On the Role of Context in Reading Time Prediction

• URL: http://arxiv.org/abs/2409.08160v3
• Date: Mon, 21 Oct 2024 15:22:58 GMT
• Title: On the Role of Context in Reading Time Prediction
• Authors: Andreas Opedal, Eleanor Chodroff, Ryan Cotterell, Ethan Gotlieb Wilcox,
• Abstract summary: We present a new perspective on how readers integrate context during real-time language comprehension. Our proposals build on surprisal theory, which posits that the processing effort of a linguistic unit is an affine function of its in-context information content.
• Score: 50.87306355705826
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a new perspective on how readers integrate context during real-time language comprehension. Our proposals build on surprisal theory, which posits that the processing effort of a linguistic unit (e.g., a word) is an affine function of its in-context information content. We first observe that surprisal is only one out of many potential ways that a contextual predictor can be derived from a language model. Another one is the pointwise mutual information (PMI) between a unit and its context, which turns out to yield the same predictive power as surprisal when controlling for unigram frequency. Moreover, both PMI and surprisal are correlated with frequency. This means that neither PMI nor surprisal contains information about context alone. In response to this, we propose a technique where we project surprisal onto the orthogonal complement of frequency, yielding a new contextual predictor that is uncorrelated with frequency. Our experiments show that the proportion of variance in reading times explained by context is a lot smaller when context is represented by the orthogonalized predictor. From an interpretability standpoint, this indicates that previous studies may have overstated the role that context has in predicting reading times.

Related papers

• The Effect of Surprisal on Reading Times in Information Seeking and Repeated Reading [1.2062053320259833]
  We use eyetracking data to examine three language processing regimes that are common in daily life. We find that the prediction of surprisal theory regarding the presence of a linear effect of surprisal on processing times, extends to these regimes. In information seeking, such estimates do not improve the predictive power of processing times compared to standard surprisals.
  arXiv  Detail & Related papers  (2024-10-10T17:43:34Z)
• Testing the Predictions of Surprisal Theory in 11 Languages [77.45204595614]
  We investigate the relationship between surprisal and reading times in eleven different languages. By focusing on a more diverse set of languages, we argue that these results offer the most robust link to-date between information theory and incremental language processing across languages.
  arXiv  Detail & Related papers  (2023-07-07T15:37:50Z)
• On the Effect of Anticipation on Reading Times [84.27103313675342]
  We operationalize anticipation as a word's contextual entropy. We find substantial evidence for effects of contextual entropy over surprisal on a word's reading time.
  arXiv  Detail & Related papers  (2022-11-25T18:58:23Z)
• Visual Answer Localization with Cross-modal Mutual Knowledge Transfer [6.895321502252051]
  We propose a cross-modal mutual knowledge transfer span localization (MutualSL) method to reduce the knowledge deviation. On this basis, we design a one-way dynamic loss function to dynamically adjust the proportion of knowledge transfer. Our method outperforms other competitive state-of-the-art (SOTA) methods, demonstrating its effectiveness.
  arXiv  Detail & Related papers  (2022-10-26T16:11:09Z)
• A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach [53.727460222955266]
  Temporal Sentence Grounding in Videos (TSGV) aims to ground a natural language sentence in an untrimmed video. Recent studies have found that current benchmark datasets may have obvious moment annotation biases. We introduce a new evaluation metric "dR@n,IoU@m" that discounts the basic recall scores to alleviate the inflating evaluation caused by biased datasets.
  arXiv  Detail & Related papers  (2022-03-10T08:58:18Z)
• An Explanation of In-context Learning as Implicit Bayesian Inference [117.19809377740188]
  We study the role of the pretraining distribution on the emergence of in-context learning. We prove that in-context learning occurs implicitly via Bayesian inference of the latent concept. We empirically find that scaling model size improves in-context accuracy even when the pretraining loss is the same.
  arXiv  Detail & Related papers  (2021-11-03T09:12:33Z)
• Temporal Common Sense Acquisition with Minimal Supervision [77.8308414884754]
  This work proposes a novel sequence modeling approach that exploits explicit and implicit mentions of temporal common sense. Our method is shown to give quality predictions of various dimensions of temporal common sense. It also produces representations of events for relevant tasks such as duration comparison, parent-child relations, event coreference and temporal QA.
  arXiv  Detail & Related papers  (2020-05-08T22:20:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.