The reconstruction of static visual forms from sparse dotted samples

Uttal, William R.; Davis, Nancy S.; Welke, Cynthia; Kakarala, Ramakrishna

doi:10.3758/bf03207868

Cited by 16 publications

(19 citation statements)

References 12 publications

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“…This occurs when observers are able to take advantage of some attribute of the stimulus that has not been programmed into the model but that allows the observer to perform far better than predicted by the model. We have encountered such a situation in work in my laboratory, which was reported in Uttal, Davis, Welke, and Kakarala (1988). We used a simple three-dimensional surface-fitting process to model the ability of an observer to reconstruct the shape of a surface from a very sparse sample of dots randomly positioned (on that surface).…”

Section: Some Possible Constraints On What Models Can Domentioning

confidence: 99%

On some two-way barriers between models and mechanisms

Uttal

1990

Perception & Psychophysics

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121

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A number of recent as well as classic ideas suggest that there are constraints and limits on the explanatory role that computational, mathematical, and neural net models of visual and other cognitive processes can play that have not been generally appreciated. These ideas come from mathematics, automata theory, chaos theory, thermodynamics, neurophysiology, and psychology. Collectively, these ideas suggest that the neural or cognitive mechanisms underlying many kinds offormal models are untestable and unverifiable. Models may be good descriptions of perceptual and other cognitive processes, but they cannot in principle be reductive explanations nor can we use them to predict behavior at the molar level from what we know of the neural primitives. This discussion is an effort to clarify the appropriate meanings of these models, not to dissuade workers from forging ahead in the modeling endeavor, which I acknowledge is progressing and is making possible our increasingly deep appreciation of plausible and interesting cognitive processes. 188What do formal models of cognitive processes mean? What can models do, and what can they not do? My purpose in this article is to consider these questions for a wide variety of formal models-mathematical, computational, neural network, statistical, cognitive flowchart, and symbolic, as well as others that are just arriving on the scene.' The past decade, in particular, has seen an explosion of interest in the development of formal models of cognitive processes. But it must not be overlooked that this kind of modeling has been with us virtually since the first availability of digital computer technology. (See, e.g., Farley & Clark, 1954;Rosenblatt, 1962;Selfridge, 1959; and Widrow, 1962, among others; and for a comprehensive review of the history of this field and the mathematical relationships among the various theories, see also Grossberg, 1988. Anderson & Rosenfeld's 1988 compendium of significant papers in neurocomputing is also a good review of the history of one cluster of formal models.) The recent impetus for the renewed interest in computer simulations and mathematical models of cognitive proThis work was supported by Contract NOOOI4-88-K0603 from the Office of Naval Research. I would like to express my thanks to several anonymous reviewers of an earlier draft of this article. In addition, I wish specifically to thank Stevan Hamad, Joseph Lappin, and James T. Townsend for their detailed and insightful commentaries, which helped make this document a clearer and more worthwhile contribution than it otherwise would have been. Correspondence should be sent to the following address: William R. Utta1,

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Section: Some Possible Constraints On What Models Can Domentioning

confidence: 99%

On some two-way barriers between models and mechanisms

Uttal

1990

Perception & Psychophysics

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121

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“…While early investigators employed a variety of surface shapes defined by binocular disparity and/or motion (e.g., Braunstein, 1966;Green, 1961;Julesz, 1971;Johansson, 1975;Ullman, 1979;Wallach & O'Connell, 1953), vision researchers did not actually measure human observers' ability to discriminate 3-D surface shape until the 1980s and 1990s (e.g., de Vries, Kappers, & Koenderink, 1993;Norman & Lappin, 1992;Norman, Lappin, & Zucker, 1991;Rogers & Graham, 1979;Sperling, Landy, Dosher, & Perkins, 1989;Uttal, Davis, Welke, & Kakarala, 1988;Van Damme & Van de Grind, 1993). Such psychophysical research into shape discrimination has continued to the present day (e.g., Norman, Beers, Holmin, & Boswell, 2010;Norman, Swindle, Jennings, Mullins, & Beers, 2009;Vreven, 2006).…”

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“…For example, Norman and Lappin (1992, Fig. 5) showed that observers can effectively perceive and discriminate 3-D surface shape even when surfaces are defined only by the motions and 3-D positions of nine points (see also Julesz, 1971;Lappin & Craft, 2000;Norman, Dawson, & Butler, 2000;Uttal et al, 1988). To achieve the perception of whole surfaces from a sparse sampling of motion and/or binocular disparity requires interpolation or approximation (see, e.g., Dinh, Turk, & Slabaugh, 2002;Howard & Rogers, 2012, pp.…”

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“…Uttal (1985) investigated the detection (i.e., presence or absence) of curved surfaces embedded in volumetric noise but did not address shape discrimination. Uttal et al (1988) did investigate stereoscopic shape discrimination but did not utilize or manipulate volumetric noise. In contrast, Norman et al (1991) required observers to discriminate the stereoscopic shape of surfaces that were embedded in various amounts of volumetric noise.…”

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Aging and the discrimination of 3-D shape from motion and binocular disparity

Norman

Holmin

Beers

et al. 2012

Atten Percept Psychophys

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Two experiments evaluated the ability of younger and older adults to visually discriminate 3-D shape as a function of surface coherence. The coherence was manipulated by embedding the 3-D surfaces in volumetric noise (e.g., for a 55 % coherent surface, 55 % of the stimulus points fell on a 3-D surface, while 45 % of the points occupied random locations within the same volume of space). The 3-D surfaces were defined by static binocular disparity, dynamic binocular disparity, and motion. The results of both experiments demonstrated significant effects of age: Older adults required more coherence (tolerated volumetric noise less) for reliable shape discrimination than did younger adults. Motion-defined and static-binoculardisparity-defined surfaces resulted in similar coherence thresholds. However, performance for dynamic-binoculardisparity-defined surfaces was superior (i.e., the observers' surface coherence thresholds were lowest for these stimuli). The results of both experiments showed that younger and older adults possess considerable tolerance to the disrupting effects of volumetric noise; the observers could reliably discriminate 3-D surface shape even when 45 % of the stimulus points (or more) constituted noise.

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“…Although considerable research has examined the minimal conditions for the perception of a surface from binocular disparity (e.g., Uttal, 1975Uttal, , 1983Uttal, , 1985Uttal, , 1987Uttal, , 1988Uttal, Davis, Welke, & Kakarala, 1988), there has been relatively little research examining the minimal conditions for surface detection from optic flow. In a recent study examining the minimal conditions for the detection of 3-D surfaces from optic flow (Andersen, 1991(Andersen, , 1993, subjects were presented with displays simulating points undergoing rigid horizontal translation.…”

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Detection of three-dimensional surfaces from optic flow: The effects of noise

Andersen

Wuestefeld

1993

Perception & Psychophysics

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Previous research (Andersen, 1989) has suggested that the recovery of 3-Dshape from nonsmooth optic flow (motion transparency) can be performed by segregating surfaces according to the distributions of velocities present in the flow field. Five experiments were conducted to examine this hypothesis in a surface detection paradigm and to determine the limitations of human observers to detect 3-D surfaces in the presence of noise. Two display types were examined: a flow field that simulated a surface corrugated in depth and a flow field that simulated a random volume. In addition, two types of noise were examined: a distribution of noise velocities that overlapped or did not overlap the velocity distribution that defined the surface. Corrugation frequency and surface density were also examined. Detection performance increased with decreasing corrugation frequency, decreasing noise density, and decreasing surface density. Overall, the subjects demonstrated remarkable tolerance to the presence of noise and, for some conditions, could discriminate surface from random conditions when noise density was twice the surface density. Discrimination accuracy was greater for the nonoverlapping than for the overlapping noise, providing support for an analysis based on the distribution of velocities.Optic flow, the perspective transformation of visible feature points during motion of the observer or objects in the environment, can be a useful source of information for the the recovery of 3-D shape and the layout of a scene (Gibson, 1950(Gibson, , 1966 Helmholtz, 1867/1%2; von Kries, 1910 von Kries, /1962. Considerable research has examined the ability of human observers to recover the shape and depth of objects in a scene from optic flow. Studies have demonstrated the effectiveness of optic flow in providing the curvature and shape of a surface (Cornilleau-Peres & Drou1ez, 1989;Eby, 1992;Norman & Lappin, 1992;Rogers & Graham, 1979;Todd, 1984), the orientation in depth of a surface (Braunstein & Andersen, 1981), the slant of a surface (Braunstein & Payne, 1969), and the depth order and relative depth between surfaces (Andersen, 1989).An important assumption in this research is that sufficient information is present in the display for the perception of a surface. Although considerable research has examined the minimal conditions for the perception of a surface from binocular disparity (e.g., Uttal, 1975Uttal, , 1983Uttal, , 1985Uttal, , 1987Uttal, , 1988Uttal, Davis, Welke, & Kakarala, 1988), there has been relatively little research examining the minimal conditions for surface detection from optic flow. In a recent study examining the minimal conditions This research was supported by National Science Foundation Grant BNS 9021081. The authors would like to thank Myron L. Braunstein, Jan Koenderink, and Jim Todd for comments on an earlier draft. We would also like to thank Myron L. Braunstein for beneficial discussions regarding the design of Experiment 5. Reprint requests should be sent to G. J. Andersen, Department of Psychol...

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The reconstruction of static visual forms from sparse dotted samples

Cited by 16 publications

References 12 publications

On some two-way barriers between models and mechanisms

On some two-way barriers between models and mechanisms

Aging and the discrimination of 3-D shape from motion and binocular disparity

Detection of three-dimensional surfaces from optic flow: The effects of noise

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