Psychological Science http://pss.sagepub.com/

Attentional-Tracking Acuity Is Modulated by Illusory Changes in Perceived Speed Welber Marinovic, Samuel L. Pearce and Derek H. Arnold Psychological Science 2013 24: 174 originally published online 9 January 2013 DOI: 10.1177/0956797612450890 The online version of this article can be found at: http://pss.sagepub.com/content/24/2/174

Published by: http://www.sagepublications.com

On behalf of:

Association for Psychological Science

Additional services and information for Psychological Science can be found at: Email Alerts: http://pss.sagepub.com/cgi/alerts Subscriptions: http://pss.sagepub.com/subscriptions Reprints: http://www.sagepub.com/journalsReprints.nav Permissions: http://www.sagepub.com/journalsPermissions.nav

>> Version of Record - Feb 8, 2013 OnlineFirst Version of Record - Jan 9, 2013 What is This?

Downloaded from pss.sagepub.com at UQ Library on February 9, 2013

Research Article

Attentional-Tracking Acuity Is Modulated by Illusory Changes in Perceived Speed

Psychological Science 24(2) 174­–180 © The Author(s) 2013 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/0956797612450890 http://pss.sagepub.com

Welber Marinovic, Samuel L. Pearce, and Derek H. Arnold University of Queensland

Abstract Many activities, such as driving or playing sports, require simultaneous monitoring of multiple, often moving, objects. Such situations tap people’s ability to attend selected objects without tracking them with their eyes—this is known as attentional tracking. It has been established that attentional tracking can be affected by the physical speed of a moving target. In the experiments reported here, we showed that this effect is primarily due to apparent speeds, as opposed to physical speeds. We used sensory adaptation—in this case, prolonged exposure to adapting stimuli moving faster or slower than standard test stimuli—to modulate perceived speed. We found performance decrements and increments for apparently sped and slowed test stimuli when participants attempted attentional tracking. Our data suggest that both perceived speed and the acuity of attention for moving objects reflect a ratio of responses in low-pass and band-pass temporal-frequency channels in human vision. Keywords attention, tracking, motion adaptation, temporal frequency Received 2/8/12; Revision accepted 4/30/12

Selective attention allows people to prioritize the processing of information concerning particular objects or regions of space. Many activities, such as driving or trying to ensure that none of a gaggle of children do themselves serious harm, require simultaneous monitoring of multiple, often moving, objects. Such situations tap people’s ability to maintain a selected object or location in attention without tracking it with their eyes (Cavanagh & Alvarez, 2005), as it is impossible to fixate multiple objects at once, particularly if they are careering in different directions (Alvarez & Franconeri, 2007; Pylyshyn & Storm, 1988; Viswanathan & Mingolla, 2002). The physical speed of a moving target can have a profound impact on one’s attentional-tracking ability (Alvarez & Franconeri, 2007; Franconeri, Lin, Pylyshyn, Fisher, & Enns, 2008; Pylyshyn & Storm, 1988; Verstraten, Cavanagh, & Labianca, 2000). Evidence suggests that it is not the physical speed of a moving object per se that is important, but rather how this speed affects the likelihood that a similar moving object will be seen at the same (Verstraten et al., 2000) or in a proximate (Franconeri et al., 2008) location. For instance, it becomes impossible to attentionally track a selected element within a repetitive pattern once stimulus motion ensures a repetition rate of approximately 8 Hz (Verstraten et al., 2000). This must reflect a limitation related to attention rather than to motion perception, as humans can see movement at much higher rates of repetition (Verstraten et al., 2000). Moreover,

this finding implicates a relatively low level of visual coding, because mechanisms that are more attuned to repetition rates, as opposed to velocity, tend to be found at lower substrates of the visual hierarchy. There is ambiguity concerning the cause of impaired performance with increasing stimulus speed, as physical movement is not the sole determinant of either perceived speed or perceived repetition rates. Illusory changes can be induced, for instance, by reducing luminance contrast (Snowden, Stimpson, & Ruddle, 1998; Vaziri-Pashkam & Cavanagh, 2008) or by eliminating luminance contrast altogether by having motion signaled solely by color changes (Cavanagh, Tyler, & Favreau, 1984). Subjective changes in speed are also doubly dissociable from measures of sensitivity (Clifford, Arnold, & Wenderoth, 2000). For instance, humans are more sensitive to changes in speed when they are provided with more independent samples of movement, but this manipulation, which enhances sensitivity, has no impact on perceived speed (Clifford et al., 2000; Verghese & Stone, 1996). In contrast, particular patterns of motion look faster than others, but this has no impact on Corresponding Author: Derek H. Arnold, University of Queensland, Perception Laboratory, School of Psychology, McElwain Building, St Lucia 4072, Brisbane, QLD, Australia E-mail: [email protected]

Downloaded from pss.sagepub.com at UQ Library on February 9, 2013

175

Attentional Tracking and Perceived Speed sensitivity to speed changes (Bex, Metha, & Makous, 1999; Clifford et al., 2000). This independence of perceived speed and measures of sensitivity led us to question whether the factor limiting attentional-tracking performance (which can be regarded as a sensitivity measure) is the physical rate of motion across the retina or the perceived rate of motion. Perhaps the most robust manipulation that induces changes in perceived speed is sensory adaptation (see Goldstein, 1957), in which a test stimulus can look faster or slower after a period of preexposure to a stimulus moving at a greater or lesser physical speed (Ledgeway & Smith, 1997; Schrater & Simoncelli, 1998; Stocker & Simoncelli, 2009). We conducted three experiments to demonstrate that the ability to attentionally track a moving object is shaped by illusory changes in perceived speed after adaptation, with performance increments and decrements, respectively, for apparently slowed and sped test stimuli.

Experiment 1: Attentional Tracking and Adaptation Method Eight volunteers participated in Experiment 1: 2 authors (W. M. and S. L. P.) and 6 participants who were naive to the

purpose of the experiment. All had normal or corrected-tonormal visual acuity and color vision. Visual stimuli were generated with Cogent 2000 Graphics (University College London Laboratory of Neurobiology; www.vislab.ucl.ac.uk/cogent.php) running in MATLAB 7.5 software (The MathWorks, Natick, MA). Stimuli were displayed on a 19-in. Sony Trinitron G420 monitor at a resolution of 1,280 × 1,024 pixels and a refresh rate of 60 Hz. Stimuli were viewed from 57 cm with the head placed in a chin rest. Responses were recorded via mouse-button presses. Stimuli consisted of circular rotating patterns comprised of 12 dots. The diameter of each dot subtended 0.88° of visual angle at the retina, and the dots were presented against a black background. The outer diameter of the pattern subtended 7.5°. At test, participants were required to attentionally track 1 of the 12 dots (the target) for an average period of 2.8 s (±300 ms; Fig. 1) as it and the other dots rotated coherently about a central fixation point. Because the dots were evenly spaced and moved at a constant speed against a black background, dot movement generated square-wave modulations of luminance at all points along the circular path of the rotating pattern. Hereafter, we will refer to the rate of these modulations as the

∆ Performance (Ratio)

0.4

12 or 30 s

0.25 s

0.2 0.0 –0.2

Time

–0.4 Slow

0.5 s

Fast

Adaptor Speed

2.5–3.1 s

0.5 s Fig. 1.  Sample trial sequence (left) and tracking performance (right) in Experiment 1. During the adaptation phase of each trial, a circular pattern of 12 dots rotated around a central fixation point for either 12 s or 30 s, alternating every 2 s between clockwise and counterclockwise rotation. After a 0.25-s interstimulus interval, a static test display appeared. The test display consisted of the same circular pattern of dots, except that a target dot was red. After 0.5 s, the circle of dots began rotating (clockwise, as shown here, or counterclockwise on alternate trials), and after another 0.2 s, the red target dot turned white. The pattern stopped rotating after an average of 2.8 s, and then one of the dots turned red. Participants judged whether the red dot in the final display was the target dot. The graph shows the mean change in tracking performance as a function of adaptor speed. Change in performance was measured by calculating the ratio between estimates from baseline blocks and estimates from adaptation blocks and then subtracting 1, so that positive values show that performance improved relative to baseline, whereas negative values signify impaired performance. Error bars show standard errors of the mean. Downloaded from pss.sagepub.com at UQ Library on February 9, 2013

176

Marinovic et al.

stimulus’s temporal frequency. Before the dots started moving, they were static for 0.5 s. The target dot was colored red (luminance = 24 cd/m2) during this period and for another 0.2 s after motion onset, after which it became identical in appearance to the other dots (white; luminance = 99 cd/m2). At the end of the tracking period, the dots stopped revolving, and either the target or one of two dots adjacent to the target became red. The participant was required to judge whether the final highlighted dot was the target (red at the beginning of the trial) or whether it preceded or followed the target—a three-alternative forced-choice task. In a preliminary procedure, we determined temporalfrequency thresholds for attentional tracking using the method of constant stimuli. In this procedure, the dots rotated in a predictable direction on each trial (alternating every 2 s between clockwise and counterclockwise) but at an unpredictable temporal frequency (3–12.5 Hz). A complete run of trials involved 8 presentations of each of 10 temporal frequencies (80 trials in total). A logistic function was fitted to the resulting distribution of correct target/nontarget discrimination as a function of the temporal frequency of the test stimuli, and the 67% point on the fitted function (midway between chance and perfect performance) was taken as an estimate of the participants’ temporal-frequency threshold for attentional tracking. Following the preliminary procedure, in Experiment 1, participants completed the attentional-tracking task for test stimuli rotating at their threshold for attentional tracking. In different blocks of trials, they did so for test stimuli viewed either with preexposure to an adapting stimulus (adaptation runs) or without preexposure to an adapting stimulus (baseline runs). All participants completed two sets of three blocks of trials. The first of each set of three blocks was a baseline run. This block was followed by two blocks of adaptation trials, one for fast adaptation and one for slow adaptation. During adaptation blocks, adapting stimuli were presented for 30 s on the first trial and for 12 s on subsequent trials. During adaptation, the direction of rotation alternated every 2 s to mitigate the buildup of a strong directional motion aftereffect at test. Half of the participants viewed a fast adaptor (animated at twice the participants’ tracking threshold) followed by a slow adaptor (animated at half the participants’ tracking threshold), and the other participants viewed a slow adaptor followed by a fast adaptor. Adaptor order was then reversed for each participant in the subsequent set of three trial blocks. Each block of trials consisted either of 40 individual trials (baseline runs) or 20 individual trials (adaptation runs). Each participant therefore completed 80 baseline trials and 40 trials each for slow and fast adaptation.

Results and discussion On average, the threshold for attentional tracking corresponded with a temporal frequency of approximately 7.3 Hz (SD = 1.7), which is broadly consistent with previous estimates (Verstraten et al., 2000). In the subsequent Experiment 1, we found that

adaptation to slow motion impaired performance (52% correct, SE = 4%) relative to baseline performance (67% correct, SE = 2%), in which participants viewed test stimuli without prior adaptation, t(7) = 3.68, p = .008, r = .81 (see Fig. 2a). Adaptation to fast motion improved performance (78% correct, SE = 5%) relative to baseline performance, t(7) = 2.69, p = .030, r = .71 (see Fig. 1). Adaptation-induced changes in tracking performance were consistent with the impact of adaptation on perceived speed. Subjectively, adapting to fast motion made test stimuli seem to move more slowly than they did without adaptation, and this effect was accompanied by improved tracking. Adapting to slow motion made test stimuli seem to move more rapidly than they did without adaptation, and this effect was associated with impaired performance. To facilitate a comparison between these illusory speed changes and matched physical speed changes, we quantified the magnitudes of illusory speed change in Experiment 2.

Experiment 2: Perceived Speed and Adaptation Method Methodological details for Experiment 2 were as for Experiment 1, with the following exceptions. Seven volunteers from Experiment 1, including 2 authors (W. M. and S. L. P.), participated. The adapting stimulus was a left-side semicircular pattern of dots presented on the left of fixation (see Fig. 2). In effect, the right side of the circular adapting stimulus used in Experiment 1 was obscured with a black overlay. The eccentricity of the dots within the adapting stimulus, relative to fixation, was 3.7°. The dots rotated, centered on fixation, in the same manner as in Experiment 1. At test, participants were presented with two successive stimuli. Both were matched in appearance to the adapting stimulus, with dots arranged to form a left-side semicircle (see Fig. 2). This configuration was presented in both the adapted location (the standard test stimulus) and in an unadapted location to the right of fixation (the comparison test stimulus). The comparison stimulus was both displaced from the adapted location and was inconsistent with a rotating stimulus centered on fixation. This was done to prevent the adaptation of mechanisms selective for rotation around fixation (Bex et al., 1999; Snowden & Milne, 1996) from affecting the apparent speed of the comparison stimulus, thereby making the comparison stimulus useful for assessing changes in apparent standardstimulus speed. Dots within the comparison stimulus were positioned at the same average eccentricity relative to fixation as in the standard stimulus. On any given trial, standard and comparison stimuli rotated in the same direction, clockwise or counterclockwise. Order of presentation (standard then comparison stimulus, or comparison then standard stimulus) was randomized on a trial-by-trial basis. Participants were required to judge which presentation was faster, a two-alternative forced-choice task.

Downloaded from pss.sagepub.com at UQ Library on February 9, 2013

177

Attentional Tracking and Perceived Speed

∆ Perceived Speed (Ratio)

0.4

12 or 30 s

0.5 s

0.2 0.0 –0.2

Time

–0.4 Slow

1s

Fast

Adaptor Speed

0.5 s

1s Fig. 2.  Sample trial sequence (left) and perceived speed of test stimuli (right) in Experiment 2. During the adaptation phase of each trial, a semicircular pattern of dots rotated around a central fixation point for either 12 s or 30 s, alternating every 2 s between clockwise and counterclockwise rotation. After a 0.5-s interstimulus interval, two test stimuli appeared consecutively. The two test stimuli consisted of the same semicircular pattern either rotating around the fixation point as during adaptation (the standard stimulus) or rotating around a point to the right of fixation (the comparison stimulus). Both test stimuli rotated in the same direction on each trial, and participants judged which stimulus was faster. The graph shows the mean change in perceived speed of the test stimulus after adaptation as a function of the average speed of the test stimulus before adaptation. Change in performance was measured by calculating the ratio between estimates from baseline blocks and estimates from adaptation blocks and then subtracting 1, so positive values show that perceived speed increased relative to baseline, whereas negative values signify reductions in perceived speed. Error bars show standard errors of the mean.

Standard and comparison test stimuli were presented for 1 s, separated by a blank interstimulus interval (ISI) of 0.5 s. On adaptation trials, there was a 0.5-s ISI between the adaptor and the first test stimulus. Standard stimuli moved at the same speed across trials (the participants’ temporal threshold for attentional tracking), whereas the speed of comparison stimuli was manipulated relative to the standard stimuli according to the method of constant stimuli. A complete block of trials involved eight presentations at each of seven comparison speeds (0.25, 0.5, 0.75, 1.0, 1.25, 1.5, and 1.75 times the speed of the standard stimulus), which yielded 56 trials in total. As in Experiment 1, participants completed two sets of three blocks of trials in the order in which they were completed in Experiment 1. Data were collated across blocks of trials to provide distributions of apparent standard-stimulus speed as a function of the comparison-stimulus speed. Estimates of the comparison-stimulus speed perceptually matched to the standard-stimulus speed were given by the 50% point on logistic functions fitted to these distributions. For each adaptor speed, the effects of adaptation were measured by calculating

the ratio between estimates from baseline blocks and estimates from adaptation blocks and then subtracting 1, with a negative value therefore signifying that the standard stimulus (adapted location) seemed to move relatively slowly after adaptation.

Results and discussion Adaptation to slow motion caused an increase in perceived speed of the test stimulus relative to baseline trials (+19%, SE = 0.8%), t(6) = 7.92, p = .0002, r = .95 (see Fig. 2), whereas adaptation to fast motion made the test stimulus seem to move slower relative to baseline trials (–20%, SE = 0.8%), t(6) = 6.47, p = .0006, r = .92. As predicted, Experiment 2 demonstrated that our adapting stimulus caused changes not only in participants’ ability to track moving targets, but also in the targets’ perceived speed. In Experiment 3, we mimicked these illusory, adaptationinduced speed changes by presenting test stimuli at physical speeds matched to apparent postadaptation speeds of test stimuli from Experiment 2.

Downloaded from pss.sagepub.com at UQ Library on February 9, 2013

178

Marinovic et al.

Experiment 3: Attentional Tracking and Physical Speed Changes Method Methodological details for Experiment 3 were as for Experiment 1, with the following exceptions. Seven volunteers from Experiment 1, including two authors (W. M. and S. L. P.), participated in Experiment 3. In Experiment 3, there was no adaptation phase. Participants performed the tracking task for test stimuli animated at three different physical speeds: baseline, fast, and slow. The speed of the dots during baseline blocks of trials was matched to each participant’s temporal threshold for attentional tracking, as determined during the preliminary procedure in Experiment 1. The speed of the dots during fast and slow blocks of trials was matched to the apparent speeds of standard stimuli after adaptation to slow and fast motion, respectively, in Experiment 2. A block of trials consisted of 60 individual trials, with test stimuli moving at a constant speed. The order in which six blocks of trials (two for each of the three experimental conditions) were completed was randomized for each participant. The test stimulus’s direction of rotation was predictable from trial to trial (alternating between clockwise and counterclockwise). The effects of physical differences in speed were quantified by the percentage of correct responses for each condition.

Results and discussion As Figure 3 shows, physically fast test stimuli were harder to track than were baseline stimuli (baseline performance = 61% correct, SE = 5%; fast-stimuli performance = 43% correct,

∆ Performance (Ratio)

0.4 0.2 0.0 –0.2 –0.4 Fast

Slow

Test-Stimulus Speed Fig. 3. Results from Experiment 3: mean change in attentional tracking performance as a function of physical speed changes. Change in performance was measured by calculating the ratio between estimates from baseline blocks and estimates from blocks of trials in which physical test-stimulus speeds were adjusted to match apparent test-stimulus speeds after adaptation to slow and fast motion, respectively, in Experiment 2. A value of 1 was then subtracted from these ratios, so positive values show that performance improved relative to baseline, whereas negative values signify reductions in performance. Error bars show standard errors of the mean.

SE = 4), t(6) = 2.74, p = .033, r = .72, whereas slow test stimuli were easier to track than baseline stimuli (slow-stimuli performance = 78% correct, SE = 8%), t(6) = 4.32, p = .004, r = .85. Moreover, there was no difference in tracking performance after adaptation to fast motion in Experiment 1 and for the physically slow test stimuli that mimicked this condition in Experiment 3 (mean difference = 0.03%), t(6) = 0.18, p = .85, r = .06. Nor was there any difference between tracking performance after adaptation to slow motion in Experiment 1 and for the physically fast test stimuli that mimicked this condition in Experiment 3 (mean difference = 9%), t(6) = 1.13, p = .21, r = .39. These data show that the ability to attentionally track a moving element in a repetitive display is better predicted by the encoded or perceived speed of test stimuli, as opposed to their physical speed.

General Discussion Our data show that the ability to attentionally track a moving element is modulated by illusory changes in speed. In Experiment 1, we found that adaptation to dots moving faster than those in test stimuli resulted in an improvement of approximately 20% in tracking performance, whereas adaptation to dots moving slower than those in test stimuli resulted in a decrement of approximately 20%. In Experiment 2, we established that these tracking-performance changes were consistent with illusory adaptation-induced changes in speed, as adaptation to faster motion made test stimuli appear to move approximately 20% slower, whereas adaptation to slower motion made test stimuli appear to move approximately 20% faster. In Experiment 3, we found that changes in tracking performance for physically sped or slowed test stimuli were commensurate with performance modulations from matched illusory speed changes. Overall, these data establish that attentional-tracking performance for moving items in a repetitive display is better predicted by perceived, as opposed to physical, test-stimulus speeds. We believe that our data can be explained via a simple ratio model that can successfully account for adaptation-induced changes in perceived speed (Hammett, Champion, Morland, & Thompson, 2005; Perrone, 2005). In human vision, direction-selective mechanisms receive input from nondirectional cells that respond differentially to luminance modulation over time (Foster, Gaska, Nagler, & Pollen, 1985; Tolhurst & Movshon, 1975). Such cells respond well to luminance flicker, such as that generated at points in our test display by white dots rotating against a black background. These cells are often referred to as spatiotemporal filters, and they can be taken to provide input for at least two temporal-frequency channels (Cass & Alais, 2006; Hess & Snowden, 1992). One channel can be thought of as temporally low pass, as it responds maximally to slower and progressively less to higher rates of luminance flicker. The other can be thought of as a band-pass channel, as it responds minimally to very low rates, maximally to frequencies in the midrange, and progressively less to

Downloaded from pss.sagepub.com at UQ Library on February 9, 2013

179

Attentional Tracking and Perceived Speed higher rates of luminance flicker. The differential responses of these channels means that a temporal-frequency estimate can be determined by taking the ratio of responses in these two channels to any given input. Figure 4a depicts responses in an unadapted low-pass channel and in a single unadapted band-pass channel as a function of input temporal frequency. These can be contrasted with the scenario depicted in Figure 4b, in which the band-pass channels’ response has been selectively mitigated via adaptation to a high temporal frequency. This increases the response of the low-pass relative to the band-pass channel across a broad range of frequencies. Hypothetically, as perceived temporal frequency is signaled via the ratio of responses in these two channels, this tends to shift encoded temporal frequency toward slower rates of change. Thus adaptation to fast motion can result in an illusory reduction in apparent temporal frequency. The reverse situation is shown in Figure 4c, with a selective reduction in the response of the low-pass channel tending to cause an illusory increase in encoded temporal frequency. We believe that the type of ratio model depicted in Figure 4 (Hammett et al., 2005; Perrone, 2005) can also account for our attentional-tracking data if one considers another facet of the mechanisms that provide input to low- and band-pass temporal-frequency channels. As the name suggests, spatiotemporal filters respond not only to input that varies over time, but also to input that varies over space. The cells that respond to slow rates of change over time tend to be responsive to more tightly localized changes in space relative to cells that respond to higher temporal frequencies (Burr & Ross, 1982; Gegenfurtner, Kiper, & Levitt, 1997). Individuating an element from within a repetitive pattern will therefore likely depend on a robust contribution from temporally low-pass mechanisms. Selectively mitigating the contribution of these mechanisms (e.g., via adaptation to slow motion) is therefore likely to make element individuation, which is essential for attentional

tracking, more difficult. Thus, adaptation to slow motion would cause both an increase in perceived speed and a decrease in the ability to attentionally track an individual moving element within a repetitive pattern. By contrast, band-pass temporal-frequency-tuned cells tend to respond to input from across relatively broad regions of space (Gegenfurtner et al., 1997). Thus, although these mechanisms make it possible to discern motion when a stimulus generates high rates of temporal flicker, they are less suited for the task of individuating a moving element within a repetitive pattern. Consequently, mitigating their relative contribution (e.g., by adapting to fast motion) will tend to both decrease apparent speed and improve one’s ability to attentionally track an individual moving element. Our data relate specifically to the ability to use attention to track just one of a number of repetitive moving elements. We believe that attentional tracking is shaped by adaptation because analyses are biased either in favor of a system that integrates motion signals across relatively broad spatial regions after adaptation to slow motion or in favor of a system that integrates signals across small spatial regions after adaptation to fast motion. Our data may therefore have implications for other attentional-tracking tasks, in which people attempt to track multiple items at once, as such tasks also rely on individuating moving objects (Pylyshyn & Storm, 1988). However, attentionally tracking multiple moving objects concurrently might also tap additional cognitive processes relative to attempts to track just one object (see Holcombe & Chen, 2012). Hence, we predict that multiple-object tracking will be affected by adaptation, but perhaps to a lesser degree than will single-object tracking. Our data suggest that the ability to attentionally track one of a number of repetitive moving elements is better predicted by the encoded, or perceived, rate of change as opposed to the physical input. This observation is reminiscent of recent data on crowding, which shows that human ability to individuate

Low-Pass Channel Band-Pass Channel

Response Magnitude

a

No Adaptor

b

Fast Adaptor

c

Slow Adaptor

1.0 0.8 0.6 0.4 0.2 0.0 1

10

Temporal Frequency (Hz)

1

10

Temporal Frequency (Hz)

1

10

Temporal Frequency (Hz)

Fig. 4.  Schematics illustrating a ratio model: responses of low-pass and band-pass temporal-frequency channels as a function of input frequency. Scenarios are shown separately for responses following (a) no adaptation, (b) adaptation to fast motion, and (c) adaptation to slow motion. Downloaded from pss.sagepub.com at UQ Library on February 9, 2013

180

Marinovic et al.

static elements from flanking stimuli is shaped by illusory position changes (Dakin, Greenwood, Carlson, & Bex, 2011; Maus, Fischer, & Whitney, 2011). Our data are also consistent with the observation that perception depends on a combination of responses from distinct spatiotemporal mechanisms and with the observation that these mechanisms are capable of generating conflicting perceptual signals that can engage in a form of perceptual rivalry (Arnold, Erskine, Roseboom, & Wallis, 2010). Declaration of Conflicting Interests The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article.

References Alvarez, G. A., & Franconeri, S. L. (2007). How many objects can you track? Evidence for a resource-limited attentive tracking mechanism. Journal of Vision, 7(13), Article 14. Retrieved from http://www.journalofvision.org/content/7/13/14 Arnold, D. H., Erskine, H. E., Roseboom, W., & Wallis, T. S. (2010). Spatiotemporal rivalry: A perceptual conflict involving illusory moving and static forms. Psychological Science, 21, 692–699. Bex, P. J., Metha, A. B., & Makous, W. (1999). Enhanced motion aftereffect for complex motions. Vision Research, 39, 2229–2238. Burr, D. C., & Ross, J. (1982). Contrast sensitivity at high velocities. Vision Research, 22, 479–484. Cass, J., & Alais, D. (2006). Evidence for two interacting temporal channels in human visual processing. Vision Research, 46, 2859– 2868. Cavanagh, P., & Alvarez, G. A. (2005). Tracking multiple targets with multifocal attention. Trends in Cognitive Sciences, 9, 349–354. Cavanagh, P., Tyler, C. W., & Favreau, O. E. (1984). Perceived velocity of moving chromatic gratings. Journal of the Optical Society of America A: Optics, Image Science, and Vision, 1, 893–899. Clifford, C. W., Arnold, D. H., & Wenderoth, P. (2000). Dissociable factors affect speed perception and discrimination. Clinical & Experimental Ophthalmology, 28, 230–232. Dakin, S. C., Greenwood, J. A., Carlson, T. A., & Bex, P. J. (2011). Crowding is tuned for perceived (not physical) location. Journal of Vision, 11(9), Article 2. Retrieved from http://www.journalof vision.org/content/11/9/2 Foster, K. H., Gaska, J. P., Nagler, M., & Pollen, D. A. (1985). Spatial and temporal frequency selectivity of neurones in visual cortical areas V1 and V2 of the macaque monkey. Journal of Physiology, 365, 331–363. Franconeri, S. L., Lin, J. Y., Pylyshyn, Z. W., Fisher, B., & Enns, J. T. (2008). Evidence against a speed limit in multiple-object tracking. Psychonomic Bulletin & Review, 15, 802–808. Gegenfurtner, K. R., Kiper, D. C., & Levitt, J. B. (1997). Functional properties of neurons in macaque area V3. Journal of Neurophysiology, 77, 1906–1923.

Goldstein, A. G. (1957). Judgments of visual velocity as a function of length of observation time. Journal of Experimental Psychology, 54, 457–461. Hammett, S. T., Champion, R. A., Morland, A. B., & Thompson, P. G. (2005). A ratio model of perceived speed in the human visual system. Proceedings of the Royal Society of London B: Biological Sciences, 272, 2351–2356. Hess, R. F., & Snowden, R. J. (1992). Temporal properties of human visual filters: Number, shapes and spatial covariation. Vision Research, 32, 47–59. Holcombe, A. O., & Chen, W. Y. (2012). Exhausting attentional tracking resources with a single fast-moving object. Cognition, 123, 218–228. Ledgeway, T., & Smith, A. T. (1997). Changes in perceived speed following adaptation to first-order and second-order motion. Vision Research, 37, 215–224. Maus, G. W., Fischer, J., & Whitney, D. (2011). Perceived positions determine crowding. PLoS ONE, 6(5), e19796. Retrieved from http://www.plosone.org/article/info%3Adoi%2F10.1371%2 Fjournal.pone.0019796 Perrone, J. A. (2005). Economy of scale: A motion sensor with variable speed tuning. Journal of Vision, 5(1), Article 3. Retrieved from http://www.journalofvision.org/content/5/1/3 Pylyshyn, Z. W., & Storm, R. W. (1988). Tracking multiple independent targets: Evidence for a parallel tracking mechanism. Spatial Vision, 3, 179–197. Schrater, P. R., & Simoncelli, E. P. (1998). Local velocity representation: Evidence from motion adaptation. Vision Research, 38, 3899–3912. Snowden, R. J., & Milne, A. B. (1996). The effects of adapting to complex motions: Position invariance and tuning to spiral motions. Journal of Cognitive Neuroscience, 8, 435–452. Snowden, R. J., Stimpson, N., & Ruddle, R. A. (1998). Speed perception fogs up as visibility drops. Nature, 392, 450. Stocker, A. A., & Simoncelli, E. P. (2009). Visual motion aftereffects arise from a cascade of two isomorphic adaptation mechanisms. Journal of Vision, 9(9), Article 9. Retrieved from http://www .journalofvision.org/content/9/9/9 Tolhurst, D. J., & Movshon, J. A. (1975). Spatial and temporal contrast sensitivity of striate cortical neurones. Nature, 257, 674–675. Vaziri-Pashkam, M., & Cavanagh, P. (2008). Apparent speed increases at low luminance. Journal of Vision, 8(16), Article 9. Retrieved from http://www.journalofvision.org/content/8/16/9 Verghese, P., & Stone, L. S. (1996). Perceived visual speed constrained by image segmentation. Nature, 381, 161–163. Verstraten, F. A., Cavanagh, P., & Labianca, A. T. (2000). Limits of attentive tracking reveal temporal properties of attention. Vision Research, 40, 3651–3664. Viswanathan, L., & Mingolla, E. (2002). Dynamics of attention in depth: Evidence from multi-element tracking. Perception, 31, 1415–1437.

Downloaded from pss.sagepub.com at UQ Library on February 9, 2013