Direction-of-motion discrimination is facilitated by visible motion smear

Tong, Jianliang; Aydin, Murat; Bedell, Harold E.

doi:10.3758/bf03194452

Cited by 10 publications

(6 citation statements)

References 42 publications

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“…Our results cannot be explained on the basis of temporal integration creating a static spatial ‘streak’ on which to base directional motion judgments [2]–[5], [30]. This is despite the fact that we used suitably high motion velocities [5].…”

Section: Discussionmentioning

confidence: 73%

Integration across Time Determines Path Deviation Discrimination for Moving Objects

2008

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BackgroundHuman vision is vital in determining our interaction with the outside world. In this study we characterize our ability to judge changes in the direction of motion of objects–a common task which can allow us either to intercept moving objects, or else avoid them if they pose a threat.Methodology/Principal FindingsObservers were presented with objects which moved across a computer monitor on a linear path until the midline, at which point they changed their direction of motion, and observers were required to judge the direction of change. In keeping with the variety of objects we encounter in the real world, we varied characteristics of the moving stimuli such as velocity, extent of motion path and the object size. Furthermore, we compared performance for moving objects with the ability of observers to detect a deviation in a line which formed the static trace of the motion path, since it has been suggested that a form of static memory trace may form the basis for these types of judgment. The static line judgments were well described by a ‘scale invariant’ model in which any two stimuli which possess the same two-dimensional geometry (length/width) result in the same level of performance. Performance for the moving objects was entirely different. Irrespective of the path length, object size or velocity of motion, path deviation thresholds depended simply upon the duration of the motion path in seconds.Conclusions/SignificanceHuman vision has long been known to integrate information across space in order to solve spatial tasks such as judgment of orientation or position. Here we demonstrate an intriguing mechanism which integrates direction information across time in order to optimize the judgment of path deviation for moving objects.

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Section: Discussionmentioning

confidence: 73%

Integration across Time Determines Path Deviation Discrimination for Moving Objects

2008

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“…Using one-dimensional dynamic noise to mask the motion of a translating Gaussian blob, Geisler found increased luminance detection thresholds for the blob's motion when masked by parallel noise (compared to orthogonal noise) above a certain "critical speed", corresponding to a spatiotemporal integration period of roughly one "dot width" per 100 ms. A noise mask whose dominant orientation is parallel with the direction of motion should be more effective than an orthogonal mask if the translating dot leaves a trailing motion streak, as the mask would produce a large and target-irrelevant response in orientation-selective neurons aligned with the motion streak and make it harder to detect the streak's presence. Several other psychophysical studies have since supported this model Apthorp, Wenderoth, & Alais, 2009;Burr & Ross, 2002;Edwards & Crane, 2007;Krekelberg, Dannenberg, Hoffmann, Bremmer, & Ross, 2003;Ross, Badcock, & Hayes, 2000;Tong, Aydin, & Bedell, 2007). In addition, neurophysiological evidence suggests that there are direction-selective cells in V1 that respond preferentially to orientations parallel to their preferred direction when the motion stimulus is fast (Geisler, Albrecht, Crane, & Stern, 2001).…”

Section: Introductionmentioning

confidence: 77%

Orientation tuning of contrast masking caused by motion streaks

Apthorp¹,

Cass²,

Alais³

2010

Journal of Vision

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We investigated whether the oriented trails of blur left by fast-moving dots (i.e., "motion streaks") effectively mask grating targets. Using a classic overlay masking paradigm, we varied mask contrast and target orientation to reveal underlying tuning. Fast-moving Gaussian blob arrays elevated thresholds for detection of static gratings, both monoptically and dichoptically. Monoptic masking at high mask (i.e., streak) contrasts is tuned for orientation and exhibits a similar bandwidth to masking functions obtained with grating stimuli (∼30 degrees). Dichoptic masking fails to show reliable orientation-tuned masking, but dichoptic masks at very low contrast produce a narrowly tuned facilitation (∼17 degrees). For iso-oriented streak masks and grating targets, we also explored masking as a function of mask contrast. Interestingly, dichoptic masking shows a classic "dipper"-like TVC function, whereas monoptic masking shows no dip and a steeper "handle". There is a very strong unoriented component to the masking, which we attribute to transiently biased temporal frequency masking. Fourier analysis of "motion streak" images shows interesting differences between dichoptic and monoptic functions and the information in the stimulus. Our data add weight to the growing body of evidence that the oriented blur of motion streaks contributes to the processing of fast motion signals.

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“…Human observers' ability to discriminate between directions improves with increasing speed, over a wide range of speeds (0.5-64 deg/s) (De Bruyn and Orban, 1988). This has been attributed to the static orientation information that is produced by sensory persistence of fast-moving stimuli (i.e., motion streaks) (Geisler, 1999;Tong et al, 2007). However, there is no persistence that could produce something equivalent to motion streaks within MotionNet, yet the network develops the same relationship between direction and speed tuning.…”

Section: Interactions Between Direction and Speed Processingmentioning

confidence: 99%

But Still It Moves: Static Image Statistics Underlie How We See Motion

Rideaux

Welchman

2020

J. Neurosci.

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Seeing movement promotes survival. It results from an uncertain interplay between evolution and experience, making it hard to isolate the drivers of computational architectures found in brains. Here we seek insight into motion perception using a neural network (Motion-Net) trained on moving images to classify velocity. The network recapitulates key properties of motion direction and speed processing in biological brains, and we use it to derive, and test, understanding of motion (mis)perception at the computational, neural, and perceptual levels. We show that diverse motion characteristics are largely explained by the statistical structure of natural images, rather than motion per se. First, we show how neural and perceptual biases for particular motion directions can result from the orientation structure of natural images. Second, we demonstrate an interrelation between speed and direction preferences in (macaque) MT neurons that can be explained by image autocorrelation. Third, we show that natural image statistics mean that speed and image contrast are related quantities. Finally, using behavioral tests (humans, both sexes), we show that it is knowledge of the speed-contrast association that accounts for motion illusions, rather than the distribution of movements in the environment (the "slow world" prior) as premised by Bayesian accounts. Together, this provides an exposition of motion speed and direction estimation, and produces concrete predictions for future neurophysiological experiments. More broadly, we demonstrate the conceptual value of marrying artificial systems with biological characterization, moving beyond "black box" reproduction of an architecture to advance understanding of complex systems, such as the brain.

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Direction-of-motion discrimination is facilitated by visible motion smear

Cited by 10 publications

References 42 publications

Integration across Time Determines Path Deviation Discrimination for Moving Objects

Integration across Time Determines Path Deviation Discrimination for Moving Objects

Orientation tuning of contrast masking caused by motion streaks

But Still It Moves: Static Image Statistics Underlie How We See Motion

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