Pioneering studies linked the semantic composition of words to particular brain regions, such as the anterior temporal lobe (e.g., Baron and Osherson, 2011 Bemis and Pylkkänen, 2011 Westerlund and Pylkkänen, 2014 Zhang and Pylkkänen, 2015) or mid-superior temporal cortex ( Frankland and Greene, 2015).
However, how sentence-level meaning is constructed from words and represented throughout this “semantic network” remains weakly understood. Sentence comprehension is known to engage a distributed cortical network, spanning temporal, parietal, and inferior/superior temporal cortex ( Lau et al., 2008 Binder et al., 2009). This is because models have typically represented sentences as unordered “bags-of-words.” Using a deep artificial neural network that recurrently and nonlinearly combines word representations into unified propositional sentence representations, we provide evidence that sentence-level information is encoded throughout a cortical network, rather than in a single region. However, how unified sentence-level information, as opposed to word-level units, is represented throughout this network remains unclear. Such models have helped map a cortical semantic information-processing network. A recent approach, emerging from advances in functional neuroimaging, big data, and machine learning, is to computationally model meaning, and use models to predict brain activity. SIGNIFICANCE STATEMENT A modern-day scientific challenge is to understand how the human brain transforms word sequences into representations of sentence meaning. In follow-up analyses, we place results in the context of other deep network approaches (ELMo and BERT) and estimate the degree of unpredicted neural signal using an “experiential” semantic model and cross-participant encoding. This effect occurs throughout a distributed network, which suggests that propositional sentence-level meaning is represented within and across multiple cortical regions rather than at any single site. Using voxelwise encoding modeling, we demonstrate that InferSent predicts elements of fMRI activation that cannot be predicted by bag-of-words models and sentence models using grammatical rules to assemble word vectors. Recurrent connections and nonlinear filters enable InferSent to transform sequences of word vectors into unified “propositional” sentence representations suitable for evaluating intersentence entailment relations. To address this issue, we interrogated fMRI activation elicited as 240 sentences were read by 14 participants (9 female, 5 male), using sentences encoded by a recurrent deep artificial neural-network trained on a sentence inference task (InferSent). This is because models have typically represented sentences as “bags-of-words” that neglect sentence-level structure. However, it remains unclear whether activation patterns within regions reflect unified representations of sentence-level meaning, as opposed to superpositions of context-independent component words. These studies have helped map out semantic representation across a distributed brain network spanning temporal, parietal, and frontal cortex. Recent advances have begun to explain brain activation elicited by sentences using vector models of word meaning derived from patterns of word co-occurrence in text corpora. Understanding how and where in the brain sentence-level meaning is constructed from words presents a major scientific challenge.
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