The effect of high- and low-frequency previews and sentential fit on word skipping during reading

In a previous gaze-contingent boundary experiment, Angele and Rayner (2013) found that readers are likely to skip a word that appears to be the definite article the even when syntactic constraints do not allow for articles to occur in that position. In the present study, we investigated whether the word frequency of the preview of a 3-letter target word influences a reader’s decision to fixate or skip that word. We found that the word frequency rather than the felicitousness (syntactic fit) of the preview affected how often the upcoming word was skipped. These results indicate that visual information about the upcoming word trumps information from the sentence context when it comes to making a skipping decision. Skipping parafoveal instances of the therefore may simply be an extreme case of skipping high-frequency words

A distributional model of semantic context effects in lexical processinga

One of the most robust findings of experimental psycholinguistics is that the context in which a word is presented influences the effort involved in processing that word. We present a novel model of contextual facilitation based on word co-occurrence prob ability distributions, and empirically validate the model through simulation of three representative types of context manipulation: single word priming, multiple-priming and contextual constraint. In our simulations the effects of semantic context are mod eled using general-purpose techniques and representations from multivariate statistics, augmented with simple assumptions reflecting the inherently incremental nature of speech understanding. The contribution of our study is to show that special-purpose m echanisms are not necessary in order to capture the general pattern of the experimental results, and that a range of semantic context effects can be subsumed under the same principled account.›

Availability-Based Production Predicts Speakers' Real-time Choices of Mandarin Classifiers

Speakers often face choices as to how to structure their intended message into an utterance. Here we investigate the influence of contextual predictability on the encoding of linguistic content manifested by speaker choice in a classifier language. In English, a numeral modifies a noun directly (e.g., three computers). In classifier languages such as Mandarin Chinese, it is obligatory to use a classifier (CL) with the numeral and the noun (e.g., three CL.machinery computer, three CL.general computer). While different nouns are compatible with different specific classifiers, there is a general classifier "ge" (CL.general) that can be used with most nouns. When the upcoming noun is less predictable, the use of a more specific classifier would reduce surprisal at the noun thus potentially facilitate comprehension (predicted by Uniform Information Density, Levy & Jaeger, 2007), but the use of that more specific classifier may be dispreferred from a production standpoint if accessing the general classifier is always available (predicted by Availability-Based Production; Bock, 1987; Ferreira & Dell, 2000). Here we use a picture-naming experiment showing that Availability-Based Production predicts speakers' real-time choices of Mandarin classifiers.Comment: To appear in proceedings of CogSci 201

Neural evidence for Bayesian trial-by-trial adaptation on the N400 during semantic priming.

When semantic information is activated by a context prior to new bottom-up input (i.e. when a word is predicted), semantic processing of that incoming word is typically facilitated, attenuating the amplitude of the N400 event related potential (ERP) - a direct neural measure of semantic processing. N400 modulation is observed even when the context is a single semantically related "prime" word. This so-called "N400 semantic priming effect" is sensitive to the probability of encountering a related prime-target pair within an experimental block, suggesting that participants may be adapting the strength of their predictions to the predictive validity of their broader experimental environment. We formalize this adaptation using a Bayesian learning model that estimates and updates the probability of encountering a related versus an unrelated prime-target pair on each successive trial. We found that our model's trial-by-trial estimates of target word probability accounted for significant variance in trial-by-trial N400 amplitude. These findings suggest that Bayesian principles contribute to how comprehenders adapt their semantic predictions to the statistical structure of their broader environment, with implications for the functional significance of the N400 component and the predictive nature of language processing

Modeling Task Effects in Human Reading with Neural Attention

Humans read by making a sequence of fixations and saccades. They often skip words, without apparent detriment to understanding. We offer a novel explanation for skipping: readers optimize a tradeoff between performing a language-related task and fixating as few words as possible. We propose a neural architecture that combines an attention module (deciding whether to skip words) and a task module (memorizing the input). We show that our model predicts human skipping behavior, while also modeling reading times well, even though it skips 40% of the input. A key prediction of our model is that different reading tasks should result in different skipping behaviors. We confirm this prediction in an eye-tracking experiment in which participants answers questions about a text. We are able to capture these experimental results using the our model, replacing the memorization module with a task module that performs neural question answering

Modeling Fixation Behavior in Reading with Character-level Neural Attention

Humans read text in a sequence of fixations connected by saccades spanning 7–9 characters. While most words are fixated, some are skipped, and sometimes there are reverse saccades. Previous work has explained this behavior in terms of a trade-off between the accuracy of text comprehension and the efficiency of reading, and modeled this using attention-based sequence-to-sequence neural networks. We extend this line of work by modeling the locations of individual fixations down to the character level. We evaluate our model on an eye-tracking corpus and demonstrate that it reproduces human reading patterns, both quantitatively and qualitatively. It achieves good performance in predicting fixation positions and also captures lexical effects on fixation rate and landing position effects

Lexical Predictability during Natural Reading: Effects of Surprisal and Entropy Reduction

What are the effects of word‐by‐word predictability on sentence processing times during the natural reading of a text? Although information complexity metrics such as surprisal and entropy reduction have been useful in addressing this question, these metrics tend to be estimated using computational language models, which require some degree of commitment to a particular theory of language processing. Taking a different approach, this study implemented a large‐scale cumulative cloze task to collect word‐by‐word predictability data for 40 passages and compute surprisal and entropy reduction values in a theory‐neutral manner. A separate group of participants read the same texts while their eye movements were recorded. Results showed that increases in surprisal and entropy reduction were both associated with increases in reading times. Furthermore, these effects did not depend on the global difficulty of the text. The findings suggest that surprisal and entropy reduction independently contribute to variation in reading times, as these metrics seem to capture different aspects of lexical predictability

Visual, Lexical, and Syntactic Effects on Failure to Notice Word Transpositions: Evidence from Behavioral and Eye Movement Data

Evidence of systematic misreading has been taken to argue that language processing is noisy, and that readers take noise into consideration and therefore sometimes interpret sentences non-literally (rational inference over a noisy channel). The present study investigates one specific misreading phenomenon: failure to notice word transpositions in a sentence. While this phenomenon can be explained by rational inference, it also has been argued to arise due to parallel lexical processing. The study explored these two accounts. Visual, lexical, and syntactic properties of the two transposed words were manipulated in three experiments. Failure to notice the transposition was more likely when both words were short, and when readers\u27 eyes skipped, rather than directly fixated, one of the two words. Failure to notice the transposition also occurred when one word was long. The position of ungrammaticality elicited by transposition (the first vs. second transposed word) influenced tendency to miss the error; the direction of the effect, however, depended on word classes of the transposed words. Failure of detection was not more likely when the second transposed word was easier to recognize than the first transposed word. Finally, readers’ eye movements on the transposed words revealed no disruption in those trials when they ultimately accepted the sentence to be grammatical. We consider the findings to be only partially supportive of parallel lexical processing and instead propose that word recognition is serial, but integration is not perfectly incremental, and that rational inference may take place before an ungrammatical representation is constructed

Eye Movements in Reading: Models and Data

Models of eye movement control in reading and their impact on the field are discussed. Differences between the E-Z Reader model and the SWIFT model are reviewed, as are benchmark data that need to be accounted for by any model of eye movement control. Predictions made by the models and how models can sometimes account for counterintuitive findings are also discussed. Finally, the role of models and data in further understanding the reading process is considered