Tracking the dynamic word-by-word incremental reading through multimeasures.
Bibliographic record
Abstract
Reading relies on the incremental processes that occur across all words in a passage to build a global comprehension of the text. Factorial experimental designs are not well-suited to examine these incremental processes, which are influenced by multilevel factors in an overlapping manner. Exemplifying an alternative approach, we combined event-related potentials, probabilistic language models, authentic texts, and statistical methods to examine the time course of multilevel linguistic influences on the incremental processes which occur during reading each word. We found that indicators of the initial stages of word identification (N170 and P200) are sensitive to context-independent statistical information of a word, for example, word frequency. The later stages of word processing, involving processes related to meaning retrieval and integration (N400), heavily rely on the word's context-dependent information measured by word surprisal. Syntactic processing, reflected by a word's syntactic surprisal and the number of phrase structures it closes, was presented across multiple phases (an early negativity, N400, and a late positivity). Additionally, the effects of position factors at both the word and sentence levels emerged across multiple time windows (including N170, P200, and N400), suggesting their distinct influence beyond linguistic factors. These findings provide a theoretically coherent picture of incremental reading, partly convergent with conclusions from factorial studies but with novel results concerning the time courses and interactions of processing components. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".