Dimensionality and Ramping: Signatures of Sentence Integration in the Dynamics of Brains and Deep Language Models.

A sentence is more than the sum of its words: its meaning depends on how they combine with one another. The brain mechanisms underlying such semantic composition remain poorly understood. To shed light on the neural vector code underlying semantic composition, we introduce two hypotheses: (1) the intrinsic dimensionality of the space of neural representations should increase as a sentence unfolds, paralleling the growing complexity of its semantic representation; and (2) this progressive integration should be reflected in ramping and sentence-final signals. To test these predictions, we designed a dataset of closely matched normal and jabberwocky sentences (composed of meaningless pseudo words) and displayed them to deep language models and to 11 human participants (5 men and 6 women) monitored with simultaneous MEG and intracranial EEG. In both deep language models and electrophysiological data, we found that representational dimensionality was higher for meaningful sentences than jabberwocky. Furthermore, multivariate decoding of normal versus jabberwocky confirmed three dynamic patterns: (1) a phasic pattern following each word, peaking in temporal and parietal areas; (2) a ramping pattern, characteristic of bilateral inferior and middle frontal gyri; and (3) a sentence-final pattern in left superior frontal gyrus and right orbitofrontal cortex. These results provide a first glimpse into the neural geometry of semantic integration and constrain the search for a neural code of linguistic composition.SIGNIFICANCE STATEMENT Starting from general linguistic concepts, we make two sets of predictions in neural signals evoked by reading multiword sentences. First, the intrinsic dimensionality of the representation should grow with additional meaningful words. Second, the neural dynamics should exhibit signatures of encoding, maintaining, and resolving semantic composition. We successfully validated these hypotheses in deep neural language models, artificial neural networks trained on text and performing very well on many natural language processing tasks. Then, using a unique combination of MEG and intracranial electrodes, we recorded high-resolution brain data from human participants while they read a controlled set of sentences. Time-resolved dimensionality analysis showed increasing dimensionality with meaning, and multivariate decoding allowed us to isolate the three dynamical patterns we had hypothesized.

Back-to-back regression: Disentangling the influence of correlated factors from multivariate observations.

Identifying causes solely from observations can be particularly challenging when i) the factors under investigation are difficult to manipulate independently from one-another and ii) observations are high-dimensional. To address this issue, we introduce ''Back-to-Back'' regression (B2B), a linear method designed to efficiently estimate, from a set of correlated factors, those that most plausibly account for multidimensional observations. First, we prove the consistency of B2B, its links to other linear approaches, and show how it can provide a robust, unbiased and interpretable scalar estimate for each factor. Second, we use a variety of simulated data to show that B2B can outperform forward modeling ("encoding"), backward modeling ("decoding") as well as cross-decomposition modeling (i.e. canonical correlation analysis and partial least squares) on causal identification when the factors and the observations are not orthogonal. Finally, we apply B2B to a hundred magneto-encephalography recordings and to a hundred functional Magnetic Resonance Imaging recordings acquired while subjects performed a 1 â€‹h reading task. B2B successfully disentangles the respective contribution of collinear factors such as word length, word frequency in the early visual and late associative cortical responses respectively. B2B compared favorably to other standard techniques on this disentanglement. We discuss how the speed and the generality of B2B sets promising foundations to help identify the causal contributions of covarying factors from high-dimensional observations.