A feedforward architecture accounts for rapid categorization.
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ABSTRACT:
SUBMITTER: Serre T
PROVIDER: S-EPMC1847457 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Human performance at categorizing natural visual images surpasses automatic algorithms, but how and when this function arises and develops remain unanswered. We recorded scalp electrical brain activity in 4-6 months infants viewing images of objects in their natural background at a rapid rate of 6 images/second (6 Hz). Widely variable face images appearing every 5 stimuli generate an electrophysiological response over the right hemisphere exactly at 1.2 Hz (6 Hz/5). This face-selective response is absent for phase-scrambled images and therefore not due to low-level information. These findings indicate that right lateralized face-selective processes emerge well before reading acquisition in the infant brain, which can perform figure-ground segregation and generalize face-selective responses across changes in size, viewpoint, illumination as well as expression, age and gender. These observations made with a highly sensitive and objective approach open an avenue for clarifying the developmental course of natural image categorization in the human brain.
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Project description:The current dominant visual processing paradigm in both human and machine research is the feedforward, layered hierarchy of neural-like processing elements. Within this paradigm, visual saliency is seen by many to have a specific role, namely that of early selection. Early selection is thought to enable very fast visual performance by limiting processing to only the most salient candidate portions of an image. This strategy has led to a plethora of saliency algorithms that have indeed improved processing time efficiency in machine algorithms, which in turn have strengthened the suggestion that human vision also employs a similar early selection strategy. However, at least one set of critical tests of this idea has never been performed with respect to the role of early selection in human vision. How would the best of the current saliency models perform on the stimuli used by experimentalists who first provided evidence for this visual processing paradigm? Would the algorithms really provide correct candidate sub-images to enable fast categorization on those same images? Do humans really need this early selection for their impressive performance? Here, we report on a new series of tests of these questions whose results suggest that it is quite unlikely that such an early selection process has any role in human rapid visual categorization.
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Project description:Fast periodic visual stimulation combined with electroencephalography (FPVS-EEG) has unique sensitivity and objectivity in measuring rapid visual categorization processes. It constrains image processing time by presenting stimuli rapidly through brief stimulus presentation durations and short inter-stimulus intervals. However, the selective impact of these temporal parameters on visual categorization is largely unknown. Here, we presented natural images of objects at a rate of 10 or 20 per second (10 or 20 Hz), with faces appearing once per second (1 Hz), leading to two distinct frequency-tagged EEG responses. Twelve observers were tested with three squarewave image presentation conditions: 1) with an ISI, a traditional 50% duty cycle at 10 Hz (50-ms stimulus duration separated by a 50-ms ISI); 2) removing the ISI and matching the rate, a 100% duty cycle at 10 Hz (100-ms duration with 0-ms ISI); 3) removing the ISI and matching the stimulus presentation duration, a 100% duty cycle at 20 Hz (50-ms duration with 0-ms ISI). The face categorization response was significantly decreased in the 20 Hz 100% condition. The conditions at 10 Hz showed similar face-categorization responses, peaking maximally over the right occipito-temporal (ROT) cortex. However, the onset of the 10 Hz 100% response was delayed by about 20 ms over the ROT region relative to the 10 Hz 50% condition, likely due to immediate forward-masking by preceding images. Taken together, these results help to interpret how the FPVS-EEG paradigm sets temporal constraints on visual image categorization.
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Project description:Making a judgment about the semantic category of a visual scene, such as whether it contains an animal, is typically assumed to involve high-level associative brain areas. Previous explanations require progressively analyzing the scene hierarchically at increasing levels of abstraction, from edge extraction to mid-level object recognition and then object categorization. Here we show that the statistics of edge co-occurrences alone are sufficient to perform a rough yet robust (translation, scale, and rotation invariant) scene categorization. We first extracted the edges from images using a scale-space analysis coupled with a sparse coding algorithm. We then computed the "association field" for different categories (natural, man-made, or containing an animal) by computing the statistics of edge co-occurrences. These differed strongly, with animal images having more curved configurations. We show that this geometry alone is sufficient for categorization, and that the pattern of errors made by humans is consistent with this procedure. Because these statistics could be measured as early as the primary visual cortex, the results challenge widely held assumptions about the flow of computations in the visual system. The results also suggest new algorithms for image classification and signal processing that exploit correlations between low-level structure and the underlying semantic category.
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Project description:The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using this predictive model, we mapped human cortical representations to 64,000 visual objects from 80 categories with high throughput and accuracy. Such representations covered both the ventral and dorsal pathways, reflected multiple levels of object features, and preserved semantic relationships between categories. In the entire visual cortex, object representations were organized into three clusters of categories: biological objects, non-biological objects, and background scenes. In a finer scale specific to each cluster, object representations revealed sub-clusters for further categorization. Such hierarchical clustering of category representations was mostly contributed by cortical representations of object features from middle to high levels. In summary, this study demonstrates a useful computational strategy to characterize the cortical organization and representations of visual features for rapid categorization.
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