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Record W2570856954 · doi:10.1080/17470218.2016.1278456

The early processing of metaphors and similes: Evidence from eye movements

2017· article· en· W2570856954 on OpenAlexafffund
Jane Ashby, Carlos Roncero, Roberto G. de Almeida, Stephen J. Agauas

Bibliographic record

VenueQuarterly Journal of Experimental Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsConcordia UniversityMcGill UniversityJewish General Hospital
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMetaphorLiteral and figurative languageReading (process)Interpretation (philosophy)PsychologyEye movementCognitive psychologyLinguisticsMovement (music)CommunicationPhilosophyNeuroscienceAesthetics

Abstract

fetched live from OpenAlex

This eye movement study examined how people read nominal metaphors and similes in order to investigate how the surface form, or wording, of these expressions affected early processing. Participants silently read metaphors (knowledge is a river) and similes (knowledge is like a river). The identical words were used in the topic-vehicle pair (knowledge-river) in both conditions. Experiments 1 and 2 demonstrated longer reading times and a higher proportion of regressions in metaphors than in similes. Familiarity modulated later metaphor effects in Experiment 1, but not in Experiment 2. Reading ability did not modulate the metaphor effects in Experiment 2. Results indicate that readers revised their initial interpretation of metaphors before moving on to read new text. This suggests that readers did not initially hold figurative interpretations of apt nominal metaphors that are somewhat familiar. Metaphor interpretation may be fast, but it is not easy.

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 score0.813
Threshold uncertainty score0.501

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 science0.0010.000
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 head0.045
GPT teacher head0.392
Teacher spread0.347 · 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

Citations20
Published2017
Admission routes2
Has abstractyes

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