Learning-Dependent Changes in Brain Responses While Learning to Break Camouflage: A Human fMRI Study
Bibliographic record
Abstract
Camouflage represents an extreme case of figure-ground segregation whereby a target object, even when in ‘plain view’, is difficult to distinguish from its background. Neural mechanisms by which we recognize a camouflaged object, i.e., break its camouflage, are largely unclear. To characterize the neural responses underlying camouflage-breaking, we carried out two human fMRI experiments. The first experiment used a rapid, event-related design in which subjects had to detect a novel ‘digital embryo’ target camouflaged against a background of a large number of distractor digital embryos (Hegdé et al, JOV 6:677, 2006). We found that the responses in many regions of interest (ROIs), most notably fusiform gyrus (FG) and superior temporal suclus (STS) were significantly larger during those trials in which the subjects (N = 13) correctly reported the presence or absence of a target, compared to the responses during incorrect trials (p <0.05, corrected for multiple comparisons). To assess the extent to which the response patterns are independent of the experimental conditions, we carried out a second experiment using a time-resolved design and stimuli in which the target was a human face camouflaged against uniform background texture. The response patterns of many brain regions, including FG and STS, were similar to those in the first experiment, indicating that the responses were not idiosyncratic to the category of the target object or the nature of the background. On other hand, the BOLD responses in the intraparietal sulcus (IPS) were suppressed below baseline levels during behaviorally correct trials in the second experiment, while the corresponding responses were enhanced above baseline levels in the second experiment. Together, these results suggest, although do not prove, that camouflage-breaking may involve a ‘core’ set of brain regions that whose responses are largely invariant to the nature of the target and of the background. 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".