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Record W2099157253 · doi:10.1177/0956797610397957

On the Strength of Connections Between Localist Mental Modules as a Source of Frequency-of-Occurrence Effects

2011· article· en· W2099157253 on OpenAlexafffund
Derek Besner, Sarah E. Moroz, Shannon O’Malley

Bibliographic record

VenuePsychological Science · 2011
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyTask (project management)Variety (cybernetics)Cognitive psychologyArabic numeralsReading (process)CognitionReading aloudLinguisticsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Frequency-of-occurrence effects (e.g., effects of word frequency or familiarity) are widely thought to arise through differences in resting levels of activation in localist input-output modules. A different account posits that these effects at least partially reflect the strength of connections between various localist modules. Given that Arabic numerals appear more frequently than their alphabetic counterparts, we contrasted reaction times to stimuli in both formats in a naming/reading-aloud task and a parity-judgment task. The script effect (the difference between reaction times to Arabic and to alphabetic formats) was large in the parity-judgment task but absent in the naming/reading-aloud task. This script-by-task interaction follows naturally from the idea that at least part of the effect of frequency of occurrence of a printed word or digit (and other instances of familiarity) resides in the strength of connections between specialized localist input-output modules and a localist semantic module. This conclusion is likely applicable across a variety of domains.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.356
Teacher spread0.298 · 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

Citations6
Published2011
Admission routes2
Has abstractyes

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