MST Responses to Pursuit Across Optic Flow With Motion Parallax

Urmen D. Upadhyay, William K. Page, and Charles J. Duffy

Departments of Neurology, Brain and Cognitive Sciences, Neurobiology and Anatomy, and Ophthalmology and the Center for Visual Science, The University of Rochester Medical Center, Rochester, New York 14642


    ABSTRACT
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Upadhyay, Urmen D., William K. Page, and Charles J. Duffy. MST Responses to Pursuit Across Optic Flow With Motion Parallax. J. Neurophysiol. 84: 818-826, 2000. Self-movement creates the patterned visual motion of optic flow with a focus of expansion (FOE) that indicates heading direction. During pursuit eye movements, depth cues create a retinal flow field that contains multiple FOEs, potentially complicating heading perception. Paradoxically, human heading perception during pursuit is improved by depth cues. We have studied medial superior temporal (MST) neurons to see whether their heading selectivity is also improved under these conditions. The responses of 134 MST neurons were recorded during the presentation of optic flow stimuli containing one or three speed-defined depth planes. During pursuit, multiple depth-plane stimuli evoked larger responses (71% of neurons) and stronger heading selectivity (70% of neurons). Responses to the three speed-defined depth-planes presented separately showed that most neurons (54%) preferred one of the planes. Responses to multiple depth-plane stimuli were larger than the averaged responses to the three component planes, suggesting enhancing interactions between depth-planes. Thus speed preferences create selective responses to one of many depth-planes in the retinal flow field. The presence of multiple depth-planes enhances those responses. These properties might improve heading perception during pursuit and contribute to relative depth perception.


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Optic flow contains a focus of expansion (FOE) that indicates heading direction during self-movement (Gibson 1950). Concurrent pursuit eye movements add rotation to the retinal image that causes the FOE to be displaced from the heading. During pursuit, depth cues from objects at different distances from the observer can create multiple retinal flow patterns that eliminate the singular FOE (Longuet-Higgins and Prazdny 1980).

Nevertheless, human observers show improved heading perception when depth is added to optic flow during pursuit (Royden et al. 1992; Stone and Perrone 1997; Warren and Hannon 1990). This might suggest that heading perception does not always rely on FOE identification and that other mechanisms might dominate during pursuit. Feature extraction, template-matching, and probabilistic network models have been shown to be viable possibilities (Koenderink 1986; Lappe and Rauschecker 1993; Perrone 1992), but there is no consensus regarding which model is best.

Neurons in the medial superior temporal area (MST) of monkey extrastriate visual cortex respond to optic flow (Saito et al. 1986). Many show heading selectivity (Duffy and Wurtz 1991b, 1995; Graziano et al. 1994; Orban et al. 1992; Tanaka et al. 1989) that persists during pursuit across optic flow that does not have depth cues (Bradley et al. 1996; Page and Duffy 1999).

We studied MST neuronal responses to optic flow with depth-plane stimuli. As with human observers, depth-planes enhance responses to neuronal optic flow during pursuit.

A brief report of this work has appeared previously (Upadhyay et al. 1998).


    METHODS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Animal surgery and training

Single neurons were recorded from two cerebral hemispheres of two rhesus monkeys. Surgery was performed under general anesthesia using inhaled Isoflurane. Bilateral scleral search coils (Judge et al. 1980), a head holder, and bilateral recording cylinders were surgically implanted. The recording cylinders were placed over 1-cm trephine holes above area MST (AP -2 mm, ML ±15 mm, angle 0) and encased in a dental acrylic cap. Postoperative analgesia was administered as judged appropriate by veterinary staff. All protocols were approved by the University Committee on Animal Research and complied with Public Health Service Policy on laboratory animals.

The monkeys were trained to sit in a primate chair and perform a visual fixation task that was monitored with magnetic search coils (Robinson 1963). Trials began with a red fixation point (0.25° diam, 2.7 cd/m2) centered on a rear-projection tangent screen 48 cm from the monkey. The monkey fixated within 500 ms and maintained fixation (±3°) for the 1-s stimulus period and an additional variable period (0.5-1.5 s) after the stimulus. Successful trials ended with a tone and a liquid reward.

Pursuit stimuli

In pursuit eye movement trials, the monkey followed a red light-emitting diode fixation target reflected off a two-axis mirror-galvonometer system. Each trial began with target fixation at the center of the screen. The target was then extinguished and re-appeared at an eccentricity of 7.5° along the horizontal or vertical axis. The target then moved at 15°/s across the center of the screen. Stimulus illumination began 66 ms after the onset of target movement and continued for 1 s while the monkey stayed within a ±3° window. The stationary FOE maintained a constant position on the screen to provide a constant simulated heading direction in body-centered coordinates (Fig. 1A).



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Fig. 1. Stimuli used in these studies. A: pursuit trials consisted of leftward, rightward, upward, or downward pursuit at 15°/s for 1 s. Pursuit started 7.5° from the center and ended at the same eccentricity on the opposite side. The 76° × 76° optic flow stimuli (fine arrows in smaller boxes) moved with the pursuit target (heavy arrows) on the 91° × 91° projection screen (larger boxes). Fixation trials (not shown) consisted of centered fixation throughout the 1-s visual stimulus period. B: the 9 optic flow patterns consisted of 500 white dots moving at an average speed of 40°/s across a dark 90° × 90° projection screen. Straight ahead self-movement was simulated by a centered focus of expansion (FOE) stimulus. The 8 other directions of forward self-movement had FOEs at 30° eccentricity and at 45° intervals around 360°. Here, each frame represents the screen containing one of the stimuli with the FOE at the junction of the arrows and the fixation point at the center of the screen. C: triple-plane stimuli had the same FOE location in 3 transparently superimposed sets of dots. Each speed plane contained 166 dots moving at an average speed of 20, 40, or 60°/s, respectively. D: the 3 separate speed stimuli each contained 500 dots moving at an average speed of either 20, 40, or 60°/s. These were used to characterize speed effects on neuronal responses.

A 76° × 76° viewing aperture moved with the pursuit target across the 91° × 91° optic flow stimulus to ensure a constant area of retinal stimulation. The stimulus first appeared with the viewing aperture shifted by 7.5° so that its peripheral edge was 46° from the center of the screen. The viewing aperture then moved with the pursuit target at 15°/s to its final position 31° from the center. The FOE remained at the screen location specified for that optic flow stimulus. The moving frame was not seen as an additional environmental surface by human observers, presumably because it moved with the eyes.

Visual stimuli

The optic flow stimuli consisted of 500 white dots (0.19° at 2.61 cd/m2) on a black background (0.18 cd/m2) in a 480 × 480 pixelation of the central 91° × 91° of the visual field. A pseudorandom sequence of visual stimuli were presented by a 486-based personal computer driving a television projector (Electrohome ECP4100) at a frame rate of 60 Hz. The stimuli covered a 76° × 76° area centered in the visual field during fixation trials and passing through the central visual field in pursuit trials.

Dot motion in the optic flow stimuli simulated the observer's approach to a remote fronto-parallel single- or triple-plane surface as an outward radial pattern of dots emanating from its FOE. During simulated forward self-movement the observer moved at 0.5 m/s toward a single depth plane at 0.5 m or toward three depth planes at 0.38, 0.5, and 0.63 m distance. The dots in each plane were evenly distributed in a random pattern in the first frame and were assigned a random lifetime of 1-60 frames. Dots were replaced at expiration or by a smoothing algorithm to maintain a uniform dot density across all frames in all stimuli.

Nine optic flow stimuli were used that contained FOEs at the center of the screen or at one of eight positions, displaced 30° from the center, and distributed at 45° intervals around 360° (Fig. 1B). Two stimulus sets were presented containing either three superimposed speed-defined depth planes with 166 dots in each speed plane (Fig. 1C), or a single speed plane with 500 dots (Fig. 1D). Dots accelerated as a sine × cosine function of their distance from the FOE maintaining an average speed of 40°/s for the single-plane, and 20, 40, and 60°/s for the triple-plane stimuli. Finally, the individual speed-defined planes were presented to test the speed sensitivity of the neurons. All of these stimuli were presented in a fully interleaved design.

Neuron recording

Epoxy-coated tungsten microelectrodes (Microprobe) were passed through a transdural guide tube positioned within the recording cylinder (Crist et al. 1988). Neuronal activity was monitored to determine relative depth of physiological landmarks. A dual window discriminator was used to digitize neuronal discharges, and these were stored with stimulus and behavioral event markers using the REX experimental control system (Hays et al. 1982). Neuron responses were averaged across the 1-s period over six to eight stimulus presentations to characterize responses to each stimulus.

Once a neuron was isolated, its approximate receptive field boundaries were determined by a hand-held projector. MSTd neurons were identified by their physiologic characteristics including large receptive fields (>20° × 20°) containing the fixation point, direction-selective responses, and a preference for large moving patterns rather than moving bars or spots (Duffy and Wurtz 1991a,b, 1995; Komatsu and Wurtz 1988). The approximate location within area MST was confirmed with the deeper extension of the penetration across the superior temporal sulcus (STS) to obtain typical responses of medial temporal (MT) neurons. MT was characterized as having greater responsiveness to bar or spot movement than is seen in MST, with smaller receptive fields that are proportionate to the eccentricity of the receptive field center.

We then recorded responses to all nine FOEs with single- and triple-plane stimuli during centered fixation and pursuit. Blank screen and stationary-dots control trials were randomly interleaved with test stimuli to provide a standard measure of neuronal responsiveness throughout all studies.

Recording sites

Neuron recordings in cortical area MST were directed by the stereotaxic positioning of the recording chambers and the depths of microelectrode penetrations. Magnetic resonance imaging (MRI) of the brain, with microelectrodes in place, confirmed positioning in MST. MRIs were obtained in the sagital plane on a 1.5 Tesla magnet (General Electric) with fast spoiled gradient echo technique (TR = 23.5, TE = 10.3, 30° flip angle). The scans confirmed the location of the electrode tips in the anterior bank of the STS.

On the completion of neuron recording in each monkey, electrolytic lesions (25 µA × 25 s) were made in each hemisphere along penetration tracks in three different guide tubes. After perfusing the animal and fixing the tissue, we cut posterior cortical blocks in 50-µm-thick sections. Nissl and Luxol Fast Blue techniques were used to stain every fourth and fifth section, respectively. The recording sites were identified by extrapolation from the location of the electrolytic lesions. The analysis of histology from the two hemispheres studied indicates that these neurons were in the anterior bank of the superior temporal sulcus that is included in dorsal part of area MST (MSTd) (Komatsu and Wurtz 1988).

Data analysis

Trial-by-trial discharge rates for each stimulus condition were averaged across the 1-s stimulus period and entered into ANOVAs using the SAS statistics package (SAS Institute 1988) to quantify the effects of stimulus parameters on neuronal activity. The results were tested for statistical significance at the P < 0.05 level.

Neuronal responses were displayed as linear plots to emphasize relative response amplitude and as polar plots to emphasize response directionality. In the polar plots (Fig. 6A), eight thin radial lines represent responses to the optic flow stimuli with FOEs in the direction of the lines. The length of the eight radial lines is proportionate to the neuronal firing rate during the corresponding stimulus period.

The thick radial line in each polar plot indicates the vector sum of the eight individual response vectors. The angle of the net vector is the mean direction of that polar distribution. The length of the net vector, the resultant length, is a measure of the strength of the direction selectivity in that polar distribution. We used circular statistical analyses for data sampled at 45° intervals around 360° (Batschelet 1981), including the Rayleigh Z statistic to test for significant directionality in a circular response profile. A large Z-value suggests a uni-modal distribution with a clearly preferred direction.


    RESULTS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

We studied 134 neurons in the anterior bank of the STS. All neurons were more responsive to the movement of large patterns than to the movement of spots or bars. Most of the receptive fields covered more than a full quadrant of the visual field and included the fixation point as is typical of neurons in the dorsal segment of area MST (Komatsu and Wurtz 1988). All classes of optic flow neurons in MST (Duffy and Wurtz 1991b) were included because all might contribute to heading determination (Duffy and Wurtz 1995) during fixation and pursuit (Page and Duffy 1999). Limitations on recording duration precluded the testing of other flow field stimuli.

Pursuit reveals planes effects

We tested whether pursuit disrupts optic flow selectivity and whether such effects are greater with triple-plane stimuli that do not have a visible FOE during pursuit. The single- and triple-plane versions of our nine optic flow fields were presented while the monkey performed pursuit eye movements in four (horizontal and vertical axes, n = 34) or two directions (horizontal axis alone, n = 100; Fig. 2).



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Fig. 2. Pursuit across single-plane and triple-plane optic flow stimuli. Eye position records from centered fixation and each of the 4 pursuit directions tested in these studies. The horizontal traces are shown for leftward and rightward pursuit; the vertical traces are shown for upward and downward pursuit. In each frame, traces from pursuit across single-plane stimuli are shown above traces from pursuit across triple-plane stimuli. Pursuit gains were similar with single- and triple-plane stimuli: rightward pursuit, single-plane = 0.84 ± 0.10, triple-plane = 0.84 ± 0.10; leftward pursuit, single-plane = 0.86 ± 0.09, triple-plane = 0.88 ± 0.10; upward pursuit, single-plane = 0.82 ± 0.08, triple-plane = 0.81 ± 0.09; downward pursuit, single-plane = 0.74 ± 0.03, triple-plane = 0.72 ± 0.04.

MST neurons respond differently to single- and triple-plane stimuli during pursuit. Figure 3 shows the responses of a neuron with a strong preference for optic flow stimuli having FOEs in the upper visual field. During all directions of pursuit, the triple-plane stimuli with 166 dots in each plane (- - -) maintained stronger responsiveness and response selectivity than the single-plane stimuli with 500 dots (---).



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Fig. 3. Responses of a medial superior temporal (MST) neuron to single- (---) and triple-plane (- - -) stimuli during centered fixation (center) and 4 directions of pursuit. The response profiles seen in fixation were maintained during right and down pursuit across triple-plane but not single-plane stimuli. The graphs plot average response amplitude for the 1-s stimulus period (ordinate) for each of the nine FOEs (abscissa). The horizontal lines in each graph indicate control activity level when no visual stimulus was presented (solid line) or when stationary dots were presented (dashed line). Two-way ANOVAs of planes and FOE effects during each pursuit condition revealed significant planes effects in left pursuit (P = 0.0002) and significant planes × FOE interactions in right (P = 0.001) and down (P = 0.0001) pursuit. The spike density histograms (inset) show that differences between the responses to single- and triple-plane stimuli affected the entire stimulus period. The graphs show averaged responses to 6 presentations of the preferred FOE stimuli (ordinate) using single-plane (open plot) and triple-plane (filled plot) stimuli presented for 1 s (horizontal bar).

Figure 4 compares planes effects, defined as the differences between single- and triple-plane responses, in fixation (abscissas) and pursuit (ordinates) for the pursuit direction that yielded the largest effects. In the great majority of neurons, pursuit revealed larger planes effects than were observed in fixation. This was true of 63% (85/134) of the neurons when averaging planes effects across the nine FOE stimuli presented in fixation versus pursuit (Fig. 4A). This was true of 72% (96/134) of the neurons when comparing the largest planes effect for any FOE in fixation versus pursuit (Fig. 4B). Both of these distributions are significantly asymmetric by the nonparametric sign test for large samples (Fig. 4A, z = 3.1, P < 0.002; Fig. 4B, z = 5.0, P < 0.0001).



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Fig. 4. Pursuit enhances the differences between responses to single- and triple-plane stimuli. Differences between responses to single- and triple-plane stimuli are compared for fixation (abscissas) and pursuit (ordinates). A: average differences during pursuit. The absolute values of the differences averaged across all 9 FOE stimuli and expressed as a percentage of the response to single-plane stimuli in fixation. The bar graph at the end of the diagonal shows the differences between planes effects during pursuit and fixation (abscissa) vs. the percentage of neurons showing those differences (ordinate). The larger percentages on the left side of the bar graph show that the majority of studies (63%, 85/134) showed larger average differences between single- and triple-plane responses during pursuit. B: largest differences during pursuit. The largest difference from the 9 FOE stimuli and expressed as a percentage of the response to that single-plane stimulus in fixation. A still greater preponderance (72%, 96/134) of larger effects was seen during pursuit.

These differences between planes effects in fixation and pursuit were tested in each neuron using a three-way ANOVA to determine the effects of viewing condition (fixation vs. pursuit), depth cues (single vs. triple planes), and FOE location (9 FOEs). Half of the neurons (49%, 66/134) showed a significant main effect of depth cues, indicating higher response rates for triple planes or a significant interaction between viewing condition and depth cues, indicating that responses to triple-plane stimuli were larger under conditions of pursuit (P < 0.05). In many of these neurons (46%, 30/66), the effect of depth planes was restricted to the preferred FOEs as shown by a significant depth planes × FOE interaction.

Multiple planes enhance responses

Responses to triple-plane stimuli during pursuit were generally larger than the corresponding responses to single-plane stimuli. Figure 5 shows the differences in the magnitude of responses to single- and triple-plane responses during pursuit as a percentage of the single-plane responses during fixation. Averaging across the nine FOE locations, 68% (91/134) of the neurons showed larger triple-plane responses (Fig. 5A, right). Focusing on the FOE that showed the largest planes effect, 71% (95/134) of the neurons showed larger triple-plane responses (Fig. 5B, right). These distributions are highly asymmetric (Fig. 5A, sign test z = 4.1, P < 0.0001; Fig. 5B, sign test z = 5.0, P < 0.0001).



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Fig. 5. Stronger responses to triple-plane stimuli during pursuit. Bar graphs plot the differences between single- and triple-plane responses (abscissas) vs. the percentage of neurons showing those differences (ordinates). Neurons with stronger responses to single-plane stimuli are on the left, those with stronger responses to triple-plane stimuli are on the right. A: average differences during pursuit. Differences averaged across the 9 FOEs as a percentage of the single-plane response. More neurons showed stronger responses to triple-plane than to single-plane stimuli (68%, 91/134). Of the 68 neurons with more than 5% differences between single- and triple-plane responses, more preferred the triple-plane stimuli (70%, 56/68). B: largest differences during pursuit. Largest differences from the 9 FOEs as a percentage of the response to that single-plane stimulus. Again, far more neurons (71%, 95/134) showed larger responses to triple-plane stimuli.

Preferences for single- or triple-plane stimuli were verified by two-way ANOVAs of these data with planes and FOE location main effects. A total of 47% (63/134) of the neurons showed significant planes effects (P < 0.05). Of those neurons, 76% (48/63) preferred triple-plane stimuli as measured by average response effects, and 81% (51/63) preferred triple-plane stimuli as measured by peak response effects. Thus triple-plane stimuli typically evoked stronger responses from MST neurons during pursuit.

We compared the direction selectivity of single- and triple-plane responses by deriving net vectors from the polar plots. The strength of direction selectivity and the preferred direction were measured by the resultant length and mean direction of the net vectors, respectively. In neurons that showed their largest responses to the center FOE (n = 4), we compared single- and triple-plane responses to that stimulus.

Triple-plane stimuli also evoked stronger direction selectivity than single-plane stimuli, although the preferred direction was usually the same. Figure 6A shows the responses of an MST neuron that maintained its preferred direction with single- and triple-plane stimuli in leftward and rightward pursuit. The strength of this direction preference was maintained with triple-plane stimuli and dropped by 31% during rightward pursuit across single-plane stimuli.



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Fig. 6. Triple-plane stimuli evoked stronger FOE selectivity. A: FOE selectivity maintained by triple-plane stimuli. This neuron showed nearly identical responses to single- and triple-plane stimuli during leftward pursuit, somewhat stronger triple-plane responses during fixation, and substantially stronger triple-plane responses during rightward pursuit (top). The horizontal lines indicate control activity level with no visual stimulus other than the fixation target (---) or with stationary dots (- - -). A 3-way ANOVA with planes, pursuit, and FOE main effects revealed a significant 3-way interaction (P = 0.0001). Post hoc 2-way ANOVAs with planes and FOE main effects revealed significant planes × FOE interactions in fixation (P = 0.0001) and rightward pursuit (P = 0.0002). Polar plots of these responses (bottom) reveal that the strength of this neuron's direction selectivity [measured by the resultant length (RL) of the net vector] substantially decreased with single-plane stimuli during rightward pursuit. However, the preferred direction [measured by the mean direction (MD) of the net vector] was maintained across all conditions tested. Polar limbs with filled dots represent responses that were significantly different from control (Student's t-test, P < 0.05). B: differences in the strength of FOE selectivity with single- and triple-plane responses during pursuit. Differences in the resultant length (abscissa) are graphed for the percentage of neurons (ordinate) that showed significant differences (2-way ANOVA with P < 0.05, n = 63/134) between the responses to single- and triple-plane stimuli. The great majority of these studies (70%, 44/63) showed stronger direction selectivity with triple-plane stimuli. C: differences in FOE preferences with single- and triple-plane stimuli. Preferred directions were nearly the same in the responses to single- and triple-plane stimuli. Differences in the mean direction (abscissa) are graphed for the percentage of studies (ordinate) that showed significant directionality to both single- and triple-plane stimuli (Rayleigh Z with P < 0.05, n = 34/63). Almost all of these studies (91%, 31/34) showed mean directions from single- and triple-plane responses within 20°.

The directionality of single- and triple-plane responses was compared in the 63 neurons that showed significant differences between those responses. The great majority of neurons (70%, 44/63) showing stronger direction selectivity in the triple-plane responses created a highly asymmetric distribution (Fig. 6B, sign test z = 3.2, P < 0.002). In contrast, the direction that was preferred was almost always the same in single- and triple-plane responses. Neurons that showed significant direction selectivity in both single- and triple-plane responses (Rayleigh Z with P < 0.05) rarely (9%, 3/34) showed substantial changes in their preferred direction (Fig. 6C).

Thus during pursuit, triple-plane stimuli evoked larger optic flow responses with stronger direction selectivity than was evoked by single-plane stimuli.

Speed affects heading responses

To evaluate the contributions of speed sensitivity to the triple-plane responses, we presented the three component speed planes as separate stimuli. Optic flow stimuli with nine different FOEs were presented at the three speeds of 20, 40, and 60°/s, where the 40°/s stimulus was the single-plane stimulus. Speed effects were mainly seen as larger excitatory responses to the preferred FOE stimuli (Fig. 7A), although larger inhibitory responses to the anti-preferred FOEs were also observed.



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Fig. 7. Speed sensitivity in FOE responses. Sensitivity to stimulus speed was tested in 69 MST neurons by presenting separately the 20, 40, and 60°/s speed-defined depth planes. A: responses of an MST neuron to the 3 speed stimuli with stronger responses to the faster speed but a maintained profile of FOE preference across speeds. A 2-way ANOVA with speed and FOE main effects revealed a significant speed × FOE interaction (P = 0.0001). The horizontal line indicates control activity during fixation with no visual stimulus other than the fixation target. B: responses to slow and fast stimuli as a percentage of responses to moderate speed stimuli. Speed effects are shown as the amplitude of the peak response to the slow stimuli (20°/s,  and open circle ) and the fast stimuli (60°/s,  and ) as a percentage of the peak response to the moderate speed stimuli (40°/s, horizontal line at 100%).  and , neurons that had significant speed tuning as determined by 2-way ANOVA with speed main effects and speed × FOE interactions. More neurons showed a strong preference for the fast stimuli (64%, 44/69; right), with far fewer preferring the moderate (19%, 13/69; middle) or slow (17%, 12/69; left) stimuli.

We studied 69 neurons with stimuli at 3 speeds during fixation. Figure 7B shows their speed sensitivity measured as the amplitude of their largest response to the nine FOE stimuli at each of the three speeds tested. The largest response to slow ( and open circle ) and fast ( and ) stimuli are expressed as a percentage of the largest response to the moderate speed stimuli (100% line). Most neurons (64%, 44/69) preferred the fast speed stimuli, with less than half as many (19%, 13/69) preferring the moderate speed, and still fewer (17%, 12/69) preferring the slow speed.

Directional analysis of these responses showed that MST neurons maintain their preferred directions across stimulus speed changing only the strength of their direction preferences. The vast majority (81%, 56/69) of the neurons showed changes of at least 20% in the resultant lengths of their responses to different speed stimuli. In contrast, only 18% (11/62) of these neurons showed changes of mean direction >20° across stimulus speeds.

The speed preferences of MST neurons were maintained during pursuit, but the strength of their speed preferences varied substantially. Figure 8A shows the responses of an MST neuron to the three sets of speed stimuli presented during leftward pursuit, centered fixation, and rightward pursuit. In all three pursuit conditions this neuron preferred the fast stimuli, but speed effects were strongest during leftward pursuit.



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Fig. 8. Speed preferences were maintained during pursuit, although the magnitude of the speed effects often varied substantially. A: speed effects during pursuit. Responses of an MST neuron (ordinates) to the 9 FOE stimuli (abscissas) during leftward pursuit, centered fixation, and rightward pursuit. The preference for fast stimuli was maintained during fixation and pursuit but was strongest in leftward pursuit and weakest in rightward pursuit. Two-way ANOVAs revealed significant speed × FOE interactions in all 3 pursuit conditions (P = 0.0001). Horizontal lines indicate control activity level during fixation with no visual stimulus other than the fixation target. B: distribution of speed preferences in fixation and pursuit. The percentage of neurons (ordinates) that showed their largest responses to the slow (20°/s), moderate (40°/s), and fast (60°/s) speed stimuli (abscissas). Across all 3 pursuit conditions (n = 207), the fast stimuli were preferred by most neurons (an average of 61%, 126/207) with only <FR><NU>1</NU><DE>3</DE></FR> as many preferring moderate (19%, 39/207) or slow (20%, 42/207) stimuli. The percentage of neurons that showed significant speed effects (2-way ANOVA for speed and FOE, P < 0.05) in each group is represented by the filled segment of each bar.

A total of 207 speed studies were conducted (69 neurons and 3 pursuit conditions). The majority of these (59%, 123/207) yielded significant speed effects (2-way ANOVA for speed and FOE, P < 0.05). Significant speed effects were observed in neurons preferring slow, moderate, and fast stimuli in all three pursuit conditions (filled segments of bars in Fig. 8B). However, the majority of neurons consistently preferred the fast stimuli (Fig. 8B).

The pattern of speed sensitivity was maintained across pursuit conditions, although the strength of that sensitivity varied substantially (as in Fig. 8A). We made 138 comparisons between pursuit (left or right) and fixation in the 69 neurons studied with speed stimuli under all 3 conditions. In 44% (61/138) of these comparisons, both the pursuit and fixation responses showed significant speed effects. The vast majority (82%, 50/61) of these comparisons showed the same preference for slow, moderate, or fast speed stimuli in both conditions.

Thus we find that stimulus speed changes the strength of optic flow responses without changing their direction preferences. In addition, speed sensitivity persisted during pursuit, although the strength of speed preferences varied.

Responses to depth cues

We examined the relationship between speed sensitivity and responses to triple-plane stimuli to determine whether a preference for one speed plane altered responses to multiple, superimposed speed planes. Our approach is illustrated by the responses of a neuron with a strong preference for fast stimuli (Fig. 9A, left). Its responses to the separate presentation of the three speed stimuli were averaged for each FOE (Fig. 9A, right, ---) and found to be substantially smaller than its responses to triple-plane stimuli (Fig. 9A, right, - - -). The peak response with triple-plane stimuli was 22% larger than the average speed-plane responses, and it was nearly as large as the response to the preferred fast speed-plane stimulus presented alone.



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Fig. 9. Motion parallax effects in FOE responses. Many responses to triple-plane stimuli were greater than the average response to the 3 speed planes, suggesting an effect of the motion parallax in the triple-plane stimuli. A: responses of an MST neuron (ordinates) to the 9 FOEs (abscissas) in the 3 speed stimuli (left) and in the triple-plane stimuli (right). The fast stimuli evoked the strongest responses with a significant speed × FOE interaction in a 2-way ANOVA (P = 0.0001). The responses for the 3 speed planes were averaged for each FOE and plotted on the right (---). The responses to the triple plane stimuli (right, - - -) indicate that the triple plane evoked larger responses than the average. This difference yielded a significant response × FOE interaction in a 2-way ANOVA (P = 0.0013). Horizontal lines indicate control activity level during fixation with no visual stimulus other than the fixation target. B: differences between triple-plane and averaged speed responses. We compared the averaged speed responses to the triple-plane responses in 138 pursuit studies from 69 neurons tested with the interleaving of both stimulus sets. Most of the studies (59%, 81/138) showed triple-plane responses that were similar to the averaged speed responses (±20%). However, almost all of the other studies (96%, 55/57) showed substantially larger triple-plane responses. ANOVAs showed that 38% (26/69) showed significant differences (P < 0.05) between the triple-plane and averaged speed responses with 81% (21/26) of those showing larger triple-plane responses.

Relationships between the averaged responses to the 3 speed planes and the responses to triple-plane stimuli were tested in 69 neurons recorded during leftward and rightward pursuit. Figure 9B shows differences between the triple-plane and averaged speed responses as a percentage of response amplitude (abscissa) across studies (ordinate). Many studies (41%, 57/138) showed large differences (>20%), creating a highly asymmetric distribution (sign test z = 5.6, P < 0.0001). Almost all of those with large differences (96%, 55/57) preferred the triple-plane stimuli.

We tested the significance of differences between responses to triple-plane stimuli and the averaged responses to the speed stimuli. Many neurons (38%, 26/69) showed significant stimulus effects or stimulus × FOE interaction effects in one or both pursuit directions, most of these (81%, 21/26) showing larger responses to the triple-plane stimuli. Thus the triple-plane responses were larger than the averaged responses to the individual speed planes, seemingly maintaining speed preferences even when multiple, speed planes are presented.

We considered that the effects of triple-plane stimuli might be limited by a conflict between its motion parallax depth cue and a binocular disparity cue from our flat stimulus screen. We were not able to directly manipulate stimulus disparity, so we approached this issue by comparing planes effects during binocular and monocular viewing.

Figure 10 shows the responses of an MST neuron to single- and triple-plane stimuli during leftward and rightward pursuit. We recorded responses during a sequence of viewing conditions: binocular, left-covered monocular, right-covered monocular, and a repeat of binocular viewing. Substantially larger responses to triple-plane stimuli were seen during rightward pursuit under all viewing conditions (Fig. 10, right). However, the preference for single-plane stimuli seen with binocular viewing in leftward pursuit was reversed with monocular viewing. Thus with binocular viewing, single-plane responses were larger, but during monocular viewing triple-plane responses were larger.



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Fig. 10. Effects of monocular viewing on differences between single- and triple-plane responses. The responses of a neuron to single- (---) and triple-plane stimuli (- - -) presented during leftward (left) and rightward pursuit (right). A: initial presentation during binocular viewing showed similar single- and triple-plane responses during leftward pursuit and much stronger responses to triple-plane stimuli during rightward pursuit. B: with the right eye covered, triple-plane stimuli evoked stronger responses in both pursuit directions. C: with the left eye covered, the triple-plane responses remained stronger. D: when binocular viewing was resumed, leftward pursuit again showed similar single- and triple-plane responses, and rightward pursuit continued to show stronger responses to triple-plane stimuli. Thus monocular viewing either maintained or enhanced responses to triple-plane stimuli during pursuit. Horizontal lines indicate control activity level during fixation with no visual stimulus other than the fixation target.

We conducted 67 studies in 21 neurons using single- and triple-plane stimuli with binocular and monocular viewing. Two-thirds (66%, 14/21) showed no changes in planes effects other than a uniform decrease in response amplitude during monocular viewing. The remaining seven neurons included five with greater planes effects during monocular viewing and two with lesser effects. These findings suggest that binocular disparity interacts with speed-defined depth cues in some neurons.


    DISCUSSION
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ABSTRACT
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METHODS
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DISCUSSION
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Effects of triple-plane stimuli

Pursuit alters the retinal image of optic flow. In single depth-plane stimuli the FOE is displaced from the heading. In multiple depth-plane stimuli there are multiple, overlapping, displaced FOEs (Longuet-Higgins and Prazdny 1980). Paradoxically, heading perception during pursuit is improved by the presence of multiple depth-planes (Royden et al. 1992; Warren and Hannon 1990).

We compared the responses of MST neurons to optic flow stimuli containing 1 speed-defined depth plane with 500 dots or 3 speed-defined depth planes with 166 dots in each plane. Some differences were observed during fixation, but larger differences emerged during pursuit (Figs. 3 and 4). During pursuit, triple-plane stimuli evoked stronger responses, with stronger direction selectivity, and the same preferred direction (Figs. 5 and 6). Thus as with human observers, multiple speed-defined depth-planes enhance MST neuronal heading selectivity during pursuit.

Enhanced heading perception during pursuit across depth-planes has prompted efforts to identify heading cues other than the FOE. Proposed mechanisms include decomposing optic flow into orthogonal motion components (Koenderink 1986), template matching to motion patterns (Perrone and Stone 1994), and network derivation of heading probabilities (Lappe and Rauschecker 1993). All of these hypotheses are consistent with MST's neuronal heading selectivity during pursuit (Bradley et al. 1996; Page and Duffy 1999) and our current finding that depth-planes enhance that selectivity. These hypotheses do not predict our finding that speed sensitivity is an important factor.

Speed sensitivity and pursuit

The separate presentation of three different speed-planes revealed strong speed sensitivity in most neurons with the largest number (64%, 44/69) preferring the fastest speed (60°/s). The preferred speeds evoked stronger direction selectivity and maintained similar preferred heading directions (Fig. 7). Speed sensitivity persisted during pursuit with about the same proportions of slow, moderate, and fast preferring neurons. However, many neurons showed substantial changes in the strength of their speed sensitivity across pursuit conditions (Fig. 8).

Earlier studies showed that visual speed sensitivity enhances optic flow selectivity in MST (Tanaka et al. 1989). This effect was also seen in studies that detailed MST neuronal tuning for a range of speeds (Orban et al. 1995). Both phenomena are important to enhancing responses to optic flow that simulates a particular three-dimensional layout of the visual scene (Duffy and Wurtz 1997). Our current findings show that MST neurons maintain speed sensitivity during pursuit, possibly enhancing stimulus selectivity when multiple depth-planes create a number of flow fields on the rotating retina.

Speed sensitivity during pursuit has several potential advantages. First, greater responsiveness to a particular speed-plane might effectively filter triple-plane stimuli by allowing the preferred speed to dominate the responses. Second, speed sensitivity might cause more neurons to be optimally activated by multiple speed-plane stimuli than by any single speed-plane. Third, the prevalence of fast preferring neurons might cause greater responsiveness to the faster motion in nearer depth-planes where there is less FOE displacement during pursuit.

Optic flow and depth cues

Triple-plane responses that are greater than the average response to the component speed-plane stimuli (Fig. 9) might contribute to the perception of motion parallax. Interactions between motion parallax and binocular disparity (Roy et al. 1992) in MST might explain why monocular viewing can enhance differences between single- and triple-plane responses (Fig. 10). We view monocular enhancements as reflecting the removal of conflicts between parallax and disparity cues. This suggests that MST neurons respond to speed-planes as a depth cue and supports our earlier conclusion that MST neurons are sensitive to motion parallax in optic flow (Duffy and Wurtz 1997).

Speed, motion parallax, and disparity sensitivity might all make important contributions to optic flow analysis in MST. The addition of rotation to multiple depth-plane flow fields creates multiple speed-planes on the retina with each speed-plane having a somewhat differently displaced FOE. Each speed-plane might activate a different set of speed-sensitive neurons, each of which would indicate a somewhat different FOE location. Different FOE locations indicated by neurons with different speed preferences might serve as an important signal that there is rotation in the retinal flow field.

MST's extra-retinal pursuit (Newsome et al. 1988), head movement (Shenoy et al. 1999), and vestibular (Duffy 1998) signals could then be used to apportion flow field rotation to eye, head, and body movements. The remaining rotation would reflect path curvature (Stone and Perrone 1997) that could be assessed using relative depth cues from motion parallax. Finally, the path of self-movement could be derived by scaling to environmental geometry using disparity as an absolute depth cue.


    ACKNOWLEDGMENTS

The authors appreciate the comments of M. J. Dubin, M. T. Froehler, D. J. Logan, and S. J. Tetewsky. D. Welch provided invaluable technical assistance.

C. J. Duffy was supported by National Eye Institute Grant R01-10287, Human Frontier Science Program Grant RG71/96, and Research to Prevent Blindness. W. K. Page was supported by National Institute of Mental Health National Research Service Award F31MH-11616.


    FOOTNOTES

Address for reprint requests: C. J. Duffy, Dept. of Neurology, University of Rochester Medical Center, 601 Elmwood Ave., Rochester, NY 14642-0673 (E-mail: cjd{at}cvs.rochester.edu).

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received 22 November 1999; accepted in final form 2 May 2000.


    REFERENCES
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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