W A van de Grind

Universiteit Utrecht, Utrecht, Provincie Utrecht, Netherlands

Are you W A van de Grind?

Claim your profile

Publications (79)171.92 Total impact

  • Journal of Vision 11/2010; 2(7):585-585. DOI:10.1167/2.7.585 · 2.73 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The issue of the existence of planes-understood as the carriers of a nexus of straight lines-in the monocular visual space of a stationary human observer has never been addressed. The most recent empirical data apply to binocular visual space and date from the 1960s (Foley, 1964). This appears to be both the first and the last time this basic issue was addressed empirically. Yet the question is of considerable conceptual interest. Here we report on a direct empirical test of the existence of planes in monocular visual space for a group of sixteen experienced observers. For the majority of these observers monocular visual space lacks a projective structure, albeit in qualitatively different ways. This greatly reduces the set of viable geometrical models. For example, it rules out all the classical homogeneous spaces (the Cayley-Klein geometries) such as the familiar Luneburg model. The qualitatively different behavior of experienced observers implies that the generic population might well be inhomogeneous with respect to the structure of visual space.
    Acta psychologica 05/2010; 134(1):40-7. DOI:10.1016/j.actpsy.2009.12.002 · 2.19 Impact Factor
  • Wim van de Grind
    [Show abstract] [Hide abstract]
    ABSTRACT: Is the position of a moving target "predicted"? I argue that we should regard moving targets as the natural (veridical) position references. Motion is probably perceptually absolute, whereas position and time are relative quantities, as in physics. According to this view, processing delays are incorporated in the abstract local signs of motion signals. The flash-lag effect is one case in point.
    Behavioral and Brain Sciences 05/2008; 31(2):218-219. DOI:10.1017/S0140525X08004020 · 14.96 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Temporal interactions in direction-sensitive complex cells in area 18 and the posteromedial lateral suprasylvian cortex (PMLS) were studied using a reverse correlation method. Reverse correlograms to combinations of two temporally separated motion directions were examined and compared in the two areas. A comparison to the first-order reverse correlograms allowed us to identify nonlinear suppression or facilitation due to pairwise combinations of motion directions. Results for area 18 and PMLS were very different. Area 18 showed a single type of nonlinear behavior: similar directions facilitated and opposite directions suppressed spike probability. This effect was most pronounced for motion steps that followed each other immediately and decreased with increasing delay between steps. In PMLS, the picture was much more diverse. Some cells exhibited nonlinear interactions, that were opposite to those in area 18 (facilitation for opposite directions and suppression for similar ones), while the majority did not show a systematic interaction profile. We conclude that nonlinear second-order reverse correlation characteristics reveal different functional properties, despite similarities in the first-order reverse correlation profiles. Directional interactions in time revealed optimal integration of similar directions in area 18, but motion opponency--at least in some cells--in PMLS.
    Visual Neuroscience 03/2006; 23(2):233-46. DOI:10.1017/S0952523806232085 · 1.68 Impact Factor
  • Source
    Ildikó Vajda, Martin J M Lankheet, Wim A van de Grind
    [Show abstract] [Hide abstract]
    ABSTRACT: The spatio-temporal requirements for direction selectivity were studied in two extrastriate motion processing areas in the cat, area 18 and the posteromedial lateral suprasylvian cortex (PMLS). Direction, velocity and pixel size of random pixel arrays (RPA) were adjusted for each neuron and direction selectivity was measured as a function of step size and delay for a given optimal velocity. A subset of direction selective complex cells in area 18 was tuned to intermediate step size and delay combinations rather than the smoothest motion (band-pass cells). Other area 18 complex cells responded best to the smallest value of step size and delay (low-pass cells). Tuning varied with the pixel size of the RPA. Cells with tuning for smaller pixels favoured a preference for non-smooth motion. Area 18 cells with lower spatial resolution showed larger optimal and maximal step sizes. For a subset of the cells in area 18, we measured direction selectivity for extensive step-delay combinations, covering multiple velocities. Results showed that most cells were tuned to narrow range of step-delay combinations, and that the optimal step size was independent of temporal delay. Direction selective complex cells in PMLS were tuned to larger pixel sizes than those in area 18, although the distributions did overlap. In contrast to area 18, PMLS cells preferred the smoothest motion, irrespective of RPA pixel size.
    Vision Research 07/2005; 45(13):1769-79. DOI:10.1016/j.visres.2005.01.011 · 2.38 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Visual latencies and temporal dynamics of area 18 and PMLS direction-selective complex cells were defined with a reverse correlation method. The method allowed us to analyze the time course of responses to motion steps, without confounding temporal integration effects. Several measures of response latency and direction tuning dynamics were quantified: optimal latency (OL), latency of first and last significant responses (FSR, LSR), the increase and decrease of direction sensitivity in time, and the change of direction tuning in time. FSR, OL and LSR values for PMLS and area 18 largely overlapped. The small differences in mean latencies (3-4 ms for FSR and OL and 11.9 ms for the LSR) were not statistically significant. All cells in area 18 and the vast majority of cells in PMLS showed no systematic changes in preferred direction (monophasic neurons). In PMLS 5 out of 41 cells showed a reversal of preferred direction after approximately 56 ms relative to their OL (biphasic neurons). Monophasic cells showed no systematic changes in direction tuning width during the interval from FSR to LSR. In both areas, development of direction sensitivity was significantly faster than return to the non-direction sensitive state, but no significant difference was found between the two areas. We conclude that, for the monophasic type of direction-selective complex cells, the dynamics of primary motion processing are highly comparable for area 18 and PMLS. This suggests that motion information is predominantly processed in parallel, presumably based on input from the fast conducting thalamocortical Y-pathway.
    Cerebral Cortex 08/2004; 14(7):759-67. DOI:10.1093/cercor/bhh036 · 8.31 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: If a motion aftereffect (MAE) for given adaptation conditions has a duration T s, and the eyes are closed after adaptation during a waiting period tw=T s before testing, an unexpected MAE of a 'residual' duration TrT s is experienced. This effect is called 'storage' and it is often quantified by a storage factor sigma=TrT/T, which can reach values up to about 0.7-0.8. The phenomenon and its name have invited explanations in terms of inhibition of recovery during darkness. We present a model based on the opposite idea, that an effective test stimulus quickens recovery relative to darkness or other ineffective test stimuli. The model is worked out in mathematical detail and proves to explain 'storage' data from the literature, on the static MAE (sMAE: an MAE experienced for static test stimuli). We also present results of a psychophysical experiment with moving random pixel arrays, quantifying storage phenomena both for the sMAE and the dynamic MAE (dMAE: an MAE experienced for a random dynamic noise test stimulus). Storage factors for the dMAE are lower than for the sMAE. Our model also gives an excellent description of these new data on storage of the dMAE. The term 'storage' might therefore be a misnomer. If an effective test stimulus influences all direction tuned motion sensors indiscriminately and thus speeds up equalization of gains, one gets the storage phenomenon for free.
    Vision Research 02/2004; 44(19):2269-84. DOI:10.1016/j.visres.2004.04.012 · 2.38 Impact Factor
  • Source
    Chris Muller, Martin J M Lankheet, Wim A Van De Grind
    [Show abstract] [Hide abstract]
    ABSTRACT: We studied the low-level interactions between motion coherence detection and binocular correlation detection. It is well-established that e.g. depth information from motion parallax and from binocular disparities is effectively integrated. The question we aimed to answer is whether such interactions also exist at the very first correlation level that both mechanisms might have in common. First we quantitatively compared motion coherence detection and binocular correlation detection using similar stimuli (random pixels arrays, RPAs) and the same noise masking paradigm (luminance signal to noise ratio, LSNR). This showed that human observers are much more sensitive to motion than to binocular correlation. Adding noise therefore has a much stronger effect on binocular correlation than on motion detection. Next we manipulated the shape of the stimulus aperture to equalize LSNR thresholds for motion and binocular correlation. Motion sensitivity could be progressively reduced by shortening the length of the motion path, while keeping the aperture area constant. Changing the shape of the aperture did not affect binocular correlation sensitivity. A 'balanced' stimulus, one with equal strengths of motion and binocular correlation signals was then used to study the mutual interactions. In accordance with previous results, motion was found to greatly facilitate binocular correlation. Binocular correlation, however did not facilitate motion detection. We conclude that interactions are asymmetrical; fronto-parallel motion is primarily detected monocularly and this information can then be used to facilitate binocular correlation, but binocular correlation cannot improve motion sensitivity.
    Vision Research 02/2004; 44(16):1961-9. DOI:10.1016/j.visres.2004.03.013 · 2.38 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Viewing-distance invariance of visual perception has evolutionary advantages, but it is of necessity limited by spatial and temporal resolution. Even within these resolution limits viewing-distance invariance might not be perfect or even good, but there are remarkably few studies of its precise limits. Here we ask to what extent viewing-distance invariance holds for motion aftereffects (MAEs). There are (at least) two different MAEs: one can be seen on a static test pattern (sMAE) and is tuned to low speeds, the other only becomes manifest on a dynamic noise test stimulus (dMAE) and is sensitive to higher adaptation speeds. We show that each of these MAEs has a limited viewing-distance invariance, the dMAE only for higher screen-speeds and the sMAE only for lower screen-speeds. In both cases upper angular-speed limits shift to higher values for smaller viewing-distances (lower spatial frequencies, larger fields). This upper limit is constant, independent of viewing distance, if expressed in terms of screen-speed. On the other hand the lower speed limit is fixed in angular-speed and variable in screen-speed terms. Explanations for these findings are provided. We show that there is no fixed optimum viewing-distance or optimum angular stimulus-size for either of the two MAEs.
    Vision Research 11/2003; 43(23):2413-26. DOI:10.1016/S0042-6989(03)00431-0 · 2.38 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We introduce the motion reverse correlation method (MRC), a novel stimulus paradigm based on a random sequence of motion impulses. The method is tailored to investigate the spatio-temporal dynamics of motion selectivity in cells responding to moving random dot patterns. Effectiveness of the MRC method is illustrated with results obtained from recordings in both anesthetized cats and an awake, fixating macaque monkey. Motion tuning functions are computed by reverse correlating the response of single cells with a rapid sequence of displacements of a random pixel array (RPA). Significant correlations between the cell's responses and various aspects of stimulus motion are obtained at high temporal resolution. These correlations provide a detailed description of the temporal dynamics of, for example, direction tuning and velocity tuning. In addition, with a spatial array of independently moving RPAs, the MRC method can be used to measure spatial as well as temporal receptive field properties. We demonstrate that MRC serves as a powerful and time-efficient tool for quantifying receptive field properties of motion selective cells that yields temporal information that cannot be derived from existing methods.
    Journal of Neuroscience Methods 04/2003; 123(2):153-66. · 1.96 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We introduce the motion reverse correlation method (MRC), a novel stimulus paradigm based on a random sequence of motion impulses. The method is tailored to investigate the spatio-temporal dynamics of motion selectivity in cells responding to moving random dot patterns. Effectiveness of the MRC method is illustrated with results obtained from recordings in both anesthetized cats and an awake, fixating macaque monkey. Motion tuning functions are computed by reverse correlating the response of single cells with a rapid sequence of displacements of a random pixel array (RPA). Significant correlations between the cell's responses and various aspects of stimulus motion are obtained at high temporal resolution. These correlations provide a detailed description of the temporal dynamics of, for example, direction tuning and velocity tuning. In addition, with a spatial array of independently moving RPAs, the MRC method can be used to measure spatial as well as temporal receptive field properties. We demonstrate that MRC serves as a powerful and time-efficient tool for quantifying receptive field properties of motion selective cells that yields temporal information that cannot be derived from existing methods.
    Journal of Neuroscience Methods 03/2003; 123(2):153-166. DOI:10.1016/S0165-0270(02)00347-3 · 1.96 Impact Factor
  • Source
    W A van de Grind, M J M Lankheet, R Tao
    [Show abstract] [Hide abstract]
    ABSTRACT: Strength of the motion aftereffect (MAE) is most often quantified by its duration, a high-variance and rather 'subjective' measure. With the help of an automatic gain-control model we quantitatively relate nulling-thresholds, adaptation strength, direction discrimination threshold, and duration of the dynamic MAE (dMAE). This shows how the nulling threshold, a more objective two-alternative forced-choice measure, relates to the same system property as MAE-durations. Two psychophysical experiments to test the model use moving random-pixel-arrays with an adjustable luminance signal-to-noise ratio. We measure MAE-duration as a function of adaptation strength and compare the results to the model prediction. We then do the same for nulling-thresholds. Model predictions are strongly supported by the psychophysical findings. In a third experiment we test formulae coupling nulling threshold, MAE-duration, and direction-discrimination thresholds, by measuring these quantities as a function of speed. For the medium-to-high speed range of these experiments we found that nulling thresholds increase and dMAE-durations decrease about linearly, whereas direction discrimination thresholds increase exponentially with speed. The model description then suggests that the motion-gain decreases, while the noise-gain and model's threshold increase with speed.
    Vision Research 02/2003; 43(2):117-33. DOI:10.1016/S0042-6989(02)00495-9 · 2.38 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: 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.
    Perception 02/2003; 32(7):855-66. DOI:10.1068/p3442 · 1.11 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Unlike simple cells, complex cells of area 18 give a directionally selective response to motion of random textures, indicating that they may play a special role in motion detection. We therefore investigated how texture motion, and especially its velocity, is represented by area 18 complex cells. Do these cells have separable spatial and temporal tunings or are these nonseparable? To answer this question, we measured responses to moving random pixel arrays as a function of both pixel size and velocity, for a set of 63 directionally selective complex cells. Complex cells generally responded to a fairly wide range of pixel sizes and velocities. Variations in pixel size of the random pixel array only caused minor changes in the cells' preferred velocity. For nearly all cells the data much better fitted a model in which pixel size and velocity act separately, than a model in which pixel size and velocity interact so as to keep temporal-frequency sensitivity constant. Our conclusion is that the studied population of special complex cells in area 18 are true motion detectors, rather than temporal-frequency tuned neurons.
    Visual Neuroscience 09/2002; 19(5):651-9. DOI:10.1017/S0952523802195101 · 1.68 Impact Factor
  • Source
    Wim van de Grind
    [Show abstract] [Hide abstract]
    ABSTRACT: The conclusions drawn by Benjamin Libet from his work with colleagues on the timing of somatosensorial conscious experiences has met with a lot of praise and criticism. In this issue we find three examples of the latter. Here I attempt to place the divide between the two opponent camps in a broader perspective by analyzing the question of the relation between physical timing, neural timing, and experiential (mental) timing. The nervous system does a sophisticated job of recombining and recoding messages from the sensorial surfaces and if these processes are slighted in a theory, it might become necessary to postulate weird operations, including subjective back-referral. Neuroscientifically inspired theories are of necessity still based on guesses, extrapolations, and philosophically dubious manners of speech. They often assume some neural correlate of consciousness (NCC) as a part of the nervous system that transforms neural activity in reportable experiences. The majority of neuroscientists appear to assume that the NCC can compare and bind activity patterns only if they arrive simultaneously at the NCC. This leads to a search for synchrony or to theories in terms of the compensation of differences in neural delays (latencies). This is the main dimension of the Libet discussion. Examples from vision research, such as "temporal-binding-by-synchrony" and the "flash-lag" effect, are then used to illustrate these reasoning patterns in more detail. Alternatively one could assume symbolic representations of time and space (symbolic "tags") that are not coded in their own dimension (not time in time and space in space). Unless such tags are multiplexed with the quality message (tickle, color, or motion), one gets a binding problem for tags. One of the hidden aspects of the discussion between Libet and opponents appears to be the following. Is the NCC smarter than the rest of the nervous system, so that it can solve the problems of local sign (e.g., "where is the event"?) and timing (e.g., "when did it occur?" and "how long did it last?") on its own, or are these pieces of information coded symbolically early on in the system? A supersmart NCC appears to be the assumption of Libet's camp (which includes Descartes, but also mystics). The wish to distribute the smartness evenly across all stages of processing in the nervous system (smart recodings) appears to motivate the opponents. I argue that there are reasons to side with the latter group.
    Consciousness and Cognition 07/2002; 11(2):241-64; discussion 308-13. DOI:10.1006/ccog.2002.0560 · 2.31 Impact Factor
  • Source
    M J M Lankheet, A J van Doorn, W A van de Grind
    [Show abstract] [Hide abstract]
    ABSTRACT: We studied effects of dark adaptation on spatial and temporal tuning for motion coherence detection. We compared tuning for step size and delay for moving random pixel arrays (RPAs) at two adaptation levels, one light adapted (50 cd/m(2)) and the other relatively dark adapted (0.05 cd/m(2)). To study coherence detection rather than contrast detection, RPAs were scaled for equal contrast detection at each luminance level, and a signal-to-noise ratio paradigm was used in which the RPA is always at a fixed, supra-threshold contrast level. The noise consists of a spatio-temporally incoherent RPA added to the moving RPA on a pixel-by-pixel basis. Spatial and temporal limits for coherence detection were measured using a single step pattern lifetime stimulus, in which patterns on alternate frames make a coherent step and are being refreshed. Therefore, the stimulus contains coherent motion at a single combination of step size and delay only. The main effect of dark adaptation is a large shift in step size, slightly less than the adjustment of spatial scale required for maintaining equal contrast sensitivity. A similar change of preferred step size occurs also for scaled stimuli at a light-adapted level, indicating that the spatial effect is not directly linked to dark adaptation, but more generally related to changes in the available low-level spatial information. Dark-adaptation shifts temporal tuning by about a factor of 2. Long delays are more effective at low luminance levels, whereas short delays no longer support motion coherence detection. Luminance-invariant velocity tuning curves, as reported previously [Lankheet, M.J.M., van Doorn, A.J., Bouman, M.A., & van de Grind, W.A. (2000) Motion coherence detection as a function of luminance in human central vision. Vision Research, 40, 3599-3611], result from recruitment of different sets of motion detectors, and an adjustment of their temporal properties.
    Vision Research 01/2002; 42(1):65-73. DOI:10.1016/S0042-6989(01)00265-6 · 2.38 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We studied the changes and invariances of foveal motion detection upon dark adaptation. It is well-documented that dark adaptation affects both spatial and temporal aspects of visual processing. The question we were interested in is how this alters motion coherence detection for moving random texture. To compare motion sensitivity at different adaptation levels, we adjusted the viewing distance for equal detectability of a stationary pattern. At these viewing distances we then measured velocity tuning curves for moving random pixel arrays (RPAs). Mean luminance levels ranged from 50 down to 0.005 cd m-2. Our main conclusion is that foveal velocity tuning is amazingly close to luminance-invariant, down to a level of 0.05 cd m-2. Because different viewing distances, and hence, retinal image sizes were used, we performed two control experiments to assess variations of these two parameters separately. We examined the effects of retinal inhomogeneities using discs of different size and annuli filled with RPAs. Our conclusion is that the central visual field, including the near periphery is still rather homogeneous for motion detection at 0.05 cd m-2, but the fovea becomes unresponsive at the lowest luminance level. Variations in viewing distance had marked effects on velocity tuning, both at the light adapted level and the 0.05 cd m-2 level. The size and type of these changes indicated the effectiveness of distance scaling, and show that deviations from perfect invariance of motion coherence detection were not due to inaccurate distance scaling.
    Vision Research 02/2000; 40(26):3599-611. DOI:10.1016/S0042-6989(00)00187-5 · 2.38 Impact Factor
  • Source
    Wim A van de Grind, Jan J Koenderink, Andrea J van Doorn
    [Show abstract] [Hide abstract]
    ABSTRACT: In this study we quantify the influence of adaptation luminance on the threshold for direction-detection in coherently moving random-pixel arrays (RPAs). Square RPAs of a constant rms-contrast (35%) were used and we determined their ‘critical’ or threshold-width Wc. Mean retinal illuminances were varied in 13 steps of 0.5 log unit from the low photopic range (screen luminance 0.3 cd/m2) down to 6 log units attenuation, which appeared to be about the absolute threshold of vision under the conditions of our experiment. Moving RPAs were presented at six retinal locations (0, 3, 6, 12, 24 and 48°) from the fovea to the far periphery in the temporal visual field of the right eye of three experienced observers (the authors). In order to ensure an honest comparison between these very disparate conditions, the spatial dimensions (including speed) were scaled according to the acuity, as measured separately for each of the viewing-conditions and observers. Acuity scaling proves to equate the performance for all eccentricities and luminance levels rather well. The fovea is special, but only in the sense that the absolute threshold for light detection is reached earlier than in peripheral regions. In all other respects foveal results follow the pattern found for peripheral locations. Two different regimes can be discerned in the data, one for high and one for low speeds. In the low speed range Wc is almost constant, regardless of luminance level or eccentricity. The critical ‘crossing-time’ Tc for any pixel starting at one end of the stimulus and leaving at the opposite end is therefore inversely proportional to velocity in the low-speed range (time–velocity reciprocity). At medium-to-high speeds Wc increases linearly with velocity, so Tc is constant. This constant (minimum) value of Tc differs between subjects, but in all subjects it increases somewhat with decreasing luminance level, even for our acuity-scaled stimuli. The different behaviour for low and high speeds [reported before for photopic viewing conditions by van de Grind, W. A., van Doorn, A. J., & Koenderink, J. J. (1983. Journal of the Optical Society of America, 73, 1674–1683) and van de Grind, W. A., Koenderink, J. J., & van Doorn A. J. (1986. Vision Research, 26, 797–810)] proves to hold from photopic to low scotopic luminance ranges, provided the stimuli are scaled according to acuity. We draw the general conclusion that movement detection is a very robust process that tolerates extremely low retinal illuminance levels. Moreover, the visual system appears to use the same processing principles in combination with an acuity-scaled architecture under all adaptation states and at all eccentricities.
    Vision Research 01/2000; 40(2-40):187-199. DOI:10.1016/S0042-6989(99)00167-4 · 2.38 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Daubenton's 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 Daubenton's 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 Daubenton's bats and on the ultrasound-reflecting properties of the water surface. Our study revealed the following. (1) Daubenton's bat avoids water surfaces covered with duckweed. (2) Prey abundance was related to the number of foraging Daubenton's bats but was independent of duckweed cover. (3) When mealworms were presented among standardized amounts of duckweed to naturally foraging Daubenton's 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 Daubenton's bats.
    Behavioral Ecology and Sociobiology 10/1998; 44(2):99-107. DOI:10.1007/s002650050521 · 3.05 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: . Enroth-Cugell and Robson (1966) first proposed a classification of retinal ganglion cells into X cells, which exhibit approximate linear spatial summation and largely sustained responses, and Y cells, which exhibit nonlinearities and transient responses. Gaudiano (1992a, 1992b, 1994) has suggested that the dominant characteristics of both X and Y cells can be simulated with a single model simply by changing receptive field profiles to match those of the anatomical counterparts of X and Y cells. He also proposed that a significant component of the spatial nonlinearities observed in Y (and sometimes X) cells can result from photoreceptor nonlinearities coupled with push-pull bipolar connections. Specifically, an asymmetry was predicted in the ganglion cell response to rectangular gratings presented at different locations in the receptive field under two conditions: introduction/withdrawal (on-off) or contrast reversal. When measuring the response to these patterns as a function of spatial phase, the standard difference-of-Gaussians model predicts symmetrical responses about the receptive field center, while the push-pull model predicts slight but significant asymmetry in the on-off case only. To test this hypothesis, we have recorded ganglion cell responses from the optic tract fibers of anesthetized cat. The mean and standard deviations of responses to on-off and contrast-reversed patterns were compared. We found that all but one of the cells that yielded statistically significant data confirmed the hypothesis. These results largely support the theoretical prediction.
    Biological Cybernetics 07/1998; 79(2):151-159. DOI:10.1007/s004220050467 · 1.93 Impact Factor

Publication Stats

1k Citations
171.92 Total Impact Points

Institutions

  • 1984–2010
    • Universiteit Utrecht
      • • Division of Behavioural Biology
      • • Helmholtz Institute
      • • Division of Experimental Psychology
      • • Laboratory for Physical Geography
      Utrecht, Provincie Utrecht, Netherlands
  • 1997
    • McGill University
      • Division of Ophthalmology
      Montréal, Quebec, Canada
    • Ohio University
      • Department of Biological Sciences
      Athens, Ohio, United States
  • 1996
    • Boston University
      • Cognitive and Neural Systems
      Boston, MA, United States
    • Harvard University
      • Vision Sciences Laboratory
      Boston, MA, United States
  • 1992–1996
    • Netherlands Institute for Space Research, Utrecht
      Utrecht, Utrecht, Netherlands
  • 1994–1995
    • University of North Carolina at Chapel Hill
      • Department of Computer Science
      North Carolina, United States
  • 1993
    • Erasmus Universiteit Rotterdam
      Rotterdam, South Holland, Netherlands
  • 1979–1990
    • University of Amsterdam
      • Laboratory for Physiology
      Amsterdamo, North Holland, Netherlands
  • 1977
    • Freie Universität Berlin
      Berlín, Berlin, Germany