Distinct neural sources underlying visual word form processing as revealed by steady state visual evoked potentials (SSVEP)

Abstract

EEG has been central to investigations of the time course of various neural functions underpinning visual word recognition. Recently the steady-state visual evoked potential (SSVEP) paradigm has been increasingly adopted for word recognition studies due to its high signal-to-noise ratio. Such studies, however, have been typically framed around a single source in the left ventral occipitotemporal cortex (vOT). Here, we combine SSVEP recorded from 16 adult native English speakers with a data-driven spatial filtering approach—Reliable Components Analysis (RCA)—to elucidate distinct functional sources with overlapping yet separable time courses and topographies that emerge when contrasting words with pseudofont visual controls. The first component topography was maximal over left vOT regions with a shorter latency (approximately 180 ms). A second component was maximal over more dorsal parietal regions with a longer latency (approximately 260 ms). Both components consistently emerged across a range of parameter manipulations including changes in the spatial overlap between successive stimuli, and changes in both base and deviation frequency. We then contrasted word-in-nonword and word-in-pseudoword to test the hierarchical processing mechanisms underlying visual word recognition. Results suggest that these hierarchical contrasts fail to evoke a unitary component that might be reasonably associated with lexical access.

Progress in elementary school reading linked to growth of cortical responses to familiar letter combinations within visual word forms

“…To test the association between amplitudes for different conditions at different developmental stages, we performed linear regressions of response amplitudes, separately for T1 and T2. Outliers determined by Cook’s Distance (17) based on the regression model were removed if they exceeded the 4 /n threshold ( n total data points).…”

“…We used an adjusted paradigm—which we refer to as “image alternation” mode—involving slower stimulation rates, where image exemplars from two categories of stimuli alternate at slower 1-Hz alternation and 2-Hz base rates. This slower image alternation mode has been shown to elicit responses with a higher SNR (15–17) than base/deviant approaches and may thus be more suitable for capturing reading-related brain signals in children (14).…”

“…The often-used coarse neural tuning contrast (words versus pseudofonts) is limited, however, in its connection to theorydriven insights into word recognition development, which stress the importance of multiple levels of word reading expertise (8). This limitation leads to ambiguity in recovering the unique contributions of specific properties of words to developmental changes in reading and brain circuitry, leaving open the question of whether coarse neural tuning reflects merely the aggregation of increasing word specific knowledge, or instead 31 reflects insights into word form structures, perhaps as they 32 map to phonological patterns (9). 33 Event-related potential (ERP) studies have attempted yet 34 failed to isolate di erent levels of visual word processing in 35 early readers (3,10).…”

Cortical tracking of spoken and written language structures in (dys)fluent readers

“…Because frequency-tagging leads to a narrowband response directly related to the frequency of interest, it provides a robust measure against the experimental noise spread over the entire frequency spectrum (Norcia et al, 2015). Another advantage of this approach is that it allows implicit assessment of multiple cognitive processes even in the absence of overt behavior (Lochy et al, 2018;Wang et al, 2021). Hence, the frequency-tagging paradigm is widely adopted to study the analysis, memory, comprehension and generation of temporal structures and/or regularities embedded in (non)linguistic sequences (e.g., Buiatti et al, 2009;Lu, Sheng, Liu, & Gao, 2021;Nozaradan, Peretz, Missal, & Mouraux, 2011;Volfart, Rice, Lambon Ralph, & Rossion, 2021).…”

“…The frequency-tagging approach has also been used to study visual perception, segmentation, discrimination, and integration of multiple inputs (for review, see Norcia et al, 2015). During visual word form processing, in particular, periodically inserted real words were rapidly discriminated from pseudofonts, nonwords and pseudowords with robust SSEPs elicited over the occipito-temporal and dorsal parietal regions at the frequency of deviant stimuli (Lochy, Van Reybroeck, & Rossion, 2016;Wang et al, 2021). Moreover, when words were split into two parts and flickered at different frequencies, SSEPs were stronger when the segments matched the syllabification compared to when they did not retain the constituent syllabic structure (Montani, Chanoine, Grainger, & Ziegler, 2019).…”