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Record W4234078135 · doi:10.1075/ill.17.13ken

Show me how you feel

2020· book-chapter· en· W4234078135 on OpenAlexaff
Lia Kendall, Quentin Raffaelli, Rebecca M. Todd, Alan Kingstone, Neil Cohn

Bibliographic record

VenueIconicity in language and literature · 2020
Typebook-chapter
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyComputer science

Abstract

fetched live from OpenAlex

Abstract Visual representations are an ideal place to look at the balance of iconicity and systematicity, given that visual images often look like what they represent but also can be characterized along a scale from highly photorealistic to highly schematic or cartoony. Here we examine the contrast between different styles by presenting participants with “visual morphology” of upfixes – representations like hearts or lightbulbs that float above faces – where the face and/or upfix are either cartoony or photorealistic. Overall, we find that cartoony images, relative to photorealistic images, are easier to process, and therefore demand less attention and facilitate responding. We argue that these results support the view that drawings, and visual morphology, draw on schematic knowledge stored in long-term memory.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.855
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.024
GPT teacher head0.293
Teacher spread0.269 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2020
Admission routes1
Has abstractyes

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