Reverse-Phi Experiments Support the Counterchange Model of Motion Detection

Ruda, Harald; Riesen, Guillaume; Hock, Howard S.

doi:10.1167/16.12.668

Cited by 2 publications

(6 citation statements)

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“…By contrast, we found that the speed of reverse-phi stimuli was overestimated for low displacement speeds and underestimated for high speeds ( Figure 3 d, orange markers). Some evidence for the same pattern of behavior in humans has previously been found ( Parthasarathy, 2019 ; Ruda et al, 2016 ), but more work is needed to explicitly investigate this phenomenon in biological systems.…”

Section: Resultsmentioning

confidence: 53%

“…After verifying this behavior in the artificial system, we explore how these changes influence the calculation of speed. Unpublished observations suggest that observers over and underestimate the speed of slow and fast reverse-phi motion, respectively ( Parthasarathy, 2019 ; Ruda, Riesen, & Hock, 2016 ). We find that the network exhibits the same biases, and then use our access to the system to show that this is due to the similarity between reverse-phi motion and the receptive fields of spatiotemporal neurons tuned to opposite directions.…”

Section: Introductionmentioning

confidence: 99%

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Exploring and explaining properties of motion processing in biological brains using a neural network

Rideaux

Welchman

2021

Journal of Vision

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Visual motion perception underpins behaviors ranging from navigation to depth perception and grasping. Our limited access to biological systems constrains our understanding of how motion is processed within the brain. Here we explore properties of motion perception in biological systems by training a neural network to estimate the velocity of image sequences. The network recapitulates key characteristics of motion processing in biological brains, and we use our access to its structure to explore and understand motion (mis)perception. We find that the network captures the biological response to reverse-phi motion in terms of direction. We further find that it overestimates and underestimates the speed of slow and fast reverse-phi motion, respectively, because of the correlation between reverse-phi motion and the spatiotemporal receptive fields tuned to motion in opposite directions. Second, we find that the distribution of spatiotemporal tuning properties in the V1 and middle temporal (MT) layers of the network are similar to those observed in biological systems. We then show that, in comparison to MT units tuned to fast speeds, those tuned to slow speeds primarily receive input from V1 units tuned to high spatial frequency and low temporal frequency. Next, we find that there is a positive correlation between the pattern-motion and speed selectivity of MT units. Finally, we show that the network captures human underestimation of low coherence motion stimuli, and that this is due to pooling of noise and signal motion. These findings provide biologically plausible explanations for well-known phenomena and produce concrete predictions for future psychophysical and neurophysiological experiments.

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Section: Resultsmentioning

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Exploring and explaining properties of motion processing in biological brains using a neural network

Rideaux

Welchman

2021

Journal of Vision

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show abstract

“…3d, orange markers). Some evidence for the same pattern of behaviour in humans has previously been found (Parthasarathy, 2019;Ruda et al, 2016), but more work is needed to explicitly investigate this phenomenon in biological systems.…”

Section: Component-and Pattern-motion Selectivitymentioning

confidence: 52%

“…After verifying this behaviour in the artificial system, we explore how these changes influence the calculation of speed. Unpublished observations suggest that observers over and underestimate the speed of slow and fast reverse-phi motion, respectively (Parthasarathy, 2019;Ruda, Riesen, & Hock, 2016). We find that the network exhibits the same biases, and then use our access to the system to show that this due to the similarity between reverse-phi motion and the receptive fields of spatiotemporal neurons tuned to opposite directions.…”

Section: Introductionmentioning

confidence: 50%

See 1 more Smart Citation

Exploring and explaining properties of motion processing in biological brains using a neural network

Rideaux

Welchman

2020

Preprint

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Visual motion perception underpins behaviours ranging from navigation to depth perception and grasping. Our limited access to biological systems constrain our understanding of how motion is processed within the brain. Here we explore properties of motion perception in biological systems by training a neural network (‘MotionNetxy’) to estimate the velocity image sequences. The network recapitulates key characteristics of motion processing in biological brains, and we use our complete access to its structure explore and understand motion (mis)perception at the computational-, neural-, and perceptual-levels. First, we find that the network recapitulates the biological response to reverse-phi motion in terms of direction. We further find that it overestimates the speed of slow reverse-phi motion while underestimating the speed of fast reverse-phi motion because of the correlation between reverse-phi motion and the spatiotemporal receptive fields tuned to motion in opposite directions. Second, we find that the distribution of spatiotemporal tuning properties in the V1 and MT layers of the network are similar to those observed in biological systems. We then show that compared to MT units tuned to fast speeds, those tuned to slow speeds primarily receive input from V1 units tuned to high spatial frequency and low temporal frequency. Third, we find that there is a positive correlation between the pattern-motion and speed selectivity of MT units. Finally, we show that the network captures human underestimation of low coherence motion stimuli, and that this is due to pooling of noise and signal motion. These findings provide biologically plausible explanations for well-known phenomena, and produce concrete predictions for future psychophysical and neurophysiological experiments.

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Reverse-Phi Experiments Support the Counterchange Model of Motion Detection

Cited by 2 publications

References 0 publications

Exploring and explaining properties of motion processing in biological brains using a neural network

Exploring and explaining properties of motion processing in biological brains using a neural network

Exploring and explaining properties of motion processing in biological brains using a neural network

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