The detection of coherence in moving random-dot patterns

Lappin, Joseph S.; Bell, Herbert H.

doi:10.1016/0042-6989(76)90093-6

Cited by 141 publications

(104 citation statements)

References 15 publications

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“…A detection of motion would occur if the pooled response of a number (iv say) of basic units exceeds the noise level. Simple reasoning shows that one should expect threshold signal to noise ratios approximately equal to N-f (van Doorn and Koenderink, 1987a;Lappin and Bell (1976) have derived a similar equation). We have shown that this model is able to predict many of the peculiarities of human motion detection, and we succeeded in determining the parameters A and T as a function of the velocity (van Doorn and Koenderink, 1982a, b).…”

Section: Infroductjonmentioning

confidence: 98%

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Spatiotemporal integration in the detection of coherent motion

Doorn

Koenderink

1984

Vision Research

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Abstract-We present data on the signal to noise threshold (SNR) for the impression of coherent. smooth motion in stroboscopicall? moving dot patterns (frame repetitionrate 100 Hz) degraded with additive noise (uncorrelated pattern each new frame). (Both patterns were 250 x 255 square arrays in which a pixel tv~ randomly assigned a light or dark value with probability SO%.) it is found that the threshold varies roughly as the inverse square root of the number of available basic dot correlation pairs. subject to certain constraints that can be interpreted as due to the geometry of basic correlator units subserving the detection of motion. This geometry is estimated from the data and compared with values that have been obtained with independent methods. Phenomenologically the results are simple: when the target field is enlarged in the direction perpendicular to the motion the SNR threshold follows the theoretical expectations. Otherwise the variation is stronger. which points to a recruitment of new units. For an impression of movement to occur the smaliest targets are short thin strips elongated in the direction of motion (e.g. 2.

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Section: Infroductjonmentioning

confidence: 98%

“…Hum;tn performance in the detection of coherence in moving random speckle patterns can be conveniently described with correlation models (Lappin and Bell, 1976). In previous papers (van Doorn and Koenderink, 1982a.…”

Section: Infroductjonmentioning

confidence: 99%

Spatiotemporal integration in the detection of coherent motion

Doorn

Koenderink

1984

Vision Research

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“…The processes responsible for apparent motion in classical stroboscopic displays, in contrast, operate over much longer interstimulus intervals and displacements (see Braddick (1980) for a review of the evidence for this dichotomy). Lappin and Bell (1976) independently recognized that perception of random dot kinematograms is mediated by a process different in character from classical apparent motion. But, in contradiction to Braddick (1974), they presented evidence that the limiting displacement for correct motion perception was determined by the size of the displacement expressed as a number of array positions ("pixels"), rather than the retinal angle of the displacement.…”

Section: Introductionmentioning

confidence: 99%

“…A fixed pixel limit would correspond to a limited number of possible false pairings of dots that could be handled; Lappin and Bell's (1976) cross-correlation model is essentially of this kind. A visual angle limit is more consistent with a process that only evaluates pairings over a fixed spatial range ; such a range might be determined, for instance, by the dimensions of receptive field subunits at some point in the visual pathway.…”

Section: Introductionmentioning

confidence: 99%

“…The essential idea of both Braddick's (1974) and Lappin and Bell's (1976) experiments was to vary pixel spacing, and determine whether psychophysical performance was governed by the displacement, expressed as a number of pixels, which had not changed, or by the retinal angle of the displacement, which had changed. Lappin and Bell (1976) made this comparison for cases in which the size of the displaced patch, in pixels, was held constant (with a consequent co-varying change in retinal angle of the patch area).…”

Section: Introductionmentioning

confidence: 99%

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The basis of area and dot number effects in random dot motion perception

1982

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Abstract-Random dot kinematograms present a patch of random dots, uniformly displaced from one frame to the next. We have re-examined the question of whether the limiting displacement for this short-range apparent motion is determined by the retinal angle or by the number of pixels across which the patch is displaced; our data support the former hypothesis. The number of dots in the patch is not a confounding variable, and in fact has very little effect. Increasing the area of the patch does increase the displacement limit, apparently due to the invasion of greater retinal eccentricities.

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Artificial Intelligence: Computational Approach to Vision and Motor Control

Hildreth

Hollerbach

1987

Comprehensive Physiology

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ConclusionsDURING THE PAST DECADE, it has become increasingly clear that to understand the brain we must study more than its biochemical and biophysical mechanisms and its outward perceptual and physical behavior. We also must study the brain at a theoretical level that investigates the computations that are necessaryko perform its functions. The control of movements such as reaching, grasping, and manipulating objects requires complex mechanisms that elaborate information from many sensors and control the forces generated by many muscles. The act of seeing, which intuitively seems so simple and effortless, requires information processing whose complexity we are just beginning to grasp. A computational approach to the study of vision and motor control has evolved within the field of artificial intelligence; this approach inquires directly into the nature of the information processing required to perform complex visual and motor tasks. This chapter presents a particular view of the computational approach and its relevance to experimental neuroscience. FOUNDING PRINCIPLES OF ARTIFICIAL INTELLIGENCEThe computational approach to vision and motor control is an outgrowth of the field of artificial intelligence, from which the basic tenets are derived (131). Research in artificial intelligence has two main goals: to develop computer systems that exhibit intelligent behavior and to understand the nature of intelligence itself. The field is founded on two basic principles: to separate the tasks performed by a complex information-processing system from the hardware that carries them out and to analyze natural intelligent systems through the synthesis of artificial systems that perform the same tasks.The birth of computers led to a distinction between a process specified by software and the machinery or hardware that executes the process. Computers have been built from a variety of components, including cams, relays, analog circuitry, transistors, and microchips, yet all are able to perform essentially the same computations. The thought naturally arises that neurons can be viewed as another form of computational machinery and that an intelligent process need not be limited to biological nervous systems but in principle could also be implemented in computers. If intelligent processes can be separated from hardware, then intelligence can be considered an abstract entity, subject to its own rules, laws, and structure, and can be studied in its own right. These laws are independent of the underlying computational machinery and reflect fundamental properties of particular information-processing tasks. 605

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The detection of coherence in moving random-dot patterns

Cited by 141 publications

References 15 publications

Spatiotemporal integration in the detection of coherent motion

Spatiotemporal integration in the detection of coherent motion

The basis of area and dot number effects in random dot motion perception

Artificial Intelligence: Computational Approach to Vision and Motor Control

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