Asymmetric Masking: Luminance Gratings Mask Second-Order Gratings, but Not Vice Versa

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 345-345
Author(s):  
A J Schofield ◽  
M A Georgeson
Keyword(s):  
Modulation Depth ◽  
Broad Band ◽  
Gain Control ◽  
High Amplitude ◽  
Second Order ◽  
Human Vision ◽  
First Order ◽  
Contrast Gain ◽  
Spatiotemporal Information ◽  
The Mean

Human vision can detect spatiotemporal information conveyed by first-order modulations of luminance and by second-order, non-Fourier modulations of image contrast. Models for second-order motion have suggested two filtering stages separated by a rectifying nonlinearity. We explore here the encoding of stationary first-order and second-order gratings, and their interaction. Stimuli consisted of 2-D broad-band static visual noise sinusoidally modulated in luminance (first-order, LM) or contrast (second-order, CM). Modulation thresholds were measured in a two-interval forced-choice staircase procedure. With increasing noise contrast, first-order sensitivity decreased (owing to masking) but sensitivity to contrast modulation increased. Weak background gratings present in both intervals produced order-specific facilitation: LM background facilitated LM detection (the ‘dipper function’) and CM facilitated CM detection. LM did not facilitate CM, nor vice versa, and this is strong evidence that LM and CM are detected via different mechanisms. Nevertheless, suprathreshold LM gratings masked CM detection, but not vice versa. High-amplitude CM masks had little or no effect on CM or LM detection. A broadly tuned divisive gain-control mechanism applied to the first-order filtering stage has been proposed by Foley (1994 Journal of the Optical Society of America A11 1710 – 1719) to account for masking of luminance gratings, and this might also explain the masking of second-order by first-order stimuli. First-order maskers would drive down the effective contrast of the carrier, thus reducing second-order sensitivity. But for second-order maskers the mean contrast, and hence contrast gain, remained constant, independent of modulation depth. Thus second-order gratings would produce no masking effects, as observed.

A Pedestal Blocks the Perception of Non-Fourier Motion

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 17-17 ◽  
Author(s):  
O I Ukkonen ◽  
A M Derrington
Keyword(s):  
Modulation Depth ◽  
Discrimination Performance ◽  
Spatial Variations ◽  
Second Order ◽  
First Order ◽  
Direction Discrimination ◽  
Forced Choice Task ◽  
The Mean ◽  
Texture Contrast ◽  
Moving Pattern

We wanted to know whether the mechanisms that discriminate the motion of first-order patterns (defined by spatial variations of luminance) differ from those that detect the motion of non-Fourier or second-order patterns (defined by spatial variations of contrast). To address this question we tested whether motion discrimination performance of first-order and second-order patterns was affected by a pedestal (Lu and Sperling, 1995 Vision Research35 2697 – 2722). A pedestal is a static replica of a moving pattern. We used pedestals with contrast or modulation depth twice the value at which it becomes possible to discriminate the direction of a moving pattern. A two-interval forced-choice task was used to determine how direction discrimination varies with contrast of sine gratings (1 cycle deg−1) and modulation depth of amplitude-modulated gratings presented either alone or with a pedestal. The amplitude-modulated gratings had a 5 cycles deg−1 carrier modulated at 1 cycle deg−1. Three different temporal frequencies (1, 3, and 12 Hz) were studied. Performance with sine gratings was unaffected by the pedestal at all temporal frequencies tested. For amplitude-modulated gratings the pedestal raised the modulation depth at which it became possible to discriminate the direction of motion. This elevation in threshold decreased when the mean contrast of the pattern was high. This result shows that immunity to pedestals of texture-contrast patterns (Lu and Sperling, 1996 Journal of the Optical Society of America13 2305 – 2318) does not generalise to other non-Fourier motion stimuli.

The Second-Order Master Equation

2019 ◽  
pp. 128-158
Author(s):  
Pierre Cardaliaguet ◽  
François Delarue ◽  
Jean-Michel Lasry ◽  
Pierre-Louis Lions
Keyword(s):  
Master Equation ◽  
Mean Field ◽  
Second Order ◽  
Well Posedness ◽  
Mean Field Game ◽  
First Order ◽  
Common Noise ◽  
System P ◽  
The Mean

This chapter investigates the second-order master equation with common noise, which requires the well-posedness of the mean field game (MFG) system. It also defines and analyzes the solution of the master equation. The chapter explains the forward component of the MFG system that is recognized as the characteristics of the master equation. The regularity of the solution of the master equation is explored through the tangent process that solves the linearized MFG system. It also analyzes first-order differentiability and second-order differentiability in the direction of the measure on the same model as for the first-order derivatives. This chapter concludes with further description of the derivation of the master equation and well-posedness of the stochastic MFG system.

Dynamic shifts of the contrast-response function

1997 ◽  
Vol 14 (3) ◽  
pp. 577-587 ◽  
Author(s):  
Jonathan D. Victor ◽  
Mary M. Conte ◽  
Keith P. Purpura
Keyword(s):  
Gain Control ◽  
Human Vision ◽  
High Contrast ◽  
Contrast Level ◽  
Low Contrast ◽  
Contrast Gain ◽  
Contrast Response Function ◽  
Stimuli Response ◽  
Response Phase ◽  
Contrast Response

AbstractWe recorded visual evoked potentials in response to square-wave contrast-reversal checkerboards undergoing a transition in the mean contrast level. Checkerboards were modulated at 4.22 Hz (8.45-Hz reversal rate). After each set of 16 cycles of reversals, stimulus contrast abruptly switched between a “high” contrast level (0.06 to 1.0) to a “low” contrast level (0.03 to 0.5). Higher contrasts attenuated responses to lower contrasts by up to a factor of 2 during the period immediately following the contrast change. Contrast-response functions derived from the initial second following a conditioning contrast shifted by a factor of 2–4 along the contrast axis. For low-contrast stimuli, response phase was an advancing function of the contrast level in the immediately preceding second. For high-contrast stimuli, response phase was independent of the prior contrast history. Steady stimulation for periods as long as 1 min produced only minor effects on response amplitude, and no detectable effects on response phase. These observations delineate the dynamics of a contrast gain control in human vision.

scholarly journals Gain control of synaptic transfer from second- to third-order neurons of cockroach ocelli.

1996 ◽  
Vol 107 (1) ◽  
pp. 121-131 ◽  
Author(s):  
M Mizunami
Keyword(s):  
Synaptic Transmission ◽  
Characteristic Curve ◽  
Gain Control ◽  
Second Order ◽  
The Body ◽  
Intensity Change ◽  
Third Order ◽  
The Mean ◽  
Mean Luminance ◽  
Nonlinear Nature

Synaptic transmission from second- to third-order neurons of cockroach ocelli occurs in an exponentially rising part of the overall sigmoidal characteristic curve relating pre- and postsynaptic voltage. Because of the nonlinear nature of the synapse, linear responses of second-order neurons to changes in ligh intensity are half-wave rectified, i.e., the response to a decrement in light is amplified whereas that to an increment in light is compressed. Here I report that the gain of synaptic transmission from second- to third-order neurons changes by ambient light levels and by wind stimulation applied to the cerci. Transfer characteristics of the synapse were studied by simultaneous intracellular recordings of second- and third-order neurons. Potential changes were evoked in second-order neurons by a sinusoidally modulated light with various mean luminances. With a decrease in the mean luminance (a) the mean membrane potential of second-order neurons was depolarized, (b) the synapse between the second- and third-order neurons operated in a steeper range of the exponential characteristic curve, where the gain to transmit modulatory signals was higher, and (c) the gain of third-order neurons to detect a decrement in light increased. Second-order neurons were depolarized when a wind or tactile stimulus was applied to various parts of the body including the cerci. During a wind-evoked depolarization, the synapse operated in a steeper range of the characteristic curve, which resulted in an increased gain of third-order neurons to detect light decrements. I conclude that the nonlinear nature of the synapse between the second- and third-order neurons provides an opportunity for an adjustment of gain to transmit signals of intensity change. The possibility that a similar gain control occurs in other visual systems and underlies a more advanced visual function, i.e., detection of motion, is discussed.

fMRI Adaptation Reveals Separate Mechanisms for First-Order and Second-Order Motion

2007 ◽  
Vol 97 (2) ◽  
pp. 1319-1325 ◽  
Author(s):  
Hiroshi Ashida ◽  
Angelika Lingnau ◽  
Matthew B. Wall ◽  
Andrew T. Smith
Keyword(s):  
Selective Adaptation ◽  
Second Order ◽  
Neural Mechanisms ◽  
Human Vision ◽  
Image Motion ◽  
First Order ◽  
Single Mechanism ◽  
Neural Populations ◽  
Cross Adaptation ◽  
Fmri Adaptation

A key unresolved debate in human vision concerns whether we have two different low-level mechanisms for encoding image motion. Separate neural mechanisms have been suggested for first-order (luminance modulation) and second-order (e.g., contrast modulation) motion in the retinal image but a single mechanism could handle both. Human functional magnetic resonance imaging (fMRI) has not so far convincingly revealed separate anatomical substrates. To examine whether two separate but co-localized mechanisms might exist, we used the technique of fast fMRI adaptation. We found direction-selective adaptation independently for each type of motion in the motion area V5/MT+ of the human brain. However, there was a total absence of cross-adaptation between first-order and second-order motion stimuli. This was true in both of the two subcomponents of MT+ (MT and MST) and similar results were found in V3A. This pattern of adaptation was consistent with psychophysical measurements of detection thresholds in similar stimulus sequences. The results provide strong evidence for separate neural populations that are responsible for detecting first- and second-order motion.

Perturbation Analysis of Texture Effects on Lubricated Devices

2005 ◽  
Author(s):  
Gustavo C. Buscaglia ◽  
Mohammed Jai ◽  
Sorin Ciuperca
Keyword(s):  
Friction Coefficient ◽  
Friction Force ◽  
Perturbation Analysis ◽  
Mathematical Analysis ◽  
Load Capacity ◽  
Second Order ◽  
Numerical Investigations ◽  
First Order ◽  
The Mean

Given a bearing of some specified shape, what is the effect of texturing its surfaces uniformly? Experimental and numerical investigations on this question have recently been pursued, which we complement here with a mathematical analysis. Assuming the texture length to be much smaller than the bearing’s length, we combine homogenization techniques with perturbation analysis. This allows us to consider arbitrary, 2D texture shapes. The results show that both the load capacity and the friction force depend, to first order in the amplitude, just on the mean depth/height of the texture. The dependence of the friction coefficient is thus of second order.

A Stochastic Model for Primary Settlers

1970 ◽  
Vol 5 (1) ◽  
pp. 129-170
Author(s):  
D.P. Singh ◽  
A.W. Bryson ◽  
P.L. Silveston
Keyword(s):  
Stochastic Model ◽  
Stochastic Models ◽  
Suspended Solids ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Second Order ◽  
Order Model ◽  
First Order ◽  
The Mean

Abstract Stochastic models of process units are useful where the flow and concentrations in a feed stream vary appreciably over long time periods in a random way. Models yield not only the mean, but provide a measure of the variation around the mean. Assuming sedimentation can be described by a rate equation, stochastic models are developed for zero, first and second order rate processes. The zero order model can be rejected because it cannot be made to fit plant data, while the second order model was not developed further because of its complexity. The rate parameter for the first order model was evaluated from 1968 suspended solids data for the Kitchener Sewage Treatment Plant and found to have zero variance. Testing the model against 1966 and 1967 data and shorter period for 1968 showed that the model predicted suspended solids and BOD removals differing on the average from plant results by 10%. The first order stochastic model gives, thus, a satisfactory representation of primary settler performance.

scholarly journals A common contrast pooling rule for suppression within and between the eyes

2008 ◽  
Vol 25 (4) ◽  
pp. 585-601 ◽  
Author(s):  
TIM S. MEESE ◽  
KIRSTEN L. CHALLINOR ◽  
ROBERT J. SUMMERS
Keyword(s):  
Gain Control ◽  
Sine Wave ◽  
Human Vision ◽  
Binocular Summation ◽  
Cortical Cells ◽  
After Effects ◽  
Contrast Gain ◽  
Contrast Gain Control ◽  
Spatial Pooling ◽  
Sine Wave Gratings

AbstractRecent work has revealed multiple pathways for cross-orientation suppression in cat and human vision. In particular, ipsiocular and interocular pathways appear to assert their influence before binocular summation in human but have different (1) spatial tuning, (2) temporal dependencies, and (3) adaptation after-effects. Here we use mask components that fall outside the excitatory passband of the detecting mechanism to investigate the rules for pooling multiple mask components within these pathways. We measured psychophysical contrast masking functions for vertical 1 cycle/deg sine-wave gratings in the presence of left or right oblique (±45 deg) 3 cycles/deg mask gratings with contrast C%, or a plaid made from their sum, where each component (i) had contrast 0.5Ci%. Masks and targets were presented to two eyes (binocular), one eye (monoptic), or different eyes (dichoptic). Binocular-masking functions superimposed when plotted against C, but in the monoptic and dichoptic conditions, the grating produced slightly more suppression than the plaid when Ci ≥ 16%. We tested contrast gain control models involving two types of contrast combination on the denominator: (1) spatial pooling of the mask after a local nonlinearity (to calculate either root mean square contrast or energy) and (2) “linear suppression” (Holmes & Meese, 2004, Journal of Vision4, 1080–1089), involving the linear sum of the mask component contrasts. Monoptic and dichoptic masking were typically better fit by the spatial pooling models, but binocular masking was not: it demanded strict linear summation of the Michelson contrast across mask orientation. Another scheme, in which suppressive pooling followed compressive contrast responses to the mask components (e.g., oriented cortical cells), was ruled out by all of our data. We conclude that the different processes that underlie monoptic and dichoptic masking use the same type of contrast pooling within their respective suppressive fields, but the effects do not sum to predict the binocular case.

scholarly journals Spatial summation of first-order and second-order motion in human vision

2010 ◽  
Vol 50 (17) ◽  
pp. 1766-1774 ◽  
Author(s):  
Claire V. Hutchinson ◽  
Timothy Ledgeway
Keyword(s):  
Spatial Summation ◽  
Second Order ◽  
Human Vision ◽  
First Order
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