Wim A. van de Grind

Spatial and temporal parameters of motion detection in the peripheral visual field

We present evidence that motion detectors in the peripheral visual field react to spatiotemporal structure in moving spatial white-noise patterns in a qualitatively similar fashion to those located near the center of gaze. Reichardt-type correlator mechanisms provide a simple theoretical framework in which all observed phenomena can be discussed. Two basic parameters of the correlator model are the time lag and the span. We have devised paradigms in which these parameters may be measured as a function of velocity and position in the visual field. Our results indicate that these parameters are primarily a function of the magnitude of the velocity (the span increasing and the lag decreasing as the velocity is increased) rather than of the retinal eccentricity. In another experiment, we determined the spatial resolution for apparent segregation of the visual image that is due to differential motion of detail in stationary rectangular gratings in which the pixels moved one way in the even bars and the other way in the odd bars. The limit of resolution occurs when the pixels traverse a bar of the grating in a given time, irrespective of the velocity or the retinal eccentricity. For higher velocities, the resolution is uniform over the entire visual field. For slower motion, the region of uniform (and relatively high) resolution shrinks to a region around the center of gaze. Resolution for segregation due to differential movement is at least 10 times worse than typical contrast-grating acuity.

Prey detection in trawling insectivorous bats: duckweed affects hunting behaviour in Daubenton's bat, Myotis daubentonii

Abstract Daubentons bat, a trawling vespertilionid bat species, hunts for insects that fly close to, or rest on, the water surface. During summer, many ponds at which Daubentons bats hunt become gradually covered with duckweed. The purpose of this study was to investigate the effects of duckweed cover on the hunting behaviour of Daubentons bats and on the ultrasound-reflecting properties of the water surface. Our study revealed the following. (1) Daubentons bat avoids water surfaces covered with duckweed. (2) Prey abundance was related to the number of foraging Daubentons bats but was independent of duckweed cover. (3) When mealworms were presented among standardized amounts of duckweed to naturally foraging Daubentons bats, they caught significantly less mealworms when the duckweed cover was increased. (4) Measurements with ultrasonic signals show that a water surface covered with duckweed returns a much stronger background echo at small angles (i.e. parallel to the water surface) compared to an uncovered water surface. It seems likely that a cover of duckweed on the water surface interferes with prey detection by masking the echoes returning from prey. (5) It was relatively difficult for the bats to discriminate small patches of duckweed from mealworms. The proposed discrimination mechanism for this trawling bat species suggests that single duckweed patches can also be mistaken for natural prey by Daubentons bats.

Movement aftereffect of bi-vectorial transparent motion.

Two moving random-pixel arrays (RPAs) were presented simultaneously in the same target field. These RPAs are perceived as two superimposed transparent moving sheets. Although two directions are perceived simultaneously during stimulus presentation, the movement aftereffect (MAE) is unidirectional. The visual system averages both motion signals in the MAE. For motion vectors of equal magnitude and perpendicular direction the MAE direction is the inverse of the sum of both vectors. In the first experiment we measured perceived direction of the MAE of transparent motion for a range of speed combinations. Results indicate that vector summation only predicts the correct MAE direction for combinations of equal speeds. It is suggested that the direction of the MAE of transparent motion is a resultant of the weighted summation of the component inducing vectors. The question then arises what determines the weighting factors. Directional sensitivity and MAE duration of the individual vectors under transparent conditions were measured and used to weigh the vectors and predict the MAE direction of transparent motion. Statistical analyses showed that MAE duration is a better basis to determine the weighting factors predicting the direction of the MAE of transparent motion than component sensitivity. The direction of the MAE of transparent motion thus seems to be determined by the amount of adaptation to the component vectors as reflected by MAE duration. The results suggest that this gain control cannot be located in the individual motion detectors and must be situated at or after some subsequent cooperation stage of the human motion analysis system.

Aftereffect of High-Speed Motion

A visual illusion known as the motion aftereffect is considered to be the perceptual manifestation of motion sensors that are recovering from adaptation. This aftereffect can be obtained for a specific range of adaptation speeds with its magnitude generally peaking for speeds around 3 deg s−1. The classic motion aftereffect is usually measured with a static test pattern. Here, we measured the magnitude of the motion aftereffect for a large range of velocities covering also higher speeds, using both static and dynamic test patterns. The results suggest that at least two (sub)populations of motion-sensitive neurons underlie these motion aftereffects. One population shows itself under static test conditions and is dominant for low adaptation speeds, and the other is prevalent under dynamic test conditions after adaptation to high speeds. The dynamic motion aftereffect can be perceived for adaptation speeds up to three times as fast as the static motion aftereffect. We tested predictions that follow from the hypothesised division in neuronal substrates. We found that for exactly the same adaptation conditions (oppositely directed transparent motion with different speeds), the aftereffect direction differs by 180° depending on the test pattern. The motion aftereffect is opposite to the pattern moving at low speed when the test pattern is static, and opposite to the high-speed pattern for a dynamic test pattern. The determining factor is the combination of adaptation speed and type of test pattern.

Perception of Movement and Correlation in Stroboscopically Presented Noise Patterns

The detection of spatiotemporal correlation in visual displays has been studied with stroboscopically presented random-noise patterns and with a signal-to-noise ratio paradigm in which the moving pattern was masked with spatiotemporal white noise. These methods reveal the ability of the visual system to detect correlation of spatiotemporal structures, rather than luminance contrast. The effects of stroboscopic rate, exposure duration, target size, and the extent of discrete spatial shifts were studied in both the central and the peripheral visual field. Evidence for orientation-selective and speed-selective mechanisms was found, as well as for extensive spatiotemporal integration. Bounds on parameters of spatial and temporal correlation and integration were obtained. The results are similar to those reported earlier, and also extend them. Their relation to results obtained through other paradigms (eg the motion aftereffect) is explored.

Integration after adaptation to transparent motion : static and dynamic test patterns result in different aftereffect directions

One of the many interesting questions in motion aftereffect (MAE) research is concerned with the location(s) along the pathway of visual processing at which certain perceptual manifestations of this illusory motion originate. One such manifestation is the unidirectionality of the MAE after adaptation to moving plaids or transparent motion. This unidirectionality has led to the suggestion that the origin of this MAE might be a single source (gain control) located at, or beyond areas that are believed to be responsible for the integration of motion signals. In this report we present evidence against this suggestion using a simple experiment. For the same adaptation pattern, which consisted of two orthogonally moving transparent patterns with different speeds, we show that the direction of the resulting unidirectional MAE depends on the nature of the test stimulus. We used two kinds of test patterns: static and dynamic. For exactly the same adaptation conditions, the difference in MAE direction between testing with static and dynamic patterns can be as large as 50 degrees. This finding suggests that this MAE is not just a perceptual manifestation of a passive recovery of adapted motion sensors but an active integrative process using the output of different gain controls. A process which takes place after adaptation. These findings are in line with the idea that there are several sites of adaptation along the pathway of visual motion processing and that the nature of the test pattern determines the fate of our perceptual experience of the MAE.

Recovery from adaptation for dynamic and static motion aftereffects: Evidence for two mechanisms

The motion aftereffect (MAE) is an illusory drift of a physically stationary pattern induced by prolonged viewing of a moving pattern. Depending on the nature of the test pattern the MAE can be phenomenally different. This difference in appearance has led to the suggestion that different underlying mechanisms may be responsible and several reports show that this might be the case. Here, we tested whether differences in MAE duration obtained with stationary test patterns and dynamic test patterns can be explained by a single underlying mechanism. We find the results support the existence of (at least) two mechanisms. The two mechanisms show different characteristics: the static MAE (i.e. the MAE tested with a static test pattern) is almost completely stored when the static test is preceded by a dynamic test; in contradistinction, the dynamic MAE is not stored when dynamic testing is preceded by a static test pattern.

Binocular Visual Direction

We examine whether the rules of cyclopean visual direction, as expressed by Hering and others, correctly predict the percept of visual direction for structured visual stimuli. Theoretical inspection of the rules of cyclopean visual direction shows a paradox for the binocular visual directions of stimuli in which objects partly occlude each other. We investigate how the rules of cyclopean direction are violated during viewing of structured random-dot stereograms with different depth planes. The directions of monocular and binocular visual elements are determined in an alignment task. Subjects align a monocular/binocular slider with a monocular/binocular test line present in the random-dot stereograms. The results show that the available rules of cyclopean direction are not sufficient for human vision in this general situation. The available rules can only be used for alignment of two binocularly visible lines or two monocular lines presented to the same eye. Alignment in these cases is predicted by almost any set of rules that transforms visible lines to a cyclopean eye. Stimulus conditions, in which either one line is presented to one eye and the other line to the other eye or one line is presented to one eye and the other line to two eyes, provide a more critical test for validity of the cyclopean rules. Our results show that the rules of cyclopean direction fail to predict alignment precisely in these conditions. Inspection of the data shows that binocular alignment is achieved by alignment of two monocular lines presented to a single eye.

A Transparent Motion Aftereffect Contingent on Binocular Disparity

Under transparent motion conditions overlapping surfaces are perceived simultaneously, each with its own direction. The motion aftereffect (MAE) of transparent motion, however, is unidirectional and its direction is opposite to that of a sensitivity-weighted vector sum of both inducing vectors. Here we report a bidirectional and transparent MAE contingent on binocular disparity. Depth (from retinal disparity) was introduced between two patterns. A fixation dot was presented at zero disparity, that is, located between the two adaptation patterns. After adaptation to such a stimulus configuration testing was carried out with two stationary test patterns at the same depths as the preceding moving patterns. For opposite directions a clear transparent MAE was perceived. However, if the adaptation directions were orthogonal the chance of a transparent MAE being perceived decreased substantially. This was subject dependent. Some subjects perceived an orthogonal transparent MAE whereas others saw the negative vector sum—an integrated MAE. In addition the behaviour of the MAE when the distance in depth between adapting and test patterns was increased was investigated: it was found that the visibility of the MAE then decreased. Visibility is defined in this paper as: (i) the percentage of the trials in which MAEs are perceived and (ii) the average MAE duration. Both measures decreased with increasing distance. The results suggest that segregation and integration may be mediated by direction-tuned channels that interact with disparity-tuned channels.

Responses of Complex Cells in Area 17 of the Cat to Bi-vectorial Transparent Motion

We examined the responses to transparent motion of complex cells in cat area 17 which show directional selectivity to moving random pixel arrays (RPAs). The response to an RPA moving in the cells preferred direction is inhibited when a second RPA is transparently moving in another direction. The inhibition by the second pattern is quantified as a function of its direction. The response to a pattern moving in the preferred direction is never completely suppressed, not even when a second pattern is moving transparently in the opposite direction. To the extent that supra-spontaneous firing rates signal the presence of the optimal velocity vector, these cells therefore still signal the presence of this line-label stimulus despite additional opposing, or otherwise directed, motion components. The results confirm previous suggestions that, for the computation of motion energy in cat area 17 complex cells, a full opponent stage is not plausible. Furthermore, we show that the response to a combination of two motion vectors can be predicted by the average of the responses to the individual components.