What a Simple Letter-Detection Task Can Tell Us About Cognitive Processes in Reading
Bibliographic record
Abstract
Understanding reading is a central issue for psychology, with major societal implications. Over the past five decades, a simple letter-detection task has been used as a window on the psycholinguistic processes involved in reading. When readers are asked to read a text for comprehension while marking with a pencil all instances of a target letter, they miss some of the letters in a systematic way known as the missing-letter effect. In the current article, we review evidence from studies that have emphasized neuroimaging, eye movement, rapid serial visual presentation, and auditory passages. As we review, the missing-letter effect captures a wide variety of cognitive processes, including lexical activation, attention, and extraction of phrase structure. To account for the large set of findings generated by studies of the missing-letter effect, we advanced an attentional-disengagement model that is rooted in how attention is allocated to and disengaged from lexical items during reading, which we have recently shown applies equally to listening.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".