A large body of work in psycholinguistics has focused on the idea that online language comprehension can be shallow or `good enough': given constraints on time or available computation, comprehenders may form interpretations of their input that are plausible but inaccurate. However, this idea has not yet been linked with formal theories of computation under resource constraints. Here we use information theory to formulate a model of language comprehension as an optimal trade-off between accuracy and processing depth, formalized as bits of information extracted from the input, which increases with processing time. The model provides a measure of processing effort as the change in processing depth, which we link to EEG signals and reading times. We validate our theory against a large-scale dataset of garden path sentence reading times, and EEG experiments featuring N400, P600 and biphasic ERP effects. By quantifying the timecourse of language processing as it proceeds from shallow to deep, our model provides a unified framework to explain behavioral and neural signatures of language comprehension.

The functional interpretation of language-related ERP components has been a central debate in psycholinguistics for decades. We advance an information-theoretic model of human language processing in the brain, in which incoming linguistic input is processed at two levels, in terms of a heuristic interpretation and in terms of error correction. We propose that these two kinds of information processing have distinct electroencephalographic signatures, corresponding to the well-documented N400 and P600 components of language-related event-related potentials (ERPs). Formally, we show that the information content (surprisal) of a word in context can be decomposed into two quantities: (A) heuristic surprise, which signals the processing difficulty of word given its inferred context, and corresponds with the N400 signal; and (B) discrepancy signal, which reflects divergence between the true context and the inferred context, and corresponds to the P600 signal. Both of these quantities can be estimated using modern NLP techniques. We validate our theory by successfully simulating ERP patterns elicited by a variety of linguistic manipulations in previously-reported experimental data from four experiments. Our theory is in principle compatible with traditional cognitive theories assuming the existence of a `good-enough' heuristic interpretation, but with a precise information-theoretic formulation.

Syntactic adaptation to short-term exposure has been documented with both single-trial priming and cumulative priming paradigms. These studies usually involve repeated exposure to the same structure (e.g. reduced relative clauses), and therefore it remains open whether people can track context-dependent regularities through short-term exposure. In the current study, we present a self-paced-reading experiment that investigates context-dependent syntactic adaptation by manipulating the relationship between the animacy feature of the subject NP (animate vs. inanimate) and the corresponding parse of a verb following a subject NP. We analyzed the results in terms of a log-linear model for context-dependent syntactic adaptation. The results suggest that comprehenders can track and adapt to cue-based distributional regularities, but only when the short-term regularities are consistent with the long-term ones existent in their native language.

Geophysical characterization of calderas is fundamental in assessing their potential for future catastrophic volcanic eruptions. The mechanism behind the unrest of Long Valley Caldera in California remains highly debated, with recent periods of uplift and seismicity driven either by the release of aqueous fluids from the magma chamber or by the intrusion of magma into the upper crust. We use distributed acoustic sensing data recorded along a 100-kilometer fiber-optic cable traversing the caldera to image its subsurface structure. Our images highlight a definite separation between the shallow hydrothermal system and the large magma chamber located at ~12-kilometer depth. The combination of the geological evidence with our results shows how fluids exsolved through second boiling provide the source of the observed uplift and seismicity.

Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. However, its distinct characteristics, such as unknown ground coupling and high noise level, pose challenges to signal processing. Existing machine learning models optimized for conventional seismic data struggle with DAS data due to its ultra-dense spatial sampling and limited manual labels. We introduce a semi-supervised learning approach to address the phase-picking task of DAS data. We use the pre-trained PhaseNet model to generate noisy labels of P/S arrivals in DAS data and apply the Gaussian mixture model phase association (GaMMA) method to refine these noisy labels and build training datasets. We develop PhaseNet-DAS, a deep learning model designed to process 2D spatio-temporal DAS data to achieve accurate phase picking and efficient earthquake detection. Our study demonstrates a method to develop deep learning models for DAS data, unlocking the potential of integrating DAS in enhancing earthquake monitoring.

Continuous microseismic monitoring using three-component (3C) accelerometers deployed in multiple boreholes allows for tracking the detailed evaluation of mesoscale (∼10 m scale) fracture growth during the fracture stimulation experiments at the first Enhanced Geothermal Systems (EGS) Collab testbed. Building on a well-constrained microseismic event catalog, we invert for moment tensor of the events to better understand the fracture geometry and stress orientations. However, it is challenging because of the unknown orientation of 3C accelerometers and low signal-to-noise-ratio nature of high-frequency (several kHz) monitoring. To address these challenges, we first perform the hodogram analysis on the continuous active-source seismic monitoring (CASSM) data to determine the orientations of the 18 3C accelerometers. We then apply the principal component analysis (PCA) to the observed microseismic waveforms to improve the signal-to-noise ratios. We perform a grid search for the full moment tensor by fitting the PCA-denoised waveforms at a frequency range of 5 to 8 kHz. The moment tensor results show both the creation of hydraulic fractures and the reactivation of natural fractures during the hydraulic stimulations. Our stress inversion based on the inverted moment tensors reveals the alteration of stress regime caused by hydraulic fracture stimulations.