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Record W4405181073 · doi:10.1075/ml.24015.man

The influence of semantic primes on the typing of word targets

2024· article· en· W4405181073 on OpenAlexafffund
Ajay Mangat, Alexander Taikh

Bibliographic record

VenueThe Mental Lexicon · 2024
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsBrock UniversityConcordia University of Edmonton
FundersConcordia University of Edmonton
KeywordsKeystroke loggingPrime (order theory)Computer sciencePriming (agriculture)Interval (graph theory)Word (group theory)Natural language processingFacilitationTypingArtificial intelligenceInterstimulus intervalSpeech recognitionPsychologyLinguisticsMathematicsBiologyNeuroscience

Abstract

fetched live from OpenAlex

Abstract Typing is a complex activity involving identifying and encoding the to-be-typed word, planning, and executing the keystrokes. Different mechanisms have been proposed to explain how contextual information about meaning influences the processing of a target word (for example, semantic priming), and it is unclear how this information influences the typing output of the target word. When the interval between the prime and target is short, the prime is thought to automatically activate the target. With a longer interval, the facilitation may be more strategic. The influence of the prime on the output of the target may thus depend on the interval between the two. We found that at both short and long intervals, related semantic primes facilitated the speed of the first keystroke of the target word. However, there was no effect on how quickly the non-initial letters of the target were entered, suggesting that information from semantic primes influences the planning and initiation of typing, but not the execution of remaining keystrokes. Interestingly, the initial and non-initial keystrokes were faster when the interstimulus interval was long, suggesting participants encode the letters of the prime which could interfere with encoding and typing the target if the interval between them is short.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.200

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.017
GPT teacher head0.304
Teacher spread0.287 · 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 designTheoretical or conceptual
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

Citations1
Published2024
Admission routes2
Has abstractyes

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