Publications by Year: 2022

In people with schizophrenia and related disorders, impairments in communication and social functioning can negatively impact social interactions and quality of life. In the present study, we investigated the cognitive basis of a specific aspect of linguistic communication—lexical alignment— in people with schizophrenia and bipolar disorder. We probed lexical alignment as participants played a collaborative picture-naming game with the experimenter, in which the two players alternated between naming a dual-name picture (e.g., rabbit/bunny) and listening to their partner name a picture. We found evidence of lexical alignment in all three groups, with no differences between the patient groups and the controls. We argue that these typical patterns of lexical alignment in patients were supported by preserved—and in some cases increased—bottom-up mechanisms, which balanced out impairments in top-down perspective-taking.

We used magnetoencephalography (MEG) and event-related potentials (ERPs) to track the time-course and localization of evoked activity produced by expected, unexpected plausible, and implausible words during incremental language comprehension. We suggest that the full pattern of results can be explained within a hierarchical predictive coding framework in which increased evoked activity reflects the activation of residual information that was not already represented at a given level of the fronto-temporal hierarchy (“error” activity). Between 300 and 500 ms, the three conditions produced progressively larger responses within left temporal cortex (lexico-semantic prediction error), whereas implausible inputs produced a selectively enhanced response within inferior frontal cortex (prediction error at the level of the event model). Between 600 and 1,000 ms, unexpected plausible words activated left inferior frontal and middle temporal cortices (feedback activity that produced top-down error), whereas highly implausible inputs activated left inferior frontal cortex, posterior fusiform (unsuppressed orthographic prediction error/reprocessing), and medial temporal cortex (possibly supporting new learning). Therefore, predictive coding may provide a unifying theory that links language comprehension to other domains of cognition.

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.

The N400 event-related brain potential is elicited by each word in a sentence and offers an important window into the mechanisms of real-time language comprehension. Since the 1980s, studies investigating the N400 have expanded our understanding of how bottom-up linguistic inputs interact with top-down contextual constraints. More recently, a growing body of computational modeling research has aimed to formalize theoretical accounts of the N400 to better understand the neural and functional basis of this component. Here, we provide a comprehensive review of this literature. We discuss “word-level” models that focus on the N400’s sensitivity to lexical factors and simple priming manipulations, as well as more recent sentence-level models that explain its sensitivity to broader context. We discuss each model’s insights and limitations in relation to a set of cognitive and biological constraints that have informed our understanding of language comprehension and the N400 over the past few decades. We then review a novel computational model of the N400 that is based on the principles of predictive coding, which can accurately simulate both word-level and sentence-level phenomena. In this predictive coding account, the N400 is conceptualized as the magnitude of lexico-semantic prediction error produced by incoming words during the process of inferring their meaning. Finally, we highlight important directions for future research, including a discussion of how these computational models can be expanded to explain language-related ERP effects outside the N400 time window, and variation in N400 modulation across different populations.