Dynamics of distributed 1D and 2D motion representations for short-latency ocular following

Abstract

Integrating information is essential to measure the physical 2D motion of a surface from both ambiguous local 1D motion of its elongated edges and non-ambiguous 2D motion of its features such as corners or texture elements. The dynamics of this motion integration shows a complex time course as read from tracking eye movements: first, local 1D motion signals are extracted and pooled to initiate ocular responses, then 2D motion signals are integrated to adjust the tracking direction until it matches the surface motion direction. The nature of these 1D and 2D motion computations are still unclear. One hypothesis is that their different dynamics may be explained from different contrast sensitivities. To test this, we measured contrast-response functions of early, 1D-driven and late, 2D-driven components of ocular following responses to different motion stimuli: gratings, plaids and barberpoles. We found that contrast dynamics of 1D-driven responses are nearly identical across the different stimuli. On the contrary, late 2D-driven components with either plaids or barberpoles have similar latencies but different contrast dynamics. Temporal dynamics of both 1D- and 2D-driven responses demonstrates that the different contrast gains are set very early during the response time course. Running a Bayesian model of motion integration, we show that a large family of contrast-response functions can be predicted from the probability distributions of 1D and 2D motion signals for each stimulus and by the shape of the prior distribution. However, the pure delay (i.e. largely independent upon contrast) observed between 1D- and 2D-motion supports the fact that 1D and 2D probability distributions are computed independently. This two-pathway Bayesian model supports the idea that 1D and 2D mechanisms represent edges and features motion in parallel.

Temporal Evolution of Pattern Disparity Processing in Humans

“…The simplest explanation for this finding is that the IOC-like neural computation of pattern disparity from component responses takes 10–15 ms. This finding parallels the observation by Masson and colleagues (Masson and Castet, 2002; Masson, 2004; Barthelemy, Fleuriet, and Masson, 2010; Barthelemy et al, 2008) that when subjects (humans and monkeys) are presented with moving plaid stimuli the resulting Ocular Following Response (OFR) is characterized by two components: an initial one driven by the motion of the components, and a delayed one in the direction of pattern motion. In those studies, which inspired ours, the delay was of approximately 20 ms, and thus comparable to ours.…”

Neural Mechanisms of Tactile Motion Integration in Somatosensory Cortex

“…Then again, the motion integration problem faced by the somatosensory system may be adequately solved using a VA mechanism, i.e., by computing the average of motion signals stemming from local motion detectors, a computation that can be implemented using a relatively simple neural network. Many of the mechanisms that are thought to contribute to visual motion integration, including non-linear interactions between simple motion detectors (Busse et al, 2009;Heeger, 1992;Rust et al, 2006;Simoncelli and Heeger, 1998) and/or parallel pathways for processing edges and terminators (Barthelemy et al, 2008;Wilson et al, 1992), thus seem unnecessary to explain tactile motion integration.…”

Bifurcation study of a neural field competition model with an application to perceptual switching in motion integration

“…It was shown in Barthélemy et al (2008) that the motion signals from 1D cues stimulate a broad range of directions when compared with 2D cues that stimulate a more localised range of directions; see arrows in Figs. 4a and b.…”

“…Figure 4(e) shows the complex input I ext represented as a summation of 1D and 2D motion signals with maximum normalised to 1 and a smaller weighting w 1D ∈ [0, 1] given to 1D cues: The weighting w 1D translates the fact that in motion integration experiments 2D cues play a more significant role that 1D cues in driving perceived direction of motion (Masson et al 2000; Barthélemy et al 2010). Here we represent this weighting in a simple linear relationship, but in future studies it may be relevant to consider the contrast response functions for 1D and 2D cues separately (Barthélemy et al 2008). …”