Visual processing

Résumé

Visual processing is a term that is used to refer to the brain's ability to use and interpret visual information from the world around us. The process of converting light energy into a meaningful image is a complex process that is facilitated by numerous brain structures and higher level cognitive processes. On an anatomical level, light energy first enters the eye through the cornea, where the light is bent. After passing through the cornea, light passes through the pupil and then lens of the eye, where it is bent to a greater degree and focused upon the retina. The retina is where a group of light-sensing cells, called photoreceptors are located. There are two types of photoreceptors: rods and cones. Rods are sensitive to dim light and cones are better able to transduce bright light. Photoreceptors connect to bipolar cells, which induce action potentials in retinal ganglion cells. These retinal ganglion cells form a bundle at the optic disc, which is a part of the optic nerve. The tw

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https://en.wikipedia.org/wiki/Visual_processing

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Conscious and unconscious processing are two of the most hot topics in neuroscience. In unconscious processing research, usually a direct measure is used to show that a target is not consciously perceived. An indirect measure is used to show that still the target can influence visual processing. This unconscious processing is usually related to fast motor priming in a short period before conscious processing takes over. Here, I show that features of unconscious elements can influence visual information processing and are not related to motor priming. Interestingly, these features can become conscious even though the carriers of the features are not

2009

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Act Quickly, Decide Later: Long-latency Visual Processing Underlies Perceptual Decisions but Not Reflexive Behavior

Humans largely guide their behavior by their visual representation of the world. Recent studies have shown that visual information can trigger behavior within 150 msec, suggesting that visually guided responses to external events, in fact, precede conscious awareness of those events. However, is such a view correct? By using a texture discrimination task, we show that the brain relies on long-latency visual processing in order to guide perceptual decisions. Decreasing stimulus saliency leads to selective changes in long-latency visually evoked potential components reflecting scene segmentation. These latency changes are accompanied by almost equal changes in simple RTs and points of subjective simultaneity. Furthermore, we find a strong correlation between individual RTs and the latencies of scene segmentation related components in the visually evoked potentials, showing that the processes underlying these late brain potentials are critical in triggering a response. However, using the same texture stimuli in an antisaccade task, we found that reflexive, but erroneous, prosaccades, but not antisaccades, can be triggered by earlier visual processes. In other words: The brain can act quickly, but decides late. Differences between our study and earlier findings suggesting that action precedes conscious awareness can be explained by assuming that task demands determine whether a fast and unconscious, or a slower and conscious, representation is used to initiate a visually guided response.

2011

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Capsule networks explain complex spatial processing

Adrien Christophe Doerig, Michael Herzog, Lynn Schmittwilken

Classically, visual processing is described as a cascade of local feedforward computations. This view has received striking support from the success of convolutional neural networks (CNNs). However, CNNs only roughly mimic human vision. For example, CNNs do not take the global spatial configuration of visual elements into account and thus fail at simple tasks such as explaining crowding and uncrowding. In crowding, the perception of a target deteriorates in the presence of neighboring elements. Classically, adding flanking elements is thought to always decreases performance. However, adding flankers even far away from the target can improve performance, depending on the global configuration (uncrowding). We showed previously that no classic model of crowding, including CNNs, can explain uncrowding. Here, we show that capsule networks, a type of deep network combining CNNs and object segmentation, explain both crowding and uncrowding. We trained capsule networks to recognize targets and groups of shapes. There were no crowding/uncrowding stimuli in the training set. When we subsequently tested the network on crowding/uncrowding stimuli, both crowding and uncrowding occurred. We show theoretically how crowding and uncrowding naturally emerge from neural dynamics in capsule networks. These powerful recurrent models offer a new framework to understand previously unexplained experimental results.

SAGE PUBLICATIONS LTD2019

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