Neural bases of proactive and predictive processing of meaningful sub-word units in speech comprehension

To comprehend speech, human brains identify meaningful units in the speech stream. But whereas the English ‘ She believed him. ’ has 3 words, the Arabic equivalent ‘ ṣaddaqathu. ’ is a single word with 3 meaningful sub-word units, called morphemes: a verb stem (‘ ṣaddaqa ’), a subject suffix (‘- t -’), and a direct object pronoun (‘- hu ’). It remains unclear whether and how the brain processes morphemes, above and beyond other language units, during speech comprehension. Here, we propose and test hierarchically-nested encoding models of speech comprehension: a NAÏVE model with word-, syllable-, and sound-level information; a BOTTOM-UP model with additional morpheme boundary information; and PREDICTIVE models that process morphemes before these boundaries. We recorded magnetoencephalography (MEG) data as participants listened to Arabic sentences like ‘ ṣaddaqathu. ’. A temporal response function (TRF) analysis revealed that in temporal and left inferior frontal regions PREDICTIVE models outperform the BOTTOM-UP model, which outperforms the NAÏVE model. Moreover, verb stems were either length-AMBIGUOUS (e.g., ‘ ṣaddaqa ’ could initially be mistaken for the shorter stem ‘ ṣadda ’=‘ blocked ’) or length-UNAMBIGUOUS (e.g., ‘ qayyama ’=‘ evaluated ’ cannot be mistaken for a shorter stem), but shared a uniqueness point, at which stem identity is fully disambiguated. Evoked analyses revealed differences between conditions before the uniqueness point, suggesting that, rather than await disambiguation, the brain employs PROACTIVE PREDICTIVE strategies, processing the accumulated input as soon as any possible stem is identifiable, even if not unique. These findings highlight the role of morpheme processing in speech comprehension, and the importance of including morpheme-level information in neural and computational models of speech comprehension.