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Record W2460548332 · doi:10.3389/fnhum.2016.00346

Imaging Imageability: Behavioral Effects and Neural Correlates of Its Interaction with Affect and Context

2016· article· en· W2460548332 on OpenAlexafffund
Chris Westbury, Ivor Cribben, Jacqueline Cummine

Bibliographic record

VenueFrontiers in Human Neuroscience · 2016
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Health Services
KeywordsAffect (linguistics)Neural correlates of consciousnessContext (archaeology)PsychologyCognitive psychologyFunctional Brain ImagingNeuroimagingNeuroscienceCognitionCommunicationBiology

Abstract

fetched live from OpenAlex

The construct of imageability refers to the extent to which a word evokes a tangible sensation. Previous research (Westbury et al., 2013) suggests that the behavioral effects attributed to a word's imageability can be largely or wholly explained by two objective constructs, contextual density and estimated affect. Here, we extend these previous findings in two ways. First, we show that closely matched stimuli on the three measures of contextual density, estimated affect, and human-judged imageability show a three-way interaction in explaining variance in LD RTs, but that imagebility accounts for no additional variance after contextual density and estimated affect are entered first. Secondly, we demonstrate that the loci and functional connectivity (via graphical models) of the brain regions implicated in processing the three variables during that task are largely over-lapping and similar. These two lines of evidence support the conclusion that the effect usually attributed to human-judged imageability is largely or entirely due to the effects of other correlated measures that are directly computable.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.031
GPT teacher head0.298
Teacher spread0.267 · 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 designBench or experimental
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

Citations21
Published2016
Admission routes2
Has abstractyes

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