Bivectorial transparent stimuli simultaneously adapt mechanisms at different levels of the motion pathway

Contribution of the slow motion mechanism to global motion revealed by an MAE technique

Two different motion mechanisms have been identified with motion aftereffect (MAE). (1) A slow motion mechanism, accessed by a static MAE, is sensitive to high-spatial and low-temporal frequency; (2) a fast motion mechanism, accessed by a flicker MAE, is sensitive to low-spatial and high-temporal frequency. We examined their respective responses to global motion after adapting to a global motion pattern constructed of multiple compound Gabor patches arranged circularly. Each compound Gabor patch contained two gratings at different spatial frequencies (0.53 and 2.13 cpd) drifting in opposite directions. The participants reported the direction and duration of the MAE for a variety of global motion patterns. We discovered that static MAE durations depended on the global motion patterns, e.g., longer MAE duration to patches arranged to see rotation than to random motion (Exp 1), and increase with global motion strength (patch number in Exp 2). In contrast, flicker MAEs durations are similar across different patterns and adaptation strength. Further, the global integration occurred at the adaptation stage, rather than at the test stage (Exp 3). These results suggest that slow motion mechanism, assessed by static MAE, integrate motion signals over space while fast motion mechanisms do not, at least under the conditions used.

The direction after-effect is a global motion phenomenon

Prior experience influences visual perception. For example, extended viewing of a moving stimulus results in the misperception of a subsequent stimulus's motion direction-the direction after-effect (DAE). There has been an ongoing debate regarding the locus of the neural mechanisms underlying the DAE. We know the mechanisms are cortical, but there is uncertainty about where in the visual cortex they are located-at relatively early local motion processing stages, or at later global motion stages. We used a unikinetic plaid as an adapting stimulus, then measured the DAE experienced with a drifting random dot test stimulus. A unikinetic plaid comprises a static grating superimposed on a drifting grating of a different orientation. Observers cannot see the true motion direction of the moving component; instead they see pattern motion running parallel to the static component. The pattern motion of unikinetic plaids is encoded at the global processing level-specifically, in cortical areas MT and MST-and the local motion component is encoded earlier. We measured the direction after-effect as a function of the plaid's local and pattern motion directions. The DAE was induced by the plaid's pattern motion, but not by its component motion. This points to the neural mechanisms underlying the DAE being located at the global motion processing level, and no earlier than area MT.

Motion-form interactions beyond the motion integration level: evidence for interactions between orientation and optic flow signals

Motion and form encoding are closely coupled in the visual system. A number of physiological studies have shown that neurons in the striate and extrastriate cortex (e.g., V1 and MT) are selective for motion direction parallel to their preferred orientation, but some neurons also respond to motion orthogonal to their preferred spatial orientation. Recent psychophysical research (Mather, Pavan, Bellacosa, & Casco, 2012) has demonstrated that the strength of adaptation to two fields of transparently moving dots is modulated by simultaneously presented orientation signals, suggesting that the interaction occurs at the level of motion integrating receptive fields in the extrastriate cortex. In the present psychophysical study, we investigated whether motion-form interactions take place at a higher level of neural processing where optic flow components are extracted. In Experiment 1, we measured the duration of the motion aftereffect (MAE) generated by contracting or expanding dot fields in the presence of either radial (parallel) or concentric (orthogonal) counterphase pedestal gratings. To tap the stage at which optic flow is extracted, we measured the duration of the phantom MAE (Weisstein, Maguire, & Berbaum, 1977) in which we adapted and tested different parts of the visual field, with orientation signals presented either in the adapting (Experiment 2) or nonadapting (Experiments 3 and 4) sectors. Overall, the results showed that motion adaptation is suppressed most by orientation signals orthogonal to optic flow direction, suggesting that motion-form interactions also take place at the global motion level where optic flow is extracted.

Interactions between motion and form processing in the human visual system

The predominant view of motion and form processing in the human visual system assumes that these two attributes are handled by separate and independent modules. Motion processing involves filtering by direction-selective sensors, followed by integration to solve the aperture problem. Form processing involves filtering by orientation-selective and size-selective receptive fields, followed by integration to encode object shape. It has long been known that motion signals can influence form processing in the well-known Gestalt principle of common fate; texture elements which share a common motion property are grouped into a single contour or texture region. However, recent research in psychophysics and neuroscience indicates that the influence of form signals on motion processing is more extensive than previously thought. First, the salience and apparent direction of moving lines depends on how the local orientation and direction of motion combine to match the receptive field properties of motion-selective neurons. Second, orientation signals generated by "motion-streaks" influence motion processing; motion sensitivity, apparent direction and adaptation are affected by simultaneously present orientation signals. Third, form signals generated by human body shape influence biological motion processing, as revealed by studies using point-light motion stimuli. Thus, form-motion integration seems to occur at several different levels of cortical processing, from V1 to STS.

Motion aftereffect duration is not changed by perceptual learning: evidence against the representation modification hypothesis

The representation modification hypothesis of perceptual learning attributes the practice-induced improvements in sensitivity and/or discriminability to changes in the early visual areas. We used motion aftereffects (MAE) to probe the representations of motion direction. In two experiments, four practice sessions on a fine direction-discrimination task caused large stimulus-specific improvements in d' but no significant stimulus-specific changes in either static or dynamic MAE duration at posttest relative to a pretest. Power analysis indicated that the data were approximately 100 times more likely given the hypothesis of no MAE change than the hypothesis of a 10% relative change. In light of converging evidence in the MAE literature, this suggests that little or no change occurred in the cortical representations of visual motion up to and including area MT. The task specificity of the learning effect challenges the representation modification hypothesis and supports an alternative-selective reweighting.

Test stimulus characteristics determine the perceived speed of the dynamic motion aftereffect

Using a speed-matching task, we measured the speed tuning of the dynamic motion aftereffect (MAE). The results of our first experiment, in which we co-varied dot speed in the adaptation and test stimuli, revealed a speed tuning function. We sought to tease apart what contribution, if any, the test stimulus makes towards the observed speed tuning. This was examined by independently manipulating dot speed in the adaptation and test stimuli, and measuring the effect this had on the perceived speed of the dynamic MAE. The results revealed that the speed tuning of the dynamic MAE is determined, not by the speed of the adaptation stimulus, but by the local motion characteristics of the dynamic test stimulus. The role of the test stimulus in determining the perceived speed of the dynamic MAE was confirmed by showing that, if one uses a test stimulus containing two sources of local speed information, observers report seeing a transparent MAE; this is despite the fact that adaptation is induced using a single-speed stimulus. Thus while the adaptation stimulus necessarily determines perceived direction of the dynamic MAE, its perceived speed is determined by the test stimulus. This dissociation of speed and direction supports the notion that the processing of these two visual attributes may be partially independent.

Velocity dependence of the interocular transfer of dynamic motion aftereffects

It is well established that motion aftereffects (MAEs) can show interocular transfer (IOT); that is, motion adaptation in one eye can give a MAE in the other eye. Different quantification methods and different test stimuli have been shown to give different IOT magnitudes, varying from no to almost full IOT. In this study, we examine to what extent IOT of the dynamic MAE (dMAE), that is the MAE seen with a dynamic noise test pattern, varies with velocity of the adaptation stimulus. We measured strength of dMAE by a nulling method. The aftereffect induced by adaptation to a moving random-pixel array was compensated (nulled), during a brief dynamic test period, by the same kind of motion stimulus of variable luminance signal-to-noise ratio (LSNR). The LSNR nulling value was determined in a Quest-staircase procedure. We found that velocity has a strong effect on the magnitude of IOT for the dMAE. For increasing speeds from 1.5 deg s(-1) to 24 deg s(-1) average IOT values increased about linearly from 18% to 63% or from 32% to 83%, depending on IOT definition. The finding that dMAEs transfer to an increasing extent as speed increases, suggests that binocular cells play a more dominant role at higher speeds.