Endstopping and curvature

Dobbins, Allan C.; Zucker, Steven W.; Cynader, Max S.

doi:10.1016/0042-6989(89)90193-4

Cited by 118 publications

(47 citation statements)

References 35 publications

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“…Many models of end-stopped cells employ some form of implicit or explicit lateral inhibition to achieve their end-stopping capabilities (Dobbins, Zucker, & Cynader, 1989;Heitger et al, 1992). Similarly, the first competitive stage ofthe BCS consists of spatial competitions between like-oriented cells.…”

Section: Brightness Effectsmentioning

confidence: 99%

Illusory contours: Toward a neurally based perceptual theory

Lesher

1995

Psychon Bull Rev

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Although illusory contours were first described nearly a century ago, researchers have only recently begun to approach a consensus on the processes underlying their formation. Neurophysiological and psychophysical evidence indicate that neural mechanisms of the early visual cortex subserve illusory contour generation, although cognitive factors play important roles in determining the fmal percept. I summarize experiments concerning the determinants of illusory contour strength and form, concentrating on findings particularly relevant to modeling. After establishing arguments for the early generation of illusory contours, I provide an overview of formation theories, culminating with descriptions of neural models. The constraints that experimental data place on models are outlined, and neural models are evaluated with respect to these constraints. Throughout the review,I indicate where further experimental and modeling research are critical.Schumann introduced the first illusory contour, depicted in Figure 1a, in 1900, noting that a central "white rectangle with sharply defined contours appears, which objectively are not there." Schumann had discovered and noted two salient features of illusory figures: sharp edges in regions of homogeneous luminance, and a brightening within the figure. Perhaps because this stimulus failed to yield particularly convincing illusory contours, it was not until more than 50 years later that illusory contours became a topic for activeresearch. Although Ehrenstein (1941) demonstrated stimuli with salient illusory contours, it is remarkable that he failed to note their existence in the text of his article explicitly, commenting instead only on illusory brightening effects. Kanizsa's (1955) stunning figures, examples of which are depicted in Figures Iband 1c, produced extremely sharp, salient contours along with obvious brightening, initiating a wave of research.Although there have been a number ofexcellent illusory contour review papers Meyer & Petry, 1987;Parks, 1984), research advances in the psychophysical, neurophysiological, and modeling domains have been more than sufficient to mandate a new summary ofthe literature. Inthis review, I will not only present more recent data, but also employ an approach to the summaries that is markedly different from those previously taken. At the time of the earlier reviews, determination of the locus of formation of illusory contours was the central issue in illusory contour research, a fact reflected in the structure of the review papers. Experimental results were presented primarily in the historical context of supporting or refut-I would like to thank Ennio Mmgolla of Boston University for generously providing many helpful comments and suggestions during the development ofthis review.

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Section: Brightness Effectsmentioning

confidence: 99%

Illusory contours: Toward a neurally based perceptual theory

Lesher

1995

Psychon Bull Rev

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“…Thus, FRF mechanisms have been proposed to mediate effortless texture segmentation irrespective of whether the textures are defined by orientation (Malik & Perona, 1990; or by the arrangement of their elements (Graham et al, 1992;Sutter & Graham, 1995;Graham & Sutter, 1998). FRF mechanisms have been proposed to mediate the detection of textures that are smoothly modulated in contrast , orientation (Kingdom & Keeble, 1996), and spatial frequency (Arsenault et al, 1999), and have also been proposed to mediate the detection of moving contrastmodulated patterns (Chubb & Sperling, 1988), illusory contours (Wilson, 1999), glass patterns (Wilson et al, 1997;Wilson & Wilkinson, 1998), and boundary curvature (Dobbins et al, 1989;Wilson & Richards, 1992).…”

Section: Introductionmentioning

confidence: 99%

Mechanism independence for texture-modulation detection is consistent with a filter-rectify-filter mechanism

2003

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The ability of the visual system to detect stimuli that vary along dimensions other than luminance or color-"second-order" stimuli-has been of considerable interest in recent years. An important unresolved issue is whether different types of second-order stimuli are detected by a single, all purpose, mechanism, or by mechanisms that are specific to stimulus type. Using a conventional psychophysical paradigm, we show that for a class of second-order stimuli-textures sinusoidally modulated in orientation (OM), spatial frequency (FM), and contrast (CM)-the human visual system employs mechanisms that are selective to stimulus type. Whereas the addition of a subthreshold mask to a test pattern of the same stimulus type was found to facilitate the detection of the test, no facilitation was observed when mask and test were of different types, suggesting mechanism independence for the different types of stimulus. This finding raises the important question of whether mechanism independence is compatible with the well-known filter-rectify-filter (FRF) model of second-order stimulus detection, since FRF mechanisms, in principle, do not discriminate between stimulus types. We show that for all mask0test combinations except those with CM masks, the FRF mechanism giving the largest response to the test modulation is largely unaffected by subthreshold levels of a different stimulus-type mask. For this reason, we cannot rule out the possibility that FRF mechanisms mediate the detection of our stimuli. For combinations involving CM masks, however, we propose that a process of contrast normalization renders the test stimulus insensitive to the mask stimulus.

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“…Among earlier research, Dobbins et al proposed the most promising mechanism: an end-stopping mechanism [1,26,27], well-known for being curvature-selective. However, due to ambiguities in responses obtained using Dobbins et al's method, this paper proposes that it is more accurate to distinguish the curvature and length of line segments in comparison to curvature detectors [10][11][12]28].…”

Section: Discussionmentioning

confidence: 99%

The curvature calculation mechanism based on simple cell model

Yu¹,

Fan²,

Song³

2017

THC

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Abstract. A conclusion has not yet been reached on how exactly the human visual system detects curvature. This paper demonstrates how orientation-selective simple cells can be used to construct curvature-detecting neural units. Through fixed arrangements, multiple plurality cells were constructed to simulate curvature cells with a proportional output to their curvature. In addition, this paper offers a solution to the problem of narrow detection range under fixed resolution by selecting an output value under multiple resolution. Curvature cells can be treated as concrete models of an end-stopped mechanism, and they can be used to further understand "curvature-selective" characteristics and to explain basic psychophysical findings and perceptual phenomena in current studies.

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Endstopping and curvature

Cited by 118 publications

References 35 publications

Illusory contours: Toward a neurally based perceptual theory

Illusory contours: Toward a neurally based perceptual theory

Mechanism independence for texture-modulation detection is consistent with a filter-rectify-filter mechanism

The curvature calculation mechanism based on simple cell model

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