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![random dot stereogram random dot stereogram](https://i.pinimg.com/originals/a7/16/48/a716489d8f42c9bd990c2487aeed37ef.gif)
#Random dot stereogram code
The work is made available under the Creative Commons CC0 public domain dedication.ĭata Availability: The raw psychophysical data, code for analysing the data, and the code for running all the simulations is available on.
#Random dot stereogram free
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. Received: JAccepted: ApPublished: May 19, 2016 PLoS Comput Biol 12(5):Įditor: Karl Gegenfurtner, Justus-Leibig University, GERMANY This reconciles the properties of human depth perception with the properties of neurons in V1, bringing us closer to understanding how neuronal activity causes perception.Ĭitation: Henriksen S, Cumming BG, Read JCA (2016) A Single Mechanism Can Account for Human Perception of Depth in Mixed Correlation Random Dot Stereograms. That is, our computer model of a brain area, based on experimental data about real neurons and using only a single type of depth computation, successfully explains and predicts human depth judgments in novel stimuli.
#Random dot stereogram series
Our model cells mimic the response properties of real cells in the primate brain, and importantly, we show that a perceptual decision model that uses these cells as its basic elements can capture the performance of human observers on a series of visual tasks. In this article, we show how a simple modification to model neurons that compute correlation can account for depth perception in these stimuli. However, recent psychophysical experiments have reported depth perception in stimuli for which this correlation is zero, suggesting that another mechanism might be responsible for matching the left and right images in this case. It has long been believed that neurons in V1 achieve this by computing the correlation between small patches of each eye’s image. This process is believed to begin in primary visual cortex (V1). In essence, the brain has to work out which elements in the left eye’s image correspond to which in the right image. Stereopsis-the ability of many animals to see in stereoscopic 3D-is a particularly tractable problem because the computational and geometric challenges faced by the brain are very well understood.
![random dot stereogram random dot stereogram](http://retina.umh.es/Webvision/imageswv/KallDepth8.jpg)
Relating neural activity to perception is one of the most challenging tasks in neuroscience. We conclude that a single correlation-based computation, based directly on already-known properties of V1 neurons, can account for the literature on mixed correlation random dot stereograms. The model makes predictions about how performance should change with dot size in half-matched stereograms and temporal alternation in correlation, which we test in human observers. We then show that a simple decision model using this single mechanism can reproduce psychometric functions generated by human observers, including reduced performance to large disparities and rapidly updating dot patterns. Here we show that a straightforward modification to the binocular energy model-adding a point output nonlinearity-is by itself sufficient to produce cells that are disparity-tuned to half-matched random dot stereograms. This has led to the proposition that a second, match-based computation must be extracting disparity in these stimuli. Half-matched random dot stereograms are made up of an equal number of correlated and anticorrelated dots, and the binocular energy model-a well-known model of V1 binocular complex cells-fails to signal disparity here. However, recent work by Doi et al suggests that human observers can see depth in a class of stimuli where the mean binocular correlation is 0 (half-matched random dot stereograms). The initial stage of the solution to the correspondence problem is generally thought to consist of a correlation-based computation. This computationally demanding task is known as the stereo correspondence problem. In order to extract retinal disparity from a visual scene, the brain must match corresponding points in the left and right retinae.
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