Using stochastic language models (SLM) to map lexical, syntactic, and phonological information processing in the brain

Abstract

Language comprehension involves the simultaneous processing of information at the phonological, syntactic, and lexical level. We track these three distinct streams of information in the brain by using stochastic measures derived from computational language models to detect neural correlates of phoneme, part-of-speech, and word processing in an fMRI experiment. Probabilistic language models have proven to be useful tools for studying how language is processed as a sequence of symbols unfolding in time. Conditional probabilities between sequences of words are at the basis of probabilistic measures such as surprisal and perplexity which have been successfully used as predictors of several behavioural and neural correlates of sentence processing. Here we computed perplexity from sequences of words and their parts of speech, and their phonemic transcriptions. Brain activity time-locked to each word is regressed on the three model-derived measures. We observe that the brain keeps track of the statistical structure of lexical, syntactic and phonological information in distinct areas.

Brain network profiling defines functionally specialized cortical networks

“…Lan was engaged in language and social tasks and was strongly connected with both TPOJN and Aud. In fact, the superior temporal gyrus (Lan) is part of the semantic stream (Lopopolo, Frank, Van Den Bosch, & Willems, 2017) and is primarily implicated in passive listening but not reading (Hagoort & Indefrey, 2014). Together, Lan and lDMN encompass the cortical language system (Friederici, 2017;Skeide & Friederici, 2016).…”

“…Moreover, it was significantly more connected with lDMN than with other default networks. In contrast, the middle temporal gyrus (lDMN) is recruited during passive reading as well as in listening (Hagoort & Indefrey, 2014), and is associated with the syntactic stream (Lopopolo et al, 2017). However, temporal regions of these two networks are involved in different functions.…”

“…However, temporal regions of these two networks are involved in different functions. In fact, the superior temporal gyrus (Lan) is part of the semantic stream (Lopopolo, Frank, Van Den Bosch, & Willems, 2017) and is primarily implicated in passive listening but not reading (Hagoort & Indefrey, 2014). In contrast, the middle temporal gyrus (lDMN) is recruited during passive reading as well as in listening (Hagoort & Indefrey, 2014), and is associated with the syntactic stream (Lopopolo et al, 2017).…”

“…In fact, the superior temporal gyrus (Lan) is part of the semantic stream (Lopopolo, Frank, Van Den Bosch, & Willems, 2017) and is primarily implicated in passive listening but not reading (Hagoort & Indefrey, 2014). In contrast, the middle temporal gyrus (lDMN) is recruited during passive reading as well as in listening (Hagoort & Indefrey, 2014), and is associated with the syntactic stream (Lopopolo et al, 2017). Furthermore, regions of the lDMN are involved in tasks with higher semantic demands (Hagoort & Indefrey, 2014), and in the initial stages of learning new languages (Weber, Christiansen, Petersson, Indefrey, & Hagoort, 2016).…”

Probabilistic language models in cognitive neuroscience: Promises and pitfalls

“…Typically, these stages are investigated in separate experiments with different experimental paradigms. Can probabilistic language models be used as a tool for investigating expectation-based processing at distinct representational and temporal levels of complexity concurrently in a single experiment within the same dataset (e.g., Lopopolo et al, 2017)?…”