Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Material Perception (2)
- 3D shape inference (1)
- Attention (1)
- Bayesian model fitting (1)
- Bayesian modeling (1)
-
- Binocular vision; Stereopsis; Inverse optics; Multiple view geometry; Inverse projective geometry; (1)
- Capture (1)
- Classification images (1)
- Computational Rationality (1)
- Decision making (1)
- Depth perception (1)
- EEG (1)
- Flanker experiment (1)
- Frequency estimation (1)
- Intuitive Physics (1)
- Lightness (1)
- Line drawing interpretation (1)
- Log odds (1)
- Mid-level Vision (1)
- Modeling (1)
- Motion (1)
- N2pc (1)
- Object Perception (1)
- Pd (1)
- Perception (1)
- Probability distortion (1)
- Response bias (1)
- Sensitivity (1)
- Shape (1)
- Shape perception (1)
Articles 1 - 9 of 9
Full-Text Articles in Cognition and Perception
Efficient Perception Of Physical Object Properties With Visual Heuristics, Vivian C. Paulun, Florian S. Bayer, Joshua B. Tenenbaum, Roland W. Fleming
Efficient Perception Of Physical Object Properties With Visual Heuristics, Vivian C. Paulun, Florian S. Bayer, Joshua B. Tenenbaum, Roland W. Fleming
MODVIS Workshop
No abstract provided.
Understanding The Influence Of Perceptual Noise On Visual Flanker Effects Through Bayesian Model Fitting, Jordan Deakin, Dietmar Heinke
Understanding The Influence Of Perceptual Noise On Visual Flanker Effects Through Bayesian Model Fitting, Jordan Deakin, Dietmar Heinke
MODVIS Workshop
No abstract provided.
Recovering Depth From Stereo Without Using Any Oculomotor Information, Tadamasa Sawada
Recovering Depth From Stereo Without Using Any Oculomotor Information, Tadamasa Sawada
MODVIS Workshop
The human visual system uses binocular disparity to perceive depth within 3D scenes. It is commonly assumed that the visual system needs oculomotor information about the relative orientation of the two eyes to perceive depth on the basis of binocular disparity. The necessary oculomotor information can be obtained from an efference copy of the oculomotor signals, or from a 2D distribution of the vertical disparity, specifically, from the vertical component of binocular disparity. It is known that oculomotor information from the efference copy and from the vertical disparity distribution can affect the perception of depth based on binocular disparity. But, …
A Feature-Based Model Of Visually Perceiving Deformable Objects, Vivian C. Paulun, Filipp Schmidt, Roland W. Fleming
A Feature-Based Model Of Visually Perceiving Deformable Objects, Vivian C. Paulun, Filipp Schmidt, Roland W. Fleming
MODVIS Workshop
No abstract provided.
Using Classification Images To Understand Models Of Lightness Perception, Minjung Kim, Jason M. Gold, Richard F. Murray
Using Classification Images To Understand Models Of Lightness Perception, Minjung Kim, Jason M. Gold, Richard F. Murray
MODVIS Workshop
No abstract provided.
Modeling The Neural Circuitry Underlying The Behavioral And Eeg Correlates Of Attentional Capture, Chloe Callahan-Flintoft, Brad Wyble
Modeling The Neural Circuitry Underlying The Behavioral And Eeg Correlates Of Attentional Capture, Chloe Callahan-Flintoft, Brad Wyble
MODVIS Workshop
The Reactive-Convergent Gradient Field model (R-CGF) is a unique approach to modeling spatial attention in that it links neural mechanisms to event related potentials (ERPs) from scalp EEG. This model was developed with the aim of explaining different, sometimes conflicting, findings in the attention literature. Specifically, this model address conflicting findings showing both simultaneous and serial deployment of attention. Another argument addressed by the model is whether attention to a location invokes a suppression of the spatial surround, or the selective inhibition of distractors. With the R-CGF, we have found that these results are not as incompatible as they appear …
The Bounded Log-Odds Model Of Frequency And Probability Distortion, Hang Zhang, Laurence T. Maloney
The Bounded Log-Odds Model Of Frequency And Probability Distortion, Hang Zhang, Laurence T. Maloney
MODVIS Workshop
No abstract provided.
A Signal Detection Experiment With Limited Number Of Trials, Tadamasa Sawada
A Signal Detection Experiment With Limited Number Of Trials, Tadamasa Sawada
MODVIS Workshop
Signal detection theory has been well accepted in vision science to measure human sensitivity to stimuli in a Psychophysical experiment. The theory is formulated so that the measured sensitivity is independent from a response bias (criterion). The formulation is based on an assumption that number of trials in the experiment is infinite but this assumption cannot be satisfied in practice. The assumption came from two normal distributions used in the formulation. The distributions respectively represent a set of signal trial and that of noise trials in the experiment. In this study, I will show how the violation of the assumption …
Bayesian Modeling Of 3d Shape Inference From Line Drawings, Seha Kim, Jacob Feldman, Manish Singh
Bayesian Modeling Of 3d Shape Inference From Line Drawings, Seha Kim, Jacob Feldman, Manish Singh
MODVIS Workshop
Human depth comparisons in line drawings reflect the underlying uncertainty of perceived 3D shape. We propose a Bayesian model that estimates the 3D shape from line drawings based on the local and non-local contour cues. This model estimates the posterior distribution over depth differences at two points on a line drawing. The likelihood is numerically computed by assuming a generative model, which generates random 3D surfaces and, via projection, random line drawings. The 3D surfaces are inflated from random skeletons and projected into line drawings. Given a novel line drawing, the model samples probable local surfaces based on the relations …