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Record W2559927057 · doi:10.1177/0963721416661173

What a Simple Letter-Detection Task Can Tell Us About Cognitive Processes in Reading

2016· article· en· W2559927057 on OpenAlexaff
Raymond M. Klein, Jean Saint‐Aubin

Bibliographic record

VenueCurrent Directions in Psychological Science · 2016
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversité de MonctonDalhousie University
Fundersnot available
KeywordsPsychologyCognitive psychologyReading (process)CognitionSet (abstract data type)PhraseActive listeningTask (project management)Cognitive scienceLinguisticsComputer scienceNatural language processingCommunication

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.409
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2016
Admission routes1
Has abstractyes

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