Presentations

Abstract

Knowledge structures are flexible. This is exemplified by language. The sentence “She got cash from the bank” does not make us think about the bank of a river. Our brains must house representations of word meaning that are constrained by the stable form of the word, but also by flexibly changing contexts. Here, we quantified the effect of flexibly changing sentential context on word representations relative to that of stored word form.
Participants learned to associate symbols with homonyms, where they derived the homonym’s meaning from its sentence context (e.g.,“she got cash from the [bank]”). Word meaning flexibility was estimated with: (1) a spatial multi-arrangement task, where participants positioned the symbols in a space based on their associated word meaning, and (2) a repetition priming task, where participants made speeded judgements about a sequence of symbols.
The spatial multi-arrangement task successfully captured the flexibility of word meaning, showing larger distances between symbols associated with homonyms (e.g., river- vs cash-bank) than between isonyms (e.g., river- vs river-bank). This effect was as large as that of stable word-form (e.g., “bank” vs “chair”). Ongoing fMRI work will address the role of hippocampus-medial frontal circuitry in representing these flexible context-dependent versus stored word-form representations.

Abstract

When we comprehend language from speech, the phase of the neural response aligns with particular features of the speech input, resulting in a phenomenon referred to as neural tracking. In recent years, a large body of work has demonstrated the tracking of the acoustic and linguistic units at the phoneme and word levels, and beyond. However, the degree to which speech tracking is driven by acoustic edges of the signal or by internally-generated linguistic units, or by the interplay of both, remains contentious. We used naturalistic story-listening to investigate whether phoneme-level features are tracked over and above acoustic edges and whether word entropy, which can reflect sentence- and discourse-level constraints, impacted the encoding of acoustic and phoneme-level features of a first language (Dutch) compared to a statistically-familiar but uncomprehended language (French). We show that encoding models with phoneme-level linguistic features uncovered an increased neural tracking response; this signal was further amplified in a comprehended language, putatively reflecting the transformation of acoustic features into internally-generated phoneme-level representations. Phonemes were tracked more strongly in a comprehended language, suggesting that language comprehension functions as a neural filter over acoustic edges of the speech signal as it transforms sensory signals into abstract linguistic units.