The Death of of General Wolfe: Investigating the influence of artistic compositional techniques on eye movement control and interpretation of paintings
Bibliographic record
Abstract
Research in natural scene perception has demonstrated the involvement of both saliency and cognitive control in directing gaze patterns (Parkhurst et al., 2002; Henderson et al., 2007). In paintings, the artist has control over both the salient and semantic aspects of the "scene". For example, artistic techniques such as brush strokes are used to add texture, increase salience and guide gaze to the artistic center of focus (Enns et al., 2010). In the current study, we used three versions of the painting "The Death of General Wolfe" by artist Benjamin West (1738-1820) as stimuli. All these versions represent the same scene with slightly different compositions between versions – new background, changes in brightness and textural details and their locations. While all versions share a common central salient region, the number of additional salient regions varies. Using the Wolfe paintings, we investigated whether an artist can manipulate salience through compositional differences to induce different interpretations across versions. Three groups of participants (n=16/group) viewed one version of the Wolfe painting along with five filler paintings while their eye movements were tracked. Results indicated a significant difference in overall fixation duration (F (2,310)=4.57, p=0.01) between versions of the Wolfe paintings that was not present in filler paintings. Additionally, there was a difference in the dispersion between versions during the initial examination of the painting (first five fixations) (F (2,12) = 42.89, p<0.001). These results suggest that artists can differentially guide viewer gaze while maintaining the same semantic information. We hypothesized that changes in number of salient regions that affect gaze may also affect the perceived importance of accompanying figures in the painting. Further research will investigate whether differences in eye movement patterns result in subtle interpretation differences across versions of the painting. Meeting abstract presented at VSS 2012
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".