Medial Superior Temporal Area Neurons Respond to Speed Patterns in Optic Flow

Effects of mean speed in normal gradient stimuli

We varied the mean speed of radial or circular motion using the same five mean speeds between 10 and 80°/sec for all neurons. In all of these stimuli, we maintained the normal gradient of speed profiles (slower motion in the center) as shown for the example of outward radial motion in Figure 1. More than two-thirds of the neurons that showed some responses to these stimuli (68%, or 83 of 122 neurons) showed strong speed preferences; the amplitude of their response to one speed was at least twice that to another speed. The curves relating response amplitude to mean speed (Fig. 2) showed a variety of shapes that we placed into five categories, which resemble five simple filters. If no response was significantly greater than any other, the neuron was classified as having a flat response profile (broad band; Fig. 2A). If the largest response was at one end of the curve and the smallest response was at the other end, the neuron was classified as having either an increasing or decreasing response profile (low-pass or high-pass; Fig. 2B,C). If the intermediate mean speeds evoked the smallest or largest response, the neuron was classified as having a trough or peak response profile, respectively (bandpass or band-reject; Fig. 2D,E). These response profile classifications do not bear a one-to-one correspondence with classification by optimal stimulus speed (e.g., neurons preferring the fastest speed might show a high-pass or band-reject profile).

Across the sample, speed had similar effects on the responses to both radial and circular stimuli, and Figure 3A shows the frequency of the five response classes shown in Figure 2. The most common speed response profile is that showing increasing response with increasing mean speed (42% of radial neurons and 38% of circular neurons). The frequency of optimal speed preferences is shown in Figure3B, with the most commonly preferred speeds being the fastest and slowest (64% of radial neurons and 66% of circular neurons). Although we usually tested neurons only to their preferred radial or circular stimuli, those that responded to both had comparable speed profiles to both stimulus patterns. Figure 3C shows the speed profiles of such a neuron that showed preferences for slower stimulus speeds for both radial (left) and circular (right) motion.

Thus, we have made the following observations: (1) the responses of more than two-thirds of the MSTd neurons are strongly modulated by changes in the mean speed of the stimulus gradient; (2) this modulation is equally present for neurons preferring radial or circular stimuli; and (3) the profile of this modulation with change of speeds can be regarded as falling into classes resembling simple filter characteristics.

Effects of speed gradients in optic flow stimuli

In normal gradient stimuli, speed varies as a function of viewing angle from fixation at the center of the stimulus to a given dot: a sine × cosine function for radial stimuli and a tangent function for circular stimuli. To determine whether these speed gradients influence MSTd responses, we created radial and circular stimuli without the normal gradient; i.e., stimuli with the same speed of dot motion throughout the stimulus (Fig. 4A). We see these stimuli with and without the speed gradient as looking alike, but with a clear sense that there are speed differences between them.

We compared the response of 105 neurons to normal gradient and nongradient stimuli at five mean speeds. Overall, the nongradient stimuli evoked only slightly less responsiveness than did the gradient stimuli; approximately two-thirds still showed a response that was at least twice the amplitude of the weakest response (62%, or 58 of 94 for the nongradient stimuli compared with 68% for the gradient stimuli). Figure 4B shows an example of a neuron that responded similarly to the normal gradient (left) and nongradient (right) stimuli. Nevertheless, some neurons showed substantial differences between their responses to normal gradient and nongradient stimuli. For example, the neuron in Figure4C shows a decrease in the response to the lowest speed when the stimulus had no gradient, and the neuron in Figure4D shows a different profile of responses, with a decrease at the fastest speed and an increase at the slowest speed for a nongradient stimulus.

Figure 5 shows the extent of these response differences between normal gradient and nongradient stimuli for our sample of neurons. The preference for faster and (to a lesser extent) slower speeds seen for gradient stimuli is preserved for nongradient stimuli (Fig.5A). When the number of speeds at which the response differs for the normal gradient and the nongradient stimuli are compared (Fig.5B), we find that almost two-thirds of the neurons (64%, or 67 of 105) show significant differences in their responses to at least one of the five speeds. Figure 5C shows the magnitude of the response differences between gradient and nongradient stimuli. Two-thirds of the neurons (69%, or 73 of 105) had speeds at which the larger of the responses to either stimulus was more than one and a half times the amplitude of the smaller response. Thus, in two-thirds of the neurons, the presence of a speed gradient produced substantial changes in neuronal responses.

Because eliminating the speed gradient alters the responses of many MSTd neurons, we next determined whether maintaining the gradient but varying its shape also would alter the responses. We used the same mean speed, but for negative speed gradients, the speed decreased with increasing distance from the center of the stimulus (inverted gradients; Fig. 6A), whereas for positive gradients, the speed increased with increasing distance from the center (normal or exaggerated gradients; Fig. 6B). The speed gradients were altered by multiplying the effect of distance from the center by a value from −2 to +2, creating seven speed gradient stimuli covering a segment of the range of naturalistic speed gradients. Figure 6,C and D, shows the responses of two neurons that show the most common response profiles observed. The neuron in Figure6C shows strong responses to the positive speed gradients, in contrast to the neuron in Figure 6D, which shows a clear preference for negative speed gradients.

In 122 MSTd neurons, we found a fairly uniform preference for negative and positive speed gradients (Fig. 7A). All seven gradients are well represented in the sample, with 89% of the neurons preferring either a negative or positive gradient over stimuli with no speed gradient. A somewhat greater number of neurons preferred positive gradients (53%, or 65 of 122) over negative gradients (36%, or 44 of 122), which suggests that the population of neurons might be skewed toward the more commonly encountered self-movement flow fields. To measure the magnitude of the gradient effect, we compared the largest response amplitude to the smallest response amplitude across the seven speed gradients. Figure 7B shows the distribution of the ratios of the largest versus the smallest responses among neurons preferring negative gradients (shaded bars) and neurons preferring positive gradients (filled bars). In both groups, almost half of the neurons (45% for neurons preferring negative gradients, 40% for neurons preferring positive gradients) showed contrast ratios greater than 0.25; i.e., the largest response was more than 1.5 times the amplitude of the smallest response.

Taken together, these results show that approximately two-thirds of MSTd neurons show substantial effects of eliminating speed gradients and that almost 9 of 10 neurons prefer a positive or inverted speed gradient to stimuli without a speed gradient. This indicates that the spatial distribution of speed across these radial and circular optic flow stimuli can have substantial effects on many MSTd neurons.

Potential explanations of gradient preferences

Speed gradient preferences could result from local differences in speed tuning profiles, such as a preference for slow motion in the center of the stimulus and fast motion in the periphery. To test this hypothesis, we presented stimuli separately to the central 50°² stimulus segment and the peripheral segment outside that area, dividing the stimuli approximately at the point at which local speeds are the same in all of the speed gradients (the pivotal points in Fig. 6A,B). Figure 8 illustrates the results of such experiments in two neurons having distinctly different speed gradient preferences. Both neurons showed increasing responses with increasing speeds in nongradient stimuli covering the full stimulus area (Fig. 8A), and continued to show this same increase whether the stimulus was limited to the central segment of the field (Fig. 8B) or to the peripheral segment (Fig. 8C). Thus, these neurons show no evidence of the kind of substantial differences in central and peripheral speed tuning that would seem necessary to account for the normal speed gradient preferences. This point is reinforced by noting that these neurons had very different speed gradient preferences; neuron 26KL22 showed a strong preference for negative speed gradients (Fig. 8D, left), whereas neuron 26KR5 showed a strong preference for positive speed gradients (Fig. 8D, right).

None of the 48 neurons tested had substantially different speed profiles in the central and peripheral segments. However, we saw substantial differences in overall response amplitude with six neurons showing no significant responses to stimulation of the peripheral segment. The scatter plot in Figure 9 shows the responses of the remaining 42 neurons as the slope from a least squares fit to the response profiles evoked by stimuli in the central (abscissa) and peripheral (ordinate) stimulus segments. Although this measure is insensitive to the details of a few of the response profiles included, it demonstrates the comparability of the responses evoked from the central and peripheral segments (the regression line for the sample has a slope of 0.78; r = 0.77). Thus, we see no evidence of a spatial segregation of speed preferences (e.g., preferring slow motion in the center and faster motion in the periphery) as the basis of speed gradient preferences in MSTd neurons.

One factor that might contribute to speed gradient preferences is interactions between responses that are simultaneously evoked from stimuli in different parts of the stimulus area, such as those between the central and peripheral segments. To test this hypothesis, we presented the five speeds of central motion either with no stimuli in the periphery or with slow motion in the periphery. Figure 10 shows the results of such studies in two neurons with nongradient full-field (Fig. 10A), central (Fig. 10B), and peripheral (Fig. 10C) responses, as well as responses to the combination of slow motion (10°/sec) in the periphery with the five speeds tested in the center (Fig. 10D). The middle column shows the responses of a neuron that preferred positive gradient stimuli and that had similar preferences for higher speeds in both central and peripheral stimuli but gave no responses when the periphery contained slow motion, even though the central segment contained the otherwise preferred fast motion. In contrast, the neuron in the right column preferred negative gradient stimuli and had similar preferences for faster speeds in both central and peripheral stimuli, but this neuron’s strongest responses were recorded when the periphery contained slow motion and the central segment contained fast motion. The peripheral stimulus, therefore, clearly can alter the response to different speeds of motion in the central segment.

To examine such effects more fully, we studied 44 neurons with speed testing in the central segment at our usual five speeds, while either slow motion (as in Fig. 9D) or fast motion was presented in the periphery. The results from the sample are summarized in Figure 11as the slopes of the response profiles for the five speeds in the central segment presented with either slow motion (abscissa) or fast motion (ordinate) in the periphery. The wider distribution of points in Figure 11, with a relatively flat regression line (slope = 0.26) and low correlation coefficient (r = 0.36) (i.e., compared with Fig. 9), reflects substantial differences between the responses to central stimulation when the peripheral segment contained slow versus fast motion. Some neurons appeared activated by fast motion in the periphery, whereas others appeared to be suppressed, and the same is true for slow motion in the periphery. Thus, MSTd neurons have different responses to a local speed stimulus depending on the speed of motion elsewhere in the stimulus, an effect that might contribute to the speed gradient preferences of these neurons.

With the stimuli used so far, we have been able to demonstrate speed interactions between spatially segregated parts of the stimulus area. To determine whether such spatial segregation of speeds is needed to elicit speed interactions, we combined different speeds as transparently overlapping planes of optic flow, with each plane having a different mean speed. Such stimuli are shown schematically in Figure12A, with the radial pattern of three nongradient speed planes (20°/sec, 40°/sec, and 60°/sec) shown to indicate transparent overlap throughout the 100°² stimulus area. Figure 12B shows that the responses of a neuron to such overlapping stimuli decrease with increasing numbers of speed planes.

We tested 22 neurons with stimuli containing multiple speed planes and summarized the magnitude of response variation as contrast ratios across multiple-plane stimuli (Fig. 12C). A total of 41% (9 of 22) of the neurons tested showed clear effects of the number of speed planes, with multiple-plane stimuli evoking responses with contrast ratios of more than 0.3 (Fig. 12C, right bar). In the sample, as many neurons preferred decreasing numbers of planes as preferred increasing numbers of planes. Thus, when a number of speeds are presented simultaneously in the same area, MSTd neurons do not respond merely to some preferred speed. Rather, their responses reflect, at least to some degree, the variety of different speeds presented.

The effects of the number of speed planes might rely on the particular speeds in the multiple speed plane stimuli or on the magnitude of the speed difference between those speeds. Figure 13 compares the response to the speed of a single speed plane (A) with the response to the differences between two simultaneously presented speed planes (B) for the same neuron shown in Figure12B. Figure 13A shows the responses to normal gradient stimuli with different mean speeds, whereas Figure13B shows the responses of the same neuron to thedifference between the speeds of two transparently overlapping planes. This neuron gave approximately the same response to all but the slowest single plane stimulus (Fig. 13A), but it showed consistently decreasing responses as speed differences increased between the two-plane stimuli (Fig. 13B). The responses to the difference in the mean speed of two-plane stimuli are not equivalent to the averaged response to those mean speeds in single-plane stimuli. However, the decline in response amplitude with increasing difference between speeds in two-plane stimuli closely resembles the profile for multiple-plane responses in that neuron (Fig.12B). This similarity is consistent with a response of this neuron to the relative motion within the optic flow stimulus.

We found that neurons that show sensitivity to the relative motion with increasing numbers of planes (Fig. 12B) showed similar profiles of relative motion sensitivity to the speed difference between two superimposed planes of motion (Fig. 13B). Such similarities were evident in seven of the nine neurons that had contrast ratios greater than 0.3 for multiple-plane responses (Fig.12C). Thus, the effects of multiple speed planes can be mimicked by presenting two planes having the same overall differences in speed that are presented in the multiple speed planes stimuli. This does not appear to reflect preferences for a particular speed as much as a preference for a combination of speeds, a preference that might be related to interactions between simultaneously presented speeds.

Speed preferences of MSTd neurons

We first examined the sensitivity of MSTd neurons to the pattern of speed in optic flow stimuli by varying the mean speed within the stimuli from 10 to 80°/sec. The radial stimuli would approximate the visual experience of an observer (at our viewing distance of 50 cm from the stimulus) moving forward at speeds from 0.2 to 1.5 m/sec, a range that is included in naturalistic experience. More than two-thirds of the MSTd neurons were strongly modulated by changes in the mean speed of the stimulus, and this modulation was similar in neurons preferring radial stimuli and circular stimuli. The shape of the response profile to stimulus speeds varied across the sample of neurons, but they could be placed into five classes resembling simple filter characteristics. The most common response profile showed increasing response amplitude with increasing mean speed. These findings establish the effect of stimulus speed on the responses of many MSTd neurons, just as previous experiments established the effects of the direction of stimulus motion.

The range of MSTd speed preferences we observed is consistent with those demonstrated by Orban et al. (1995). However, they reported optimal responses mainly in the range of 15–20°/sec, whereas our results (Fig. 3B) suggest that a substantial number of MST neurons prefer faster speeds. It is worth noting that Maunsell and Van Essen (1983) found that neurons in the middle temporal area (MT) also show a broad range of speed preferences, with some neurons preferring slower speeds (10–50°/sec) and others preferring faster speeds (>100°/sec). This conclusion is supported by the broad range of speed preferences demonstrated by Cheng et al. (1994) in MT and V4 neurons.

Our MSTd speed profiles are similar to those reported for striate cortex by Orban et al. (1981), who described speed profiles by analogy to filter characteristics, specifically low-pass, high-pass, and bandpass filters. However, MSTd neurons show an additional profile that can be termed band-reject (Figs. 2E, 3A), in an extension of the analogy to filters. The number of these neurons is small (11% of the total), but their presence might be taken as completing the spectrum of simple filters and might be viewed as supporting a filter model of these responses. The low-pass and high-pass filters provide orthogonal representations of stimulus speed that might interact to create other response characteristics, such as the more selective bandpass and band-reject filters. The band-reject filters are also noteworthy because they might play a role in refining MSTd responses, since a complete set of simple filters provides a greater potential for focusing bandwidth to complex stimuli. The range of MSTd response profiles is also consistent with the observation that response profile shape varies along a continuum in MT (Maunsell and Van Essen, 1983).

The most salient point is that many MSTd neurons are sensitive to the mean speed of the optic flow stimulus, which is in contrast to the impression given by several previous studies of MSTd, including our own (Duffy and Wurtz, 1991a), based on much smaller samples of neurons.

Effects of speed gradients

To assess the effects of speed gradients, we created stimuli with the same radial and circular patterns of motion direction, but with no speed gradient (Fig. 4), inverted speed gradients (faster motion in the center than in the periphery; Fig. 6A), or exaggerated gradients (much slower motion in the center; Fig.6B). Approximately two-thirds of the MSTd neurons showed substantial effects of eliminating speed gradients, and almost 9 of 10 neurons preferred a positive gradient or inverted speed gradient to stimuli having no speed gradient. This indicates that the spatial distribution of speed across optic flow stimuli has substantial effects on many MSTd neurons and could contribute to the role these neurons play in the analysis of optic flow.

This conclusion differs from those of previous studies in ways that may be accounted for by differences in the experiments and in the size of the sample of neurons. In the first study of speed gradient effects on MSTd neurons (Tanaka et al., 1989), one of eight radial segments in an expansion/contraction stimulus was modified to eliminate the local speed gradient, and little effect was observed. We were able to present a series of gradient stimuli that cover more of the natural range of speed gradients in optic flow, and this may have revealed effects that otherwise would not be apparent. In addition, our sample contained some MSTd neurons with relatively little sensitivity to speed, raising the possibility that studies with smaller sample sizes might have included such neurons.

We altered speed in optic flow stimuli to determine whether MSTd neurons might use this parameter as a cue for self-movement perception. However, these stimuli might also be interpreted as simulating the movement of large objects at various speeds relative to a stationary observer. Our findings revealed a third potential role for MSTd neurons: the analysis of visual motion cues about the three-dimensional structure of the environment. For example, MSTd neurons preferring positive speed gradients might be most active when nearby features of the scene are in the peripheral visual field (i.e., the trees lining the approach to the house in Fig. 14A) with remote features in the central visual field (i.e., the house in Fig.14A). In contrast, neurons preferring negative speed gradients might be most active when nearby features are in the central visual field (i.e., the house in Fig. 14B) with remote features in the periphery (i.e., the trees in Fig.14B). This interpretation suggests that MSTd neurons might contribute to previously demonstrated perceptual capacities to discriminate between differently structured environments based on the visual motion in optic flow (Braunstein and Andersen, 1981; Harris et al., 1992). These findings are also consistent with the notion that MSTd neurons could serve as the hypothesized higher-order elements needed to integrate speed and direction information from optic flow to support visual space perception (Nakayama and Loomis, 1974).

Speed interactions in MSTd neurons

A simple explanation of speed gradient preferences would be that central and peripheral stimulus segments have different speed preferences (e.g., preferring slow movement in the center and fast movement in the periphery). We compared responses evoked by stimuli in the central 50°² of the stimulus area with those evoked by stimuli outside that area and found no substantial differences in their speed profiles (Fig. 9). Furthermore, neurons with very different gradient preferences were found to have very similar central and peripheral speed profiles (Fig. 8A–D).

The absence of support for the simplest explanation of speed gradient preferences prompted us to consider whether interactions between speeds simultaneously presented in the central and peripheral stimulus segments might shape the responses of these neurons. To test this hypothesis, we compared the speed profiles evoked by stimuli in the central segment when presented along with either slow or fast motion in the periphery. In most of the neurons tested with these stimuli, there were substantial differences between responses to stimuli presented in the central segment, depending on the speed of motion in the periphery (Figs. 10, 11). Such interactions could contribute to speed gradient preferences by altering responses to particular combinations of central and peripheral speeds.

Because these experiments suggested that speed gradient preferences might rely on interactions between simultaneously presented speed stimuli, we wanted to determine whether those interactions required some particular spatial structure of the stimuli (e.g., slow motion in the center and fast motion in the periphery). To test this possibility, we presented different speeds together as transparently overlapping speed planes each covering the entire stimulus area (Fig.12A). We found that 41% of the neurons showed substantial changes in response amplitude as the number of speed planes increased from one to five (Fig. 12B,C), with similar effects induced by increasing the magnitude of the speed difference between two overlapping speed planes (Fig. 13). Thus, there is evidence for speed interactions even in the absence of the spatial separation of speeds in the stimulus.

The perceptual utility of neuronal sensitivity to the overlap of multiple speed planes might relate to the motion parallax in these stimuli. Figure 14C illustrates how a visual scene with substantial depth of field (i.e., the trees at various distances from the approach to the house) presents spatially overlapping features at different speeds. Motion parallax can reflect the three-dimensional structure of the environment (Gibson, 1966) and support detection of heading direction during self-movement (Rogers and Graham, 1979;Cutting et al., 1992), possibly relating the speed and direction preferences of MSTd neurons. Our experiments provide some clues about motion interactions that might underlie speed gradient and speed overlap preferences in MST neurons. However, further studies in which these preferences and interaction effects are tested together in a substantial number of neurons would be required to characterize the underlying mechanisms.