ArticleCognition2017
Modeling the N400 ERP component as transient semantic over-activation within a neural network model of word comprehension.
Article in Cognition, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- From form to concept: a dynamic spatiotemporal brain network cascade supports language comprehension.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- Prediction in reading: A review of predictability effects, their theoretical implications, and beyond.Psychonomic bulletin & review · 2025Review
- Event-related potentials and behavioral correlates of emotional recognition memory in late pregnancy.Archives of women's mental health · 2025Article
- Evaluating frontoparietal network topography for diagnostic markers of Alzheimer's disease.Scientific reports · 2024Article
- A predictive coding model of the N400.Cognition · 2024Article
- Better Together: Integrating Multivariate with Univariate Methods, and MEG with EEG to Study Language Comprehension.Language, cognition and neuroscience · 2024Article
- Explaining the Sentence Superiority Effect and N400s Elicited by Words and Short Sentences with OB1-Reader.Journal of cognition · 2024Article
- Tracking Lexical and Semantic Prediction Error Underlying the N400 Using Artificial Neural Network Models of Sentence Processing.Neurobiology of language (Cambridge, Mass.) · 2024Article
- Accumulating evidence for myriad alternatives: Modeling the generation of free association.Psychological review · 2023Article
- From decomposition to distributed theories of morphological processing in reading.Psychonomic bulletin & review · 2022Review
- Connecting and considering: Electrophysiology provides insights into comprehension.Psychophysiology · 2022Article
- Age-related dissociation of N400 effect and lexical priming.Scientific reports · 2020Observational
- A Comprehension- or a Production-Based Monitor? Response to Roelofs (2020).Journal of cognition · 2020Article
- To catch a Snitch: Brain potentials reveal variability in the functional organization of (fictional) world knowledge during reading.Journal of memory and language · 2020Article
- Neural habituation enhances novelty detection: an EEG study of rapidly presented words.Computational brain & behavior · 2020Article
- Quasi-compositional mapping from form to meaning: a neural network-based approach to capturing neural responses during human language comprehension.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020Article
- Neural Components of Reading Revealed by Distributed and Symbolic Computational Models.Neurobiology of language (Cambridge, Mass.) · 2020Article
- Toward a Neurobiologically Plausible Model of Language-Related, Negative Event-Related Potentials.Frontiers in psychology · 2019Article
Corrections and comments
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2 authors.
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Abstract
The study of the N400 event-related brain potential has provided fundamental insights into the nature of real-time comprehension processes, and its amplitude is modulated by a wide variety of stimulus and context factors. It is generally thought to reflect the difficulty of semantic access, but formulating a precise characterization of this process has proved difficult. Laszlo and colleagues (Laszlo & Plaut, 2012; Laszlo & Armstrong, 2014) used physiologically constrained neural networks to model the N400 as transient over-activation within semantic representations, arising as a consequence of the distribution of excitation and inhibition within and between cortical areas. The current work extends this approach to successfully model effects on both N400 amplitudes and behavior of word frequency, semantic richness, repetition, semantic and associative priming, and orthographic neighborhood size. The account is argued to be preferable to one based on "implicit semantic prediction error" (Rabovsky & McRae, 2014) for a number of reasons, the most fundamental of which is that the current model actually produces N400-like waveforms in its real-time activation dynamics.
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