scholarly journals Spatial vision by macaque midbrain

2017 ◽  
Author(s):  
Chih-Yang Chen ◽  
Lukas Sonnenberg ◽  
Simone Weller ◽  
Thede Witschel ◽  
Ziad M. Hafed
Keyword(s):  
Superior Colliculus ◽  
Spatial Vision ◽  
Population Level ◽  
Visual Exploration ◽  
Natural Scenes ◽  
Natural Environments ◽  
Natural Scene ◽  
Natural Scene Statistics ◽  
Brain Areas ◽  
Spatial Frequencies

Visual brain areas exhibit tuning characteristics that are well suited for image statistics present in our natural environment. However, visual sensation is an active process, and if there are any brain areas that ought to be particularly 'in tune' with natural scene statistics, it would be sensory-motor areas critical for guiding behavior. Here we found that the primate superior colliculus, a structure instrumental for rapid visual exploration with saccades, detects low spatial frequencies, which are the most prevalent in natural scenes, much more rapidly than high spatial frequencies. Importantly, this accelerated detection happens independently of whether a neuron is more or less sensitive to low spatial frequencies to begin with. At the population level, the superior colliculus additionally over-represents low spatial frequencies in neural response sensitivity, even at near-foveal eccentricities. Thus, the superior colliculus possesses both temporal and response gain mechanisms for efficient gaze realignment in low-spatial-frequency dominated natural environments.

Independence of color and luminance edges in natural scenes

2009 ◽  
Vol 26 (1) ◽  
pp. 35-49 ◽  
Author(s):  
THORSTEN HANSEN ◽  
KARL R. GEGENFURTNER
Keyword(s):  
Visual Processing ◽  
Artificial Vision ◽  
Natural Scenes ◽  
Natural Scene ◽  
Natural Scene Statistics ◽  
Form Vision ◽  
Independent Information ◽  
Edge Contrast ◽  
Source Of Information ◽  
Gain Access

AbstractForm vision is traditionally regarded as processing primarily achromatic information. Previous investigations into the statistics of color and luminance in natural scenes have claimed that luminance and chromatic edges are not independent of each other and that any chromatic edge most likely occurs together with a luminance edge of similar strength. Here we computed the joint statistics of luminance and chromatic edges in over 700 calibrated color images from natural scenes. We found that isoluminant edges exist in natural scenes and were not rarer than pure luminance edges. Most edges combined luminance and chromatic information but to varying degrees such that luminance and chromatic edges were statistically independent of each other. Independence increased along successive stages of visual processing from cones via postreceptoral color-opponent channels to edges. The results show that chromatic edge contrast is an independent source of information that can be linearly combined with other cues for the proper segmentation of objects in natural and artificial vision systems. Color vision may have evolved in response to the natural scene statistics to gain access to this independent information.

scholarly journals Mice use robust and common strategies to discriminate natural scenes

2017 ◽  
Author(s):  
Yiyi Yu ◽  
Riichiro Hira ◽  
Jeffrey N. Stirman ◽  
Waylin Yu ◽  
Ikuko T. Smith ◽  
...  
Keyword(s):  
Gabor Filter ◽  
Discrimination Performance ◽  
Structural Similarity ◽  
Orientation Dependence ◽  
Natural Scenes ◽  
Natural Environments ◽  
Natural Scene ◽  
Image Metrics ◽  
Screen System ◽  
Mouse Vision

AbstractMice use vision to navigate and avoid predators in natural environments. However, the spatial resolution of mouse vision is poor compared to primates, and mice lack a fovea. Thus, it is unclear how well mice can discriminate ethologically relevant scenes. Here, we examined natural scene discrimination in mice using an automated touch-screen system. We estimated the discrimination difficulty using the computational metric structural similarity (SSIM), and constructed psychometric curves. However, the performance of each mouse was better predicted by the population mean than SSIM. This high inter-mouse agreement indicates that mice use common and robust strategies to discriminate natural scenes. We tested several other image metrics to find an alternative to SSIM for predicting discrimination performance. We found that a simple, primary visual cortex (V1)-inspired model predicted mouse performance with fidelity approaching the inter-mouse agreement. The model involved convolving the images with Gabor filters, and its performance varied with the orientation of the Gabor filter. This orientation dependence was driven by the stimuli, rather than an innate biological feature. Together, these results indicate that mice are adept at discriminating natural scenes, and their performance is well predicted by simple models of V1 processing.

scholarly journals Residual abilities in age-related macular degeneration to process spatial frequencies during natural scene categorization

2011 ◽  
Vol 28 (6) ◽  
pp. 529-541 ◽  
Author(s):  
BENOIT MUSEL ◽  
RUXANDRA HERA ◽  
SYLVIE CHOKRON ◽  
DAVID ALLEYSSON ◽  
CHRISTOPHE CHIQUET ◽  
...  
Keyword(s):  
Spatial Frequency ◽  
Macular Degeneration ◽  
Reaction Times ◽  
Age Related Macular Degeneration ◽  
Natural Scenes ◽  
Natural Scene ◽  
Scene Categorization ◽  
Age Related ◽  
Spatial Frequencies ◽  
Healthy Participants

AbstractAge-related macular degeneration (AMD) is characterized by a central vision loss. We explored the relationship between the retinal lesions in AMD patients and the processing of spatial frequencies in natural scene categorization. Since the lesion on the retina is central, we expected preservation of low spatial frequency (LSF) processing and the impairment of high spatial frequency (HSF) processing. We conducted two experiments that differed in the set of scene stimuli used and their exposure duration. Twelve AMD patients and 12 healthy age-matched participants in Experiment 1 and 10 different AMD patients and 10 healthy age-matched participants in Experiment 2 performed categorization tasks of natural scenes (Indoors vs. Outdoors) filtered in LSF and HSF. Experiment 1 revealed that AMD patients made more no-responses to categorize HSF than LSF scenes, irrespective of the scene category. In addition, AMD patients had longer reaction times to categorize HSF than LSF scenes only for indoors. Healthy participants’ performance was not differentially affected by spatial frequency content of the scenes. In Experiment 2, AMD patients demonstrated the same pattern of errors as in Experiment 1. Furthermore, AMD patients had longer reaction times to categorize HSF than LSF scenes, irrespective of the scene category. Again, spatial frequency processing was equivalent for healthy participants. The present findings point to a specific deficit in the processing of HSF information contained in photographs of natural scenes in AMD patients. The processing of LSF information is relatively preserved. Moreover, the fact that the deficit is more important when categorizing HSF indoors, may lead to new perspectives for rehabilitation procedures in AMD.

scholarly journals Cortical Thickness and Natural Scene Recognition in the Child’s Brain

2020 ◽  
Vol 10 (6) ◽  
pp. 329
Author(s):  
François Orliac ◽  
Grégoire Borst ◽  
Grégory Simon ◽  
Katell Mevel ◽  
Julie Vidal ◽  
...  
Keyword(s):  
Cortical Thickness ◽  
Reaction Times ◽  
Imaging Study ◽  
Scene Recognition ◽  
Natural Scenes ◽  
Natural Scene ◽  
Processing Efficiency ◽  
Parahippocampal Cortex ◽  
Spatial Frequencies ◽  
Coarse Information

Visual scenes are processed in terms of spatial frequencies. Low spatial frequencies (LSF) carry coarse information, whereas high spatial frequencies (HSF) subsequently carry information about fine details. The present magnetic resonance imaging study investigated how cortical thickness covaried with LSF/HSF processing abilities in ten-year-old children and adults. Participants indicated whether natural scenes that were filtered in either LSF or HSF represented outdoor or indoor scenes, while reaction times (RTs) and accuracy measures were recorded. In adults, faster RTs for LSF and HSF images were consistently associated with a thicker cortex (parahippocampal cortex, middle frontal gyrus, and precentral and insula regions for LSF; parahippocampal cortex and fronto-marginal and supramarginal gyri for HSF). On the other hand, in children, faster RTs for HSF were associated with a thicker cortex (posterior cingulate, supramarginal and calcarine cortical regions), whereas faster RTs for LSF were associated with a thinner cortex (subcallosal and insula regions). Increased cortical thickness in adults and children could correspond to an expansion mechanism linked to visual scene processing efficiency. In contrast, lower cortical thickness associated with LSF efficiency in children could correspond to a pruning mechanism reflecting an ongoing maturational process, in agreement with the view that LSF efficiency continues to be refined during childhood. This differing pattern between children and adults appeared to be particularly significant in anterior regions of the brain, in line with the proposed existence of a postero-anterior gradient of brain development. Taken together, our results highlight the dynamic brain processes that allow children and adults to perceive a visual natural scene in a coherent way.

scholarly journals Low-level image statistics in natural scenes influence perceptual decision-making

2018 ◽  
Author(s):  
Noor Seijdel ◽  
Sara Jahfari ◽  
Iris I.A. Groen ◽  
H. Steven Scholte
Keyword(s):  
Decision Making ◽  
Real World ◽  
Detection Task ◽  
Natural Scenes ◽  
Natural Scene ◽  
Natural Scene Statistics ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Low Level ◽  
Animal Detection

A fundamental component of interacting with our environment is gathering and interpretation of sensory information. When investigating how perceptual information shapes the mechanisms of decision-making, most researchers have relied on the use of manipulated or unnatural information as perceptual input, resulting in findings that may not generalize to real-world scenes. Unlike simplified, artificial stimuli, real-world scenes contain low-level regularities (natural scene statistics) that are informative about the structural complexity of a scene, which the brain could exploit during perceptual decision-making. In this study, participants performed an animal detection task on low, medium or high complexity scenes as determined by two biologically plausible natural scene statistics, contrast energy (CE) or spatial coherence (SC). In experiment 1, stimuli were sampled such that CE and SC both influenced scene complexity. Diffusion modeling showed that both the speed of information processing and the required evidence were affected by the low-level scene complexity. Experiment 2a/b refined these observations by showing how the isolated manipulation of SC alone resulted in weaker but comparable effects on decision-making, whereas the manipulation of only CE had no effect. Overall, performance was best for scenes with intermediate complexity. Our systematic definition of natural scene statistics quantifies how complexity of natural scenes interacts with decision-making in an animal detection task. We speculate that the computation of CE and SC could serve as an indication to adjust perceptual decision-making based on the complexity of the input.

scholarly journals What is visible across the visual field?

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Andrew M Haun
Keyword(s):  
Visual Field ◽  
Visual Perception ◽  
Peripheral Vision ◽  
Spatial Vision ◽  
Natural Scenes ◽  
Natural Scene ◽  
Foveal Vision ◽  
Edge Information ◽  
Detailed Model ◽  
Natural Scene Images

Abstract It is sometimes claimed that because the resolution and sensitivity of visual perception are better in the fovea than in the periphery, peripheral vision cannot support the same kinds of colour and sharpness percepts as foveal vision. The fact that a scene nevertheless seems colourful and sharp throughout the visual field then poses a puzzle. In this study, I use a detailed model of human spatial vision to estimate the visibility of certain properties of natural scenes, including aspects of colourfulness, sharpness, and blurriness, across the visual field. The model is constructed to reproduce basic aspects of human contrast and colour sensitivity over a range of retinal eccentricities. I apply the model to colourful, complex natural scene images, and estimate the degree to which colour and edge information are present in the model’s representation of the scenes. I find that, aside from the intrinsic drift in the spatial scale of the representation, there are not large qualitative differences between foveal and peripheral representations of ‘colourfulness’ and ‘sharpness’.

scholarly journals Build-up of serial dependence in color working memory

2018 ◽  
Author(s):  
João Barbosa ◽  
Albert Compte
Keyword(s):  
Visual Features ◽  
Experimental Session ◽  
Natural Scenes ◽  
Strongly Correlated ◽  
Natural Scene ◽  
Serial Dependence ◽  
Natural Scene Statistics ◽  
Short Term ◽  
Total N ◽  
The Face

AbstractSerial dependence, how recent experiences bias our current estimations, has been described experimentally during delayed-estimation of many different visual features, with subjects tending to make estimates biased towards previous ones. It has been proposed that these attractive biases help perception stabilization in the face of correlated natural scene statistics as an adaptive mechanism, although this remains mostly theoretical. Color, which is strongly correlated in natural scenes, has never been studied with regard to its serial dependencies. Here, we found significant serial dependence in 6 out of 7 datasets with behavioral data of humans (total n=111) performing delayed-estimation of color with uncorrelated sequential stimuli. Consistent with a drifting memory model, serial dependence was stronger when referenced relative to previous report, rather than to previous stimulus. In addition, it built up through the experimental session, suggesting metaplastic mechanisms operating at a slower time scale than previously proposed (e.g. short-term synaptic facilitation). Because, in contrast with natural scenes, stimuli were temporally uncorrelated, this build-up casts doubt on serial dependencies being an ongoing adaptation to the stable statistics of the environment.

Categorization Goals Modulate the Use of Natural Scene Statistics

2019 ◽  
Vol 31 (1) ◽  
pp. 109-125
Author(s):  
Andrea De Cesarei ◽  
Shari Cavicchi ◽  
Antonia Micucci ◽  
Maurizio Codispoti
Keyword(s):  
Natural Scenes ◽  
Natural Scene ◽  
Natural Scene Statistics ◽  
Top Down ◽  
Bottom Up ◽  
Computational Data

Understanding natural scenes involves the contribution of bottom–up analysis and top–down modulatory processes. However, the interaction of these processes during the categorization of natural scenes is not well understood. In the current study, we approached this issue using ERPs and behavioral and computational data. We presented pictures of natural scenes and asked participants to categorize them in response to different questions (Is it an animal/vehicle? Is it indoors/outdoors? Are there one/two foreground elements?). ERPs for target scenes requiring a “yes” response began to differ from those of nontarget scenes, beginning at 250 msec from picture onset, and this ERP difference was unmodulated by the categorization questions. Earlier ERPs showed category-specific differences (e.g., between animals and vehicles), which were associated with the processing of scene statistics. From 180 msec after scene onset, these category-specific ERP differences were modulated by the categorization question that was asked. Categorization goals do not modulate only later stages associated with target/nontarget decision but also earlier perceptual stages, which are involved in the processing of scene statistics.

scholarly journals Perception of 3D structure and natural scene statistics: The Southampton-York Natural Scenes (SYNS) dataset.

2015 ◽  
Vol 15 (12) ◽  
pp. 726 ◽  
Author(s):  
Wendy Adams ◽  
James Elder ◽  
Erich Graf ◽  
Alex Muryy ◽  
Arthur Lugtigheid
Keyword(s):  
3D Structure ◽  
Natural Scenes ◽  
Natural Scene ◽  
Natural Scene Statistics

scholarly journals Natural scene statistics predict how humans pool information across space in the estimation of surface tilt

2019 ◽  
Author(s):  
Seha Kim ◽  
Johannes Burge
Keyword(s):  
Hierarchical Model ◽  
Human Performance ◽  
Target Location ◽  
3D Structure ◽  
Natural Scenes ◽  
Natural Scene ◽  
Natural Scene Statistics ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Visual Systems

ABSTRACTVisual systems estimate the three-dimensional (3D) structure of scenes from information in two-dimensional (2D) retinal images. Visual systems use multiple sources of information to improve the accuracy of these estimates, including statistical knowledge of the probable spatial arrangements of natural scenes. Here, we examine how 3D surface tilts are spatially related in real-world scenes, and show that humans pool information across space when estimating surface tilt in accordance with these spatial relationships. We develop a hierarchical model of surface tilt estimation that is grounded in the statistics of tilt in natural scenes and images. The model computes a global tilt estimate by pooling local tilt estimates within an adaptive spatial neighborhood. The spatial neighborhood in which local estimates are pooled changes according to the value of the local estimate at a target location. The hierarchical model provides more accurate estimates of groundtruth tilt in natural scenes and provides a better account of human performance than the local model. Taken together, the results imply that the human visual system pools information about surface tilt across space in accordance with natural scene statistics.

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