scholarly journals Variance misperception under skewed empirical noise statistics explains overconfidence in the visual periphery

2021 ◽  
Author(s):  
Charles J. Winter ◽  
Megan A. K. Peters
Keyword(s):  
Visual Periphery ◽  
Noise Statistics

scholarly journals Hidden by bias: how standard psychophysical procedures conceal crucial aspects of peripheral visual appearance

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fazilet Zeynep Yildirim ◽  
Daniel R. Coates ◽  
Bilge Sayim
Keyword(s):  
Visual Perception ◽  
Line Pair ◽  
Crucial Aspect ◽  
Time And Space ◽  
Contextual Modulation ◽  
Visual Appearance ◽  
Single Time ◽  
Visual Periphery ◽  
Contextual Stimuli ◽  
Biased Perception

AbstractThe perception of a target depends on other stimuli surrounding it in time and space. This contextual modulation is ubiquitous in visual perception, and is usually quantified by measuring performance on sets of highly similar stimuli. Implicit or explicit comparisons among the stimuli may, however, inadvertently bias responses and conceal strong variability of target appearance. Here, we investigated the influence of contextual stimuli on the perception of a repeating pattern (a line triplet), presented in the visual periphery. In the neutral condition, the triplet was presented a single time to capture its minimally biased perception. In the similar and dissimilar conditions, it was presented within stimulus sets composed of lines similar to the triplet, and distinct shapes, respectively. The majority of observers reported perceiving a line pair in the neutral and dissimilar conditions, revealing ‘redundancy masking’, the reduction of the perceived number of repeating items. In the similar condition, by contrast, the number of lines was overestimated. Our results show that the similar context did not reveal redundancy masking which was only observed in the neutral and dissimilar context. We suggest that the influence of contextual stimuli has inadvertently concealed this crucial aspect of peripheral appearance.

On Why Objects Appear Smaller in the Visual Periphery

2019 ◽  
Vol 31 (1) ◽  
pp. 88-96 ◽  
Author(s):  
Wladimir Kirsch ◽  
Roland Pfister ◽  
Wilfried Kunde
Keyword(s):  
Visual Field ◽  
Visual System ◽  
Structural Properties ◽  
Spatial Attention ◽  
Visuospatial Attention ◽  
Peripheral Target ◽  
Perceptual Distortion ◽  
Visual Periphery ◽  
Peripheral Location ◽  
Exogenous Cue

An object appears smaller in the periphery than in the center of the visual field. In two experiments ( N = 24), we demonstrated that visuospatial attention contributes substantially to this perceptual distortion. Participants judged the size of central and peripheral target objects after a transient, exogenous cue directed their attention to either the central or the peripheral location. Peripheral target objects were judged to be smaller following a central cue, whereas this effect disappeared completely when the peripheral target was cued. This outcome suggests that objects appear smaller in the visual periphery not only because of the structural properties of the visual system but also because of a lack of spatial attention.

Online Adaptive Kalman Filter for Target Tracking with Unknown Noise Statistics

2021 ◽  
pp. 1-1
Author(s):  
Yuming Chen ◽  
Wei Li ◽  
Yuqiao Wang
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Adaptive Kalman Filter ◽  
Noise Statistics

scholarly journals Learning Based Signal Detection for MIMO Systems with Unknown Noise Statistics

2021 ◽  
pp. 1-1
Author(s):  
Ke He ◽  
Le He ◽  
Lisheng Fan ◽  
Yansha Deng ◽  
George K. Karagiannidis ◽  
...  
Keyword(s):  
Signal Detection ◽  
Mimo Systems ◽  
Noise Statistics

scholarly journals Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Lijun Song ◽  
Zhongxing Duan ◽  
Bo He ◽  
Zhe Li
Keyword(s):  
Neural Networks ◽  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Estimation Error ◽  
Inertial Navigation ◽  
Transfer Alignment ◽  
The Neural Networks ◽  
Initial Attitude ◽  
Noise Statistics

The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.

Photomultiplier noise statistics

1972 ◽  
Vol 5 (5) ◽  
pp. 915-930 ◽  
Author(s):  
P B Coates
Keyword(s):  
Noise Statistics

An Adaptive State Filtering Algorithm for Systems With Partially Known Dynamics

2002 ◽  
Vol 124 (3) ◽  
pp. 364-374 ◽  
Author(s):  
Alexander G. Parlos ◽  
Sunil K. Menon ◽  
Amir F. Atiya
Keyword(s):  
Neural Networks ◽  
Case Studies ◽  
Feedforward Neural Networks ◽  
Complex Case ◽  
State Equations ◽  
Estimation Errors ◽  
Filter Performance ◽  
On Line ◽  
Noise Statistics ◽  
On Line Learning

On-line filtering of stochastic variables that are difficult or expensive to directly measure has been widely studied. In this paper a practical algorithm is presented for adaptive state filtering when the underlying nonlinear state equations are partially known. The unknown dynamics are constructively approximated using neural networks. The proposed algorithm is based on the two-step prediction-update approach of the Kalman Filter. The algorithm accounts for the unmodeled nonlinear dynamics and makes no assumptions regarding the system noise statistics. The proposed filter is implemented using static and dynamic feedforward neural networks. Both off-line and on-line learning algorithms are presented for training the filter networks. Two case studies are considered and comparisons with Extended Kalman Filters (EKFs) performed. For one of the case studies, the EKF converges but it results in higher state estimation errors than the equivalent neural filter with on-line learning. For another, more complex case study, the developed EKF does not converge. For both case studies, the off-line trained neural state filters converge quite rapidly and exhibit acceptable performance. On-line training further enhances filter performance, decoupling the eventual filter accuracy from the accuracy of the assumed system model.

Sign in / Sign up

Export Citation Format

Share Document