Bibliographic record
Abstract
The visual search paradigm is widely recognized as a means of assessing the salience of image features, and distinguishing properties that are processed pre-attentively. Recent research has used face stimuli to show high-level influences on putative pre-attentive processing (Hershler & Hochstein, 2005; Reddy, Wilken & Koch, 2004). Others have shown similar advantages with natural images (Rousselet et al., 2004; Li et al., 2002), however, with such complex stimuli it is often difficult to determine the basis of the effect. Here we use meaningful, non-face, stimuli to evaluate high-level influences in a visual search task. Targets were black and white images of food and everyday objects; distractor stimuli were scrambled versions of the target items in which local features were re-positioned. The two classes of images and their distractors did not different in their low-level image properties (RMS contrast, frequency content). In a visual search experiment, observers indicated if a target was present in a set of distractors. Within a session all image types and distractor levels were randomly interleaved. Reaction times for non-food images increased with the number of distractors over the full range tested (n = 5−80). Reaction times for food images initially increased, but flattened at approximately 20 distractors. Further increases in the number of distractors had no effect on performance for this class of stimuli. This food specific pop-out effect is robust, and shows no effect of gender. Moreover, an image identification task shows that there is no difference in the discriminability of the two groups of images. Our results show that so-called pre-attentive processing is not restricted to low-level image properties, but is clearly influenced by meaning. These data provide another piece of evidence against simple hierarchical models of visual information processing, and for more integrative models, like that proposed by Lee and Mumford (2003).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".