Comics and Cognitive Systems : The processing of visual narratives
Bibliographic record
Abstract
Humans have drawn sequential images as a means ofexpression throughout history, from cave paintings andfrescoes to wall-carvings and tapestries (McCloud, 1993). Incontemporary society, we find them most prevalently incomics of the world, and over the past two decades,increasing attention has turned to this communicativesystem in the cognitive sciences.Earlier work often focused on theory alone, drawing fromparadigms in linguistics or semiotics (for review, see Cohn,2012; Wildfeuer & Bateman, 2016) or from theoristsoutside academia (e.g., McCloud, 1993). However, newerstudies test theoretical predictions with empirical corpusanalyses and both behavioral and neurocognitiveexperimentation. As in language research, combining thesemethodologies provides converging evidence on thestructure of visual narratives, their diversity across theworld, and their comprehension by minds and brains.Recent research has especially focused on the overlappingcognition between the processing of the “visual languages”constituting drawn visual narratives and the linguisticsystems of verbal and signed languages (Cohn, 2013;Magliano, Larson, Higgs, & Loschky, 2015). Thesepresentations further such analyses, and explore questionsrelated to the degree to which these visual languages sharemechanisms with linguistic and other cognitive systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".