On the effectiveness of noise masks: Naturalistic vs. un-naturalistic image statistics

AbstractIt has been argued that the human visual system is optimized for identification of broadband objects embedded in stimuli possessing orientation averaged power spectra fall-offs that obey the 1/fβ relationship typically observed in natural scene imagery (i.e., β=2.0 on logarithmic axes). Here, we were interested in whether individual spatial channels leading to recognition are functionally optimized for narrowband targets when masked by noise possessing naturalistic image statistics (β=2.0). The current study therefore explores the impact of variable β noise masks on the identification of narrowband target stimuli ranging in spatial complexity, while simultaneously controlling for physical or perceived differences between the masks. The results show that β=2.0 noise masks produce the largest identification thresholds regardless of target complexity, and thus do not seem to yield functionally optimized channel processing. The differential masking effects are discussed in the context of contrast gain control

Spatial Masking Does Not Reveal Mechanisms Selective to Combined Luminance and Red-Green Color

Detection thresholds plotted in the L and M cone-contrast plane have shown that there are two primary detection mechanisms, a red–green hue mechanism and a light–dark luminance mechanism. However, previous masking results suggest there may be additional mechanisms, responsive to combined features like bright and red or dark and green. We measured detection thresholds for a 1.2 c deg−1 sine-wave grating in the presence of a spatially matched mask grating which was either stationary, dynamically jittered or flickered. The stimuli could be set to any direction in the L,M plane. The appearance of selectivity for combined hue and luminance arose only in conditions where adding the test to the mask modified the spatial phase offset between the luminance and red–green stimulus components. Sensitivity was very high for detecting this spatial phase offset. When this extra cue was eliminated, masking contours in the L,M plane could be largely described by the classical red–green and luminance mechanisms

Colour adaptation modifies the long-wave versus middle-wave cone weights and temporal phases in human luminance (but not red-green) mechanism.

1. The human luminance (LUM) mechanism detects rapid flicker and motion, responding to a linear sum of contrast signals, L' and M', from the long-wave (L) and middle-wave (M) cones. The red-green mechanism detects hue variations, responding to a linear difference of L' and M' contrast signals. 2. The two detection mechanisms were isolated to assess how chromatic adaptation affects summation of L' and M' signals in each mechanism. On coloured background (from blue to red), we measured, as a function of temporal frequency, both the relative temporal phase of the L' and M' signals producing optimal summation and the relative L' and M' contrast weights of the signals (at the optimal phase for summation). 3. Within the red-green mechanism at 6 Hz, the phase shift between the L' and M' signals was negligible on each coloured field, and the L' and M' contrast weights were equal and of opposite sign. 4. Relative phase shifts between the L' and M' signals in the LUM mechanism were markedly affected by adapting field colour. For stimuli of 1 cycle deg-1 and 9 Hz, the temporal phase shift was zero on a green-yellow field (approximately 570 nm). On an orange field, the L' signal lagged M' by as much as 70 deg phase while on a green field M' lagged L' by as much as 70 deg. The asymmetric phase shift about yellow adaptation reveals a spectrally opponent process which controls the phase shift. The phase shift occurs at an early site, for colour adaptation of the other eye had no effect, and the phase shift measured monocularly was identical for flicker and motion, thus occurring before the motion signal is extracted (this requires an extra delay). 5. The L' versus M' phase shift in the LUM mechanism was generally greatest at intermediate temporal frequencies (4-12 Hz) and was small at high frequencies (20-25 Hz). The phase shift was greatest at low spatial frequencies and strongly reduced at high spatial frequencies (5 cycle deg-1), indicating that the receptive field surround of neurones is important for the phase shift. 6. These temporal phase shifts were confirmed by measuring motion contrast thresholds for drifting L cone and M cone gratings summed in different spatial phases. Owing to the large phase shifts on green or orange fields, the L and M components were detected about equally well by the LUM mechanism (at 1 cycle deg-1 and 9 Hz) when summed spatially in phase or in antiphase. Antiphase summation is typically thought to produce an equiluminant red-green grating. 7. At low spatial frequency, the relative L' and M' contrast weights in the LUM mechanism (assessed at the optimal phase for summation) changed strongly with field colour and temporal frequency. 8. The phase shifts and changing contrast weights were modelled with phasic retinal ganglion cells, with chromatic adaptation strongly modifying the receptive field surround. The cells summate L' and M' in their centre, while the surround L' and M' signals are both antagonistic to the centre for approximately 570 nm yellow adaptation. Green or orange adaptation is assumed to modify the L and M surround inputs, causing them to be opponent with respect to each other, but with reversed polarity on the green versus orange field (to explain the chromatic reversal of the phase shift). Large changes in the relative L' and M' weights on green versus orange fields indicate the clear presence of the spectrally opponent surround even at 20 Hz. The spectrally opponent surround appears sluggish, with a long delay (approximately 20 ms) relative to the centre