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Research Article  |   March 2008
Motion processing at low light levels: Differential effects on the perception of specific motion types
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Journal of Vision March 2008, Vol.8, 14. doi:10.1167/8.3.14
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      Jutta Billino, Frank Bremmer, Karl R. Gegenfurtner; Motion processing at low light levels: Differential effects on the perception of specific motion types. Journal of Vision 2008;8(3):14. doi: 10.1167/8.3.14.

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Abstract

While many aspects of human vision at low light levels have been studied in great detail, motion perception has rarely been investigated so far. Here we address differential effects of light level on the perception of coherent motion, heading from radial flow, and biological motion. We determined detection thresholds under photopic, mesopic, and scotopic conditions. Results indicate that the perception of specific motion types differs in vulnerability to changes in light level. Thresholds for coherent motion and heading from radial flow increased monotonically from photopic to mesopic and scotopic light levels. We suppose that observed deficits are due to temporal pooling under rod-dominated vision. In contrast, detection thresholds for biological motion, which is distinguished by temporal dynamics and a specific spatial distribution of nearby signals, were exclusively elevated under mesopic conditions. Thresholds under scotopic conditions matched those under photopic conditions. Selective constraints under mesopic conditions might be explained by a detrimental interaction of rod and cone vision as well as by activity of different rod pathways. Findings suggest that very early retinal signal processing can have complex effects on the perception of different motion types, which is generally considered to rely on cortical areas.

Introduction
Our ability to process visual information over a wide range of light intensities, from bright daylight to faint starlight at night, reflects a remarkable flexibility of the human visual system. The maintenance of visual performance despite decreasing light levels relies on temporal and spatial summation which allows to capture more photons and thus preserves the ability to detect visual information. Adaptive information integration is accomplished by the transition from cone-mediated to rod-mediated photoreception. While many aspects of rod-dominated vision in humans have been studied in great detail (for review, see Hess, Sharpe, & Nordby, 1990), motion perception has rarely been investigated so far. 
Indeed, specialized processing of motion information in the visual pathways does not occur before striate cortex (Albright, 1984; Albright & Stoner, 1995; Felleman & Van Essen, 1991). Since rods and cones make connections to the same post-receptoral pathways, it might be presumed that cortical processing of their signals is identical (for a review, see Bloomfield & Dacheux, 2001; D'Zmura & Lennie, 1986). van de Grind, Koenderink, and van Doorn (2000) have sophisticatedly compensated for effects of changes in retinal signal transmission by scaling stimuli according to temporal and spatial acuity. Their study has shown robustness of the central motion analysis system at low light levels. However, central motion analysis ultimately relies on input determined by retinal photoreceptors with specific spatio-temporal sensitivities and transmission characteristics. 
Motion perception is most obviously affected by temporal changes under rod-dominated vision. The response of the visual system becomes more sluggish with decreasing light intensities (Dawson & Di Lollo, 1990; Takeuchi & De Valois, 1997). Several studies have established that perceived speed and speed discrimination are deficient under dim light conditions (Hammett, Champion, Thompson, & Morland, 2007; Raghuram, Lakshminarayanan, & Khanna, 2005; Takeuchi & De Valois, 2000). Some results indicate that impairment is more pronounced for velocities above 4°/s (Hammett et al., 2007; Takeuchi & De Valois, 2000). With regard to global motion detection (see Newsome & Paré, 1988), to our best knowledge, only Grossman and Blake (1999) have considered thresholds under scotopic conditions and found thresholds comparable to those under photopic conditions. They argued that motion detection in noise depends on pooling of local motion signals and therefore increased spatial pooling at low light levels should not affect performance. However, applied stimuli were restricted to a relatively low velocity range (3.2°/s to 8.0°/s) in which the effect of temporal averaging might not be evaluable conclusively. Although manipulating light levels represents an ecological valid approach to explore differences in perceptual performance, exact control over cone and rod activity cannot be achieved. Gegenfurtner, Mayser, and Sharpe (1999, 2000) avoided this limitation by isolating rod and cone activity under silent substitution conditions. They investigated velocity perception at mesopic light levels and reported that rod-mediated stimuli were perceived approximately 20% slower compared to cone-mediated stimuli. They proposed that temporal averaging attenuates motion signals in central detectors tuned to high velocities which in turn causes the reduction of perceived velocity. 
Most of the above studies were not concerned with differential effects of light level on the perception of different types of motion information but focused on the perception of basic translational motion which has been supposed to be determined early in the central visual pathways. Brain imaging and lesion studies in humans have identified human MT (V5) as a critical functional region (Dumoulin et al., 2000; Schenk & Zihl, 1997; Sunaert, Van Hecke, Marchal, & Orban, 1999; Vaina, Cowey, Eskew, LeMay, & Kemper, 2001). Electrophysiological data supports the idea that input to MT is dominated by the magnocellular pathway (Maunsell, Nealey, & DePriest, 1990). More complex types of motion information, e.g., form-from-motion, biological motion, optic flow, or depth-from-motion, are characterized by specific spatio-temporal characteristics. Specialized processing mechanisms comprise neural pathways that might differ in their vulnerability to changes in retinal signal transmission during decrease of light intensity. There is currently a lack of knowledge of how perception of high-level motion types is challenged at low light levels. Some studies provided evidence that perception of form-from-motion and of biological motion is impaired at low light levels (Grossman & Blake, 1999; Takeuchi, Yokosawa, & De Valois, 2004). Grossman and Blake (1999) speculated that due to spatial pooling, the distinction between nearby motion signals becomes difficult and the configural structure is consequently lost. 
Radial flow and biological motion represent two types of high-level motion that bear particular ecological relevance. Radial flow occurs when an observer moves through the environment. Expanding radial flow generated by forward motion is important for heading detection and navigation in space. Findings of imaging and lesion studies in humans suggest that heading perception engages a network of multiple neural regions including human MT (V5), parietal, and frontal regions (Beardsley & Vaina, 2005; de Jong, Shipp, Skidmore, Frackowiak, & Zeki, 1994; Greenlee, 2000; Peuskens, Sunaert, Dupont, Van Hecke, & Orban, 2001; Royden & Vaina, 2004; Vaina & Soloviev, 2004; Wunderlich et al., 2002). Heading perception requires integration of large field visual information but also analysis of a complex velocity distribution. Hence, though robust to spatial pooling, it might be vulnerable to deficient velocity processing under rod vision. Biological motion is elicited by the moving form of a human figure and contributes to social interaction. Whereas human MT (V5) is not required for biological motion perception, activity in the posterior superior temporal sulcus has been shown to be of functional importance (Grossman, Battelli, & Pascual-Leone, 2005; Grossman et al., 2000; Vaina & Gross, 2004; Vaina, LeMay, Bienfang, Choi, & Nakayama, 1990). Physiological evidence suggests that temporal areas receive magnocellular as well as parvocellular input (Nealey & Maunsell, 1994). The global perception of biological activity depends on exact spatial and temporal differentiation of visual signals. Summation under rod vision might therefore interfere with this specialized analysis. 
Decreasing light intensity is associated with a gradual transition from cone- to rod-dominated vision. However, it seems necessary to bear in mind that this represents an oversimplification of the underlying physiological changes. There exist two rod pathways that differ in signal transmission to the ganglion cells (Sharpe & Stockman, 1999; Stockman, Sharpe, Rüther, & Nordby, 1995; Stockman, Sharpe, Zrenner, & Nordby, 1991). At mesopic and high scotopic light levels, fast transmission is accomplished by rod–cone gaps. At low scotopic light levels, rod signals are conveyed via a slow pathway involving rod bipolars and A2 amacrine cells. In addition, signal processing at mesopic light levels is supposed to be particularly complex because interaction between cone and rod systems might disturb transmission (Bloomfield & Dacheux, 2001; Stockman & Sharpe, 2006). Hence, when considering visual abilities at dim light levels, a range of light intensities which potentially engage different processing pathways should be taken into account. 
In the present study, we aimed to evaluate the effect of light level on the perception of different motion types. We investigated the perception of (i) basic coherent motion, (ii) heading from radial flow, and (iii) biological motion. Light levels were chosen to trigger differential contributions of cones and rods to signal processing. 
Method
Subjects
A total of 6 subjects (3 females) with a mean age of 23.0 years ( SD = 2.3) participated in these experiments. All subjects had normal or corrected-to-normal vision and normal color vision. Informed consent was obtained prior to the start of the experiments. 
Apparatus
Stimuli were generated by a Dell Latitude 600 at a frame rate of 35 Hz and displayed on a 21-inch Iiyama Vision Master Pro 513 CRT monitor driven by an NVIDIA Quadro NVS 285 graphics card. The monitor resolution was set to 1,154 × 864 pixels. A gamma correction ensured linearity of gray levels. Light levels were manipulated by placing neutral-density filters (LEE filters) in front of the display. Experiments were run at three different light levels to which we refer in the following as photopic, mesopic, and scotopic light levels. For the different light levels, white pixels had a photopic luminance of 98.5, 0.285, and 0.018 cd/m 2, respectively. Resulting Michelson contrast of the stimuli lay above 98%. Luminances were checked by measurement with a photometer (UDT Instruments Optometer 370). The spectrum of each of the three monitor primaries at its maximum intensity was measured with a Photo Research PR 650 spectroradiometer. Since stimuli were viewed with natural pupils, exact retinal illumination cannot be specified. However, assuming average dark-adapted pupil diameters of 6 mm (Reeves, 1920; Spring & Stiles, 1948), retinal illuminance approximated 8.1 photopic trolands and 0.5 photopic trolands in our mesopic and scotopic condition, respectively. The conversion of photopic to scotopic units depends on the exact wavelength distribution of the monitor primaries (for details, compare Wyszecki and Stiles, 2000). For the broadband spectra of our particular monitor, we computed the conversion factor to be 0.999, resulting in scotopic trolands equivalent to photopic trolands. Therefore, it appears justified to assume that vision at our “photopic” light level was primarily based on cone activity, at our “mesopic” light level on cone and rod activity, and at our “scotopic” light level mainly on rod activity (Stockman & Sharpe, 2006; see also Hood & Finkelstein, 1986). 
Stimuli
Random dot kinematograms were used to present coherent motion, radial flow, and biological motion. They were composed of white dots with a diameter of 0.1° on a black background. Dot size and density were chosen to ensure visibility under all luminance conditions. Figure 1 illustrates the stimuli. 
Figure 1
 
Static representation of motion stimuli. Signal dots are shown in gray and noise dots in white for clarification. In the actual stimuli, all dots were white. Coherent motion was defined as horizontal motion of the signal dots either to the right or to the left. In the radial flow stimulus, signal dots expanded with the focus of expansion (FOE) either right or left of the fixation cross. Small gray arrows indicate the motion direction of the signal dots but were not present in the actual stimulus. The biological motion stimulus consisted of a canonical point-light walker embedded in noise dots. It moved as if on a treadmill, facing either to the right or to the left.
Figure 1
 
Static representation of motion stimuli. Signal dots are shown in gray and noise dots in white for clarification. In the actual stimuli, all dots were white. Coherent motion was defined as horizontal motion of the signal dots either to the right or to the left. In the radial flow stimulus, signal dots expanded with the focus of expansion (FOE) either right or left of the fixation cross. Small gray arrows indicate the motion direction of the signal dots but were not present in the actual stimulus. The biological motion stimulus consisted of a canonical point-light walker embedded in noise dots. It moved as if on a treadmill, facing either to the right or to the left.
The coherent motion stimulus was formed by a circular aperture with a diameter of 9.4° containing 60 dots. A certain percentage of dots moved in the same horizontal direction, either to the right or to the left, resulting in coherent motion. The other dots moved in random direction. Dots moving out of the aperture reappeared at random position. Dots had a limited lifetime of four frames. Signal intensity was defined by percentage of coherently moving dots. Three different velocities were applied, namely 3.3°/s, 6.6°/s, and 13.2°/s. 
The radial flow stimulus consisted of 100 dots expanding within a rectangular aperture, 37.5° × 28.5°, simulating forward motion on a straight path. A certain percentage of dots expanded coherently whereas the rest moved in random direction. Focus of expansion was shifted horizontally 5.6° either to the right or to the left of the center of the field. Again, dots had a limited lifetime of four frames and dots moving out of the aperture reappeared at random position. Signal intensity was defined by percentage of coherently expanding dots. Velocity of expansion increased linearly from the focus of expansion. Three different maximum velocities were applied, namely, 9.3°/s, 18.6°/s, and 37.2°/s. 
A point-light walker represented the biological motion stimulus. The walker consisted of eleven dots and was defined by the point-light walker algorithm described by Cutting (1978). It subtended a visual angle of 5.3° in height and 2.0° in width. It was shown in a sagittal view and moved in place as if on a treadmill with either left- or rightward gait. The walker appeared in a circular aperture with a diameter of 9.4° and was camouflaged by noise dots that moved randomly. Noise dots had a limited lifetime of four frames and reappeared at random position when moving out of the aperture. Signal intensity was defined by percentage of walker dots relative to the total number of dots. Duration of a stride cycle was set to 1 s, which falls in the range for normal human walking as reported by Inman, Ralston, and Todd (1981). 
Experimental procedure
Experiments took place in a thoroughly darkened and light-shielded room. Subjects were seated at a distance of 60 cm to the monitor. Viewing was binocular and subjects' head was stabilized by using a chin rest. Testing at each light level was scheduled in a separate session, which spanned approximately 90 minutes plus 20 minutes for dark adaptation in the mesopic and scotopic conditions. Sequence of light levels and sequence of different stimuli velocities within sessions were counterbalanced across subjects. 
Stimuli were presented in spatial 2-alternative-forced-choice-paradigms. Subjects were instructed to fixate at the center of the screen. A white fixation cross subtending 3.3° was provided 500 ms before stimulus onset. In order to avoid irritations during fixation at dim light levels, the cross had a central gap of 1.1°, omitting the area for which rod vision is absent. Stimuli were displayed for 400 ms. In the coherent motion task, two apertures appeared simultaneously right and left of the fixation cross. They were shifted horizontally to an eccentricity of 7.5°. One aperture contained coherent motion whereas in the other one all dots moved randomly. Subjects had to indicate on which side they had seen coherent motion. In the radial flow task, subjects had to detect the direction of heading within a single central aperture, that means whether the focus of expansion was shifted to the right or to the left of the fixation cross. In the biological motion task, again two apertures appeared simultaneously right and left of the fixation cross and were shifted horizontally to an eccentricity of 7.5°. In one aperture, a canonical point-light walker occurred camouflaged by noise dots. In the other one, a scrambled walker and the same amount of noise dots were presented. The scrambled walker consisted also of eleven dots whose motion matched the motion of the dots in the canonical walker. However, dots' spatial position was randomized so that the canonical structure was lost. Subjects had to indicate at which side they had seen the canonical walker. 
Responses were entered without time constraints directly on the keyboard after stimulus presentation. Feedback on performance was not provided. Subjects started each new trial by pressing the space bar. Before obtaining threshold data, sufficient practice trials were given so that subjects got used to the task and could handle the keyboard blindly. 
We used the method of constant stimuli to measure perception thresholds. Signal intensity was varied by seven different noise levels. Each signal-to-noise level was presented in 32 trials, resulting in a total of 224 trials. The number of correct responses at each noise level was recorded. Thresholds were obtained by fitting the percentage of correct responses with a Weibull function for a performance level of 75%. We used the psignifit toolbox in Matlab (Wichmann & Hill, 2001a, 2001b) and assessed the goodness of fit of the psychometric function. Summary statistics yielded a good fit between the model and the data. 
Results
Effects of light level on perceptual thresholds for different motion types were examined for single subject data as well as for group data. Table 1 summarizes mean detection thresholds in the different motion tasks. 
Table 1
 
Mean detection thresholds for different motion types in dependence on light level. Standard deviations are given in parentheses. N = 6.
Table 1
 
Mean detection thresholds for different motion types in dependence on light level. Standard deviations are given in parentheses. N = 6.
Motion type Light level
Photopic Mesopic Scotopic
Coherent motion
3.3°/s 23.7 (9.3) 24.1 (5.5) 25.3 (11.0)
6.6°/s 12.5 (2.8) 17.4 (2.3) 22.7 (9.1)
13.2°/s 8.1 (0.7) 13.2 (1.6) 18.8 (5.3)
Radial flow
<9.3°/s 16.2 (1.8) 27.7 (5.9) 26.3 (3.9)
<18.6°/s 8.3 (1.8) 18.0 (5.7) 20.1 (3.4)
<37.2°/s 5.0 (1.4) 9.1 (2.3) 10.9 (3.5)
Biological motion
9.4 (2.5) 14.9 (2.2) 10.9 (3.6)
Coherent motion perception
Figure 2 demonstrates the effect of light level on coherent motion perception by performance of a typical subject. Psychometric functions for detection performance at photopic (light gray), mesopic (dark gray), and scotopic (black) light levels are plotted together. Performance at low velocity appeared quite similar at different light levels ( Figure 2A). At medium velocity, psychometric functions indicate increasing thresholds at low light levels ( Figure 2B). This decline in performance is observed more clearly at high velocity ( Figure 2C). 
Figure 2
 
Psychometric functions for the detection of coherent motion at photopic, mesopic, and scotopic light levels. Data of a typical subject are shown. Detection accuracy is plotted as a function of signal intensity. Parts A to C correspond to different velocities of translation.
Figure 2
 
Psychometric functions for the detection of coherent motion at photopic, mesopic, and scotopic light levels. Data of a typical subject are shown. Detection accuracy is plotted as a function of signal intensity. Parts A to C correspond to different velocities of translation.
Group data mirrored single subject data. Figure 3 displays mean thresholds at different light levels for the three applied velocities. Perception of coherent motion at low velocities was unaffected by light level. However, at higher velocities detection thresholds for coherent motion increased under dim light conditions. Thresholds under mesopic and scotopic conditions were comparable. 
Figure 3
 
Mean detection thresholds for coherent motion plotted by light level and velocity of translation. Error bars indicate standard errors.
Figure 3
 
Mean detection thresholds for coherent motion plotted by light level and velocity of translation. Error bars indicate standard errors.
Heading perception from radial flow
Figure 4 depicts psychometric functions of a typical subject for heading perception from radial flow at different light levels. At all applied maximum velocities ( Figures 4A to 4C), mesopic and scotopic functions were concordant with each other, but clearly indicated higher thresholds than photopic functions. 
Figure 4
 
Psychometric functions for the detection of heading from radial flow at photopic, mesopic, and scotopic light levels. Data of a typical subject are shown. Detection accuracy is plotted as a function of signal intensity. Parts A to C correspond to different velocities of expansion.
Figure 4
 
Psychometric functions for the detection of heading from radial flow at photopic, mesopic, and scotopic light levels. Data of a typical subject are shown. Detection accuracy is plotted as a function of signal intensity. Parts A to C correspond to different velocities of expansion.
Group data are illustrated in Figure 5. Mean thresholds at different light levels for the three applied maximum velocities are shown. Heading perception from radial flow was impaired at dim light levels. Increase in thresholds occurred at all applied velocities of expansion. Similar thresholds were observed under mesopic and scotopic conditions. 
Figure 5
 
Mean detection thresholds for heading from radial flow plotted by light level and velocity of expansion. Error bars indicate standard errors.
Figure 5
 
Mean detection thresholds for heading from radial flow plotted by light level and velocity of expansion. Error bars indicate standard errors.
Biological motion perception
Psychometric functions of a typical subject for biological motion detection at different light levels are plotted in Figure 6. Functions appeared separated, but a decrease of luminance was not associated with a continuous decline in performance. Whereas best performance was achieved under photopic conditions, worst performance occurred under mesopic conditions. The scotopic function lay between both other functions. 
Figure 6
 
Psychometric functions for the detection of biological motion at photopic, mesopic, and scotopic light levels. Data of a typical subject are shown. Detection accuracy is plotted as a function of signal intensity.
Figure 6
 
Psychometric functions for the detection of biological motion at photopic, mesopic, and scotopic light levels. Data of a typical subject are shown. Detection accuracy is plotted as a function of signal intensity.
Figure 7 illustrates group data for biological motion perception at different light levels. We found comparable mean thresholds at photopic and scotopic light levels. In contrast, mesopic thresholds showed a pronounced elevation. 
Figure 7
 
Mean detection thresholds for biological motion plotted by light level. Error bars indicate standard errors.
Figure 7
 
Mean detection thresholds for biological motion plotted by light level. Error bars indicate standard errors.
Discussion
The present study was concerned with the question how motion processing is affected by low light levels. In comparison to previous research, we considered not only translational motion information, but also more complex motion types. We determined detection thresholds for coherent motion, heading from radial flow, and biological motion in noise. Light levels were manipulated in order to trigger photopic, mesopic, and scotopic transmission pathways. We found that the effects of light level differ in dependence on the motion type which has to be detected. Results can be discussed with regard to processing characteristics of different motion types and specific vulnerabilities at dim light levels. 
The perception of coherent motion was affected by light level in dependence on stimulus velocity. At a low velocity of 3.3°/s, detection of coherent motion was comparable under cone- and rod-dominated vision. In contrast, the detection of stimuli at faster velocities of 6.6°/s and 13.2°/s, respectively, deteriorated when luminance decreased. These results corroborate to some extent the findings of Grossman and Blake (1999). They applied coherent motion at a velocity of 3.2°/s and reported robust detection thresholds at dim light levels. Unfortunately, they considered higher velocities up to 8.0°/s only in a single observer who showed steady performance independent of luminance condition. Further support that motion analysis at low light levels is particularly impaired for stimuli at higher velocities comes from two other studies. Hammett and colleagues (2007) have described distorted speed perception at speeds above 4°/s, whereas speed perception at lower speeds has been found unaltered. Takeuchi and De Valois (2000) have shown preserved discrimination of velocities below 3°/s but worsened performance for higher velocities. In summary, our data provides evidence that rod-dominated signal transmission does not interfere with perception of coherent motion at low velocities but leads to impairment for stimuli at higher velocities. We agree with the argument of Grossman and Blake (1999) that coherent motion perception relies on spatial pooling of local information and therefore is presumably not compromised by enlarged pooling zones under rod-dominated vision. However, our results suggest that increased temporal integration impairs detection of stimuli at higher velocities. It should be noted that thresholds increased monotonically under mesopic and scotopic conditions. Mesopic light levels were not associated with an outstanding perceptual deficit. 
Heading detection from radial flow might be expected to deteriorate at low light levels for several reasons. Velocity in expanding radial flow patterns increases with increasing distance from the focus of expansion. High velocities dominate and are consequently of particular importance for extraction of heading information. Hence, considering the described effects of light level on coherent motion perception, heading detection appears prone to impairment at low light levels. Furthermore, radial flow patterns possess a complex velocity distribution and heading detection relies on the exact analysis of this profile. Several studies have confirmed deficits in velocity perception under rod-dominated vision (Hammett et al., 2007; Raghuram et al., 2005; Takeuchi & De Valois, 2000). Although these studies differ in experimental details, they agree in their conclusion that velocity analysis suffers from changes in temporal filtering. Our present findings are in line with the assumption that impaired velocity perception might be detrimental to heading perception. Observers showed increasing detection thresholds for heading from radial flow when light intensity decreased. Sensitivity at low light levels was reduced under all velocity conditions. Again, there was no evidence for an outstanding perceptual deficit at mesopic light levels. Although our heading task represents just a limited model for actual heading perception while moving in the environment (for a discussion, see Wurtz, 1998), results indicate that a decrease in light intensity interferes with navigation in space. We suppose that the observed deficits in heading detection under rod-dominated vision are mainly due to an impaired analysis of the complex velocity distribution in the radial flow field. 
The effect of light level on biological motion perception differed considerably from the deterioration of performance in both other motion tasks. Whereas the change from photopic to mesopic light levels resulted in elevated detection thresholds, further reduction of luminance was associated with decreasing thresholds. Detection thresholds for biological motion at scotopic conditions matched those under photopic conditions. Grossman and Blake (1999) also investigated biological motion perception at dim light levels but considered only two different light intensities. They reported impaired discrimination performance in a low luminance condition corresponding to the scotopic range. Their data might not be in conflict with the present findings because we chose a lower light level in our scotopic condition, namely, 0.018 cd/m2 (in photopic units) compared to 0.036 cd/m2 (in photopic units) in their study. Although the absolute difference in luminance appears rather small, it is conceivable that it triggers a possible transition between different signal processing mechanisms. Hence, it remains to be resolved which characteristics of rod-dominated vision contribute to the particular course of biological motion perception when light level decreases. 
Considering our biological motion stimulus, it seems unlikely that the detection of signal dots per se is impaired by temporal summation. Signal dots moved at rather low velocities of maximally 4°/s. Detection of basic motion signals at such low velocities was deficient neither in the study of Grossman and Blake (1999) nor in the present study. In addition, perception of low velocities has been shown to be preserved under dim light levels (Hammett et al., 2007; Takeuchi & De Valois, 2000). 
However, biological motion perception might be especially vulnerable to impreciseness of velocity analysis because the extraction of the critical structure relies on its exact temporal dynamics. We suppose that co-activity of rods and cones or rod–cone interaction at mesopic light levels could result in noisy velocity perception. Gegenfurtner and colleagues (1999, 2000) investigated low velocities of 0.5 to 4.0°/s and provided evidence that velocity is perceived significantly slower under rod vision than under cone vision. Simultaneous activity of rods and cones at mesopic light levels might explain the observed threshold elevation. Incongruent velocity information exerts a detrimental effect on the analysis of temporal dynamics so that it becomes more difficult to extract the biological motion structure. Perception under selective rod vision could be expected to be unimpaired because just slower velocities do not change the inherent temporal dynamics of a biological motion structure. Our finding that detection thresholds for biological motion are equivalent under photopic and scotopic conditions supports this expectation. In addition to co-activity of rods and cones, it might also be speculated that rod–cone interaction at mesopic light levels contributes to noisy velocity perception. Several psychophysical studies have demonstrated a transition between the slow and the fast rod pathways near a retinal illuminance of 1 scotopic troland (Sharpe & Stockman, 1999; Stockman et al., 1995, 1991). Therefore, we assume that signal processing in our mesopic condition (8.1 scotopic trolands) involved primarily the fast rod pathway, whereas the slow rod pathway was dominant in our scotopic condition (0.5 scotopic trolands). Transmission via the fast rod pathway is accomplished by rod–cone gaps while cones are still active. The twofold function of cones at mesopic light levels might add further noise to velocity analysis. The potentially detrimental interaction is absent at low scotopic light levels when cone threshold is reached and rod signals are conveyed via the slow pathway involving rod bipolars and A2 amacrine cells. 
The absence of pronounced impairment of coherent motion and radial flow perception under mesopic conditions does not contradict the assumption of conflicting rod–cone activity and resulting noisy velocity perception. Coherent motion detection does not require exact velocity perception. Radial flow analysis relies on complex velocity perception, but includes a large field distribution of signals. Rod–cone conflicts can be expected to be most pronounced for analysis of extrafoveal signals approximately up to 4° eccentricity where rod and cone densities are about equal. With respect to the signal distribution in the radial flow field, the critical area makes just a minor contribution so that velocity information from other areas less prone to rod–cone conflicts might compensate for enhanced noise. Furthermore, radial flow is dominated by high velocities so that the threshold increase due to temporal pooling might mask an additional effect of noisy velocity perception. 
Co-activity of rods and cones or interaction between both receptor types could well account for elevated biological motion thresholds under mesopic conditions; however, our interpretations have to be ultimately confirmed in an experimental design that allows selective stimulation of rods and cones. 
Finally, the observed course of biological motion perception at low light levels can be appraised with regard to the idea of differential involvement of the parvocellular and the magnocellular systems. Temporal areas relevant for biological motion perception receive to a large degree parvocellular input (Nealey & Maunsell, 1994) so that a specific reduction might affect performance. Studies that investigated the relative contribution of rod signals to the both ganglion systems, i.e., whether rod signals feed rather into midget or parasol ganglion cells, have shown that rods provide primarily input to the magnocellular pathway and only very weak input to the parvocellular pathway (Lee, Pokorny, Smith, Martin, & Valberg, 1990; Lee, Smith, Pokorny, & Kremers, 1997; Purpura, Kaplan, & Shapley, 1988; Sun, Pokorny, & Smith, 2001). Our results indicate that biological motion perception is comparable under photopic and scotopic conditions. Hence, a reduction of parvocellular input under rod vision does not seem to result necessarily in reduced performance. 
Conclusion
We conclude that motion processing is affected by light level in dependence on the spatio-temporal characteristics of a specific motion type. Temporal pooling under rod-dominated vision primarily impairs detection of signals at high velocities and complex velocity discrimination. Deficits occur already at mesopic light levels and do not change when luminance decreases further. Furthermore, we suppose that co-activity of rods and cones as well as rod–cone interaction at mesopic light levels contribute to noisy velocity perception. In particular, the analysis of temporal dynamics, e.g., inherent to biological motion stimuli, seems to be vulnerable to interacting rod–cone vision. Since we manipulated light levels and did not control activity of photoreceptors directly, our conclusions have to remain preliminary. The effects of specific transmission mechanisms on motion perception could be clarified further by stimulating rods or cones selectively. Our results provide valuable clues to specific perceptual constraints at low light levels. They suggest that very early retinal signal processing can have complex effects on the perception of different motion types which is generally considered to rely on cortical areas. 
Acknowledgments
This research was supported by the research training group “Neuronal Representation and Action Control—NeuroAct” (DFG 885/1). 
Commercial relationships: none. 
Corresponding author: Jutta Billino. 
Email: jutta.billino@psychol.uni-giessen.de. 
Address: Justus Liebig University Giessen, Experimental Psychology, Otto Behaghel Str. 10F, D-35394 Giessen, Germany. 
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Figure 1
 
Static representation of motion stimuli. Signal dots are shown in gray and noise dots in white for clarification. In the actual stimuli, all dots were white. Coherent motion was defined as horizontal motion of the signal dots either to the right or to the left. In the radial flow stimulus, signal dots expanded with the focus of expansion (FOE) either right or left of the fixation cross. Small gray arrows indicate the motion direction of the signal dots but were not present in the actual stimulus. The biological motion stimulus consisted of a canonical point-light walker embedded in noise dots. It moved as if on a treadmill, facing either to the right or to the left.
Figure 1
 
Static representation of motion stimuli. Signal dots are shown in gray and noise dots in white for clarification. In the actual stimuli, all dots were white. Coherent motion was defined as horizontal motion of the signal dots either to the right or to the left. In the radial flow stimulus, signal dots expanded with the focus of expansion (FOE) either right or left of the fixation cross. Small gray arrows indicate the motion direction of the signal dots but were not present in the actual stimulus. The biological motion stimulus consisted of a canonical point-light walker embedded in noise dots. It moved as if on a treadmill, facing either to the right or to the left.
Figure 2
 
Psychometric functions for the detection of coherent motion at photopic, mesopic, and scotopic light levels. Data of a typical subject are shown. Detection accuracy is plotted as a function of signal intensity. Parts A to C correspond to different velocities of translation.
Figure 2
 
Psychometric functions for the detection of coherent motion at photopic, mesopic, and scotopic light levels. Data of a typical subject are shown. Detection accuracy is plotted as a function of signal intensity. Parts A to C correspond to different velocities of translation.
Figure 3
 
Mean detection thresholds for coherent motion plotted by light level and velocity of translation. Error bars indicate standard errors.
Figure 3
 
Mean detection thresholds for coherent motion plotted by light level and velocity of translation. Error bars indicate standard errors.
Figure 4
 
Psychometric functions for the detection of heading from radial flow at photopic, mesopic, and scotopic light levels. Data of a typical subject are shown. Detection accuracy is plotted as a function of signal intensity. Parts A to C correspond to different velocities of expansion.
Figure 4
 
Psychometric functions for the detection of heading from radial flow at photopic, mesopic, and scotopic light levels. Data of a typical subject are shown. Detection accuracy is plotted as a function of signal intensity. Parts A to C correspond to different velocities of expansion.
Figure 5
 
Mean detection thresholds for heading from radial flow plotted by light level and velocity of expansion. Error bars indicate standard errors.
Figure 5
 
Mean detection thresholds for heading from radial flow plotted by light level and velocity of expansion. Error bars indicate standard errors.
Figure 6
 
Psychometric functions for the detection of biological motion at photopic, mesopic, and scotopic light levels. Data of a typical subject are shown. Detection accuracy is plotted as a function of signal intensity.
Figure 6
 
Psychometric functions for the detection of biological motion at photopic, mesopic, and scotopic light levels. Data of a typical subject are shown. Detection accuracy is plotted as a function of signal intensity.
Figure 7
 
Mean detection thresholds for biological motion plotted by light level. Error bars indicate standard errors.
Figure 7
 
Mean detection thresholds for biological motion plotted by light level. Error bars indicate standard errors.
Table 1
 
Mean detection thresholds for different motion types in dependence on light level. Standard deviations are given in parentheses. N = 6.
Table 1
 
Mean detection thresholds for different motion types in dependence on light level. Standard deviations are given in parentheses. N = 6.
Motion type Light level
Photopic Mesopic Scotopic
Coherent motion
3.3°/s 23.7 (9.3) 24.1 (5.5) 25.3 (11.0)
6.6°/s 12.5 (2.8) 17.4 (2.3) 22.7 (9.1)
13.2°/s 8.1 (0.7) 13.2 (1.6) 18.8 (5.3)
Radial flow
<9.3°/s 16.2 (1.8) 27.7 (5.9) 26.3 (3.9)
<18.6°/s 8.3 (1.8) 18.0 (5.7) 20.1 (3.4)
<37.2°/s 5.0 (1.4) 9.1 (2.3) 10.9 (3.5)
Biological motion
9.4 (2.5) 14.9 (2.2) 10.9 (3.6)
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