Palmer, E. M., & Kellman, P. J. (2003).

:
This model makes an unintuitive prediction: If two parts of a moving object appear together perfectly aligned, and then one part becomes occluded, there will be a strong illusion of misalignment. Results from 2 experiments confirm this prediction, and suggest that it takes approximately 40ms to bind visible and occluded regions of an image.
:


References
Palmer, E. M., & Kellman, P. J. (2003). (Mis)Perception of motion and form after occlusion: Anorthoscopic perception revisited [Abstract]. Journal of Vision, 3(9), 251a, http://journalofvision.org/3/9/251/, doi:10.1167/3.9.251.
http://journalofvision.org/3/9/251/

Yazdanbakhsh, A., and Grossberg, S. (2004).

The LAMINART model
pp.4
:
key properties of this model depend upon the existence of balanced excitatory and inhibitory signals in different cortical layers. In particular, a balance between excitation and inhibition is needed in the bipole circuit in layer 2/3 to ensure that perceptual groupings can form inwardly between pairs or greater numbers of inducers, but not outwardly from a single inducer.
:
On the other hand, balanced excitatory and inhibitory connections have also been used to explain the observed variability in the number and temporal distribution of spikes emitted by cortical neurons. Several model studies have shown how balanced excitation and inhibition can produce the highly variable interspike intervals that are found in cortical data.


References
Yazdanbakhsh, A., and Grossberg, S. (2004). Fast synchronization of perceptual grouping in laminar visual cortical circuits. Technical Report CAS/CNS TR-2004-005 , Boston University . Neural Networks, 17 , 707-718.

Yazdanbakhsh & Grossberg (2004)

Yazdanbakhsh, A., and Grossberg, S. (2004). Fast synchronization of perceptual grouping in laminar visual cortical circuits. Technical Report CAS/CNS TR-2004-005 , Boston University . Neural Networks, 17 , 707-718.

Abstract
Perceptual grouping is well-known to be a fundamental process during visual perception, notably grouping across scenic regions that do not receive contrastive visual inputs. Illusory contours are a classical example of such groupings. Recent psychophysical and neurophysiological evidence have shown that the grouping process can facilitate rapid synchronization of the cells that are bound together by a grouping, even when the grouping must be completed across regions that receive no contrastive inputs. Synchronous grouping can hereby bind together different object parts that may have become desynchronized due to a variety of factors, and can enhance the efficiency of cortical transmission. Neural models of perceptual grouping have clarified how such fast synchronization may occur by using bipole grouping cells, whose predicted properties have been supported by psychophysical, anatomical, and neurophysiological experiments. These models have not, however, incorporated some of the realistic constraints on which groupings in the brain are conditioned, notably the measured spatial extent of long-range interactions in layer 2/3 of a grouping network, and realistic synaptic and axonal signaling delays within and across cells in different cortical layers. This work addresses the question: Can long-range interactions that obey the bipole constraint achieve fast synchronization under realistic anatomical and neurophysiological constraints that initially desynchronize grouping signals? Can the cells that synchronize retain their analog sensitivity to changing input amplitudes? Can the grouping process complete and synchronize illusory contours across gaps in bottom-up inputs? Our simulations show that the answer to these questions is Yes.

KEY WORDS: perceptual grouping, binding problem, visual cortex, synchronization, illusory contours, bipole cell, horizontal connections, adaptive resonance, LAMINART model

References
Stephen Grossberg, Boston University

Good continuation and relatability

:
Relatability -- which governs contour interpolation across gaps -- differs in 5 ways from good continuation.
:


References
Kellman, P. J., Garrigan, P. B., Kalar, D., & Shipley, T. F. (2003). Good continuation and relatability: Related but distinct principles [Abstract]. Journal of Vision, 3(9), 120a, http://journalofvision.org/3/9/120/, doi:10.1167/3.9.120.

Watson, A. B. & Ahumada, A. J., Jr. (2005)

Watson, A. B. & Ahumada, A. J., Jr. (2005). A standard model for foveal detection of spatial contrast. Journal of Vision, 5(9), 717-740.
Keywords
vision, spatial, pattern, detection, threshold, contrast, contrast sensitivity, model, ModelFest


References
http://journalofvision.org/5/9/6/

Watson, AB (1979)

Watson, A. B. (1979 ). Probability summation over time. Vision Research, 19, 515-522.

Abstract

Frequency-of-seeing and sensitivity-duration curves were collected for temporal signals of limited spectral extent. A comparison of the two sorts of data suggests that a stimulus is detected whenever the excursions of its linearly filtered, noise-perturbed temporal waveform exceed some fixed magnitude.

References
Elsevier.com - Vision Research
http://www.elsevier.com/wps/find/journaldescription.cws_home/263/description#description

R -- ワイブル分布のパラメータの最尤推定

目的
ワイブル分布のパラメータを最尤推定する。

使用法
weibull.par(x, epsilon=1e-7)

引数
x データベクトル
epsilon 収束判定値（省略時には 1e-7 が仮定される）
max.loop 収束計算の上限回数（省略時には 500 回が仮定される）

このプログラムで計算される尺度パラメータは，
分布関数が F(x) = 1 - exp(- (x/b)^a) と表現されるときの b である.

cf.
http://blog.goo.ne.jp/hideunuma/e/b9f92b3183c7eef5767d94867fc90b1b

References
ワイブル分布のパラメータの最尤推定
http://aoki2.si.gunma-u.ac.jp/R/weibull-par.html

Rubin, Nakayama, and Shapley (1996)a

Rubin, N., Nakayama, K., and Shapley, R.M. Enhanced perception of illusory contours in the lower versus the upper visual hemifields. Science. 651-653, 1996.

　Inducing Figure の傾きの弁別と主観的輪郭線(modal interpolation) による弁別を比較している

Rubin, Nakayama, and Shapley (1996)b

Rubin, N., Nakayama, K., and Shapley, R.M. Abrupt learning and retinal size specificity in illusory-contour perception. Current Biology 7, 461-467, 1997

輪郭線の知覚
　知覚学習が網膜像の大きさに規定されるとしている

測定関数のモデル
The fraction of times that the subject judged the stimulus to be thin was plotted as a function of the inducers’ rotation angle. A sigmoid curve ([1+tanh(β(x-α))]/2) was fit to the data, with the slope(β)and bias(α) as free parameters. The threshold, defined as the inducers’ rotation angle needed to reach 82% correct discrimination, was estimated from the fitted curve.

主観的輪郭線補間の生成モデル

Ringach & Shapley (1996)は、主観的輪郭線補間の微小生成過程に以下の確率論的モデルを適用して、パラメータを最尤推定法で推定している。
p(α)=1-0.5exp{-(α/αth)^β}
ここで、
α：Indicing Figure(Packman)のスライスされた辺が水平垂直からずれた角度（傾き）（物理量、操作された変数）
αth：弁別閾>0：推定値が81.6%に達するthreshold
β>0 ：上記の点での精神測定関数の傾き
p(α)：Induced Figure が正しく同定される(Fat or Thin) 確率（推定値）
なお、この確率論的モデルについては下記の Watson (1979)を参照。

References
Ringach, D., & Shapley, R. (1996). Spatial and temporal properties of illusory contours and amodal boundary completion, Vision Research, Vol.36, No.19, pp.3037-3050.
Watson, A. B. (1979 ). Probability summation over time. Vision Research, 19, 515-522.

Spatiotemporal Boundary Formation

Spatiotemporal Boundary Formation: Boundary, Form, and Motion Perception From Transformations of Surface Elements
Thomas F. Shipley and Philip J. Kellman
Journal of Experimental Psychology; General 1994, Vol. 123, No. 1, 3-20

Continuous surface boundaries, object shape, and global motion can be perceived from information that is fragmentary in both space and time. The authors report investigations indicating that accretion and deletion of texture is only 1 member of a broader class of element transformations that produce boundaries, shape, and motion, through spatiotemporal boundary formation (SBF). The authors report 4 experiments exploring SBF. The first 3 examine the class of transformations producing SBF, indicating that local element changes in color, orientation, or location are all effective. A 4th experiment examines temporal constraints on SBF. Integration of local element changes to produce boundaries, form, and global motion appears to be confined to a 165-ms window. Two classes of spatiotemporal integration models are considered; the relation between SBF and other cases of boundary interpolation are discussed.

A response-classification technique

Deriving behavioural receptive fields for visually completed contours
Jason M. Gold, Richard F. Murray, Patrick J. Bennett and Allison B. Sekuler
19 May 2000 Current Biology2000, 10:663-666

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Previous behavioural and physiological studies suggest that the visual system treats illusory and occluded contours like luminance-defined contours in many respects. None of these studies has, however, directly shown that illusory and occluded contours are actually used to perform perceptual tasks. Here, we use a response-classification technique to answer this question directly.
Here, we use a response-classification technique to answer this question directly. This technique provides pictorial representations-- ‘classification images’ --

---

From Fragments to Objects

From Fragments to Objects : Segmentation and Grouping in Vision (Advances in Psychology (Amsterdam, Netherlands), 130,)-US-
ISBN:0444505067 (Hard cover book)
Shipley, Thomas F. (EDT) /Kellman, Philip J. (EDT) /Publisher:North-Holland Published 2001/12

Contents List
Philosophy and History of Perceptual Unit Formation"object" in perception and cognition, R. Schwartz; Balls of wax and cans of worms - the early history of object perception, M. Atherton; Development: Perceptual unit formation in infancy, M.E. Arterberry; Perceptual units and their mapping with language, B. Landau. Attention: An object substitution theory of visual masking, J.T. Enns, V. Di Lollo; Attention and unit formation - a biased competition account of object-based attention, S.P. Vecera, M. Behrmann. Models of Segmentation and Grouping: Geometric and neural models of object perception, P.J. Kellman et al; Varieties of grouping and its role in determining surface layout, B. Gillam; Amodal completion - a case study in grouping, A.B. Sekuler, R.F. Murray; Perceptual organization as generic object recognition, D.W. Jacobs; Simplicity, regularity, and perceptual interpretations - a structural information approach, R. Van Lier; Computational neural models of spatial integration in perceptual grouping, H. Neumann, E. Mingolla; Part-based representations of visual shape and implications for visual cognition, M. Singh, D.D. Hoffman. Spatiotemporal Segmentation and Grouping: Gaze control for face learning and recognition by humans and machines, J. Henderson et al; The visual interpretation of object and human movement, M. Shiffrar; Contours from apparent motion - a computational theory, W. Prophet et al; Breathing illusions and boundary formation in space-time, N. Bruno; Perception of occluding and occluded objects over time - spatiotemporal segmentation and unit formation, T.F. Shipley, D.W. Cunningham.

Murray, Gold, Bennett, Sekuler (2001)

ECVP 2001 abstract

Perceptually interpolated contours in shape discrimination
R F Murray, J M Gold, P J Bennett, A B Sekuler (Department of Psychology, University of Toronto, 100 St George Street, Toronto, Ontario M5S 3G3, Canada)

Observers use perceptually interpolated contours to perform some shape discrimination tasks, eg illusory and occluded contours to discriminate between Kanizsa squares (Murray et al, 2000 Perception 29 Supplement, 122). Do observers use contours defined only by perceptual grouping? We measured classification images in a task where observers discriminated between patterns made of four inward-facing Ls arranged as the corners of a square. The Ls were perceptually grouped as a square, but produced no illusory contours. Observers did not use whole sides of these grouping-defined squares. Rather, they mainly used the Ls, and were only slightly influenced by the empty regions between the Ls. This is in marked contrast to Murray et al's results with Kanizsa squares. Do observers use illusory contours that do not bound a region of illusory brightness? We measured classification images in a task where observers discriminated between Kanizsa squares defined by two white and two black inducers. These stimuli produced illusory contours, but the illusory square had the same brightness as the background. Observers did not use whole sides of the illusory square. Rather, they mainly used edges of the inducers. We conclude that different types of perceptually interpolated contours are not equally likely to be used to discriminate shapes.

Ringach DL, Shapley R. Vision Res. 1996

Ringach DL, Shapley R. Vision Res. 1996 Oct;36(19):3037-50.
Spatial and temporal properties of illusory contours and amodal boundary completion.

Center for Neural Science, New York University, NY 10003, USA. dario@cns.nyu.ed

Spatial and temporal properties of illusory contours and amodal completion were investigated using a shape discrimination task. Performance was characterized as accuracy of angular discrimination of the inducing figures ("pacmen") in a two-alternative forced choice (2AFC) paradigm. First, we compared performance when four "pacmen" were organized into Kanizsa-like figures (squares and small deformations of squares) which produced the percept of illusory contours (ICs), with performance obtained with all four "pacmen" facing in the same direction, when no illusory contours were seen. Then, we found that it was possible to interfere with boundary completion and degrade performance with masking lines placed between the inducers of a Kanizsa figure. From these experiments we concluded that performance in the shape discrimination task depended on boundary completion. Next, the dependence of contour-dependent performance on the spatial scale of the figures was examined. Threshold angular discrimination was approximately scale-invariant and subjects were able to integrate visual information across gaps as large as 13 deg of visual angle. Performance in the shape recognition task for illusory and amodally completed figures was also measured. Similar accuracy was obtained either when the boundaries were modally or amodally completed. Finally, we used shape discrimination in conjunction with backward masking to explore the dynamics of boundary completion. Two different phases of the boundary completion process were observed. The first phase was revealed when the inducers were locally masked, and took approximately 117 msec. A second phase lasted an additional 140-200 msec after the inducers were masked.

References
Visual Neuroscience Lab @ UCLA
http://manuelita.psych.ucla.edu/~dario/
Dario L. Ringach NEUROBIOLOGY
Office: Now at UCLA Franz Hall 8441B !!