There is an ongoing controversy over whether readers can access the meaning of multiple words, simultaneously. To date, different experimental methods have generated seemingly contradictory evidence in support of serial or parallel processing accounts. For example, dual-task studies suggest that readers can process a maximum of one word at a time (White, Palmer & Boynton, 2018), while ERP studies have demonstrated neural priming effects that are more consistent with parallel activation (Wen, Snell & Grainger, 2019). To help reconcile these views, I measured neural responses and behavioral accuracy in a dual-task sentence comprehension paradigm. Participants saw masked sentences and two-word phrases and had to judge whether or not they were grammatical. Grammatically correct sentences (This girl is neat) produced smaller N400 responses compared to scrambled sentences (Those girl is fled): an N400 sentence superiority effect. Critically, participants' grammaticality judgements on the same trials showed striking capacity limitations, with dual-task deficits closely matching the predictions of a serial, all-or-none processing account. Together, these findings suggest that the N400 sentence superiority effect is fully compatible with serial word recognition, and that readers are unable to process multiple sentence positions simultaneously.

During language comprehension, we routinely use information from the prior context to help identify the meaning of individual words. While measures of online processing difficulty, such as reading times, are strongly influenced by contextual predictability, there is disagreement about the mechanisms underlying this lexical predictability effect, with different models predicting different linking functions – linear (Reichle, Rayner, & Pollatsek, 2003) or logarithmic (Levy, 2008). To help resolve this debate, we conducted two highly-powered experiments (self-paced reading, N = 216; cross-modal picture naming, N = 36), and a meta-analysis of prior eye-tracking while reading studies (total N = 218). We observed a robust linear relationship between lexical predictability and word processing times across all three studies. Beyond their methodological implications, these findings also place important constraints on predictive processing models of language comprehension. In particular, these results directly contradict the empirical predictions of surprisal theory, while supporting a proportional pre-activation account of lexical prediction effects in comprehension.

It has been proposed that people can generate probabilistic predictions at multiple levels of representation during language comprehension. We used magnetoencephalography (MEG) and electroencephalography (EEG), in combination with representational similarity analysis, to seek neural evidence for the prediction of animacy features. In two studies, MEG and EEG activity was measured as human participants (both sexes) read three-sentence scenarios. Verbs in the final sentences constrained for either animate or inanimate semantic features of upcoming nouns, and the broader discourse context constrained for either a specific noun or for multiple nouns belonging to the same animacy category. We quantified the similarity between spatial patterns of brain activity following the verbs until just before the presentation of the nouns. The MEG and EEG datasets revealed converging evidence that the similarity between spatial patterns of neural activity following animate-constraining verbs was greater than following inanimate-constraining verbs. This effect could not be explained by lexical-semantic processing of the verbs themselves. We therefore suggest that it reflected the inherent difference in the semantic similarity structure of the predicted animate and inanimate nouns. Moreover, the effect was present regardless of whether a specific word could be predicted, providing strong evidence for the prediction of coarse-grained semantic features that goes beyond the prediction of individual words.

Background

People with schizophrenia process language in unusual ways, but the causes of these abnormalities are unclear. In particular, it has proven difficult to empirically disentangle explanations based on impairments in the top-down processing of higher-level information from those based on the bottom-up processing of lower-level information.

Methods

To distinguish these accounts, we used visual world eye-tracking, a paradigm that measures spoken language processing during real-world interactions. Participants listened to and then acted out syntactically ambiguous spoken instructions (e.g., “tickle the frog with the feather”, which could either specify how to tickle a frog, or which frog to tickle). We contrasted how 24 people with schizophrenia and 24 demographically-matched controls used two types of lower-level information (prosody and lexical representations) and two types of higher-level information (pragmatic and discourse-level representations) to resolve the ambiguous meanings of these instructions. Eye-tracking allowed us to assess how participants arrived at their interpretation in real time, while recordings of participants’ actions measured how they ultimately interpreted the instructions.

Results

We found a striking dissociation in participants’ eye movements: the two groups were similarly adept at using lower-level information to immediately constrain their interpretations of the instructions, but only controls showed evidence of fast top-down use of higher-level information. People with schizophrenia, nonetheless, did eventually reach the same interpretations as controls.

Conclusions

Background: Schizophrenia is characterized by abnormalities in referential communication, which may be linked to more general deficits in proactive cognitive control. We used event-related potentials (ERPs) to probe the timing and nature of the neural mechanisms engaged as people with schizophrenia linked pronouns to their preceding referents during word-by-word sentence comprehension.

Methods: We measured ERPs to pronouns in two-clause sentences from 16 people with schizophrenia and 20 demographically-matched control participants. Our design crossed the number of potential referents (1-referent, 2-referent) with whether the pronoun matched the gender of its preceding referent(s) (matching, mismatching). This gave rise to four conditions: (1) 1-referent matching (“…Edward took courses in accounting but he…”), (2) 2-referent matching (“…Edward and Phillip took courses but he…”), (3) 1-referent mismatching (“…Edward took courses in accounting but she…”), and (4) 2-referent mismatching (“…Edward and Phillip took courses but she…”).

Results: Consistent with previous findings, healthy controls produced a larger left anteriorly-distributed negativity between 400-600ms to 2-referent matching than to 1-referent matching pronouns (the “Nref effect”). In contrast, people with schizophrenia produced a larger centro-posterior positivity effect between 600-800ms. Both patient and control groups produced a larger positivity between 400-800ms to mismatching than to matching pronouns.

Conclusions: These findings suggest that proactive mechanisms of referential processing, reflected by the Nref effect, are impaired in schizophrenia, while reactive mechanisms, reflected by the positivity effects, are relatively spared. Indeed, patients may compensate for proactive deficits by retro-actively engaging with context to influence the processing of inputs at a later stage of analysis.

Since the early 2000s, several ERP studies have challenged the assumption that we always use syntactic contextual information to influence semantic processing of incoming words, as reflected by the N400 component. One approach for explaining these findings is to posit distinct semantic and syntactic processing mechanisms, each with distinct time courses. While this approach can explain specific datasets, it cannot account for the wider body of findings. I propose an alternative explanation: a dynamic generative framework in which our goal is to infer the underlying event that best explains the set of inputs encountered at any given time. Within this framework, combinations of semantic and syntactic cues with varying reliabilities are used as evidence to weight probabilistic hypotheses about this event. I further argue that the computational principles of this framework can be extended to understand how we infer situation models during discourse comprehension, and intended messages during spoken communication.