and distilling from it a spatio-temporal model of word production. They were clearly aware of the challenge: “Speaking is, after all, our most complex cognitive-motor skill . A vast network of brain structures, both cortical and subcortical, contributes to the high-speed generation of utterances in never-identical communicative settings” (Indefrey & Levelt, 2000). Such a vast undertaking requires an explicit theoretical framework that captures the main established findings, and provides flexibility for further development. The strength of the link between cognitive theory and biological neuroscience is still a matter of debate (Carandini, 2012; Embick & Poeppel, 2015; Henson, 2005; Page, 2006). Recently, Embick and Poeppel (2015) argued that while fully explanatory links between computational and neurobiological levels of language are still emerging, a clear theoretical framework is also essential in order to map cognitive function onto the brain (correlational link), or to use brain data in order to constrain the theory (integrated link). The I&L model is exemplary in this regard. Levelt (1995) proposed a decomposition of the complex act of speaking into subcomponents. The model was further elaborated with the help of a computational model (WEAVER++) for the case of word production (Levelt, 1999; Levelt, Roelofs, & Meyer, 1999). The model accounted for a wealth of behavioural data from psycholinguistic experiments, mostly voice onset times in word and picture naming paradigms, and allowed for an integration of production and comprehension processes. This provided a basis for theory-guided neuroimaging research on language production. In this respect, it served two main purposes. (1) It could be used to summarise existing neuroimaging studies in a principled and systematic way, as demonstrated in several metaanalyses. (2) It allowed formulating hypotheses for novel studies in order to test and possibly extend the model. In a series of seminal meta-analyses of the neuroimaging literature (mostly based on positron emission tomography and functional magnetic resonance imaging as well as electroand magneto-encephalography (EEG/MEG) data, but also discussing transcranial magnetic stimulation literature), the authors provided evidence that their functional decomposition of word production processes is at least partly consistent with patterns of brain activation (Indefrey, 2011; Indefrey & Levelt, 2004, 2000). Furthermore, they refined their estimates for the timing of word production stages on the basis of EEG/MEG evidence. Not surprisingly, such an authoritative model did not remain unchallenged, as already reflected in the commentaries of the model presented prior to the neuroimaging meta-analyses (Levelt et al., 1999), which included comments on the role of feedback and inhibition mechanisms, the assumption of serial processing stages, effects of pragmatics, discourse and attention, the biological plausibility of the model and a number of unexplained phenomena. Strijkers and Costa (S&C) raise several critical issues about fundamental aspects of the I&L model in the light of recent evidence from the neuroscience of language. They start by asking “Is the data supporting the model or the model supporting the data? ” – a question that is certainly relevant for most areas of cognitive science. The apparent plausibility and elegance of a model may