Binocular summation at contrast threshold:a new look

Contrast sensitivity is better with two eyes than one. The standard view is that thresholds are about 1.4 (v2) times better with two eyes, and that this arises from monocular responses that, near threshold, are proportional to the square of contrast, followed by binocular summation of the two monocular signals. However, estimates of the threshold ratio in the literature vary from about 1.2 to 1.9, and many early studies had methodological weaknesses. We collected extensive new data, and applied a general model of binocular summation to interpret the threshold ratio. We used horizontal gratings (0.25 - 4 cycles deg-1) flickering sinusoidally (1 - 16 Hz), presented to one or both eyes through frame-alternating ferroelectric goggles with negligible cross-talk, and used a 2AFC staircase method to estimate contrast thresholds and psychometric slopes. Four naive observers completed 20 000 trials each, and their mean threshold ratios were 1.63, 1.69, 1.71, 1.81 - grand mean 1.71 - well above the classical v2. Mean ratios tended to be slightly lower (~1.60) at low spatial or high temporal frequencies. We modelled contrast detection very simply by assuming a single binocular mechanism whose response is proportional to (Lm + Rm) p, followed by fixed additive noise, where L,R are contrasts in the left and right eyes, and m, p are constants. Contrast-gain-control effects were assumed to be negligible near threshold. On this model the threshold ratio is 2(?1/m), implying that m=1.3 on average, while the Weibull psychometric slope (median 3.28) equals 1.247mp, yielding p=2.0. Together, the model and data suggest that, at low contrasts across a wide spatiotemporal frequency range, monocular pathways are nearly linear in their contrast response (m close to 1), while a strongly accelerating nonlinearity (p=2, a 'soft threshold') occurs after binocular summation. [Supported by EPSRC project grant GR/S74515/01]

Contrast discrimination with simultaneous monocular and dichoptic masks

In experiments reported elsewhere at this conference, we have revealed two striking results concerning binocular interactions in a masking paradigm. First, at low mask contrasts, a dichoptic masking grating produces a small facilitatory effect on the detection of a similar test grating. Second, the psychometric slope for dichoptic masking starts high (Weibull ß~4) at detection threshold, becomes low (ß~1.2) in the facilitatory region, and then unusually steep at high mask contrasts (ß~5.5). Neither of these results is consistent with Legge's (1984 Vision Research 24 385 - 394) model of binocular summation, but they are predicted by a two-stage gain control model in which interocular suppression precedes binocular summation. Here, we pose a further challenge for this model by using a 'twin-mask' paradigm (cf Foley, 1994 Journal of the Optical Society of America A 11 1710 - 1719). In 2AFC experiments, observers detected a patch of grating (1 cycle deg-1, 200 ms) presented to one eye in the presence of a pedestal in the same eye and a spatially identical mask in the other eye. The pedestal and mask contrasts varied independently, producing a two-dimensional masking space in which the orthogonal axes (10X10 contrasts) represent conventional dichoptic and monocular masking. The resulting surface (100 thresholds) confirmed and extended the observations above, and fixed the six parameters in the model, which fitted the data well. With no adjustment of parameters, the model described performance in a further experiment where mask and test were presented to both eyes. Moreover, in both model and data, binocular summation was greater than a factor of v2 at detection threshold. We conclude that this two-stage nonlinear model, with interocular suppression, gives a good account of early binocular processes in the perception of contrast. [Supported by EPSRC Grant Reference: GR/S74515/01]

The role of texture amplitude in shape from shading

When a textured surface is modulated in depth and illuminated, parts of the surface receive different levels of illumination; the resulting variations in luminance can be used to infer the shape of the depth modulations-shape from shading. The changes in illumination also produce changes in the amplitude of the texture, although local contrast remains constant. We investigated the role of texture amplitude in supporting shape from shading. If a luminance plaid is added to a binary noise texture (LM), shape from shading produces perception of corrugations in two directions. If the amplitude of the noise is also modulated (AM) such that it is in-phase with one of the luminance sinusoids and out-of-phase with the other, the resulting surface is seen as corrugated in only one directionöthat supported by the in-phase pairing. We confirmed this subjective report experimentally, using a depth-mapping technique. Further, we asked naïve observers to indicate the direction of corrugations in plaids made up of various combinations of LM and AM. LM+AM was seen as having most depth, then LM-only, then LM-AM, and then AM-only. Our results suggest that while LM is required to see depth from shading, its phase relative to any AM component is also important.

The interaction of luminance and texture amplitude in surface depth perception

Previous studies have suggested separate channels for detection of first-order luminance modulations (LM) and second-order modulations of the local amplitude (AM) of a texture. Mixtures of LM and AM with different phase relationships appear very different: in-phase compounds (LM + AM) look like 3-D corrugated surfaces, while out-of-phase compounds (LM - AM) appear flat and/or transparent. This difference may arise because the in-phase compounds are consistent with multiplicative shading, while the out-of-phase compounds are not. We investigated the role of these modulation components in surface depth perception. We used a textured background with thin bars formed by local changes in luminance and/or texture amplitude. These stimuli appear as embossed surfaces with wide and narrow regions. Keeping the AM modulation depth fixed at a suprathreshold level, we determined the amount of luminance contrast required for observers to correctly indicate the width (narrow or wide) of 'raised' regions in the display. Performance (compared to the LM-only case) was facilitated by the presence of AM, but, unexpectedly, performance for LM - AM was as good as for LM + AM. Thus, these results suggest that there is an interaction between first-order and second-order mechanisms during depth perception based on shading cues, but the phase dependence is not yet understood.

Edges and bars: where do people see features in 1-D images?

There have been two main approaches to feature detection in human and computer vision - based either on the luminance distribution and its spatial derivatives, or on the spatial distribution of local contrast energy. Thus, bars and edges might arise from peaks of luminance and luminance gradient respectively, or bars and edges might be found at peaks of local energy, where local phases are aligned across spatial frequency. This basic issue of definition is important because it guides more detailed models and interpretations of early vision. Which approach better describes the perceived positions of features in images? We used the class of 1-D images defined by Morrone and Burr in which the amplitude spectrum is that of a (partially blurred) square-wave and all Fourier components have a common phase. Observers used a cursor to mark where bars and edges were seen for different test phases (Experiment 1) or judged the spatial alignment of contours that had different phases (e.g. 0 degrees and 45 degrees ; Experiment 2). The feature positions defined by both tasks shifted systematically to the left or right according to the sign of the phase offset, increasing with the degree of blur. These shifts were well predicted by the location of luminance peaks (bars) and gradient peaks (edges), but not by energy peaks which (by design) predicted no shift at all. These results encourage models based on a Gaussian-derivative framework, but do not support the idea that human vision uses points of phase alignment to find local, first-order features. Nevertheless, we argue that both approaches are presently incomplete and a better understanding of early vision may combine insights from both. (C)2004 Elsevier Ltd. All rights reserved.

Contrast discrimination and pattern masking: contrast gain control with fixed additive noise

We studied the visual mechanisms that serve to encode spatial contrast at threshold and supra-threshold levels. In a 2AFC contrast-discrimination task, observers had to detect the presence of a vertical 1 cycle deg-1 test grating (of contrast dc) that was superimposed on a similar vertical 1 cycle deg-1 pedestal grating, whereas in pattern masking the test grating was accompanied by a very different masking grating (horizontal 1 cycle deg-1, or oblique 3 cycles deg-1). When expressed as threshold contrast (dc at 75% correct) versus mask contrast (c) our results confirm previous ones in showing a characteristic 'dipper function' for contrast discrimination but a smoothly increasing threshold for pattern masking. However, fresh insight is gained by analysing and modelling performance (p; percent correct) as a joint function of (c, dc) - the performance surface. In contrast discrimination, psychometric functions (p versus logdc) are markedly less steep when c is above threshold, but in pattern masking this reduction of slope did not occur. We explored a standard gain-control model with six free parameters. Three parameters control the contrast response of the detection mechanism and one parameter weights the mask contrast in the cross-channel suppression effect. We assume that signal-detection performance (d') is limited by additive noise of constant variance. Noise level and lapse rate are also fitted parameters of the model. We show that this model accounts very accurately for the whole performance surface in both types of masking, and thus explains the threshold functions and the pattern of variation in psychometric slopes. The cross-channel weight is about 0.20. The model shows that the mechanism response to contrast increment (dc) is linearised by the presence of pedestal contrasts but remains nonlinear in pattern masking.

The interaction of luminance and texture amplitude in depth perception

Previous studies have suggested separate channels for the detection of first-order luminance (LM) and second-order modulations of the local amplitude (AM) of a texture (Schofield and Georgeson, 1999 Vision Research 39 2697 - 2716; Georgeson and Schofield, 2002 Spatial Vision 16 59). It has also been shown that LM and AM mixtures with different phase relationships are easily separated in identification tasks, and (informally) appear very different with the in-phase compound (LM + AM), producing the most realistic depth percept. We investigated the role of these LM and AM components in depth perception. Stimuli consisted of a noise texture background with thin bars formed as local increments or decrements in luminance and/or noise amplitude. These stimuli appear as embossed surfaces with wide and narrow regions. When luminance and amplitude changes have the same sign and magnitude (LM + AM) the overall modulation is consistent with multiplicative shading, but this is not so when the two modulations have opposite sign (LM - AM). Keeping the AM modulation depth fixed at a suprathreshold level, we determined the amount of luminance contrast required for observers to correctly indicate the width (narrow or wide) of raised regions in the display. Performance (compared to the LM-only case) was facilitated by the presence of AM, but, unexpectedly, performance for LM - AM was even better than for LM + AM. Further tests suggested that this improvement in performance is not due to an increase in the detectability of luminance in the compound stimuli. Thus, contrary to previous findings, these results suggest the possibility of interaction between first-order and second-order mechanisms in depth perception.

Bars and edges: what makes a feature for human vision?

There have been two main approaches to feature detection in human and computer vision - luminance-based and energy-based. Bars and edges might arise from peaks of luminance and luminance gradient respectively, or bars and edges might be found at peaks of local energy, where local phases are aligned across spatial frequency. This basic issue of definition is important because it guides more detailed models and interpretations of early vision. Which approach better describes the perceived positions of elements in a 3-element contour-alignment task? We used the class of 1-D images defined by Morrone and Burr in which the amplitude spectrum is that of a (partially blurred) square wave and Fourier components in a given image have a common phase. Observers judged whether the centre element (eg ±458 phase) was to the left or right of the flanking pair (eg 0º phase). Lateral offset of the centre element was varied to find the point of subjective alignment from the fitted psychometric function. This point shifted systematically to the left or right according to the sign of the centre phase, increasing with the degree of blur. These shifts were well predicted by the location of luminance peaks and other derivative-based features, but not by energy peaks which (by design) predicted no shift at all. These results on contour alignment agree well with earlier ones from a more explicit feature-marking task, and strongly suggest that human vision does not use local energy peaks to locate basic first-order features. [Supported by the Wellcome Trust (ref: 056093)]

Neural adjustments to image blur

Blur is an intrinsic feature of retina images that varies widely across images and observers, yet the world still typically appears 'in focus'. Here we examine the putative role of neural adaptation1 in the human perception of image focus by measuring how blur judgments depended on the state of adaptation. Exposure to unfocused images has previously been shown to influence acuity and contrast sensitivity and here we show that adaptation can also profoundly affect the actual perception of image focus.

Seeing edge blur: receptive fields as multiscale neural templates

Edge blur is an important perceptual cue, but how does the visual system encode the degree of blur at edges? Blur could be measured by the width of the luminance gradient profile, peak ^ trough separation in the 2nd derivative profile, or the ratio of 1st-to-3rd derivative magnitudes. In template models, the system would store a set of templates of different sizes and find which one best fits the `signature' of the edge. The signature could be the luminance profile itself, or one of its spatial derivatives. I tested these possibilities in blur-matching experiments. In a 2AFC staircase procedure, observers adjusted the blur of Gaussian edges (30% contrast) to match the perceived blur of various non-Gaussian test edges. In experiment 1, test stimuli were mixtures of 2 Gaussian edges (eg 10 and 30 min of arc blur) at the same location, while in experiment 2, test stimuli were formed from a blurred edge sharpened to different extents by a compressive transformation. Predictions of the various models were tested against the blur-matching data, but only one model was strongly supported. This was the template model, in which the input signature is the 2nd derivative of the luminance profile, and the templates are applied to this signature at the zero-crossings. The templates are Gaussian derivative receptive fields that covary in width and length to form a self-similar set (ie same shape, different sizes). This naturally predicts that shorter edges should look sharper. As edge length gets shorter, responses of longer templates drop more than shorter ones, and so the response distribution shifts towards shorter (smaller) templates, signalling a sharper edge. The data confirmed this, including the scale-invariance implied by self-similarity, and a good fit was obtained from templates with a length-to-width ratio of about 1. The simultaneous analysis of edge blur and edge location may offer a new solution to the multiscale problem in edge detection.

Contrast masking in strabismic amblyopia: attenuation, noise, interocular suppression and binocular summation

To investigate amblyopic contrast vision at threshold and above we performed pedestal-masking (contrastdiscrimination) experiments with a group of eight strabismic amblyopes using horizontal sinusoidal gratings (mainly 3 c/deg) in monocular, binocular and dichoptic configurations balanced across eye (i.e. five conditions). With some exceptions in some observers, the four main results were as follows. (1) For the monocular and dichoptic conditions, sensitivity was less in the amblyopic eye than in the good eye at all mask contrasts. (2) Binocular and monocular dipper functions superimposed in the good eye. (3) Monocular masking functions had a normal dipper shape in the good eye, but facilitation was diminished in the amblyopic eye. (4) A less consistent result was normal facilitation in dichoptic masking when testing the good eye, but a loss of this when testing the amblyopic eye. This pattern of amblyopic results was replicated in a normal observer by placing a neutral density filter in front of one eye. The two-stage model of binocular contrast gain control [Meese, T.S., Georgeson, M.A. & Baker, D.H. (2006). Binocular contrast vision at and above threshold. Journal of Vision 6, 1224--1243.] was `lesioned' in several ways to assess the form of the amblyopic deficit. The most successful model involves attenuation of signal and an increase in noise in the amblyopic eye, and intact stages of interocular suppression and binocular summation. This implies a behavioural influence from monocular noise in the amblyopic visual system as well as in normal observers with an ND filter over one eye.

Mach Bands: multi-scale spatial filtering and co-operative coding of edges and bars

Perception of Mach bands may be explained by spatial filtering ('lateral inhibition') that can be approximated by 2nd derivative computation, and several alternative models have been proposed. To distinguish between them, we used a novel set of ‘generalised Gaussian’ images, in which the sharp ramp-plateau junction of the Mach ramp was replaced by smoother transitions. The images ranged from a slightly blurred Mach ramp to a Gaussian edge and beyond, and also included a sine-wave edge. The probability of seeing Mach Bands increased with the (relative) sharpness of the junction, but was largely independent of absolute spatial scale. These data did not fit the predictions of MIRAGE, nor 2nd derivative computation at a single fine scale. In experiment 2, observers used a cursor to mark features on the same set of images. Data on perceived position of Mach bands did not support the local energy model. Perceived width of Mach bands was poorly explained by a single-scale edge detection model, despite its previous success with Mach edges (Wallis & Georgeson, 2009, Vision Research, 49, 1886-1893). A more successful model used separate (odd and even) scale-space filtering for edges and bars, local peak detection to find candidate features, and the MAX operator to compare odd- and even-filter response maps (Georgeson, VSS 2006, Journal of Vision 6(6), 191a). Mach bands are seen when there is a local peak in the even-filter (bar) response map, AND that peak value exceeds corresponding responses in the odd-filter (edge) maps.

The influence of fixation points on contrast detection and discrimination of patches of grating: Masking and facilitation

The use of fixation points (FPs) in visual psychophysics is common practice, though the costs and benefits of different fixation regimens have not been compared. Here we investigate the influence of several different types of FP configurations on the contrast detection of patches of sine-wave gratings. We find that for small targets (1°), the addition of a superimposed central FP can increase thresholds by a factor of 1.3 (2.5 dB) in comparison with no FP, and a factor of 1.5 (3.6 dB) in comparison with FPs that surround the target. These results are consistent with (i) a suppressive influence on the central region of the target from a central FP, and (ii) facilitatory influences from surrounding FPs. Our analysis of the slope of the psychometric function suggests that the facilitatory influence is not due to reduction of uncertainty. Plausible candidate causes for the facilitation are: (i) sensory interactions, (ii) aids to ocular accommodation and convergence, (iii) a reduction in eye-movements and (iv) more accurate placement of the observer’s window of attention. Masking by a central FP is not found for the suprathreshold task of contrast discrimination, suggesting that the masking effects of pedestal and FP do not combine linearly. This means that estimates of the level of masking produced by a contrast pedestal can depend on the details of the fixation point.

Size matters, but not for everyone:Individual differences for contrast discrimination

It is very well known that contrast detection thresholds improve with the size of a grating-type stimulus, but it is thought that the benefit of size is abolished for contrast discriminations well above threshold (e.g., Legge, G. E., & Foley, J. M. (1980)]. Here we challenge the generality of this view. We performed contrast detection and contrast discrimination for circular patches of sine wave grating as a function of stimulus size. We confirm that sensitivity improves with approximately the fourth-root of stimulus area at detection threshold (a log-log slope of -0.25) but find individual differences (IDs) for the suprathreshold discrimination task. For several observers, performance was largely unaffected by area, but for others performance first improved (by as much as a log-log slope of -0.5) and then reached a plateau. We replicated these different results several times on the same observers. All of these results were described in the context of a recent gain control model of area summation [Meese, T. S. (2004)], extended to accommodate the multiple stimulus sizes used here. In this model, (i) excitation increased with the fourth-root of stimulus area for all observers, and (ii) IDs in the discrimination data were described by IDs in the relation between suppression and area. This means that empirical summation in the contrast discrimination task can be attributed to growth in suppression with stimulus size that does not keep pace with the growth in excitation. © 2005 ARVO.

Neuromagnetic evoked responses to complex motions are greatest for expansion

We analysed evoked magnetic responses to moving random dot stimuli, initially using a 19-channel magnetoencephalography (MEG) system, and subsequently using a 151-channel MEG system. Random dot displays were used to construct complex motion sequences, which we refer to as expansion, contraction, deformation, and rotation. We also investigated lateral translation and a condition in which the directions of the dots were randomised. In all stimulus conditions, the dots were first stationary, then traveled for a brief period (317s or 542 ms), and were then stationary again. In all conditions, evoked magnetic responses were observed with a widespread bilateral distribution over the observers' heads. Initial recordings revealed a substantially larger evoked magnetic response to the expansion condition than the other conditions. In a revised study, we used a 151-channel MEG system and two stimulus diameters (9.3 and 48 deg), the smaller comparable with the first experiment. The responses were analysed using a nonparametric approach and confirmed our initial observations. In a third study, speed gradients were removed and a new design permitted direct comparisons between motion conditions. The results from all three experiments are consistent with the greater ecological validity of the expansion stimulus. © 2004 Elsevier B.V. All rights reserved.