Systematic misperceptions of 3-D motion explained by Bayesian inference

Rokers, Bas; Fulvio, Jacqueline M.; Cooper, Emily A.

doi:10.1167/jov.18.3.23

Cited by 19 publications

(18 citation statements)

References 59 publications

(101 reference statements)

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“…This effect is thought to be caused by the visual system to process stereo information (e.g., binocular disparity) first and then processing binocular motion 56 . Previously, these biases have been shown in trajectory, extent and speed 14 , 25 – 28 and, to our knowledge, we now show that timing responses can also be affected by MID.…”

Section: Discussionsupporting

confidence: 74%

“…However, all these monocular and binocular retinal cues need the support of extra-retinal signals 23 for the estimation of 3D motion, that is MID. In addition to this diversity of cues, the perceived speed of MID depends on which part of the retina is stimulated 16 , 24 and the perceived spatial 3D trajectories or motion extent are affected by well known biases 14 , 16 , 25 – 28 . The variability in the perception of MID, including speed and direction, makes it worth studying the performance of response timing when dealing with objects moving in depth.…”

Section: Introductionmentioning

confidence: 99%

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Motion-in-depth effects on interceptive timing errors in an immersive environment

López‐Moliner

Malla

2021

Sci Rep

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We often need to interact with targets that move along arbitrary trajectories in the 3D scene. In these situations, information of parameters like speed, time-to-contact, or motion direction is required to solve a broad class of timing tasks (e.g., shooting, or interception). There is a large body of literature addressing how we estimate different parameters when objects move both in the fronto-parallel plane and in depth. However, we do not know to which extent the timing of interceptive actions is affected when motion-in-depth (MID) is involved. Unlike previous studies that have looked at the timing of interceptive actions using constant distances and fronto-parallel motion, we here use immersive virtual reality to look at how differences in the above-mentioned variables influence timing errors in a shooting task performed in a 3D environment. Participants had to shoot at targets that moved following different angles of approach with respect to the observer when those reached designated shooting locations. We recorded the shooting time, the temporal and spatial errors and the head’s position and orientation in two conditions that differed in the interval between the shot and the interception of the target’s path. Results show a consistent change in the temporal error across approaching angles: the larger the angle, the earlier the error. Interestingly, we also found different error patterns within a given angle that depended on whether participants tracked the whole target’s trajectory or only its end-point. These differences had larger impact when the target moved in depth and are consistent with underestimating motion-in-depth in the periphery. We conclude that the strategy participants use to track the target’s trajectory interacts with MID and affects timing performance.

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Section: Discussionsupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Motion-in-depth effects on interceptive timing errors in an immersive environment

López‐Moliner

Malla

2021

Sci Rep

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“…The earlier responses for faster speeds when motion was fronto-parallel progressively became late responses with MID. This pattern does not bode well with participants only misjudging the direction in depth (Aguado & López-Moliner, 2019;Harris & Dean, 2003;Lages, 2006;Rokers et al, 2018;Welchman et al, 2004), that is underestimating , because the target position would have been perceived more advanced than it actually was, leading to earlier responses. However, not only is speed in depth underestimated with respect to lateral movement (Brenner, Van Den Berg, & Van Damme, 1997;Brooks & Stone, 2006;Rushton & Duke, 2009;Welchman, Lam, & Bulthoff, 2008), but its discrimination thresholds are also known to be higher relative to fronto-parallel speed (Aguado & López-Moliner, 2019) which makes more difficult to discriminate the different speeds for larger angles of approach.…”

Section: Discussionmentioning

confidence: 91%

“…Moreover, speed discrimination thresholds are usually higher for MID than for lateral motion (Aguado & López-Moliner, 2019;Rushton & Duke, 2009)). It has also been shown that differences in perceived speed depend on which part of the retina is stimulated (Brooks & Mather, 2000;Murdison, Leclercq, Lefèvre, & Blohm, 2019) and well known biases in the perceived spatial trajectories (Aguado & López-Moliner, 2019;Harris & Dean, 2003;Lages, 2006;Murdison et al, 2019;Rokers, Fulvio, Pillow, & Cooper, 2018;Welchman, Tuck, & Harris, 2004) or in motion extent in depth (Lages, 2006). The variability in the perception of MID, including speed and direction, makes it worth studying the performance of response timing when dealing with objects moving in depth.…”

Section: Introductionmentioning

confidence: 99%

Motion-in-depth effects on interceptive timing errors in an immersive enviornment

López‐Moliner

Malla

2021

Preprint

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We often need to interact with targets that move along arbitrary trajectories in the 3D scene. In these situations, information of parameters like speed, time-to-contact, or motion direction is required to solve a broad class of timing tasks (e.g., shooting, or interception). There is a large body of literature addressing how we estimate different parameters when objects move both in the fronto-parallel plane and in depth. However, we do not know to which extent the timing of interceptive actions is affected when there is MID involved. Unlike previous studies that have looked at the timing of interceptive actions using constant distances and fronto-parallel motion, we here use immersive virtual reality to look at how differences in the above-mentioned variables influence timing errors in a shooting task performed in a 3D environment. Participants had to shoot at targets that moved following different angles of approach with respect to the observer when those reached designated shooting locations. We recorded the shooting time, the temporal and spatial errors and the head’s position and orientation in two conditions that differed in the interval between the shot and the interception of the target’s path. Results show a consistent change of the temporal error across approaching angles: the larger the angle, the earlier the error. Interestingly, we also found different error patterns within a given angle that depended on whether participants tracked the whole target’s trajectory or only its end-point. These differences had larger impact when the target moved in depth and are consistent with underestimating motion-in-depth in the periphery. We conclude that the strategy participants use to track the trajectory interacts with MID and affects timing performance.

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“…Lastly, previous models have mainly focused on the contrast-induced speed biases and how they can be attributed to a slow-speed prior that shifts the percept toward slower speeds for increasing levels of uncertainty caused by sensory noise or the ambiguity of the stimulus itself [3,5,6,9,[44][45][46]. While this is still the case for our new model, the monotonic increase in threshold is now also a direct consequence of the slow-speed prior: Since higher speeds are less likely, efficient coding dictates that less (neural) resources are allocated for their representation, resulting in a larger threshold.…”

Section: Extracting the "Behavioral Prior"mentioning

confidence: 99%

Prior expectations in visual speed perception predict encoding characteristics of neurons in area MT

Zhang

Stocker

2021

Preprint

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Bayesian inference provides an elegant theoretical framework for understanding the characteristic biases and discrimination thresholds in visual speed perception. However, the framework is difficult to validate due to its flexibility and the fact that suitable constraints on the structure of the sensory uncertainty have been missing. Here, we demonstrate that a Bayesian observer model constrained by efficient coding not only well fits extensive psychophysical data of human visual speed perception but also provides an accurate quantitative account of the tuning characteristics of neurons known for representing visual speed. Specifically, we found that the population coding accuracy for visual speed in area MT ("neural prior") is precisely predicted by the power-law, slow-speed prior extracted from fitting the Bayesian model to the psychophysical data ("behavioral prior"), to the point that they are indistinguishable in a model cross-validation comparison. Our results demonstrate a quantitative validation of the Bayesian observer model constrained by efficient coding at both the behavioral and neural levels.

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Systematic misperceptions of 3-D motion explained by Bayesian inference

Cited by 19 publications

References 59 publications

Motion-in-depth effects on interceptive timing errors in an immersive environment

Motion-in-depth effects on interceptive timing errors in an immersive environment

Motion-in-depth effects on interceptive timing errors in an immersive enviornment

Prior expectations in visual speed perception predict encoding characteristics of neurons in area MT

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