Context-Based Facilitation in Visual Word Recognition: Evidence for Visual and Lexical But Not Pre-Lexical Contributions

Abstract Word familiarity and predictive context facilitate visual word processing, leading to faster recognition times and reduced neuronal responses. Previously, models with and without top-down connections, including lexical-semantic, pre-lexical (e.g., orthographic/phonological), and visual processing levels were successful in accounting for these facilitation effects. Here we systematically assessed context-based facilitation with a repetition priming task and explicitly dissociated pre-lexical and lexical processing levels using a pseudoword (PW) familiarization procedure. Experiment 1 investigated the temporal dynamics of neuronal facilitation effects with magnetoencephalography (MEG; N = 38 human participants), while experiment 2 assessed behavioral facilitation effects (N = 24 human participants). Across all stimulus conditions, MEG demonstrated context-based facilitation across multiple time windows starting at 100 ms, in occipital brain areas. This finding indicates context-based facilitation at an early visual processing level. In both experiments, we furthermore found an interaction of context and lexical familiarity, such that stimuli with associated meaning showed the strongest context-dependent facilitation in brain activation and behavior. Using MEG, this facilitation effect could be localized to the left anterior temporal lobe at around 400 ms, indicating within-level (i.e., exclusively lexical-semantic) facilitation but no top-down effects on earlier processing stages. Increased pre-lexical familiarity (in PWs familiarized utilizing training) did not enhance or reduce context effects significantly. We conclude that context-based facilitation is achieved within visual and lexical processing levels. Finally, by testing alternative hypotheses derived from mechanistic accounts of repetition suppression, we suggest that the facilitatory context effects found here are implemented using a predictive coding mechanism.

[1]  F. Waszak,et al.  The processing of mispredicted and unpredicted sensory inputs interact differently with attention , 2018, Neuropsychologia.

[2]  Matthew H. Davis,et al.  Prediction Errors but Not Sharpened Signals Simulate Multivoxel fMRI Patterns during Speech Perception , 2016, PLoS biology.

[3]  Kara D. Federmeier,et al.  Never seem to find the time: evaluating the physiological time course of visual word recognition with regression analysis of single-item event-related potentials , 2014 .

[4]  F. Cuetos,et al.  Repeated Exposure to “meaningless” Pseudowords Modulates LPC, but Not N(FN)400 , 2014, Brain Topography.

[5]  M. Rugg.  The effects of semantic priming and work repetition on event-related potentials. , 1985, Psychophysiology.

[6]  Walter Ritter,et al.  Repetition and semantic priming of nonwords: implications for theories of N400 and word recognition. , 2004, Psychophysiology.

[7]  Stefan Knecht,et al.  New Names for Known Things: On the Association of Novel Word Forms with Existing Semantic Information , 2010, Journal of Cognitive Neuroscience.

[8]  R. Baayen,et al.  Mixed-effects modeling with crossed random effects for subjects and items , 2008 .

[9]  Albert Kim,et al.  Rapid Interactions between Lexical Semantic and Word Form Analysis during Word Recognition in Context: Evidence from ERPs , 2012, Journal of Cognitive Neuroscience.

[10]  David Poeppel,et al.  Word-specific repetition effects revealed by MEG and the implications for lexical access , 2013, Brain and Language.

[11]  Katherine Guérard,et al.  Bank of Standardized Stimuli (BOSS) Phase II: 930 New Normative Photos , 2014, PloS one.

[12]  A. Jacobs,et al.  The word frequency effect: a review of recent developments and implications for the choice of frequency estimates in German. , 2011, Experimental psychology.

[13]  Timothy E. J. Behrens,et al.  Perceptual Classification in a Rapidly Changing Environment , 2011, Neuron.

[14]  Reinhold Kliegl,et al.  Experimental Effects and Individual Differences in Linear Mixed Models: Estimating the Relationship between Spatial, Object, and Attraction Effects in Visual Attention , 2010, Front. Psychology.

[15]  Maximilian Riesenhuber,et al.  Adding Words to the Brain's Visual Dictionary: Novel Word Learning Selectively Sharpens Orthographic Representations in the VWFA , 2015, The Journal of Neuroscience.

[16]  Tzyy-Ping Jung,et al.  Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.

[17]  Marta Kutas,et al.  Pre-Processing in Sentence Comprehension: Sensitivity to Likely Upcoming Meaning and Structure , 2014, Lang. Linguistics Compass.

[18]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[19]  Ralf Engbert,et al.  Tracking the mind during reading: the influence of past, present, and future words on fixation durations. , 2006, Journal of experimental psychology. General.

[20]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[21]  Pienie Zwitserlood,et al.  Five days versus a lifetime: intense associative vocabulary training generates lexically integrated words. , 2007, Restorative neurology and neuroscience.

[22]  W. Drongelen,et al.  Localization of brain electrical activity via linearly constrained minimum variance spatial filtering , 1997, IEEE Transactions on Biomedical Engineering.

[23]  D. Collins,et al.  Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space , 1994, Journal of computer assisted tomography.

[24]  Russell A. Poldrack,et al.  The Neural Substrates of Visual Perceptual Learning of Words: Implications for the Visual Word Form Area Hypothesis , 2007, Journal of Cognitive Neuroscience.

[25]  Floris P. de Lange,et al.  Differential temporal dynamics during visual imagery and perception , 2017, bioRxiv.

[26]  Phillip M. Alday,et al.  A common misapplication of statistical inference: Nuisance control with null-hypothesis significance tests , 2016, Brain and Language.

[27]  Pierluigi Zoccolotti,et al.  Reading development in an orthographically regular language: effects of length, frequency, lexicality and global processing ability , 2009 .

[28]  M. Gareth Gaskell,et al.  Consolidation of vocabulary during sleep: The rich get richer? , 2017, Neuroscience & Biobehavioral Reviews.

[29]  A. Jacobs,et al.  Frequency and predictability effects on event-related potentials during reading , 2006, Brain Research.

[30]  Ellen F. Lau,et al.  A cortical network for semantics: (de)constructing the N400 , 2008, Nature Reviews Neuroscience.

[31]  Kara D. Federmeier,et al.  Revisiting the incremental effects of context on word processing: Evidence from single-word event-related brain potentials. , 2015, Psychophysiology.

[32]  R. Salmelin,et al.  Spatiotemporal Convergence of Semantic Processing in Reading and Speech Perception , 2009, The Journal of Neuroscience.

[33]  Mante S. Nieuwland,et al.  Do ‘early’ brain responses reveal word form prediction during language comprehension? A critical review , 2019, Neuroscience & Biobehavioral Reviews.

[34]  Ellen F. Lau,et al.  Automatic Semantic Facilitation in Anterior Temporal Cortex Revealed through Multimodal Neuroimaging , 2013, The Journal of Neuroscience.

[35]  R. Salmelin,et al.  Distinct time courses of word and context comprehension in the left temporal cortex. , 1998, Brain : a journal of neurology.

[36]  Julia M. Huntenburg,et al.  Loading and plotting of cortical surface representations in Nilearn , 2017 .

[37]  Anke Marit Albers,et al.  Eye Movement-Related Confounds in Neural Decoding of Visual Working Memory Representations , 2017, eNeuro.

[38]  D. Balota,et al.  Moving beyond Coltheart’s N: A new measure of orthographic similarity , 2008, Psychonomic bulletin & review.

[39]  Joachim Gross,et al.  Good practice for conducting and reporting MEG research , 2013, NeuroImage.

[40]  Mareike Grotheer,et al.  Repetition Probability Effects Depend on Prior Experiences , 2014, The Journal of Neuroscience.

[41]  Blair C. Armstrong,et al.  PSPs and ERPs: Applying the dynamics of post-synaptic potentials to individual units in simulation of temporally extended Event-Related Potential reading data , 2014, Brain and Language.

[42]  Anke Marit Albers,et al.  Eye movement-related confounds in neural decoding of visual working memory representations , 2017 .

[43]  Christian J. Fiebach,et al.  The lexical categorization model: A computational model of left ventral occipito-temporal cortex activation in visual word recognition , 2020, bioRxiv.

[44]  Kim Plunkett,et al.  The influence of consistency, frequency, and semantics on learning to read: an artificial orthography paradigm. , 2011, Journal of experimental psychology. Learning, memory, and cognition.

[45]  Kara D. Federmeier,et al.  Never Seem to Find the Time: Evaluating the Physiological Time Course of Visual Word Recognition with Regression Analysis of Single Item ERPs. , 2014, Language and cognitive processes.

[46]  Kara D. Federmeier,et al.  Won't get fooled again: An event-related potential study of task and repetition effects on the semantic processing of items without semantics , 2012, Language and cognitive processes.

[47]  Heinz Wimmer,et al.  On Sources of the Word Length Effect in Young Readers , 2015 .

[48]  Floris P. de Lange,et al.  Stimulus familiarity and expectation jointly modulate neural activity in the visual ventral stream , 2017, bioRxiv.

[49]  Ellen F. Lau,et al.  A lexical basis for N400 context effects: Evidence from MEG , 2009, Brain and Language.

[50]  Jarmo A. Hämäläinen,et al.  Distinctive Representation of Mispredicted and Unpredicted Prediction Errors in Human Electroencephalography , 2015, The Journal of Neuroscience.

[51]  Hans Knutsson,et al.  Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates , 2016, Proceedings of the National Academy of Sciences.

[52]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[53]  Rajesh P. N. Rao,et al.  Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. , 1999 .

[54]  Martin Luessi,et al.  MNE software for processing MEG and EEG data , 2014, NeuroImage.

[55]  H. Wimmer,et al.  A dual-route perspective on eye movements of dyslexic readers , 2010, Cognition.

[56]  Heinz Wimmer,et al.  Salzburger Lese-Screening für die Klassenstufen 1-4 (SLS 1-4) , 2003 .

[57]  Jim M. Monti,et al.  Neural repetition suppression reflects fulfilled perceptual expectations , 2008, Nature Neuroscience.

[58]  G. Nolte.  The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors. , 2003, Physics in medicine and biology.

[59]  Marc Brysbaert,et al.  Wuggy: A multilingual pseudoword generator , 2010, Behavior research methods.

[60]  Milena Rabovsky,et al.  The time course of semantic richness effects in visual word recognition , 2012, Front. Hum. Neurosci..

[61]  Kara D. Federmeier,et al.  Thirty years and counting: finding meaning in the N400 component of the event-related brain potential (ERP). , 2011, Annual review of psychology.

[62]  K. Grill-Spector,et al.  Repetition and the brain: neural models of stimulus-specific effects , 2006, Trends in Cognitive Sciences.

[63]  Keith Rayner,et al.  Parafoveal processing in reading , 2011, Attention, Perception, & Psychophysics.

[64]  Marta Kutas,et al.  Interplay between computational models and cognitive electrophysiology in visual word recognition , 2007, Brain Research Reviews.

[65]  J Grainger,et al.  Orthographic processing in visual word recognition: a multiple read-out model. , 1996, Psychological review.

[66]  A. Staub,et al.  Dissociating word frequency and predictability effects in reading: Evidence from coregistration of eye movements and EEG. , 2015, Journal of experimental psychology. Learning, memory, and cognition.

[67]  Martin Kronbichler,et al.  Neural repetition suppression: evidence for perceptual expectation in object-selective regions , 2014, Front. Hum. Neurosci..

[68]  E. Maris,et al.  Prior Expectation Mediates Neural Adaptation to Repeated Sounds in the Auditory Cortex: An MEG Study , 2011, The Journal of Neuroscience.

[69]  R. Oostenveld,et al.  Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.

[70]  M. Traxler,et al.  Effects of prediction and contextual support on lexical processing: Prediction takes precedence , 2015, Cognition.

[71]  M Coltheart,et al.  DRC: a dual route cascaded model of visual word recognition and reading aloud. , 2001, Psychological review.

[72]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[73]  R. C. Oldfield.  The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[74]  Melvin J. Yap,et al.  Semantic Richness Effects in Spoken Word Recognition: A Lexical Decision and Semantic Categorization Megastudy , 2016, Front. Psychol..

[75]  Russell A. Epstein,et al.  Expectation modulates repetition priming under high stimulus variability , 2017, Journal of vision.

[76]  Ellen F. Lau,et al.  The role of temporal predictability in semantic expectation: An MEG investigation , 2015, Cortex.

[77]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[78]  Stefan Haufe,et al.  Single-trial analysis and classification of ERP components — A tutorial , 2011, NeuroImage.

[79]  Janneke F. M. Jehee,et al.  Less Is More: Expectation Sharpens Representations in the Primary Visual Cortex , 2012, Neuron.

[80]  Jakke Tamminen,et al.  Novel word integration in the mental lexicon: Evidence from unmasked and masked semantic priming , 2013, Quarterly journal of experimental psychology.

[81]  Ellen F. Lau,et al.  Neural evidence for Bayesian trial-by-trial adaptation on the N400 during semantic priming , 2019, Cognition.

[82]  Kara D. Federmeier,et al.  The acronym superiority effect , 2007, Psychonomic bulletin & review.

[83]  Ellen F. Lau,et al.  Spatiotemporal Signatures of Lexical-Semantic Prediction. , 2016, Cerebral cortex.

[84]  Martin Luessi,et al.  MEG and EEG data analysis with MNE-Python , 2013, Front. Neuroinform..

[85]  Christian J Fiebach,et al.  Neuronal Mechanisms of Repetition Priming in Occipitotemporal Cortex: Spatiotemporal Evidence from Functional Magnetic Resonance Imaging and Electroencephalography , 2005, The Journal of Neuroscience.

[86]  Karl J. Friston,et al.  A theory of cortical responses , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[87]  K. Rayner.  Eye movements in reading and information processing: 20 years of research. , 1998, Psychological bulletin.

[88]  Ole Tange,et al.  GNU Parallel: The Command-Line Power Tool , 2011, login Usenix Mag..

[89]  Floris P. de Lange,et al.  Suppressed Sensory Response to Predictable Object Stimuli throughout the Ventral Visual Stream , 2017, The Journal of Neuroscience.

[90]  Andrew C Papanicolaou,et al.  Source localization of the N400 response in a sentence-reading paradigm using evoked magnetic fields and magnetic resonance imaging , 1997, Brain Research.

[91]  Fabio Richlan,et al.  Parafoveal preprocessing in reading revisited: evidence from a novel preview manipulation. , 2014, Journal of experimental psychology. Learning, memory, and cognition.

[92]  Suzanne Dikker,et al.  Predicting language: MEG evidence for lexical preactivation , 2013, Brain and Language.

[93]  Christian J. Fiebach,et al.  Visual word recognition relies on an orthographic prediction error signal , 2018 .

[94]  Ellen F. Lau,et al.  Dissociating N400 Effects of Prediction from Association in Single-word Contexts , 2013, Journal of Cognitive Neuroscience.

[95]  Jie-Li Tsai,et al.  The Time Course of Contextual Effects on Visual Word Recognition , 2012, Front. Psychology.

[96]  Mark J. Huff,et al.  An Abundance of Riches: Cross-Task Comparisons of Semantic Richness Effects in Visual Word Recognition , 2012, Front. Hum. Neurosci..

[97]  Eliane Segers,et al.  Learning Word Meanings: Overnight Integration and Study Modality Effects , 2015, PloS one.

[98]  Blair C. Armstrong,et al.  The what, when, where, and how of visual word recognition , 2014, Trends in Cognitive Sciences.

[99]  Jonathan Grainger,et al.  A Thousand Words Are Worth a Picture , 2015, Psychological science.

[100]  Christian J. Fiebach,et al.  An orthographic prediction error as the basis for efficient visual word recognition , 2018, NeuroImage.

[101]  Leonardo S. Barbosa,et al.  Prior Expectation Modulates Repetition Suppression without Perceptual Awareness , 2018, Scientific Reports.

[102]  Caterina Breitenstein,et al.  Development and validation of a language learning model for behavioral and functional-imaging studies , 2002, Journal of Neuroscience Methods.

[103]  Anders M. Dale,et al.  N400-like Magnetoencephalography Responses Modulated by Semantic Context, Word Frequency, and Lexical Class in Sentences , 2002, NeuroImage.

[104]  Matthew H. Davis,et al.  Can cognitive models explain brain activation during word and pseudoword reading? A meta-analysis of 36 neuroimaging studies. , 2013, Psychological bulletin.

[105]  M. Brodeur,et al.  The Bank of Standardized Stimuli (BOSS), a New Set of 480 Normative Photos of Objects to Be Used as Visual Stimuli in Cognitive Research , 2010, PloS one.