In the post on phoneme surprisal, we asked whether the processing of morphemes within words, as measured by phoneme surprisal (the neural response to a cohort-based phoneme surprisal variable), might be less sensitive to contextual variables than we might have assumed. For example, the recognition of a suffix might involve surprisal computed not over a cohort of morphemes that are compatible with the stem (with candidate frequency within the cohort itself modulated by the transition probability from the stem to the candidate suffix), but over a cohort of morphemes compatible simply with the first phoneme of the suffix independent of morphological context.

Similar considerations might apply for visual word recognition and the M170 response from the Visual Word Form Area (VWFA). Instead of trying to explain the M170 using a single surprisal value computed from the likelihood of the word given the grammar, we should explore the possibility that the M170 is modulated by a number of variables that aren’t fully captured by a single surprisal measure.

The landmark paper exploring morphological variables modulating the M170 is Solomyak and Marantz (2010), which itself built on the work of Zweig and Pylkkänen (2009), which demonstrated that morphologically complex words yielded larger M170 responses than matched morphologically simple words. Solomyak and Marantz explored M170 reactions to a diverse set of derived English words, with a variety of suffixes. We included both free stem words (farmer, where farm can occur by itself) and bound stem words (tolerable, where toler– occurs also in e.g. tolerate, but not without a suffix). We also included “unique stem” words, like amity, whose stem arguably occurs only in the presented word. This last group did not yield absolutely clear results, but a return to this type of word in Gwilliams and Marantz (2018) provided evidence that they, too, are decomposed at the M170 response.

The oft-cited result from Solomyak and Marantz is that, for free stem and bound stem words, the M170 is modulated by the transition probability from stem to suffix but not by the surface frequency of the word, once the variance associated with transition probability is removed. What is not often remembered is that other variables did in fact modulate the M170 independent of transition probability, specifically stem and affix frequencies. That is, in addition to the contextual variable transition probability, a-contextual unigram stem and affix frequencies also showed significant effects at the M170 response. These results are variously replicated in subsequent studies.

Consider the possibility that in the general M170 brain region and time interval, multiple connected processes are performed. The look-up of the representations of morphological forms is pursued along with the evaluation of the syntactic structure connecting multiple morphemes, if more than one morpheme is recovered from the input. Here, as suggested for auditory processing, the recognition of individual morphemes based on the visual input might be governed by contextual variables as well as a-contextual variables. If this suggestion is correct, we should be able to distinguish a number of sub-responses to different stimulus variables in perhaps different sub-regions of the VWFA and neighboring cortex and at different time points within the general M170 interval.

Another possibility exists, where a single measure of visual word surprisal, computed by us from the variables discussed and perhaps other variables, can account for all the relevant variation in the M170 response, without decomposition of this response. However, if our cognitive understanding of the various variables involved (e.g., stem frequency, affix frequency, transition probability) implicate different processes in a cognitive model, then even if we’re successful in accounting for the M170 response in terms of this single, composite variable, we can’t be said to have explained the M170 – we won’t understand why this variable works. Rather, we would need to re-think our cognitive theory of complex word recognition to make sense of why that single variable would be key.

 

References

Gwilliams, L., & Marantz, A. (2018). Morphological representations are extrapolated from morpho-syntactic rules. Neuropsychologia, 114, 77-87.

Solomyak, O., & Marantz, A. (2010). Evidence for early morphological decomposition in visual word recognition. Journal of Cognitive Neuroscience, 22(9), 2042-2057.

Zweig, E., & Pylkkänen, L. (2009). A visual M170 effect of morphological complexity. Language and Cognitive Processes, 24(3), 412-439.