Biology subjects

Willems, R. M.

Publications and source records attributed to Willems, R. M..

4 recordsLinked to original sources

bioRxiv2020

Words in context: tracking context-processing during language comprehension using computational language models and MEG

The meaning of a word depends on its lexical semantics and on the context in which it is embedded. At the basis of this lays the distinction between lexical retrieval and integration, two basic operations supporting language comprehension. In this paper, we investigate how lexical retrieval and integration are implemented in the brain by comparing MEG activity to word representations generated by computational language models. We test both non-contextualized embeddings, representing words independently from their context, and contextualized embeddings, which instead integrate contextual information in their representations. Using representational similarity analysis over cortical regions and over time, we observed that brain activity in the left anterior temporal pole and inferior frontal regions shows higher similarity with contextualized word embeddings compared to non-contextualized embeddings, between 300 and 500 ms after word presentation. On the other hand, non-contextualized word embeddings show higher similarity with brain activity in the left lateral and anterior temporal lobe at earlier latencies - areas and latencies related to lexical retrieval. Our results highlight how lexical retrieval and context integration can be tracked in the brain using word embeddings obtained with computational models. These results also suggest that the distinction between lexical retrieval and integration might be framed in terms of context-independent and contextualized representations.

neuroscience↗

bioRxiv2020

Amount of fiction reading correlates with higher connectivity between cortical areas for language and mentalizing

Behavioral evidence suggests that engaging with fiction is positively correlated with social abilities. The rationale behind this link is that engaging with fiction and fictional characters may offer a training mode for mentalizing and empathizing with sentient agents in the real world, analogous to a flight simulator for pilots. In this study, we explored the relationship between reading fiction and mentalizing by looking at brain network dynamics in 57 participants who varied on how much fiction they read in their daily lives. The hypothesis was that if reading fiction indeed trains mentalizing, a task that requires mentalizing -Like immersing in a fictional story and engaging with a protagonist-should elicit differences in brain network dynamics depending on how much people read. More specifically, more frequent readers should show increased connectivity within the theory of mind network (ToM) or between the ToM network and other brain networks. While brain activation was measured with fMRI, participants listened to two literary narratives. We computed time-course correlations between brain regions and compared the correlation values from listening to narratives to listening to an auditory baseline condition. The between-region correlations were then related to individual differences measures including the amount of fiction that participants consume in their daily lives. Our results show that there is a linear relationship between how much people read and the functional connectivity in areas known to be involved in language and mentalizing. This adds neurobiological credibility to the fiction influences mentalizing abilities hypothesis as suggested on the basis of conceptual analysis.

scientific communication and education↗

bioRxiv2018

Frequency-specific brain dynamics related to prediction during language comprehension

The brains remarkable capacity to process spoken language virtually in real time requires fast and efficient information processing machinery. In this study, we investigated how frequency-specific brain dynamics relate to models of probabilistic language prediction during auditory narrative comprehension. We recorded MEG activity while participants were listening to auditory stories in Dutch. Using trigram statistical language models, we estimated for every word in a story its conditional probability of occurrence. On the basis of word probabilities, we computed how unexpected the current word is given its context (word perplexity) and how (un)predictable the current linguistic context is (word entropy). We then evaluated whether source-reconstructed MEG oscillations at different frequency bands are modulated as a function of these language processing metrics. We show that theta-band source dynamics are increased in high relative to low entropy states, likely reflecting lexical computations. Beta-band dynamics are increased in situations of low word entropy and perplexity possibly reflecting maintenance of ongoing cognitive context. These findings lend support to the idea that the brain engages in the active generation and evaluation of predicted language based on the statistical properties of the input signal.

neuroscience↗

bioRxiv2017

Probabilistic language models in cognitive neuroscience: promises and pitfalls

Cognitive neuroscientists of language comprehension study how neural computations relate to cognitive computations during comprehension. On the cognitive part of the equation, it is important that the computations and processing complexity are explicitly defined. Probabilistic language models can be used to give a computationally explicit account of language complexity during comprehension. Whereas such models have so far predominantly been evaluated against behavioral data, only recently have the models been used to explain neurobiological signals. Measures obtained from these models emphasize the probabilistic, information-processing view of language understanding and provide a set of tools that can be used for testing neural hypotheses about language comprehension. Here, we provide a cursory review of the theoretical foundations and example neuroimaging studies employing probabilistic language models. We high-light the advantages and potential pitfalls of this approach and indicate avenues for future research.

neuroscience↗