Photographic Clarity and Blur Influences Person Perception
Bibliographic record
Abstract
The selective blurring and sharpening of images is used by filmmakers, photographers, and artists (1) to guide visual attention and (2) to influence the emotional experience of the viewer. This study tests this two-part hypothesis by first using an eye tracker to measure the influence of selective blurring and sharpening on looking behavior. Thirty participants viewed twenty-four photos of couples for seven seconds each, before being asked to answer four Likert-scale questions about the personality of one of the people in the photo. The results showed that although viewers were instructed to look equally at both people, they generally looked first, and more often, at faces rendered in sharper focus relative to other faces. In the second phase, we measured the consequences of this selective looking on the attributions viewers make to the people depicted in photos. The results indicated that both longer looking times and image clarity played a role, although their relative importance depended on the dimension being queried. For example, while attractiveness ratings were positively correlated with overall viewing time there was an additional effect linked to image features (i.e., slightly blurred persons were judged as more attractive than an equivalent slight sharpening). For dimensions tied more closely to personality, viewing time seemed to play no role, but image features did (e.g., sharper faces received higher sociability ratings but lower trustworthiness ratings). These findings imply that person perception is influenced by superficial image features in much the same way that it is influenced by a person’s physiognomy (Willis & Todorov, 2006). Our interpretation is that person perception is susceptible to inverse inferences deriving both from our own actions (e.g., looking longer leads to increased interest and value) and from a false understanding of the source of the image features (e.g., more self-revealing people are sharper in photos). Meeting abstract presented at VSS 2013
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".