Combined studies of N170/M170 responses to single letters and pseudoletters
Bibliographic record
Abstract
ABSTRACT To read efficiently, individuals must be able to rapidly identify letters within their visual networks, which occurs through forming line segments into letters and then letters into words. The temporal processes and utilized brain areas that engage in this process are widely thought to be left-lateralized within the brain. However, a range of studies demonstrate that the processing of unfamiliar stimuli, such as pseudoletters, is temporally delayed and bilaterally processed when compared to letters. The present study investigated the contributions of both hemispheres and how these interactions impact the temporal dynamics of implicit visual processing of single-letters as compared to unfamiliar pseudoletters (false fonts). The results of 5 “in-house” studies are presented within a meta-analysis (synthesis analysis), 3 High-density EEG studies and 2 MEG studies. Of the 3 EEG studies, 2 focused on measuring event related potentials (ERPs) while the participants performed an orthographic discrimination task (letters vs pseudoletters) and the other was a target detection task in which the participants detected infrequent, simple perceptual targets within a series of pseudoletter and letter strings. Of the 2 MEG studies, one was a discrimination task and the other a target detection task. Delayed N170 waveforms to pseudoletters as compared to letters were exhibited across all studies. Lateralization of the ERP differences between letter-evoked and pseudoletter-evoked responses were bilaterally distributed, whereas lateralization measure separately for letters and pseudoletters were primarily left-lateralized. As a whole, these in-house studies indicate that ERPs to letters occur earlier than to pseudoletters, and that interpretation of hemispheric laterality depends on whether the researcher is assessing ERP differences between letters and pseudoletters or the ERP waveforms of the separate letter and pseudoletter conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".