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.

An Orthographic Prediction Error as the basis for efficient Visual Word Recognition

“…This would, in principle, allow for optimized sensory processing as described by the PEMoR in natural reading situations. This hypothesis must be tested in future studies but fits with previous theoretical proposals which have acknowledged the integration of top-down predictions from multiple linguistic domains (for example at the phonological, semantic, or syntactic level DeLong et al, 2005;Eisenhauer, Fiebach, & Gagl, 2019;Nieuwland et al, 2018;Price & Devlin, 2011). Critically, our results go beyond these earlier models by demonstrating that top-down guided expectations are implemented already onto early visual-orthographic processing stages.…”

Predictive pre-activation of orthographic and lexical-semantic representations facilitates visual word recognition

“…Table 2), which shows that the effect of context-dependent facilitation is generally much stronger for words than for pseudowords (see also Fig. 2a; compare also Eisenhauer et al, 2019). To summarize, these behavioral results suggest that context-based facilitation involves processes at the pre-lexical orthographic and the lexical-semantic stages of visual word recognition, while no evidence was found for context effects at the level of visual or prelexical phonological processing.…”

“…However, note that around 90 min prior to the priming experiment described here, these pseudowords had been presented to the participants in another experiment not of interest for the present study, without any learning instruction. In addition, the experiment contained two further lists of 60 pseudowords each, which were familiarized prior to the experiment as described in Eisenhauer et al (2019; behavioral experiment). The assignment of pseudowords to the novel condition was varied across participants so that three different lists of novel pseudowords were used in the present study.…”

“…For source localization, structural magnetic resonance (MR) images obtained with a 1.5 T Siemens magnetom Allegra scanner (Siemens Medical Systems, Erlangen, Germany) using a standard T1 sequence (3D MPRAGE, 176 slices, 1 x 1 x 1 mm voxel size, 2.2 s TR, 3.93 ms TE, 9° flip angle) were available for 34 participants (Eisenhauer et al, 2019). The MR images contained fiducial markers for the two auricular MEG head localization coils to improve co-registration with the MEG data.…”

“…Source localization was performed using linearly constrained minimum variance (LCMV) spatial filters (Van Veen et al, 1997) in FieldTrip, Version 2016 10-24, for those 34 participants for whom anatomical MR images were available. The procedure closely followed our earlier study (Eisenhauer et al, 2019) and is based on Manahova et al, (2018); see also Dijkstra et al, 2018;Mostert et al, 2018). The approach comprised a permutation procedure with 1,000 analyses on shuffled data to account for noise.…”