Right for the wrong reason; wrong for the right reason: Gibson and Arnheim on picture perception
Bibliographic record
Abstract
Although J J Gibson’s theory of picture perception was often crude and biased toward naturalism, its fundamental division between the visual world and the visual field made it a semiotic theory. Contrariwise, although Arnheim wrote sensitively on pictures, he never seemed to admit that they were signs. This paper reviews both Gibson’s and Arnheim’s theories of picture perception, and explains where Arnheim’s biases caused him to lose the possibility of framing his approach in the most basic semiotic terms. Nevertheless, using the phenomenological semiotics of Sonesson and his theory of the Lifeworld Hierarchy, I demonstrate latent semiotic elements in Arnheim’s theory, due perhaps to Alfred Schutz’s influence. Hoping to argue against the brute theory of denotation, Arnheim instead sought to delay invocation of (conventional) signs as long as possible, and his idea of iconic pictorialization assumes but does not name signification. Nevertheless, I propose that Arnheim has a kind of theory of the Lifeworld Hierarchy inside the picture. Thus, he (wrongly) does not see the picture as overtly signifying but interestingly gives hints about how to treat the objects of the virtual world of the picture based on their relationship to the overall style of the work.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".