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Languages are governed by syntactic constraints-structural rules that determine which sentences are grammatical in the language. In English, one such constraint is subject-verb agreement, which dictates that the number of a verb must match the number of its corresponding subject: "the dogs run", but "the dog runs". While this constraint appears to be simple, in practice speakers make agreement errors, particularly when a noun phrase near the verb differs in number from the subject (for example, a speaker might produce the ungrammatical sentence "the key to the cabinets are rusty"). This phenomenon, referred to as agreement attraction, is sensitive to a wide range of properties of the sentence; no single existing model is able to generate predictions for the wide variety of materials studied in the human experimental literature. We explore the viability of neural network language models-broad-coverage systems trained to predict the next word in a corpus-as a framework for addressing this limitation. We analyze the agreement errors made by Long Short-Term Memory (LSTM) networks and compare them to those of humans. The models successfully simulate certain results, such as the so-called number asymmetry and the difference between attraction strength in grammatical and ungrammatical sentences, but failed to simulate others, such as the effect of syntactic distance or notional (conceptual) number. We further evaluate networks trained with explicit syntactic supervision, and find that this form of supervision does not always lead to more human-like syntactic behavior. Finally, we show that the corpus used to train a network significantly affects the pattern of agreement errors produced by the network, and discuss the strengths and limitations of neural networks as a tool for understanding human syntactic processing.

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Temporarily ambiguous sentences that are disambiguated in favor of a less preferred parse are read more slowly than their unambiguous counterparts. This slowdown is referred to as a garden path effect. Recent self-paced reading studies have found that this effect decreased over the course of the experiment as participants were exposed to such syntactically ambiguous sentences. This decrease in the magnitude of the effect has been interpreted as evidence that readers calibrate their expectations to the context; this minimizes their surprise when they encounter these initially unexpected syntactic structures. Such recalibration of syntactic expectations, referred to as syntactic adaptation, is only one possible explanation for the decrease in garden path effect, however; this decrease could also be driven instead by increased familiarity with the self-paced reading paradigm (task adaptation). The goal of this article is to adjudicate between these two explanations. In a large between-group study (n = 642), we find evidence for syntactic adaptation over and above task adaptation. The magnitude of syntactic adaptation compared to task adaptation is very small, however. Power analyses show that a large number of participants is required to detect, with adequate power, syntactic adaptation in future between-subjects self-paced reading studies. This issue is exacerbated in experiments designed to detect modulations of the basic syntactic adaptation effect; such experiments are likely to be underpowered even with more than 1,200 participants. We conclude that while, contrary to recent suggestions, syntactic adaptation can be detected using self-paced reading, this paradigm is not very effective for studying this phenomenon. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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The disambiguation of a syntactically ambiguous sentence in favor of a less preferred parse can lead to slower reading at the disambiguation point. This phenomenon, referred to as a garden-path effect, has motivated models in which readers initially maintain only a subset of the possible parses of the sentence, and subsequently require time-consuming reanalysis to reconstruct a discarded parse. A more recent proposal argues that the garden-path effect can be reduced to surprisal arising in a fully parallel parser: words consistent with the initially dispreferred but ultimately correct parse are simply less predictable than those consistent with the incorrect parse. Since predictability has pervasive effects in reading far beyond garden-path sentences, this account, which dispenses with reanalysis mechanisms, is more parsimonious. Crucially, it predicts a linear effect of surprisal: the garden-path effect is expected to be proportional to the difference in word surprisal between the ultimately correct and ultimately incorrect interpretations. To test this prediction, we used recurrent neural network language models to estimate word-by-word surprisal for three temporarily ambiguous constructions. We then estimated the slowdown attributed to each bit of surprisal from human self-paced reading times, and used that quantity to predict syntactic disambiguation difficulty. Surprisal successfully predicted the existence of garden-path effects, but drastically underpredicted their magnitude, and failed to predict their relative severity across constructions. We conclude that a full explanation of syntactic disambiguation difficulty may require recovery mechanisms beyond predictability.

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Speech is an inherently noisy and ambiguous signal. To fluently derive meaning, a listener must integrate contextual information to guide interpretations of the sensory input. Although many studies have demonstrated the influence of prior context on speech perception, the neural mechanisms supporting the integration of subsequent context remain unknown. Using MEG to record from human auditory cortex, we analyzed responses to spoken words with a varyingly ambiguous onset phoneme, the identity of which is later disambiguated at the lexical uniqueness point. Fifty participants (both male and female) were recruited across two MEG experiments. Our findings suggest that primary auditory cortex is sensitive to phonological ambiguity very early during processing at just 50 ms after onset. Subphonemic detail is preserved in auditory cortex over long timescales and re-evoked at subsequent phoneme positions. Commitments to phonological categories occur in parallel, resolving on the shorter timescale of ∼450 ms. These findings provide evidence that future input determines the perception of earlier speech sounds by maintaining sensory features until they can be integrated with top-down lexical information.SIGNIFICANCE STATEMENT The perception of a speech sound is determined by its surrounding context in the form of words, sentences, and other speech sounds. Often, such contextual information becomes available later than the sensory input. The present study is the first to unveil how the brain uses this subsequent information to aid speech comprehension. Concretely, we found that the auditory system actively maintains the acoustic signal in auditory cortex while concurrently making guesses about the identity of the words being said. Such a processing strategy allows the content of the message to be accessed quickly while also permitting reanalysis of the acoustic signal to minimize parsing mistakes.

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There is now considerable evidence that human sentence processing is expectation based: As people read a sentence, they use their statistical experience with their language to generate predictions about upcoming syntactic structure. This study examines how sentence processing is affected by readers' uncertainty about those expectations. In a self-paced reading study, we use lexical subcategorization distributions to factorially manipulate both the strength of expectations and the uncertainty about them. We compare two types of uncertainty: uncertainty about the verb's complement, reflecting the next prediction step; and uncertainty about the full sentence, reflecting an unbounded number of prediction steps. We find that uncertainty about the full structure, but not about the next step, was a significant predictor of processing difficulty: Greater reduction in uncertainty was correlated with increased reading times (RTs). We additionally replicated previously observed effects of expectation violation (surprisal), orthogonal to the effect of uncertainty. This suggests that both surprisal and uncertainty affect human RTs. We discuss the consequences for theories of sentence comprehension.

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Many previous studies have shown that predictable words are read faster and lead to reduced neural activation, consistent with a model of reading in which words are activated in advance of being encountered. The nature of such preactivation, however, has typically been studied indirectly through its subsequent effect on word recognition. Here, we use magnetoencephalography to study the dynamics of prediction within serially presented adjective-noun phrases, beginning at the point at which the predictive information is first available to the reader. Using corpus transitional probability to estimate the predictability of a noun, we found an increase in activity in the left middle temporal gyrus in response to the presentation of highly predictive adjectives (i.e., adjectives that license a strong noun prediction). Moreover, we found that adjective predictivity and expected noun frequency interacted, such that the response to the highly predictive adjectives (e.g., stainless) was modulated by the frequency of the expected noun (steel). These results likely reflect preactivation of nouns in highly predictive contexts. The fact that the preactivation process was modulated by the frequency of the predicted item is argued to provide support for a frequency-sensitive lexicon.

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There is substantial neural evidence for the role of morphology (word-internal structure) in visual word recognition. We extend this work to auditory word recognition, drawing on recent evidence that phoneme prediction is central to this process. In a magnetoencephalography (MEG) study, we crossed morphological complexity (bruis-er vs. bourbon) with the predictability of the word ending (bourbon vs. burble). High prediction error (surprisal) led to increased auditory cortex activity. This effect was enhanced for morphologically complex words. Additionally, we calculated for each timepoint the surprisal corresponding to the phoneme perceived at that timepoint, as well as the cohort entropy, which quantifies the competition among words compatible with the string prefix up to that timepoint. Higher surprisal increased neural activity at the end of the word, and higher entropy decreased neural activity shortly after word onset. These results reinforce the role of morphology and phoneme prediction in spoken word recognition.

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