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Never seem to find the time: evaluating the physiological time course of visual word recognition with regression analysis of single-item event-related potentials

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TLDR
This article examined the time course of influence of variables ranging from relatively perceptual (e.g., bigram frequency) to relatively semantic on ERP responses, analysed at the single-item level.
Abstract
Visual word recognition is a process that, both hierarchically and in parallel, draws on different types of information ranging from perceptual to orthographic to semantic. A central question concerns when and how these different types of information come online and interact after a word form is initially perceived. Numerous studies addressing aspects of this question have been conducted with a variety of techniques [e.g., behaviour, eye-tracking, event-related potentials (ERPs)], and divergent theoretical models, suggesting different overall speeds of word processing, have coalesced around clusters of mostly method-specific results. Here, we examine the time course of influence of variables ranging from relatively perceptual (e.g., bigram frequency) to relatively semantic (e.g., the number of lexical associates) on ERP responses, analysed at the single-item level. Our results, in combination with a critical review of the literature, suggest methodological, analytic and theoretical factors that may have led to inconsistency in results of past studies; we will argue that consideration of these factors may lead to a reconciliation between divergent views of the speed of word recognition.

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Citations
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Journal ArticleDOI

On the Time Course of Visual Word Recognition: An Event-related Potential Investigation using Masked Repetition Priming

TL;DR: A strong modulation of the N400 and three earlier ERP components (P150, N250, and the P325) that the authors propose reflect sequential overlapping steps in the processing of printed words are shown.
Journal ArticleDOI

Brainprint: Assessing the uniqueness, collectability, and permanence of a novel method for ERP biometrics

TL;DR: There are robustly identifiable features of the ERP that enable labeling of ERPs as belonging to individuals with accuracy reliably above chance, and these features are stable over time, as indicated by continued accurate identification of individuals from ERPs after a lag of up to six months.
Journal ArticleDOI

CEREBRE: A Novel Method for Very High Accuracy Event-Related Potential Biometric Identification

TL;DR: It is argued that the averaged event-related potential (ERP) may provide the potential for more accurate biometric identification, as its elicitation allows for some control over the cognitive state of the user to be obtained through the design of the challenge protocol.
Journal ArticleDOI

Regression-based estimation of ERP waveforms: I. The rERP framework.

TL;DR: The regression-based rERP framework is introduced, which extends ERP averaging to handle arbitrary combinations of categorical and continuous covariates, partial confounding, nonlinear effects, and overlapping responses to distinct events, all within a single unified system.
Journal ArticleDOI

Revisiting the incremental effects of context on word processing: Evidence from single‐word event‐related brain potentials

TL;DR: Modelling word-level variability in ERPs reveals mechanisms by which different sources of information simultaneously contribute to the unfolding neural dynamics of comprehension, as well as probing the continuous and incremental effects of semantic and syntactic context on multiple aspects of lexical processing during sentence comprehension.
References
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Journal ArticleDOI

Words in the brain's language.

TL;DR: These results support a neurobiological model of language in the Hebbian tradition and provide evidence for processing differences between words and matched meaningless pseudowords, and between word classes, such as concrete content and abstract function words, and words evoking visual or motor associations.
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Probabilistic word pre-activation during language comprehension inferred from electrical brain activity.

TL;DR: A phonological regularity of English indefinite articles is exploited in combination with event-related brain potential recordings from the human scalp to show that readers' brains can pre-activate individual words in a graded fashion to a degree that can be estimated from the probability that each word is given as a continuation for a sentence fragment offline.
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SWIFT: a dynamical model of saccade generation during reading.

TL;DR: An advanced version of SWIFT is presented that integrates properties of the oculomotor system and effects of word recognition to explain many of the experimental phenomena faced in reading research and an analysis of the transition from parallel to serial processing.
Journal ArticleDOI

Visual word recognition of single-syllable words.

TL;DR: Large-scale regression studies were used to investigate the unique predictive variance of phonological features in the onsets, lexical variables, and semantic variables to investigate visual word recognition, shedding light on recent empirical controversies in the available word recognition literature.
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