It is easier to find a tilted bar amongst vertical bars than vice-versa, but this asymmetry can be abolished or reversed by surrounding the bars with a tilted frame. The frame effect is important because it challenges bottom-up models of saliency. We conducted two experiments to investigate the causes of this effect. In Experiment 1, we removed different components of a square frame, and concluded that the frame effect was caused by a combination of (1) high-level configural cues that provided a frame of reference, and (2) bottom-up iso-orientation competition from the sides of the frame parallel to the bars. The iso-orientation competition could have arisen from (1) diversion of attention to the parts of the frame parallel to the target, or (2) iso-orientation suppression between nearby units selective for the same orientation. Experiment 2 investigated the nature of the iso-orientation competition process. In this experiment, we used a single line (the "axis") embedded in a circular field of bar elements, rather than a square frame surrounding them. The effect of the axis declined rapidly to zero with increasing target-axis distance, suggesting that the iso-orientation competition was caused entirely by iso-orientation suppression between nearby units tuned to the same orientation.

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In this paper, we examine the mechanisms underlying the perceptual integration of two types of contour: snakes (composed of Gabor elements parallel to the path of the contour) and ladders (with elements perpendicular to the path). We varied the element separation and carrier wavelength. Increasing the element separation impaired detection of snakes but did not affect ladders; at high separations, snakes and ladders were closely matched in difficulty. One subject showed no effect of carrier wavelength, and the other showed a decline in performance as the wavelength increased. We discuss how these results might be accommodated by association field models. We also present a new model in which the linkage results from overlap in the filter responses to adjacent elements. We show that, if 1st-order filters are used, the model's performance on widely spaced snake contours deteriorates greatly as the carrier wavelength of the elements decreases, in contrast to our psychophysical results. To integrate widely spaced contours with short carrier wavelengths, the model requires a 2nd-order process, in which a nonlinearity intervenes between small-scale 1st-stage filters and large-scale 2nd-stage filters. This model detects snakes when the 1st and 2nd stage filters have the same orientation, and detects ladders when they are orthogonal.

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We studied the relationship between the decline in sensitivity that occurs with eccentricity for stimuli of different spatial scale defined by either luminance (LM) or contrast (CM) modulation. We show that the detectability of CM stimuli declines with eccentricity in a spatial frequency-dependent manner, and that the rate of sensitivity decline for CM stimuli is roughly that expected from their 1st order carriers, except, possibly, at finer scales. Using an equivalent noise paradigm, we investigated the possible reasons for why the foveal sensitivity for detecting LM and CM stimuli differs as well as the reason why the detectability of 1st order stimuli declines with eccentricity. We show the former can be modeled by an increase in internal noise whereas the latter involves both an increase in internal noise and a loss of efficiency. To encompass both the threshold and suprathreshold transfer properties of peripheral vision, we propose a model in terms of the contrast gain of the underlying mechanisms.

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We studied the perceptual integration of contours consisting of Gabor elements positioned along a smooth path, embedded among distractor elements. Contour elements either formed tangents to the path (“snakes”) or were perpendicular to it (“ladders”). Perfectly straight snakes and ladders were easily detected in the fovea but, at an eccentricity of 6°, only the snakes were detectable. The disproportionate impairment of peripheral ladder detection remained when we brought foveal performance away from ceiling by jittering the orientations of the elements. We propose that the failure to detect peripheral ladders is a form of crowding, the phenomenon observed when identification of peripherally located letters is disrupted by flanking letters. D. G. Pelli, M. Palomares, and N. J. Majaj (2004) outlined a model in which simple feature detectors are followed by integration fields, which are involved in tasks, such as letter identification, that require the outputs of several detectors. They proposed that crowding occurs because small integration fields are absent from the periphery, leading to inappropriate feature integration by large peripheral integration fields. We argue that the “association field,” which has been proposed to mediate contour integration (D. J. Field, A. Hayes, & R. F. Hess, 1993), is a type of integration field. Our data are explained by an elaboration of Pelli et al.'s model, in which weak ladder integration competes with strong snake integration. In the fovea, the association fields were small, and the model integrated snakes and ladders with little interference. In the periphery, the association fields were large, and integration of ladders was severely disrupted by interference from spurious snake contours. In contrast, the model easily detected snake contours in the periphery. In a further demonstration of the possible link between contour integration and crowding, we ran our contour integration model on groups of three-letter stimuli made from short line segments. Our model showed several key properties of crowding: The critical spacing for crowding to occur was independent of the size of the target letter, scaled with eccentricity, and was greater on the peripheral side of the target.

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D. J. Field, A. Hayes, and R. F. Hess (1993) introduced two types of stimulus to study the perceptual integration of contours. Both types of stimulus consist of a smooth path of spatially separate elements, embedded in a field of randomly oriented elements. In one type of stimulus (“snakes”), the elements form tangents to the path of the contour; in the other type (“ladders”), the elements are orthogonal to the path. Little is currently known about the relative integration speeds of these two types of contour. We investigated this issue by temporally modulating the orientations of the contour elements. Our results suggest that snakes and ladders are integrated at similar speeds.

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To make vision possible, the visual nervous system must represent the most informative features in the light pattern captured by the eye. Here we use Gaussian scale–space theory to derive a multiscale model for edge analysis and we test it in perceptual experiments. At all scales there are two stages of spatial filtering. An odd-symmetric, Gaussian first derivative filter provides the input to a Gaussian second derivative filter. Crucially, the output at each stage is half-wave rectified before feeding forward to the next. This creates nonlinear channels selectively responsive to one edge polarity while suppressing spurious or “phantom” edges. The two stages have properties analogous to simple and complex cells in the visual cortex. Edges are found as peaks in a scale–space response map that is the output of the second stage. The position and scale of the peak response identify the location and blur of the edge. The model predicts remarkably accurately our results on human perception of edge location and blur for a wide range of luminance profiles, including the surprising finding that blurred edges look sharper when their length is made shorter. The model enhances our understanding of early vision by integrating computational, physiological, and psychophysical approaches.

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In many models of edge analysis in biological vision, the initial stage is a linear 2nd derivative operation. Such models predict that adding a linear luminance ramp to an edge will have no effect on the edge’s appearance, since the ramp has no effect on the 2nd derivative. Our experiments did not support this prediction: adding a negative-going ramp to a positive-going edge (or vice-versa) greatly reduced the perceived blur and contrast of the edge. The effects on a fairly sharp edge were accurately predicted by a nonlinear multi-scale model of edge processing [Georgeson, M. A., May, K. A., Freeman, T. C. A., & Hesse, G. S. (in press). From filters to features: Scale-space analysis of edge and blur coding in human vision. Journal of Vision], in which a half-wave rectifier comes after the 1st derivative filter. But we also found that the ramp affected perceived blur more profoundly when the edge blur was large, and this greater effect was not predicted by the existing model. The model’s fit to these data was much improved when the simple half-wave rectifier was replaced by a threshold-like transducer [May, K. A. & Georgeson, M. A. (2007). Blurred edges look faint, and faint edges look sharp: The effect of a gradient threshold in a multi-scale edge coding model. Vision Research, 47, 1705–1720.]. This modified model correctly predicted that the interaction between ramp gradient and edge scale would be much larger for blur perception than for contrast perception. In our model, the ramp narrows an internal representation of the gradient profile, leading to a reduction in perceived blur. This in turn reduces perceived contrast because estimated blur plays a role in the model’s estimation of contrast. Interestingly, the model predicts that analogous effects should occur when the width of the window containing the edge is made narrower. This has already been confirmed for blur perception; here, we further support the model by showing a similar effect for contrast perception.

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A multi-scale model of edge coding based on normalized Gaussian derivative filters successfully predicts perceived scale (blur) for a wide variety of edge profiles [Georgeson, M. A., May, K. A., Freeman, T. C. A., & Hesse, G. S. (in press). From filters to features: Scale-space analysis of edge and blur coding in human vision. Journal of Vision]. Our model spatially differentiates the luminance profile, half-wave rectifies the 1st derivative, and then differentiates twice more, to give the 3rd derivative of all regions with a positive gradient. This process is implemented by a set of Gaussian derivative filters with a range of scales. Peaks in the inverted normalized 3rd derivative across space and scale indicate the positions and scales of the edges. The edge contrast can be estimated from the height of the peak. The model provides a veridical estimate of the scale and contrast of edges that have a Gaussian integral profile. Therefore, since scale and contrast are independent stimulus parameters, the model predicts that the perceived value of either of these parameters should be unaffected by changes in the other. This prediction was found to be incorrect: reducing the contrast of an edge made it look sharper, and increasing its scale led to a decrease in the perceived contrast. Our model can account for these effects when the simple half-wave rectifier after the 1st derivative is replaced by a smoothed threshold function described by two parameters. For each subject, one pair of parameters provided a satisfactory fit to the data from all the experiments presented here and in the accompanying paper [May, K. A. & Georgeson, M. A. (2007). Added luminance ramp alters perceived edge blur and contrast: A critical test for derivative-based models of edge coding. Vision Research, 47, 1721–1731]. Thus, when we allow for the visual system’s insensitivity to very shallow luminance gradients, our multi-scale model can be extended to edge coding over a wide range of contrasts and blurs.

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A unique vertical bar among horizontal bars is salient and pops out perceptually. Physiological data have suggested that mechanisms in the primary visual cortex (V1) contribute to the high saliency of such a unique basic feature, but indicated little regarding whether V1 plays an essential or peripheral role in input-driven or bottom-up saliency. Meanwhile, a biologically based V1 model has suggested that V1 mechanisms can also explain bottom-up saliencies beyond the pop-out of basic features, such as the low saliency of a unique conjunction feature such as a red vertical bar among red horizontal and green vertical bars, under the hypothesis that the bottom-up saliency at any location is signaled by the activity of the most active cell responding to it regardless of the cell's preferred features such as color and orientation. The model can account for phenomena such as the difficulties in conjunction feature search, asymmetries in visual search, and how background irregularities affect ease of search. In this paper, we report nontrivial predictions from the V1 saliency hypothesis, and their psychophysical tests and confirmations. The prediction that most clearly distinguishes the V1 saliency hypothesis from other models is that task-irrelevant features could interfere in visual search or segmentation tasks which rely significantly on bottom-up saliency. For instance, irrelevant colors can interfere in an orientation-based task, and the presence of horizontal and vertical bars can impair performance in a task based on oblique bars. Furthermore, properties of the intracortical interactions and neural selectivities in V1 predict specific emergent phenomena associated with visual grouping. Our findings support the idea that a bottom-up saliency map can be at a lower visual area than traditionally expected, with implications for top-down selection mechanisms.

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THE finding that photographic^1–4 and digital⁵ composites (blends) of faces are considered to be attractive has led to the claim that attractiveness is averageness⁵. This would encourage stabilizing selection, favouring phenotypes with an average facial structure⁵. The 'averageness hypothesis' would account for the low distinctiveness of attractive faces⁶ but is difficult to reconcile with the finding that some facial measurements correlate with attractiveness^7,8. An average face shape is attractive but may not be optimally attractive⁹. Human preferences may exert directional selection pressures, as with the phenomena of optimal outbreeding and sexual selection for extreme characteristics^10–14. Using composite faces, we show here that, contrary to the averageness hypothesis, the mean shape of a set of attractive faces is preferred to the mean shape of the sample from which the faces were selected. In addition, attractive composites can be made more attractive by exaggerating the shape differences from the sample mean. Japanese and Caucasian observers showed the same direction of preferences for the same facial composites, suggesting that aesthetic judgements of face shape are similar across different cultural backgrounds. Our finding that highly attractive facial configurations are not average shows that preferences could exert a directional selection pressure on the evolution of human face shape.

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Colours on a flat two-dimensional surface can appear to lie in different depth planes. This phenomenon, readily seen on a computer monitor, is called chromostereopsis. Typically, red objects appear closer to the observer than blue objects. Although research on chromostereopsis has a history of over one hundred years, there are still aspects of it that are not fully explained. The simplest (and earliest) explanation proposes that a combination of chromatic aberration and the displacement of the fovea from the eye's optical axis is responsible for the illusion. Recent research supports the notion that other factors need to be taken into account, for example the eccentric location of the pupils and the Stiles-Crawford effect. We describe some of our own research that suggests that in many displays at least part of any perceived depth is due to luminance differences, bright objects appearing closer than dim ones.

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