How does noise influence the estimation of speed?

Abstract

Local motion signals have to be combined in space and time, to yield a coherent motion percept as it is involved in a variety of visual tasks. This combination necessarily means to trade-off between loosing spatio-temporal resolution by pooling local signals and maintaining perceptually significant segmentation between them. When signals are pooled to detect the presence of coherent motion in large amounts of random noise, the question raised is how the noise affects the perceived quality, in particular speed, of the coherent motion. Is there an analogy to the well-known reduction in the perceived speed of moving gratings at low contrast? Using a two-interval forced-choice procedure, we have investigated the assessment of speed in random-dot kinematograms containing different proportions of noise. Under the conditions investigated, there is no strong reduction of perceived speed with increasing noise, as long as coherence levels remain well above the thresholds for directional judgements. This basic result, which could suggest considerable but not perfect segregation of signal and noise motion components in the pooling process leading to speed estimation, is discussed in relation to a model that is designed to decode speed from a population of elementary motion detectors (EMDs) of the correlation type. A strategy to estimate speed from a set of EMDs with a variety of spatio-temporal tuning does not only provide a velocity predictor unambiguous with the spatial structure of the stimulus, but also is largely independent of noise.

Movement-induced motion signal distributions in outdoor scenes

“…At a video frame-rate of 25 Hz, the default time constant was chosen to be 80 ms (two frames), which bestows the EMD with a temporal frequency optimum of approx. 2 Hz (see, e.g., Zanker & Braddick 1999). This tuning is well within the range of temporal frequency characteristics described for a variety of motion sensitive neurones in insects (O'Carroll et al 1997).…”

“…This model has been used previously to analyse species-specific movement signals in crabs (Zeil & Zanker 1997), and to simulate a variety of psychophysical phenomena (Zanker 1997;Zanker & Braddick 1999;Zanker 2004). The basic building blocks of the 2DMD model are elementary motion detectors (EMDs) of the correlation type, which have been shown to describe the computational structure of biological motion detectors at least in insects (for review, see Reichardt 1987;.…”

The Effect of Between-Probe Variability in Haptic Feedback on Stiffness Perception and Grip Force Adjustment

“…That is, the addition of the variability did not lead to an increase or decrease in the perceived stiffness of the force field. This is consistent with the findings of Zanker et al [49] who created different levels of noise in visual speed feedback. They observed that while the noise had no effect on the perceived speed, the noise impacted the certainty.…”

Effect of Repetitive Transcranial Magnetic Stimulation on a Target Moving in Front of a Static or Random Dynamic Visual Noise

“…Unexpectedly, when the CS moved in front of the rDVN with the same physical speed as the SS, the proportion of "CS faster" responses was not above chance (0.5), even when the TMS was applied over the control site Cz. This is important because, statistically speaking, it is not possible to conclude that visual dynamic noise actually enhanced the perceived speed of a target as in previous studies (Edwards & Grainger, 2006;Zanker & Braddick, 1999). 1 On the other hand, in the SVN condition, with rTMS delivered over the Cz (control condition) and with the CS as fast as the SS, the proportion of "CS faster" responses was not below chance (0.5) and the BF (3.55) was clearly in favour of the null hypothesis.…”