Does the motor cortex differentiate between linguistic symbols and scribbles?

How the human brain distinguishes between linguistic symbols (i.e., letters and characters) and scribbles is not entirely understood. Some experimental evidence indicates the existence of a specialized visuo-motor network (comprising the left premotor cortex) that serves the perception of written language symbols as well as their production (e.g., Anderson et al., 1990; Starrfelt, 2007). Damage to this network impairs the identification of single symbols whilst sparing the ability to read, for instance, numbers. In a recent paper, Heimann et al. (2013) used Electroencephalography (EEG) to deepen our understanding of the role of visuo-motor cortices in handwriting categorization. In their experiment, Heimann et al. asked individuals to observe Roman letters (belonging to the alphabet of the participant’s mother language), Chinese (unfamiliar) characters, and scribbles (judged by the participants as not being linguistic symbols). As a marker of sensorimotor activity, they used the central alpha event-related desynchronization in the EEG (ERD; see Pfurtscheller and Lopes da Silva, 1999), which describes the contralateral attenuation of the EEG power during preparation and execution of movements (Jasper and Penfield, 1949; Chatrian et al., 1959; Pfurtscheller and Berghold, 1989). On the one hand, Heimann and colleagues found that all handwriting categories evoked activity in the observer’s cortical motor system (see their Figure 3). This first result suggests that the brain interpreted the handwritings “motorically” as traces of hand movements executed by another individual (see also Umiltà et al., 2012), regardless of whether they were language symbols (i.e., Roman letters and Chinese characters) or scribbles, and regardless of whether the observer was familiar or trained in the handwriting style. On the other hand, the time-course of the ERD (see their Figure 6) showed that scribbles evoked less sensorimotor activity than linguistic symbols. More specifically, alpha desynchronization (ERD) was generally weaker and resynchronization (return to the baseline level) faster for scribbles than for symbols. Why was this so? Heimann et al. hypothesized that this “might depend on either sensory-motor training in a broad sense, separating symbols in general from scribbles, or on visual features allowing a categorical distinction between symbols and scribbles. . . . Since a further major difference among the employed stimuli is their symbolic value, present in Roman letters and Chinese characters and absent in scribbles, central alpha ERD modulation in the temporal domain could depend on visual features marking the stimuli as linguistic symbols” (my ellipsis; see also Longcamp et al., 2005 and Wong et al., 2008). As far as the first consideration (sensorimotor training) is concerned, it is improbable that the difference observed between scribbles and symbols can be explained by previous experience, since the participants were indeed experienced in Roman letters but naïve to Chinese characters (see the results of their Rating task). The second consideration supports a crucial role of visuo-motor associations in the ability to read language symbols (see also Freyd, 1983; Babcock and Freyd, 1988; Longcamp et al., 2003, 2006, 2008; James and Gauthier, 2009) and suggests the presence of visual features in symbols that “are the outcome of the way they are written and which precisely make them recognizable as symbols (not scribbles).” In the present article, I argue that a difference between the visual features of the two groups of writings does exist, but that it is not necessarily language-related. Indeed, although the three groups of handwritings were matched in size and stroke-thickness, mere visual inspection reveals that the scribbles contain less ink (or number of pixels, NP; see their Figure 1S). The stimuli being matched, the difference in NP necessarily signifies that the length or the number of strokes differed between the stimuli. This of course may have affected the results by providing the participants with dissimilar information about the hand actions that had been executed to draw the handwritings. To quantify and compare the amount of ink in the 3 categories of handwritings (20 Roman letters, 20 Chinese characters and 20 scribbles), I analyzed the differences in their NP. First, I identified the contours of each handwriting with Color Photo Paint (Corel Draw 9) using the option Color Mask (tolerance = 30, smoothing = 0, black color). Next, I calculated the total NP with the option Histogram. A Oneway ANOVA with the factor Category (letters, characters, scribbles) showed a main effect, F(2, 57) = 52.49, p < 0.001, ηp = 0.65, indicating that the NP differed between the types of handwritings (a significance threshold of p < 0.05 was set for all statistical tests). Post-hoc comparisons (Newman-Keuls’s post-hoc test) revealed that NP between Roman letters (280.1 ± 9.5; Mean ± S.E.M.) and Chinese characters (286.2 ± 9.9) did not differ (p = 0.63; see Figure 1). Instead, their NP was