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.

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.

Face Detection in Natural Scenes

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 67-67 ◽  
Author(s):  
H Hill ◽  
R Watt
Keyword(s):  
Face Detection ◽  
Face Processing ◽  
Feature Detection ◽  
Processing System ◽  
Decision Task ◽  
Natural Scenes ◽  
Natural Scene ◽  
Energy Models ◽  
The Face ◽  
And Performance

The first task of any face processing system is detection of the face. We studied how the human visual system achieves face detection using a 2AFC task in which subjects were required to detect a face in the image of a natural scene. Luminance noise was added to the stimuli and performance was measured as a function of orientation and orientation bandwidth of the noise. Sensitivity levels and the effects of orientation bandwidth were similar for horizontally and vertically oriented noise. Performance was reduced for the smallest orientation bandwidth (5.6°) noise but sensitivity did not decrease further with increasing bandwidth until a point between 45° and 90°. The results suggest that important information may be oriented close to the vertical and horizontal. To test whether the results were specific to the task of face detection the same noise was added to the images in a man-made natural decision task. Equivalent levels of noise were found to be more disruptive and the effect of orientation bandwidth was different. The results are discussed in terms of models of face processing making use of oriented filters (eg Watt and Dakin, 1993 Perception22 Supplement, 12) and local energy models of feature detection (Morrone and Burr, 1988 Proceedings of the Royal Society of London B235 221 – 245).

scholarly journals Rediscovering Partisanship as the Long Term Force in the Vote Decision

2016 ◽  
Vol 35 (1) ◽  
pp. 1-17
Author(s):  
Michael D Martinez
Keyword(s):  
Vote Choice ◽  
Strongly Correlated ◽  
Short Term ◽  
Party Change ◽  
Vote Decision ◽  
Explanatory Variables ◽  
The Face ◽  
Voter Choice ◽  
Panel Studies

While partisanship is commonly conceived as the long term force in the voting decision, most models of voter choice include contemporaneous measures of partisanship, as well as issue preferences and retrospective evaluations as explanatory variables. In this paper, I use four multiyear panel studies spanning half a century to examine how well prior partisanship predicts future vote. Prior partisanship is strongly correlated with later vote choice, but that relationship is weaker during periods of party change, for younger voters and those who do not see differences between the parties, and in the face of strong short term forces. Despite evidence of the endogeneity of partisanship, we should also not lose sight of its long-term value as a predictor of vote choice. 

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.

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 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.

scholarly journals Rediscovering Partisanship as the Long Term Force in the Vote Decision

2016 ◽  
Vol 35 (1) ◽  
pp. 1
Author(s):  
Michael D Martinez
Keyword(s):  
Title Page ◽  
Vote Choice ◽  
Strongly Correlated ◽  
Short Term ◽  
Party Change ◽  
Vote Decision ◽  
Explanatory Variables ◽  
The Face ◽  
Voter Choice

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>While partisanship is commonly conceived as the long term force in the voting decision, most models of voter choice include contemporaneous measures of partisanship, as well as issue preferences and retrospective evaluations as explanatory variables. In this paper, I use four multiyear panel studies spanning half a century to examine how well prior partisanship predicts future vote. Prior partisanship is strongly correlated with later vote choice, but that relationship is weaker during periods of party change, for younger voters and those who do not see differences between the parties, and in the face of strong short term forces. Despite evidence of the endogeneity of partisanship, we should also not lose sight of its long-term value as a predictor of vote choice. </span></p></div></div></div>

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 Hue tuning curves in V4 change with visual context

2019 ◽  
Author(s):  
Ari S. Benjamin ◽  
Pavan Ramkumar ◽  
Hugo Fernandes ◽  
Matthew Smith ◽  
Konrad P. Kording
Keyword(s):  
Visual Cortex ◽  
Natural Images ◽  
Visual Features ◽  
Natural Scenes ◽  
Visual Context ◽  
Natural Scene ◽  
Neural Responses ◽  
Tuning Curves ◽  
Feature Interactions ◽  
General Response

SummaryTo understand activity in the higher visual cortex, researchers typically investigate how parametric changes in stimuli affect neural activity. These experiments reveal neurons’ general response properties only when the effect of a parameter in synthetic stimuli is representative of its effect in other visual contexts. However, in higher visual cortex it is rarely verified how well tuning to parameters of simplified experimental stimuli represents tuning to those parameters in complex or naturalistic stimuli. To evaluate precisely how much tuning curves can change with context, we developed a methodology to estimate tuning from neural responses to natural scenes. For neurons in macaque V4, we then estimated tuning curves for hue from both natural scene responses and responses to artificial stimuli of varying hue. We found that neurons’ hue tuning on artificial stimuli was not representative of their hue tuning on natural images, even if the neurons were strongly modulated by hue. These neurons thus respond strongly to interactions between hue and other visual features. We argue that such feature interactions are generally to be expected if the cortex takes an optimal coding strategy. This finding illustrates that tuning curves in higher visual cortex may only be accurate for similar stimuli as shown in the lab, and do not generalize for all neurons to naturalistic and behaviorally relevant stimuli.

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