Parsing as a Cue-Based Retrieval Model

Evaluation of an Algorithmic-Level Left-Corner Parsing Account of Surprisal Effects

This article evaluates the predictions of an algorithmic-level distributed associative memory model as it introduces, propagates, and resolves ambiguity, and compares it to the predictions of computational-level parallel parsing models in which ambiguous analyses are accounted separately in discrete distributions. By superposing activation patterns that serve as cues to other activation patterns, the model is able to maintain multiple syntactically complex analyses superposed in a finite working memory, propagate this ambiguity through multiple intervening words, then resolve this ambiguity in a way that produces a measurable predictor that is proportional to the log conditional probability of the disambiguating word given its context, marginalizing over all remaining analyses. The results are indeed consistent in cases of complex structural ambiguity with computational-level parallel parsing models producing this same probability as a predictor, which have been shown reliably to predict human reading times.

The Plausibility of Sampling as an Algorithmic Theory of Sentence Processing

Words that are more surprising given context take longer to process. However, no incremental parsing algorithm has been shown to directly predict this phenomenon. In this work, we focus on a class of algorithms whose runtime does naturally scale in surprisal-those that involve repeatedly sampling from the prior. Our first contribution is to show that simple examples of such algorithms predict runtime to increase superlinearly with surprisal, and also predict variance in runtime to increase. These two predictions stand in contrast with literature on surprisal theory (Hale, 2001; Levy, 2008a) which assumes that the expected processing cost increases linearly with surprisal, and makes no prediction about variance. In the second part of this paper, we conduct an empirical study of the relationship between surprisal and reading time, using a collection of modern language models to estimate surprisal. We find that with better language models, reading time increases superlinearly in surprisal, and also that variance increases. These results are consistent with the predictions of sampling-based algorithms.

Robust Effects of Working Memory Demand during Naturalistic Language Comprehension in Language-Selective Cortex

To understand language, we must infer structured meanings from real-time auditory or visual signals. Researchers have long focused on word-by-word structure building in working memory as a mechanism that might enable this feat. However, some have argued that language processing does not typically involve rich word-by-word structure building, and/or that apparent working memory effects are underlyingly driven by surprisal (how predictable a word is in context). Consistent with this alternative, some recent behavioral studies of naturalistic language processing that control for surprisal have not shown clear working memory effects. In this fMRI study, we investigate a range of theory-driven predictors of word-by-word working memory demand during naturalistic language comprehension in humans of both sexes under rigorous surprisal controls. In addition, we address a related debate about whether the working memory mechanisms involved in language comprehension are language specialized or domain general. To do so, in each participant, we functionally localize (1) the language-selective network and (2) the "multiple-demand" network, which supports working memory across domains. Results show robust surprisal-independent effects of memory demand in the language network and no effect of memory demand in the multiple-demand network. Our findings thus support the view that language comprehension involves computationally demanding word-by-word structure building operations in working memory, in addition to any prediction-related mechanisms. Further, these memory operations appear to be primarily conducted by the same neural resources that store linguistic knowledge, with no evidence of involvement of brain regions known to support working memory across domains.SIGNIFICANCE STATEMENT This study uses fMRI to investigate signatures of working memory (WM) demand during naturalistic story listening, using a broad range of theoretically motivated estimates of WM demand. Results support a strong effect of WM demand in the brain that is distinct from effects of word predictability. Further, these WM demands register primarily in language-selective regions, rather than in "multiple-demand" regions that have previously been associated with WM in nonlinguistic domains. Our findings support a core role for WM in incremental language processing, using WM resources that are specialized for language.