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Researchers have long debated whether salient distractors have the power to automatically capture attention. Recent research has suggested a potential resolution, called the signal suppression hypothesis, whereby salient distractors produce a bottom-up salience signal, but can be suppressed to prevent visual distraction. This account, however, has been criticized on the grounds that previous studies may have used distractors that were only weakly salient. This claim has been difficult to empirically test because there are currently no well-established measures of salience. The current study addresses this by introducing a psychophysical technique to measure salience. First, we generated displays that aimed to manipulate the salience of two color singletons via color contrast. We then verified that this manipulation was successful using a psychophysical technique to determine the minimum exposure duration required to detect each color singleton. The key finding was that high-contrast singletons were detected at briefer exposure thresholds than low-contrast singletons, suggesting that high-contrast singletons were more salient. Next, we evaluated the participants’ ability to ignore these singletons in a task in which they were task irrelevant. The results showed that, if anything, high-salience singletons were more strongly suppressed than low-salience singletons. These results generally support the signal suppression hypothesis and refute claims that highly salient singletons cannot be ignored.
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Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures
Previously, in Hermundstad et al., 2014, we showed that when sampling is limiting, the efficient coding principle leads to a ‘variance is salience’ hypothesis, and that this hypothesis accounts for visual sensitivity to binary image statistics. Here, using extensive new psychophysical data and image analysis, we show that this hypothesis accounts for visual sensitivity to a large set of grayscale image statistics at a striking level of detail, and also identify the limits of the prediction. We define a 66-dimensional space of local grayscale light-intensity correlations, and measure the relevance of each direction to natural scenes. The ‘variance is salience’ hypothesis predicts that two-point correlations are most salient, and predicts their relative salience. We tested these predictions in a texture-segregation task using un-natural, synthetic textures. As predicted, correlations beyond second order are not salient, and predicted thresholds for over 300 second-order correlations match psychophysical thresholds closely (median fractional error <0.13).
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- Award ID(s):
- 1734030
- PAR ID:
- 10248747
- Date Published:
- Journal Name:
- eLife
- Volume:
- 9
- ISSN:
- 2050-084X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Purpose Prior studies show convolutional neural networks predicting self-reported race using x-rays of chest, hand and spine, chest computed tomography, and mammogram. We seek an understanding of the mechanism that reveals race within x-ray images, investigating the possibility that race is not predicted using the physical structure in x-ray images but is embedded in the grayscale pixel intensities. Approach Retrospective full year 2021, 298,827 AP/PA chest x-ray images from 3 academic health centers across the United States and MIMIC-CXR, labeled by self-reported race, were used in this study. The image structure is removed by summing the number of each grayscale value and scaling to percent per image (PPI). The resulting data are tested using multivariate analysis of variance (MANOVA) with Bonferroni multiple-comparison adjustment and class-balanced MANOVA. Machine learning (ML) feed-forward networks (FFN) and decision trees were built to predict race (binary Black or White and binary Black or other) using only grayscale value counts. Stratified analysis by body mass index, age, sex, gender, patient type, make/model of scanner, exposure, and kilovoltage peak setting was run to study the impact of these factors on race prediction following the same methodology. Results MANOVA rejects the null hypothesis that classes are the same with 95% confidence (F 7.38, P < 0.0001) and balanced MANOVA (F 2.02, P < 0.0001). The best FFN performance is limited [area under the receiver operating characteristic (AUROC) of 69.18%]. Gradient boosted trees predict self-reported race using grayscale PPI (AUROC 77.24%). Conclusions Within chest x-rays, pixel intensity value counts alone are statistically significant indicators and enough for ML classification tasks of patient self-reported race.more » « less
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Abstract There has been a longstanding debate as to whether salient stimuli have the power to involuntarily capture attention. As a potential resolution to this debate, the signal suppression hypothesis proposes that salient items generate a bottom–up signal that automatically attracts attention, but that salient items can be suppressed by top–down mechanisms to prevent attentional capture. Despite much support, the signal suppression hypothesis has been challenged on the grounds that many prior studies may have used color singletons with relatively low salience that are too weak to capture attention. The current study addressed this by using previous methods to study suppression but increased the set size to improve the relative salience of the color singletons. To assess whether salient distractors captured attention, electrophysiological markers of attentional allocation (the N2pc component) and suppression (the PD component) were measured. The results provided no evidence of attentional capture, but instead indicated suppression of the highly salient singleton distractors, as indexed by the PD component. This suppression occurred even though a computational model of saliency confirmed that the color singleton was highly salient. Altogether, this supports the signal suppression hypothesis and is inconsistent with stimulus-driven models of attentional capture.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.7554/eLife.54347
