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Itsy bitsy spider?

2016· article· en· W2287376978 on OpenAlexaff
Tali Leibovich, Noga Cohen, Avishai Henik

Bibliographic record

VenueBiological Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsWestern University
FundersEuropean Research CouncilMultidisciplinary University Research Initiative
KeywordsSpiderValence (chemistry)PsychologyPerceptionMediationZoologyBiologyNeuroscience

Abstract

fetched live from OpenAlex

The current study explored the role of valence and self-relevance in size estimation of neutral and aversive animals. In Experiment 1, participants who were highly fearful of spiders and participants with low fear of spiders rated the size and unpleasantness of spiders and other neutral animals (birds and butterflies). We found that although individuals with both high and low fear of spiders rated spiders as highly unpleasant, only the highly fearful participants rated spiders as larger than butterflies. Experiment 2 included additional pictures of wasps (not self-relevant, but unpleasant) and beetles. The results of this experiment replicated those of Experiment 1 and showed a similar bias in size estimation for beetles, but not for wasps. Mediation analysis revealed that in the high-fear group both relevance and valence influenced perceived size, whereas in the low-fear group only valence affected perceived size. These findings suggest that the effect of highly relevant stimuli on size perception is both direct and mediated by valence.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.274
GPT teacher head0.502
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), 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

Citations44
Published2016
Admission routes1
Has abstractyes

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