Prior context and individual alpha frequency (IAF) influence predictive processing during language comprehension

biorxiv(2024)

引用 1|浏览12
暂无评分
摘要
The extent to which the brain predicts upcoming information during language processing remains controversial. To shed light on this debate, the present study reanalysed Nieuwland and colleagues′ (2018) replication of DeLong et al. (2005). Participants (N = 356) viewed sentences containing articles and nouns of varying predictability, while their electroencephalogram was recorded. We measured event-related potentials (ERPs) preceding the critical words (namely the semantic prediction potential; SPP), in conjunction with post-word N400 patterns and individual neural metrics. ERP activity was compared with two measures of word predictability: cloze probability and lexical surprisal. In contrast to prior literature, SPP amplitudes did not increase as cloze probability increased, suggesting that the component may not reflect prediction during natural language processing. Initial N400 results at the article provided evidence against phonological prediction in language, in line with Nieuwland and colleagues′ findings. Strikingly however, when the surprisal of the prior words in the sentence was included in the analysis, increases in article surprisal were associated with increased N400 amplitudes, consistent with prediction accounts. This relationship between surprisal and N400 amplitude was not observed when the surprisal of the two prior words was low, suggesting that expectation violations at the article may be overlooked under highly predictable conditions. Individual alpha frequency (IAF) also modulated the relationship between article surprisal and the N400, emphasising the importance of individual neural factors for prediction. The present study extends upon existing neurocognitive models of language and prediction more generally, by illuminating the flexible and subject-specific nature of predictive processing. ### Competing Interest Statement The authors have declared no competing interest.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要