William A. Simpson

Temporal properties of the visual responses to luminance and contrast modulated noise

Vision is sensitive to first-order luminance modulations and second-order modulations of carrier contrast. Our knowledge of the temporal properties of second-order vision is insufficient and contradictory. Using temporal summation and reaction time paradigms, we found that the type of visual noise (static or dynamic) determines the temporal properties of the responses to luminance and contrast modulations. In the presence of static noise, the temporal responses to both types of modulation of low and higher spatial frequencies were transient. When dynamic noise was used, the temporal responses to luminance and contrast modulations of higher spatial frequencies were sustained. At low spatial frequency, however, luminance modulations elicited transient responses, while contrast modulated dynamic noise produced sustained responses. The reaction times to near-threshold contrast modulations of low spatial frequency were slower than those to first-order patterns and they did not significantly differ at modulations of higher spatial frequency. The results suggest that the temporal characteristics of first-stage linear filters which feed the second-order pathway may determine the temporal responses to contrast modulated noise.

Spatial frequency channels derived from individual differences.

Contrast sensitivity functions differ from observer to observer. We propose that these differences arise because each observer has unique weights for the outputs of the neural channels that underlie the contrast sensitivity function. By applying principal components analysis to individual contrast sensitivity functions of 297 observers, estimates of the channel tuning curves were found. We find evidence for three broadly tuned bandpass channels with peaks at 4, 8, and 16c/deg and bandwidth near 1.3 octaves. These channel tuning curves were reproduced in a cross-validation study of 56 observers.

Sampling efficiency and internal noise for motion detection, discrimination, and summation.

By comparing real observers to an ideal observer, previous studies have found that the detection of static patterns is limited by internal noise and by imperfect sampling efficiency. We developed and applied ideal observer models for the detection, discrimination, and summation of oppositely drifting gratings in Gaussian white noise. The three tasks share a common source of internal noise. The sampling efficiencies were on the order of 1-2% except for much lower efficiency in direction discrimination for faster moving gratings. The efficiency of direction discrimination relative to detection systematically declines as the speed is increased from 1 to 6 Hz. These results suggest that observers use mismatched filters tuned to slow speeds regardless of the signal speed. Human visual motion sensing appears to use distorted representations of the incoming signals, and this distortion is a major limitation to visual performance.

Why is second-order vision less efficient than first-order vision?

Research has shown that the sensitivity to second-order modulations of carrier contrast is lower than that to first-order luminance modulations stimuli. We sought to compare the efficiency of processing first- and second-order information. Employing a phase-discrimination paradigm we found that when humans were given sufficient a priori information of signal parameters they detected both luminance and contrast modulations of 0.6 and 2c/deg by a phase-sensitive algorithm. The overall detection efficiency for second-order patterns, however, was lower that that for first-order stimuli. To study the factors which limit the efficiency of first- and second-order vision, we measured detection performance for luminance and contrast modulations of 0.6 and 2c/deg embedded in Gaussian noise. The results showed that the detection of second-order patterns had lower sampling efficiency and higher additive internal noise as compared to the detection of first-order stimuli. Classification images for detecting contrast modulations of 2c/deg resembled the side-band component of the contrast modulations which suggests that human observers may detect contrast modulations of a sinusoidal carrier using first-order luminance channels. The lower sensitivity of the mechanism detecting second-order patterns might be due to higher levels of additive internal noise and lower sampling efficiency than those of the mechanism analysing first-order patterns.

Human cortical responses to contrast modulations of visual noise

We studied visual evoked potentials (VEPs) elicited by second-order contrast modulations of binary dynamic noise and first-order luminance modulations. Using a 3-point Laplacian operator centred on Oz, we found that contrast modulations of both low and higher spatial frequencies elicited a negative component whose latency was about 200 ms. The latency of this component was significantly longer than that of the early Laplacian components to first-order luminance modulations. These findings could be due to slower first-stage linear filters and additional processing stages of the second-order pathway. The topographical analysis of scalp recorded VEPs to central and half-field stimulation has suggested that the responses to second-order patterns are likely to be generated by neuronal structures within the primary visual cortex which may have inputs from extrastriate neurons via feedback connections.

Parametric modeling of reaction time experiment data.

A simple parametric model is proposed for data from a point-process version of a reaction time experiment. It is used to statistically check for the presence and nature of nonlinear inhibition in the eye-brain-hand system, as well as to study the nature of the reaction time delay distribution. The model tells us that, in principle, the second-order intensity estimate can be used to determine whether the experimental subject is systematically observing the first or the second of two flashes transmitted in short succession. Nonparametric estimates of second-order intensity functions are used in conjunction with this model. In particular, the model allows for the computation of good bandwidths for intensity curve estimation. A parametric bootstrap can also be implemented. Our methods are illustrated with 12 runs of data from a real reaction time experiment. It is found that nonlinear inhibition is present in the eye-brain-hand system. However, there are insufficient data to distinguish between log-normality and normality in the reaction time distribution, due partly to confounding with the particular kind of nonlinear inhibition present in the system.

Efficiency and internal noise for detection of suprathreshold patterns measured using simple reaction time.

Studies of the detection of simple visual patterns at threshold contrast have found that human performance is limited by the addition of internal noise and by the sub-optimal sampling efficiency of the visual system. Many common visual tasks require the detection of a signal having a contrast well above threshold, and we sought to measure the internal noise and sampling efficiency for such signals using simple reaction time (RT). Observers were presented with suprathreshold Gabors in dynamic Gaussian white noise and were required to hit a button as soon as each was detected. By comparing the RT variances from humans to those of an ideal observer, visuomotor internal noise and sampling efficiency were measured. The internal noise remains constant and the sampling efficiency increases as the signal contrast increases.