Eq. 1
Draft version: Do not quote. Comments are welcome.
Please send correspondence to: Thomas A. Busey
Department of Psychology
Indiana University
Bloomington, IN 47405
email: busey@indiana.edu
The temporal frequencies underlying simultaneous character localization and identification tasks are measured as a test of the hypothesis that the two tasks are processed in different cortical pathways. With peripheral presentations, localization depends upon much higher temporal frequencies than character identification. These differences disappear with foveal presentations. The effects cannot be attributed to task demands or the use of different spatial frequencies. The results are consistent with a physiological model that suggests that temporal differences appear early in the visual processing stream and remain partially segregated through the primary visual cortex and on to the parietal and temporal lobe pathways. Implications for physiological research and accounts of dyslexia are discussed.
Much of the work on the properties of human vision and perception has been done with simple stimuli such as sine wave gratings or disks, with the intention of characterizing the fundamental visual mechanisms. The hope is that these findings will then generalize to more complex stimuli such as letters, objects or faces. One step in this generalization process involves asking whether processing simple aspects of an object such as its location or onset is somehow different than processing its identity. Evidence from neurophysiological work in macaques and brain imaging scans of humans suggests that object identity and location are performed in different brain areas and may receive inputs from different classes of early visual mechanisms. The goal of the present work is to determine the conditions under which tasks such as character identification and localization may rely on different sources of visual information, as suggested by neurophysiological and neuroarchitectural findings. These findings can be then used to evaluate the functional relevance of processing differences in different brain areas that have been described by single-cell recording and animal lesion experiments.
Recent neuroscience evidence suggests that visual information is processed along a pathway leading dorsally out of primary visual cortex to the parietal lobe and a second pathway leading ventrally to the temporal lobes. Monkeys with parietal or temporal lobe lesions give the impression that these different brain areas are responsible for different stimulus attributes (e.g. Ungerleider & Mishkin, 1982). Parietal lobe lesions impair the animals ability to learn to locate a well containing food, while temporal lobe lesions impair the animals ability to learn to identify an object placed near a food well (Pohl, 1973). In addition, many of the parietal lobe cells exclude the fovea in their receptive fields (Motter & Mountcastle, 1981) and appear to depend on inputs from the peripheral visual field. However, comparisons across the different tasks are difficult to make (Merigan & Maunsell, 1993), and many of the effects are weak and recoverable. Stronger evidence comes from functional brain imaging studies in humans, which show a double dissociation of the two pathways based on a face-matching or spatial vision task (Haxby, Grady, Horwitz, Ungerleider, Mishkin et al., 1991) and face identification or dot localization tasks (McIntosh, Grady, Ungerleider, Haxby, Rapoport, & Horwitz, 1994). Attending to the color or speed of motion of a stimulus also produces differential activation in the superior temporal sulcus and the inferior parietal lobe respectively (Corbetta, Miezin, Dobmeyer, Shulman & Petersen, 1991), although such differences were not as strong in experiments that isolated the pathways using different stimuli. The human lesion data also supports a processing distinction: parietal lobe lesions often cause hemifield neglect and a disruption of spatial processing (e.g. Bisiach & Vallar, 1988), while temporal lobe lesions often result in object or face recognition deficits (e.g. Landis, Regard, Bliestle & Kleihues, 1988).
The visual information contributing to the parietal and temporal lobe pathways may begin to segregate in the primary visual cortex and maintain a partial segregation through to the parietal and temporal lobe pathways (Merigan and Maunsell, 1993). If so, we might expect to see evidence of these early contributions in the form of the information used in different tasks. For example, the direction and non-direction selective cells in area V1 differ in their temporal response properties. Direction and non-direction selective cells all have similar preferred and high cutoff frequencies, but direction selective cells are almost entirely band-pass while non-direction selective cells appear equally distributed as either band-pass or low-pass (Hawken, Shapley & Grosof, 1996). As a population the direction selective cells carry higher temporal frequencies, and if these pathways remain at least partially segregated, they would propagate these different temporal frequencies through to later visual areas and on to the parietal lobe. Under this model we would that tasks performed by the parietal lobe would show evidence of this contribution of higher temporal frequencies.
This provides the central question of the current work: do tasks that are thought to be selectively processed in the temporal and parietal lobe pathways such as character identification and localization rely on different temporal frequencies? If so, this suggests several possibilities. First, processing differences might reflect differences in the tasks, such that localization could make use of higher temporal frequencies while identification could not. Second, observed differences in the temporal frequencies used in each task might reflect differences in the spatial frequencies that underlie a task. Third, such differences might reflect the response properties of different cortical areas that subserve each task. Both might receive the same input from the striate areas of the visual cortex, but higher temporal frequencies might be preserved in the parietal pathways and lost in the temporal pathways. Fourth, differences might reflect different contributions of earlier visual pathways, such that cells that maintain higher temporal frequencies in area V1 might remain segregated throughout the extra-striate regions of visual cortex and selectively influence the parietal lobe pathway. Before addressing these issues, however, we first need to ascertain whether different tasks do indeed rely on different temporal frequencies.
The notion of different parallel pathways such as the direction and non-direction selective neurons in V1 is often refereed to as labeled detectors within the psychophysical literature (e.g. Watson and Robson, 1981). Because we report behavioral data, we adopt this language as well. A stimulus is assumed to be processed by a set of labeled detectors (for example, detectors tuned to a specific spatio-temporal frequency). Higher-level visual areas that receive input from several different classes of pathways are capable of distinguishing between different types of detectors, as well as the overall output from each type of detector. Under the labeled detector hypothesis, a stimulus will be detected if a single detector or channel responds, although such a response will not differentiate between two stimuli (such as two gratings at different spatial frequencies) if only one detector responds. However, if two stimuli are mediated by different sets of labeled detectors, then the set of responding detectors uniquely discriminates between the stimulus.
Tests of this proposition involve collecting simultaneous detection and identification thresholds: if two stimuli can be differentiated as easily as they are detected, then they are mediated by different sets of labeled detectors. When asking observers to detect or differentiate different temporal frequencies, Watson and Robson (1981) found evidence of two labeled temporal frequency detectors. Interestingly, for the low-temporal frequency detectors they estimated that seven distinct sets of spatial frequency labeled detectors exist, while for high temporal frequency detectors only three sets of spatial frequency labeled detectors exist. Based on this they suggested that the high-spatial frequency labeled detectors have poorer spatial acuity resulting from much more broadly tuned spatial frequency channel bandwidths. These labeled detectors may be the psychophysical analog to the cell classes exhibiting different spatio-temporal response properties in area V1 (Hawken et. al, 1996).
The conclusions from physiological and brain imaging studies that suggest that different stimulus attributes are processed by different neural mechanisms has received some support from psychophysical studies. Articles by Tolhurst (1973) and Kulikowski and Tolhurst (1973) suggest the existence of separate "flicker-detecting" and "pattern-detecting" mechanisms. When detection was mediated by what they describe as a sustained system, the spatial pattern is evident but not the temporal variation; when a transient system mediates detection, the temporal variation is apparent but the spatial structure is not. These articles were criticized by Lennie (1980) and Derrington and Henning (1980), suggesting that pattern information is available even when participants are not aware of using spatial pattern as a cue. Thus it appears that detectors that contain high temporal frequencies also process pattern information to some degree. Gorea (1986) examined the temporal properties of the detection and discrimination of low and high spatial frequency gratings in the fovea. He found no differences in the temporal frequencies used to detect or identify a grating. Thus, the available evidence suggests the existence of labeled detectors or channels supporting different spatio-temporal frequency ranges, but the mechanisms that support higher temporal frequencies appear to process fairly high spatial frequencies as well.
Several points may be derived from the relevant psychophysical and neuroanatomical literature. First, evidence for the existence of labeled detectors processing different spatial and temporal frequency ranges is compelling, and cells in V1 with different spatial and temporal frequency response properties may provide a conduit for these different sets of detectors. Second, cortical pathways leading out of the visual cortex appear to process different aspects of the stimulus, such as the object's identity or location. Third, several authors have suggested that the early visual mechanisms in striate cortex provide only that information required by later mechanisms. Watson, Ahumada & Farrell (1986) suggest that different "windows of visibility" are made available to different cortical pathways, such that the parietal lobe pathway might take advantage of high temporal frequencies (associated with rapid velocities) while the temporal lobe pathway may be limited to lower temporal frequencies that provide a more stable percept. Finally, temporal frequency properties that begin in the direction and non-direction selective cells in area V1 may propagate through extrastriate areas and remain segregated to selectively influence the parietal and temporal lobe pathways.
Previous experiments that have examined the contributions of different sets of labeled detectors have either compared relatively simple tasks (pattern sensitivity vs. flicker detection, e.g. Kulikowski and Tolhurst, 1973) or have altered the physical stimulus to selectively isolate a cortical pathway (PET studies, e.g. Haxby et. al. 1991). Unfortunately very little research has examined the temporal frequencies underlying localization tasks, and none have compared localization and identification tasks directly. The current experiments are designed to provide evidence that different sets of labeled detectors carrying different ranges of temporal frequencies, possibly subserved by different classes of V1 cells, contribute to tasks that are processed in the parietal and temporal lobe pathways.
The general experimental strategy is as follows. Each experiment measures the temporal frequencies underlying character localization and character identification tasks, which are thought to selectively isolate the parietal and temporal lobe pathways (McIntosh et al., 1994; Haxby et al. 1991; Corbetta et al. 1991). Differences in the temporal frequencies used in each task support the hypothesis that they receive input from different sets of labeled detectors, possibly resulting from different contributions of separate classes of neurons in V1.
It may seem strange to measured the temporal frequencies underlying a character identification task when such a task in inherently defined by the spatial pattern of the stimulus. However, in addition to examining the contributions of different sets of labeled detectors to each task, the data from such experiments also bears on the issue of the role of a possible loss of high temporal frequency perception in persons with dyslexia. Evidence suggesting a correlation between dyslexia and a loss of high temporal frequencies has been reported from a number of sources (e.g. Lovegrove, Garzia & Nicholson, 1990; Williams, M. C. & Leclusyse, 1990) but a causal mechanism has not yet been identified. Lovegrove et al, (1990) have attempted to like this loss to a possible disruption of the magno pathway, but so far strong conclusions are difficult to reach without neuroanatomical evidence to support this claim. In addition, single-cell recordings made in LGN and the input layers to area V1 reveal no differences in the temporal response properties of magno and parvo cells (Hawken et al 1996). While this casts doubt on the magno hypothesis, such a selective deficit may occur higher up in the visual pathways. The current experiments characterize the temporal frequency information involved in the two basic mechanisms underlying reading: localizing characters in visual space, and identifying the letters. Once these frequencies are identified, we can then begin to address why a deficit in high temporal frequencies would affect different aspects of reading, and suggest possible deficits in the neural mechanisms that underlie dyslexia. We will return to this issue in the General Discussion.
Before describing the methods used in the current experiment, a cautionary note is in order. The motivation for the current experiments comes from neuroanatomical and brain imaging studies that suggest that different brain areas or pathways process different stimulus attributes. If localization relies on higher temporal frequencies than identification, this would be consistent with an anatomical model that suggests that temporal differences begin early in the visual cortex and remain segregated throughout the extrastriate areas. However, other anatomical models are also consistent with this finding, and links between behavioral data and anatomical models are difficult to make (e.g. Teller, 1984). As a result, we will be able to demonstrate which anatomical models are consistent with the behavioral data. These experiments can also be used to demonstrate the functional relevance of any temporal frequency differences observed in different cortical areas, and suggest future neurophysiological studies.
Several different experimental methods allow inference of the temporal frequencies underlying performance in a given task. The standard technique involves flickering a stimulus at different temporal frequencies around a gray background. This temporal contrast sensitivity (TCS) experiment involves experimentally adjusting the contrast of the stimulus such that a performance criterion is met. While the temporal contrast sensitivity function provides a direct estimate of the temporal frequencies underlying a task, measuring it requires long stimulus presentations of 500 ms or more, which introduces the possibility of contaminating eyemovements. The Two-Pulse technique is a more recent design proposed by Ikeda (1965), and has a number of advantages over the TCS paradigm. The current work adopts both techniques, with the primary emphasis on the Two-Pulse paradigm.
In the Two-Pulse paradigm, a stimulus is presented twice, in the same location, separated by a variable interstimulus interval (ISI). Typically the pulses are short duration, ranging from 2 to 30 ms (e.g. Ikeda, 1965; 1986). The first pulse engenders a response in the visual system, and for short ISI's the response to the second pulse will interact with the persisting first-pulse response. Two different pulse streams are used. In the positive/positive condition, both stimulus pulses are the same contrast (e.g. light gray pulse followed by a second light gray pulse on a gray background). The positive/negative condition reverses the polarity of the second pulse (e.g. a light gray pulse followed by a dark gray pulse). This second condition improves the parameter estimation stage that is required to recover the temporal frequencies used in a task, and demonstrates evidence for temporal inhibition (Watson, 1986). In typical Two-Pulse tasks the contrast of the pulses is systematically varied to produce a contrast threshold, although proportion-correctly recalled digits has also been used (Busey, 1994).
The LST Model
Recovering the temporal frequencies used in a given task from two-pulse data requires the adoption of a model of the early stages of visual filtering. In keeping with previous character identification modeling, we have adopted the LST model (Busey & Loftus, 1994; Busey, 1994). The assumptions underlying such a model are not strong, and various authors have adopted different formulations that all make similar quantitative predictions (e.g. Watson, 1986, Roufs & Blommaert, 1981). A brief summary of the LST model is provided below; a complete summary can be found in Busey & Loftus (1994). Evaluation of the hypothesis that two tasks rely on different temporal frequencies can be made with qualitative comparisons of the data, and thus a complete understanding of the model is not required. However, the model allows comparisons across different experimental paradigms by providing quantitative estimates of the range of temporal frequencies underlying a given task.
3.2.1 From the Physical Stimulus to the Sensory Response
Figure 1 provides an overview of the LST model. The physical stimulus, f(t) is represented as changes in contrast over time (Figure 1, top panel). The initial sensory representation engendered by this physical stimulus is generated by convolving the physical stimulus representation with an impulse response function, g(t).
Eq. 1
The result a(t), called the sensory response function, is shown the middle panel of Figure 1. The filtering operation defined by Eq. 1 is linear, so that the sensory representation contains the same overall energy as the physical stimulus, but spread out in time. The exact form of g(t) is described below, but the effect is that a(t) is a temporal blurring or filtering of the physical stimulus f(t). The shape of the impulse response function, g(t), dictates the type of temporal blurring. In keeping with previous linear filter models (e.g. Watson, 1979, 1986) I have chosen this function to be the difference of a two gamma functions, each with a different time constant,
g(t) =
- [
]
Eq. 2
where t represents the time-constant of the gamma function and provides an estimate of the temporal response properties of the mechanisms mediating a given task. The sensory response function component of the LST theory (a(t)) is based on work from Andrew Watson (Watson, 1986), George Sperling (Sperling and Sondhi, 1968) and others working in the temporal domain of perception.
The first term in Eq. 2 represents an excitatory component, and this term alone is used to produce the solid function shown in the middle panel of Figure 1. The second term of Eq. 2 represents an inhibitory component of the response, which tends to sharpen the response and allows it to respond to higher temporal frequencies; both terms together produce the dashed curve in the middle panel of Figure 1. The parameter r represents the ratio of the time-constant of the inhibitory component of the response to the excitatory component of the response, and represents the magnitude of the temporal inhibition component. The parameters n1 and n2 represents the number of stages in each process, and is usually fixed at an integer between 5-10, although the shape of the impulse-response function is relatively unchanged by the precise value chosen. For the preset work, n1 was fixed at 9 and n2 was fixed at 10.
Detection data for stimuli such as high spatial frequencies and color are often modeled by setting to 0, which gives a monotonic impulse response function g(t) as shown as a solid curve in the left panel of Figure 2 (Note that the a(t) function in Figure 1 looks like the impulse response function, g(t), in Figure 2, because the physical input leading to the Figure 1 curve is a brief rectangular pulse, similar to an impulse). An alternative way of representing the same information is by taking the Fourier transform of the impulse response function g(t), which results in a temporal contrast sensitivity function (TCSF). The TCSF plot show the sensitivity of a system to different temporal frequencies. The TCSF corresponding to the solid line in the left panel of Figure 2 is given by the solid line in the right panel. These curves represent a purely sustained response, and give a monotonically-decreasing TCSF, as shown in Figure 2, right panel.
Detection data for stimuli containing mainly low spatial frequencies, or stimuli presented on bright backgrounds, often are modeled by > 0. In this case the impulse response inhibits processing after an initial excitatory response, which results in an inhibitory lobe in the impulse response function g(t) (dashed line in Figure 2, left panel) and a characteristic TCSF with a decrease in sensitivity at low temporal frequencies (dashed curve in Figure 2, right panel).
3.2.2 Information Extraction from the Sensory Response Function
It has long been recognized that information is carried both by the positive going part of a(t) (the upper lobe of the dashed line in the middle panel of Figure 1) and by the negative going part of a(t) (the below-zero part of the dashed line in the middle panel of Figure 1). Note that the negative going portion can result either from temporal inhibition or by a negative contrast pulse as in the two-pulse study described above. Representing information in both negative and positive a(t) functions is usually handled by taking the absolute value of a(t), |a(t)|, in a process called rectification (e.g. Watson, 1986).
The critical assumption of the LST model is that information is
extracted not from |a(t)| but from that part of |a(t)|, termed
, that lies outside a sensory threshold,
Q. To be precise,
Eq. 3
A fundamental consequence of this formulation is that if the sensory response a(t) is not outside the positive or negative sensory threshold, the stimulus will not be visible to the observer. Although there is evidence against such a high-threshold formulation, the psychometric function relating contrast to performance is quite steep, and thus the sensory threshold serves as an approximation to the true mechanism.
Information is extracted from 
according
to assumptions based on the information processing literature
(e.g. Rumelhart, 1970; Townsend, 1981). Information of a given
sort is assumed to be extracted from the sensory response function
at a given rate, r(t), which is proportional to both the height
of the above-threshold sensory response and the amount of already-acquired
information:
Eq. 4
where 
represents the degree to which a(t)
lies above the sensory threshold and I(t) represents the amount
of already-acquired information at time t. The rate of
information extraction is given by a model parameter, cs.
The parameter cs determines the acquisition
rate for the different types of information required to satisfy
the demands of different tasks. Information is acquired until
time t is termed I(t), and is simply the integral of r(t) over
time. The total amount of acquired information is termed I(_),
which represents the area under each r(t) function. The form of
r(t) for two examples of a(t) is illustrated in the bottom panel
of Figure 1.
A fundamental consequence of the above formulation is that performance, in terms of proportion correctly identified stimuli (p(c)), is directly related to the above-threshold area:
Eq. 5
As the above-threshold area increases, performance increases to an asymptotic level of 1.0.
Modeling Two-Pulse Contrast Thresholds
In a threshold task, the stimulus contrast is varied according
to an adaptive search technique to compute a contrast threshold
that provides 82% correct identification. When modeling contrast
threshold data collected in a two-pulse experiment, search procedures
scale the height of the f(t) function by a predicted contrast
to produce an above-threshold area (
in
Eq. 5 via Eqs 1-4), and thus a performance prediction. This process
is repeated until the predicted performance in Eq. 5 equals 0.82.
The scaled f(t) height becomes the predicted contrast threshold
for this condition and is an exact value for a given set of model
parameters.
Quantitative predictions are computed via parameter estimation techniques. The parameters of the linear filter, t, r and , determine the shape of the impulse response function and therefore the range of temporal frequencies passed by the temporal filter. Smaller t values and larger temporal inhibitory components (as determined by the r parameter) imply faster temporal frequencies passed by the system.
The sensory nonlinearity q is not the focus of the present study, although it does in part determine the rate at which the positive/positive contrast thresholds decrease as ISI is increased. The cs parameter determines the rate at which task-relevant information is acquired by the visual system. This can also be interpreted as a sensitivity parameter which, for two-pulse data, simply moves the contrast threshold curves up and down.
The goal of Experiment 1 was to measure the temporal frequencies underlying character localization and identification tasks using the Two-Pulse technique. A character (a '2' or a '5' appeared left or right of fixation on each trial, and participants made both localization ('which side was it on') and identification (was it a '2' or a '5') judgments on each trial.
If we find that the localization tasks relies on different temporal frequencies, we would have support for the hypothesis that different sets of labeled detectors, perhaps originating from different classes of visual cortical neurons, contribute to the two tasks.
The Experiment 1 Methods follow the procedures of similar Two-Pulse experiments (e.g. Ikeda, 1986) to collect contrast thresholds using an adaptive search technique.
Stimuli and Apparatus
Stimulus presentation and response collection took place on a Macintosh II computer and a 14" monochrome monitor. Luminance control and calibration controlled via a VideoAttenuator and the VideoToolbox luminance utilities (Pelli & Zhang,1991) that provides 4096 gray levels. An oscilloscope and Pin-10 photodiode was used to verify the lack of phosphor persistence from one pulse to the next.
Participants viewed the screen from a distance of 57 cm. The two characters (a 2 or a 5) were rendered in 24 point Times font and subtended a visual angle of 0.57° vertically and 0.39° horizontally. Participants maintained fixation on a centrally located fixation point. The two letters appeared randomly 6 degrees left or right of the fovea on each trial.
Design and Procedure
Two stimulus waveform patterns form the basis of the two-pulse paradigm. In the positive/positive condition a positive-contrast 30 ms pulse of a letter is followed by a variable ISI and a second positive-contrast 30 ms pulse of the same letter. In the positive/negative condition a positive-contrast 30 ms pulse of a letter is followed by a variable ISI and a negative-contrast 30 ms pulse of the same letter. For Experiments 1-3 the contrast was defined as contrast = (Lmax - Lmin)/(Lmax + Lmin).
On each trial the contrast of the pulses was determined by an adaptive search technique (Quest, Watson & Pelli, 1983) that finds the stimulus contrast that affords 82% correct identification over trials. Each contrast threshold estimate is based on 96 replications at each condition.
Robust parameter estimates were assured using 6 ISI's between the two pulses. These allow estimation of the amount of interaction between the two pulses, and by inference an estimate of the persistence of the first pulse over time. Combined with the two types of presentations described above and the two tasks, the experiment consisted of 24 conditions.
Participants
Three participants completed 96 trials per condition. The observers were the author and two naive observers: a female staff member of the Psychology Department and a male advanced undergraduate student. All had normal or corrected-to-normal vision.
Figure 3 shows the results from Experiment 1, plotted as log (1/contrast threshold) which is interpreted as contrast sensitivity. The pattern of the data conform to other two-pulse data (e.g. Rashbass, 1970). Consider the Identification data. For the positive/positive condition, as the ISI is increased, performance decreases initially and then increases slightly. This decrease results from the sensory threshold, q, which causes more area to drop below threshold as the responses engendered by the two pulses separate with longer ISIs. For the positive/negative condition, performance is low for small ISI's, but then increases as ISI increases. At some intermediate ISI (around 30 ms) the positive/negative data cross the positive/positive data and the observer actually becomes more sensitive to the positive/negative stimulus. This crossover results from temporal inhibition in the response and the fact that the two responses engendered from the two pulses sum prior to a rectification. The inhibitory lobe from the first pulse sums with the negative-going excitatory lobe from the negative-contrast second pulse. After rectification this results in more above-threshold area and thus better sensitivity.
The ISI at which the two curves cross is a qualitative, model free estimate of the temporal frequencies used in a task. Tasks relying on higher temporal frequencies will produce curves that cross at shorter ISI's. This is clearly the case for the localization data in Figure 3. The crossover point for all three observers occurs at an ISI of 5 ms or less. Although this is a relative measure of the temporal frequencies and suggests that the two tasks rely on different temporal frequencies, the actual range of temporal frequencies requires the LST model and parameter estimation techniques. A direct test of the hypothesis that the localization and identification tasks rely on different temporal frequencies can be made by comparing the impulse-response functions engendered by the two tasks. These are shown in Figure 4.
Table 1 shows the parameter estimates for the three observers in Experiment 1. For all three observers the t, and r parameters of the impulse response functions systematically differ across tasks, and are consistent within a task across observers. These data support the hypothesis that a localization task relies on higher temporal frequencies than the identification task. This suggests that the two tasks, perhaps mediated by different visual cortical pathways, rely on different sets of labeled detectors, originating perhaps in different classes of visual cortical neurons.
One question that remains unanswered by Experiment 1 is whether the differences seen in the temporal frequencies used by localization and character identification tasks extends to foveal presentations. One major hypothesis is that the only difference between the fovea and the periphery is the spatial scale at which objects are represented (Thomas, 1987). The bandwidths of the spatial filters do not change, although the foveal stimuli provide more input to higher-spatial-frequency filters due to increased acuity. If this is indeed the case, we might expect to replicate the Experiment 1 findings in the fovea. High spatial frequency stimuli produce lower temporal frequencies, and perhaps moving the stimuli into the fovea does not add high temporal frequencies and thus would not change the pattern of data observed in Experiment 1. However, parietal cortex appears to receive much of its input from the periphery, and moving the stimulus to the fovea may cause shifts in the temporal frequencies used in the two tasks. In addition Gorea (1986) found no differences in the temporal frequencies used in detection and identification in the fovea, which contradicts our Experiment 1 findings. Experiment 2 was designed to specifically address whether the findings observed in the periphery would also be produced by foveal presentations.
Foveal presentations entail only a single location, and thus require the adoption of a two-temporal-interval presentation sequence. Tones delimited two temporal intervals, and the stimulus appeared randomly in one of the intervals. On each trial, the observer reported both which interval contained the stimulus as well as whether it was a '2' or a '5'. We assume that localizing an object in the periphery and detecting an object in the fovea are comparable operations in that they both require saying that something appeared, not which object appeared. To avoid confusion between peripheral and foveal presentations, we refer to peripheral presentations as a localization task, and foveal presentations as a detection task. Experiment 2 actually consisted of two replications, done at different background levels. The results did not differ, and thus we discuss both experiments together.
Stimuli and Apparatus
The stimuli and apparatus were identical to those of Experiment 1. Characters were presented in the fovea, 1.3 degrees below a fixation point that served to hold fixation across trials.
Design and Procedure
Three low tones delimited two temporal intervals, each of which contained the stimulus with 50% probability. The participants task on each trial was to indicate which interval contained the stimulus, and whether it was a '2' or a '5'.
Participants
The participants consisted of the author, a female staff member and a psychology graduate student.
Figure 5 shows the two-pulse data for the three observers collected at two different background levels. Contrary to the findings in Experiment 1 (Figure 3), no differences are observed in the temporal frequencies used in different tasks. All six datasets could be fit by a model that assumes only differences in sensitivity, as expressed by different cs parameters, exist between the two tasks. The impulse-response function parameters (t, r and ) that characterize the range of temporal frequencies used in each task were identical for the two tasks. The only exception is Observer NQ's data at the higher, 20 cd/m2 background level, in which a model that assumed a slightly higher range of temporal frequencies for detection vs identification produced a slightly better fit to the data. However, these differences are small, and thus we conclude that, in the fovea, detection and identification rely on the same range of temporal frequencies.
Experiments 1 and 2 delimit the conditions under which detecting the location or presence of a letter and identifying the letter depend on different temporal frequencies. We observer large differences for peripheral presentations, with localization relying on much higher temporal frequencies than character identification. In the foveal we see no differences in the temporal frequencies used in detection and identification tasks. The estimates of the linear filter parameters allow comparisons across tasks, and we see that identification appears to rely on the same temporal frequencies in the fovea and in the periphery, but localization relies on much higher temporal frequencies in the periphery than detection in the fovea.
These findings leave open two questions that are addressed in Experiments 3-5. First, are these findings somehow specific to the two-pulse paradigm, or would we see the same effects when measuring the temporal frequencies used in each task using the temporal contrast sensitivity paradigm? Second, do these differences in the use of the temporal frequencies depend on the use of different spatial frequencies in different tasks? This second question is addressed in Experiments 4 and 5 by restricting the range of available spatial frequencies by spatially filtering the letters.
Experiment 3 measures the temporal frequencies underlying localization and character identification tasks using letters flickered at different temporal frequencies. The observer adjusted the contrast of the stimulus until the letter is either just barely localizable or just barely identifiable. The resulting contrast threshold for each flicker rate is converted to a contrast sensitivity. The resulting TCSF may be directly compared to the examples given in the right panel of Figure 2. If we see differences in shapes of the temporal contrast sensitivity functions for the localization and character identification tasks, we would confirm the Experiment 1 findings.
Contrast sensitivities to eight temporal frequencies ranging from 2 to 32 Hz were obtained by flickering a letter around a gray background according to a sine-wave weighted by a gaussian envelope. Example temporal functions are shown in Figure 6. The stimuli were presented on a Tektronix 604 oscilloscope with a fast P15 phosphor at a 4 ms (250 Hz) refresh rate. The size of the oscilloscope limited the peripheral presentations to 2.7° from fixation.
Stimuli and Apparatus
The same apparatus was used for Experiments 3-5. Observers viewed two patches located left and right of a fixation point on the face of a Tektronix 604 oscilloscope. The background luminance was fixed at 20 cd/m2. Stimuli were a 2 and a 5, rendered in the same Times font used in Experiments 1-2. Observers viewed these stimuli from a distance of 86 cm, which resulted in the letters subtending 0.50° vertically and 0.37° horizontally. The center of the letters was located 2.7° or 2.3° from the fixation point.
The display device could not support the high luminance levels required to fit contrast thresholds for identification at 32 Hz, and thus this condition was eliminated for all observers in Experiments 3 and 4.
Design and Procedure
Observers viewed a series of stimuli that appeared randomly left or right of fixation and consisted of either a '2' or a '5'. The stimuli appeared about once every second. Observers maintained central fixation and adjusted the contrast of the letters until they met either a criterion of 'just barely localizable' or 'just barely identifiable'. When they were satisfied that the current contrast met the criteria for the given task, they pressed a key to continue with the next temporal frequency and task. The order of the tasks and temporal frequencies was randomized.
Participants
Two participants, the author and a graduate student, completed 4 replications of each threshold.
The data are modeled by computing the Fourier transform of the impulse response function (Eq. 2) and fitting a model that consists only of the impulse response function parameters (t, r and ) along with a sensitivity parameter s that scales the TCSF vertically. In engineering terms, this is the Transfer function G(w):
Eq 6
where w is the temporal frequency flicker rate for a condition, s is a sensitivity parameter and (t, r and ) are the impulse-response function parameters that determine the range of temporal frequencies that underlie a given task. Separate parameter values were fit for the character identification and localization tasks. Often a TCSF curve could be fit by assuming no temporal inhibition, in which case was set to zero, eliminating the second part of Eq. 6.
Figure 7 shows the TCSF data for three observers. The character identification data are characterized by a low-pass function, since the peak sensitivity is at the lowest temporal frequency. Localization appears band-pass; the peak sensitivity occurs for temporal frequencies in the range of 6-8 Hz. The localization data require a model that assumes temporal inhibition (non-zero parameter) but the character identification data do not. Direct comparison with the Experiment 1 data are possible by computing the impulse response functions using the t, r and parameters, which are directly comparable to the impulse response functions derived from the two-pulse data (Figure 4). Note that the assumptions of Eq 2 and the values of t, r and precisely determine the shape of the impulse response function. These comparisons reveal that the TCSF data replicate the Experiment 1 data: localization relies on higher temporal frequencies than character identification. In general the differences between the two tasks are less extreme than observed 6° in the periphery in Experiment 1, but the current display device only allows peripheral presentations of 2.7° in the periphery. Given that no differences exist in the fovea (Experiment 2, Figure 5), we might expect smaller differences between the two tasks as we move into the fovea.
One possible explanation for the differences observed in the temporal frequencies used by localization and character identification tasks in Experiments 1 and 3 is that the two tasks rely on different spatial frequencies. Such an explanation cannot readily account for the Experiment 2 data, since the stimuli in Experiment 1 and 2 were identical and yet only Experiment 1 demonstrates a difference between the two tasks. However, a more direct test of this possibility is to restrict the range of available spatial frequencies. Lower spatial frequencies tend to give responses that contain higher temporal frequencies (e.g. Robson, 1966) and stimuli above 13 cycles per degree tend to give monophasic rather than biphasic impulse response functions. The numbers used for Experiment 3 were low-pass filtered to restrict the range of available spatial frequencies. If the differences seen in the temporal domain in Experiments 1 and 3 result from different tasks relying on different spatial frequency bands, then restricting the spatial frequencies should also restrict the temporal frequencies.
The choice for the cutoff spatial frequency was determined according to the following logic. We want to restrict the range of available spatial frequencies. However we also require that the letters are still identifiable as characters, in order to allow comparisons with previous experiments. If the letters no longer appeared character-like, one might argue that the stimuli are somehow processed differently by the higher cortical pathways, leading to different temporal frequencies used in the task. For example, cells along the temporal lobe pathway respond to increasingly complex visual patterns as one moves down the pathway, and these cells may also differ in their temporal frequency response as they combine inputs from earlier cells (Logothesis & Sheinberg, 1996). Filtering the letters beyond legibility may result in a different class of cells responding to the stimuli.
To resolve the tension between restricting the frequencies and maintaining some degree of character legibility, we chose a cutoff frequency of 1.9 cycles per letter. Solomon and Pelli (1994) determined that letters are processed by a spatial filter one octave wide, centered at 3 cycles per letter (around 3.1 cycles per degree in their display). A cutoff frequency of 1.9 cycles per letter is 1/2 octave below 3 cycles per letter, suggesting that the filter mediating letter recognition was still partially activated. This cutoff left the characters barely legible when viewed on the display device, but containing a restricted range of spatial frequencies. Our letters are rather small due to the difficulty of presenting stimuli at a 250 Hz refresh rate, which results in filtered letters that contain spatial frequencies in the range of 1.6 to 5 cycles per degree or 0.61 to 1.9 cycles per letter. Figure 8 shows the stimuli used in Experiments 4 and 5.
Stimuli and Apparatus
The Experiment 4 stimuli were low-pass filtered using an ideal filter with cutoff frequency set to 1.9 cycles per letter (5 cycles per degree). The effective range of spatial frequencies was 0.61 to 1.9 cycles per letter due to the limited stimulus size. Because the resulting stimuli consisted of a range of luminance values instead of a single value as in Experiment 3, the formula for contrast was changed to contrast = (Lmax - Lmin)/[(Lmax + Lmin)/2]. This prevents stimulus distortions that might result from a nonlinear relationship between contrast and luminance.
Design and Procedure
The procedures were identical to those of Experiment 3, except for the use of low-pass spatially filtered letters.
A control experiment used the same Quest adaptive threshold techniques used in Experiments 1 and 2 to verify that the method of adjustment thresholds were not biased. Separate thresholds were found for each task at each temporal frequency as in Experiments 1 and 2. However, unlike previous experiments, participants made only localization or identification responses on each trial, which were blocked so that a series of trials consisted of only the localization or identification task.
Participants
Two participants, the author and a graduate student, completed 4 replications of each threshold. Observer TB also completed 80 trials at each task by temporal frequency condition in the forced-choice control experiment.
Figure 9 shows the TCSF data for two observers. Despite the fact that we have severely limited the range of spatial frequencies to the lowest frequencies that still provide character legibility, we still see differences in the patterns of temporal frequencies used in the localization and character identification tasks. The shapes of the TCSF's mirror those of Experiment 3 (Figure 7).
An important control on the subjective contrast threshold measurements used in Experiments 3-5 is the use of forced-choice techniques. Derrington and Henning (1982) used forced-choice techniques and failed to replicate earlier findings by Kulikowski and Tolhurst (1973) that dissociated pattern and flicker perception mechanisms. Derrington and Henning (1982), determined that absolute identification performance lies far below the subjective threshold, and that such differences might have contributed to Kulikowski and Tolhurst's report that pattern perception relies on different information than flicker perception. To verify that this is not a problem for the current TCSF studies, we measured absolute contrast thresholds for both localization and identification using a force-choice paradigm. Experiments 1 and 2 use force-choice techniques throughout.
The Quest procedures used in Experiments 1 and 2 were adapted to the TCSF paradigm and low-pass filtered letters of Experiment 4 to verify that the differences between localization and character identification are not simply a result of the use of subjective thresholds. Data from observer TB is shown in Figure 10, and demonstrate the same qualitative pattern observed in Figure 9. The data contain more noise than the subjective threshold technique, but a clear loss in sensitivity at the mid and high temporal frequencies is observed in the character identification data relative to the localization data (the dark dotted line in Figure 10). Thus the differences in the temporal frequencies used in the localization and identification tasks is not a result of the experimental methods employed in Experiments 3-5.
Based on the Experiment 4 data, we conclude that differences in the spatial domain are not sufficient to account for the observed differences across tasks in the temporal domain. In addition, the low-pass nature of temporal frequencies used in character identification and band-pass nature the temporal frequencies used in localization as seen in Figure 7 and Figure 9 are not due to the use of subjective thresholds, since the same conclusions are reached using forced-choice techniques (Figure 10).
Experiment 4 demonstrates that restricting the range of spatial frequencies to the lowest spatial frequencies still provides evidence that localization relies on higher temporal frequencies than does character identification. This result is consistent with a set of labeled detectors tuned to low spatial frequencies that passes higher temporal frequencies and primarily supports localization, and a set of labeled detectors that gives some response to the low spatial frequencies and passes just slower temporal frequencies to support character identification. One might ask whether these differences still exist when the range of spatial frequencies is restricted to just higher spatial frequencies. Under these conditions we might no longer see evidence of the contribution of the fast detectors tuned to just lower spatial frequencies.
Experiment 5 used band-pass filtered letters that restricted the range of spatial frequencies to an octave wide pass region centered on 3 cycles per letter. The filter included the spatial frequencies in the range of 6 to 9.5 cycles per degree or 2.3 to 3.6 cycles per letter. Figure 8 shows the stimuli used in Experiments 5.
The methods and observers were identical to Experiment 4, except that the stimuli were spatially band-pass filtered rather than low-pass filtered. This filtering reduced the power of the stimulus, and as a result the display device could not support the high luminance levels required to fit contrast thresholds at 24 and 32 Hz. These conditions were eliminated for both observers in Experiment 5.
Figure 11 shows the results from Experiment 5 for two observers. Unlike the data from Experiments 1, 3 and 4, the two tasks appear to rely on the same temporal frequencies. The data show a peak sensitivity at the lowest measured flicker rate, implying that the mechanisms that process these stimuli were temporally lowpass and did not include temporal inhibition. This finding is consistent with other two-pulse studies, that show low-pass characteristics for stimuli containing just higher spatial frequencies (Ikeda 1986).
The Experiment 5 data are consistent with the notion that the differences seen in localization and identification tasks derive from contributions of different sets of labeled detectors, and are not a result of inherent differences in the tasks. When the inputs from the fast labeled detectors are available from stimuli containing lower spatial frequencies (as in Experiments 1, 3 and 4), localization but not character identification can take advantage of this information. If not, both tasks must rely on the inputs from the labeled detectors that are sensitive to the higher spatial frequencies, which only pass slower temporal frequencies and thus give the sustained TCSF curves observed in Figure 11.
The results from the present experiments delimit the conditions under which localization and character identification tasks rely on different sets of inputs from labeled detectors carrying different temporal frequencies. With peripheral presentations, localization can take advantage of higher temporal frequencies than those used in character identification, while in foveal presentations detection appears to rely on the same temporal frequencies as character identification. As the stimulus moves into the fovea, localization relies on slower and slower temporal frequencies, while identification appears to rely on the same temporal frequencies. These peripheral differences persist in different tasks (two-pulse and TCSF) and in spatially-filtered letters that contain lower spatial frequencies.
There exist several possible explanations for observed differences in the temporal frequencies underlying localization and identification described in Experiments 1-5. First, these differences might reflect different task demands: a flickering stimulus may enhance detection but inhibit identification. Second, different tasks might use different spatial frequencies. Third, the observed differences might reflect the response properties of the neural mechanisms that underlie the processing of the different tasks, such that the pathway involved in processing location information might preserve higher temporal frequencies than the pathway processing object identity information. Finally, the observed differences might reflect different contributions of labeled detectors or classes of visual cortical neurons to the higher visual areas. Evidence for each of these possibilities is discussed below.
Differences due to the nature of the task cannot account for the findings from Expeirments 1-5, since we get different results when the stimuli and tasks are similar and the only difference is peripheral vs foveal presentations (Expeirments 1 and 2). In addition, we see no differences in the two tasks when the stimuli contain just higher spatial frequencies.
An explanation based on the use of different spatial frequencies cannot account for the findings from Experiment 1 and 2 that found differences in the periphery and no differences in the fovea, despite the fact that the same stimulus (containing the same spatial frequencies) was used in both experiments. In addition, restricting the range of spatial frequencies to lower spatial frequencies (Experiment 4) still produced a difference in the temporal frequencies used in each task.
The current data do support the anatomical model that suggests that the localization and identification tasks selectively isolate the parietal lobe and temporal lobe pathways. The parietal lobe pathway appears to receive the majority of its input from the periphery, and parietal lobe cells have been recorded with large receptive fields that exclude the fovea (Motter & Mountcastle, 1981). This would account for the differences observed between Experiments 1 and 2. The localization and identification tasks are similar to those used in brain imaging studies (McIntosh et. al., 1994, Haxby et al 1991), but the use of a single stimulus for both tasks makes differences inevitable. These data are consistent with the one study that used different tasks applied to the same stimulus to isolate the two pathways (Corbetta et al 1991).
It is difficult to distinguish between models that assume that the differences in the temporal domain are a property of the later cortical pathways, or whether such differences result from inputs from different classes of labeled detectors with different spatio-temporal properties, as suggested by Ahumada and Watson (1985). Below we review the evidence that supports the latter possibility.
Functional differences in the temporal domain appear as early as area V1. Hawken et. al. (1996) report that direction selective cells in area V1 all maintain higher temporal frequencies, while non-direction selective cells are mixed in the range of temporal frequencies that cause the cell to fire. These early differences may extend to later areas, since MT cells are overwhelmingly selective for direction (Felleman & Van Essen, 1987). Anatomical studies suggest that the MT and V4 areas may selectively influence the parietal and temporal lobe pathways, although substantial mixing does occur (Merigan and Maunsell, 1993). In addition, although it appears that the direction selective cells from layers 4B and 6 of V1 project to area MT, the direction selectivity could be generated in area MT from contributions from non-direction selective neurons (Hawkin, personal communication). While the behavioral data do not distinguish between physiological models, they can be used to determine the functional relevance of any observed differences in the temporal frequencies carried by the temporal and parietal lobe pathways.
Despite the consistencies between the findings of the current studies and the anatomical model of Ungerleider & Mishkin (1982), questions still remain about the role of the parietal lobe pathway. For example, although Experiments 1, 3, 4 and 5 all involve a localization task, this only requires coarse localization information. The cells subserving identification in the temporal lobe pathway are quite capable of coding coarse location information by virtue of the locations of their receptive fields (Desimone & Duncan, 1995). The parietal lobe may be more responsible for coding relations between objects rather than an individual object's location, or possibly performing attentional binding of either objects to locations or features to objects (Treisman, 1996).
Several authors have noted a deficit in the perception of high temporal frequencies as measured by flicker fusion and visual persistence techniques (Lovegrove, et. al, 1990; Williams & Leclusyse, 1990) in persons with specific reading disorder. Various authors have suggested that this results from a deficit in the magno pathway, although as discussed above, the magno pathway does not appear to differ from the parvo pathway in its temporal properties (Hawken et al, 1996). Other authors have suggested explanations based on eyemovements or more information processing deficits (e.g. Rayner & Pollatsek, 1989). Given that localization of peripheral presentations appears to rely on the higher temporal frequencies that persons with dyslexia lack, the current data provides more support for a model that suggests that the dyslexia syndrome results from a reduced ability to localize objects and orient attention, rather than difficulty with identifying the letters. Given that persons with dyslexia often report difficulty with the organization of text, perhaps a better anatomical model would focus on the possibility of the deficit resulting from either the inputs to the parietal lobe or the parietal lobe itself. If this pathway also mediates flicker detection, this model would also account for the deficits in the perception of high temporal frequencies. A resolution will come from testing persons with dyslexia on the two-pulse paradigm with localization and character identification, which we are currently pursuing in our laboratory.
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| Figure 1. Theoretical components of the linear filter model of character identification. Top Panel: A stimulus is characterized as its contrast over time. Middle Panel: The stimulus engenders a response in the visual system that is a function of the stimulus input function f(t) and the impulse response function g(t). Dashed and solid a(t) curves represent the responses of systems with and without temporal inhibition (see Eq 2). A sensory threshold is assessed, such that further information processing does not proceed unless the sensory response exceeds the sensory threshold. Bottom Panel: If the sensory response exceeds the threshold, further information processing takes place at a rate defined by r(t). This rate is proportional to the product of the above-threshold sensory response and the proportion of remaining stimulus information. Performance in terms of proportion correctly-recalled digits is assumed to be proportional to the area under the information-acquisition rate function, r(t) (which represents total acquired information). |
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| Figure 2. The temporal frequencies underlying a task may be characterized by an impulse response function (left panel) that characterizes the time course of the perceptual response engendered by a stimulus, or by the temporal contrast sensitivity function (right panel), that characterizes the fidelity by which the pathways subserving a given task pass different temporal frequencies. The TCSF plots are the Fourier transform of the impulse response functions into frequency space. High spatial frequency stimuli tend to elicit monophasic impulse response functions, which have no falloff at low temporal frequencies in the TCSF plot. Stimuli containing low spatial frequencies tend to elicit biphasic impulse response functions that contain an inhibitory lobe. This temporal inhibition acts to sharpen the response of the visual pathway, allowing it to respond to faster changes in the visual scene. However, this inhibition causes in a falloff at low temporal frequencies in the TCSF plot, which results from tendency for the biphasic impulse response function to inhibit itself when processing slow temporal changes. Parameters used: Monophasic: {t = 4.38, r = 0} Biphasic: {t = 3.58, r = 2.0, = 0.39} | |
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| Figure 3. Two-Pulse Data from Experiment 1 for 3 observers. For localization data, the positive/positive and positive/negative data cross at much a much shorter ISI's (0-5 ms) than the identification data (15-20 ms). These data require a model that assumes that the localization task relies on higher temporal frequencies than the identification task. |
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| Figure 4. Estimated impulse response functions for Localization and Identification Data from Experiment 1. These demonstrate that the localization task relies on higher temporal frequencies than identification, and allow comparisons across tasks. |
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| Figure 5. Data for 3 observers at two background luminances for Experiment 2. All observers demonstrate no differences across tasks, suggesting that the two tasks rely on the same temporal frequencies. The data were well-fit by a model that assumed a single set of impulse-response parameters (t, r and ) for both tasks. The exception is observer NQ's data at the higher 20 cd/m2 background luminance, which could be fit slightly better by a model with separate linear filter parameters. |
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| Figure 6. Example temporal wave forms used in Experiments 3-5. Observers scaled the contrast of these wave forms to produce an estimated contrast threshold. | |
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| Figure 7. Temporal contrast sensitivity functions (TCSF's) for three observers for Experiment 3. The localization data are bandpass, while the character identification data are lowpass. Observer TB measured contrast thresholds at 2.7° and 2.3° in the periphery, demonstrating that as the stimulus moves into the fovea, the location detection task becomes more low-pass. The insets show the recovered impulse response parameters, which can be compared with those from Experiment 1 (Figure 4) to demonstrate that the two paradigms generate the same pattern of data: localization relies on higher temporal frequencies than identification. | |
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| Figure 8. Stimuli used in Experiment 4 (top panels) and Experiment 5 (lower panels). The stimuli are enlarged somewhat, which results in the introduction of spurious high frequencies that are not present in the actual displays. |
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| Figure 9. Temporal contrast sensitivity functions (TCSF's) for low-pass filtered letters for Experiment 4. Localization might rely on lower spatial frequencies than identification in Experiment 3, and this might result in the Figure 7 data. The letters used in the Figure 9 data were lowpass filtered to restrict the range of spatial frequencies. Despite this, the data replicate the Experiments 1 and 3 data, demonstrating that localization relies on higher temporal frequencies than identification. |
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| Figure 10. Temporal contrast sensitivity function for Observer TB using the QUEST threshold-finding procedures from Experiments 1 and 2. Although the data are somewhat noisier than the subjective task data, these objective procedures replicate the Figure 9 data. The dark line is the localization data shifted vertically, demonstrating the falloff of identification performance at higher temporal frequencies. Thus, the subjective techniques used in Experiments 3-5 do not affect the conclusions. |
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| Figure 11. Temporal contrast sensitivity functions (TCSF's) for bandpass-filtered letters for Experiment 5. When the spatial frequencies are restricted to higher spatial frequencies, no differences are observed in the temporal frequencies underlying the localization and identification tasks. This demonstrates that the differences in the temporal frequencies used in different tasks observed in Experiments 1, 3 and 4 do not result from task demands alone. |
