Dynamics of word production and processing speed

In the light of the growing number of studies published in the field of word production, there is a strong need for a synopsis of the different results and for a critical analysis of the diversity of approaches. The attempt by Munding, Dubarry and Alario (M&D&A) to seek a comprehensive and critical review of MEG studies focusing on the information flow and linking the results to cascading and parallel models of word production is to be commended. The analyses carried out by M&D&A indicated largely time-overlapping functions but also some degree of seriality, which led the authors to conclude that there is not enough evidence in favour or against any of the two models of processing. In the following, I point out two caveats when collapsing the spatio-temporal results from different studies and interpreting them in terms of functional processes in word production. The first issue is related to task differences across studies. The joint analysis of results on functional activation from reading and picture-naming tasks implies the implicit assumption that the two tasks involve the same cognitive and brain processes. While this is likely to be the case for motor functions, there is a large agreement that other cognitive processes are not the same in reading and naming (Price et al., 2006). Besides different processes, the dynamics of common functional processes may also not be the same between reading and naming in particular because articulation is initiated faster in reading than in naming (Buzsáki et al., 1989; Riès, Legou, Burle, Alario, & Malfait, 2012). This leads us to the second and crucial caveat, namely the interpretation of functional timing across studies with highly variable processing speeds. Huge differences in mean production latencies are commonly observed across studies even when similar paradigms are involved. This is also the case for the studies included in the meta-analyses by M&D&A, in which the time elapsing from the presentation of the visual stimulus to the onset of articulation varies by a factor of two (see Figure 1). While the shortest mean latencies are about 450 ms following stimulus onset, production latencies beyond 850 ms are reported in several studies (in particular in those using delayed production). This means that articulation is engaged as early as 400 ms in some experiments while it is delayed after 850 ms in other studies. M&D&A reported motor functions (defined as comprising both motor planning and articulation) in a time-period largely overlapping with semantic and phonological functions. As illustrated in Figure 1, the timewindow of motor activations reported from 300 to 1000 ms covers the range of production latencies across studies. It is likely that different production latencies across studies shift the activation of motor functions. The question then is which part of the large time-window of overlap of motor functions with other functions is related to different onsets of articulation across studies. Motor function was taken here for the sake of illustration: more generally, the question relates to the variation of the dynamics of functions in the light of variability of production speed. It is usually admitted that timing of functions should be rescaled according to production speed, although different solutions have been proposed. Indefrey (2011) discussed that depending on the experimental conditions some processes may take less or more time. Schuhmann, Schiller, Goebel, and Sack (2009) suggested that all processes involved in speech production should be rescaled equally as a function of speed. Variation of spatio-temporal dynamics in relation with variability in processing speed in picture naming has been analysed within (Laganaro, Valente, & Perret, 2012) and between (Laganaro, Tzieropoulos, Frauenfelder, & Zesiger, 2015) populations. In both cases, some functional processes are lengthened in the group or population with longer production latencies. Hence, the timing of functions is bound up to the overall task processing speed, but current knowledge does not allow researchers to identify which function(s) should be rescaled and in which proportion. As a consequence, at least part of the over-