Introduction

The estimation of self-motion from visual cues is an essential component of navigation through the environment. Gibson (Gibson, 1950) defined optic flow as the complex, space-varying motion patterns produced by observer motion through visual scenes. Psychophysical experiments show that humans are extremely good at recovering the direction of heading from patterns of optic flow, even without any actual self motion. Subjects can resolve their direction of motion from random dot displays with a precision of ~1–3° (Warren, 1998). Furthermore, subjects can largely compensate for ongoing smooth pursuit eye movements, which would degrade the retinal flow-field information if left uncompensated (Warren and Hannon, 1990; Royden et al., 1992, 1994). Despite disputes over exactly which cues are utilized for this compensation, it appears that ‘extraretinal’ (e.g. oculomotor efference copy or proprioceptive feedback) signals of ongoing eye movements are used under many circumstances.

Electrophysiological experiments on the ‘motion system’ of dorsal extrastriate cortex in monkeys have identified cortical areas that might be involved both in analyzing complex optic flow information and in compensating this information for ongoing pursuit eye movements. Although it is not the only area representing such information, MST is one promising candidate. Cells in this region have large receptive fields and many are highly selective for particular optic flow patterns, such as expansion, contraction, or rotation (Saito et al., 1986; Tanaka et al., 1986; Duffy and Wurtz, 1991, 1995, 1997). Such components are present in the optic flow patterns produced by self-motion through normal scenes (Koenderink, 1986; Koenderink and van Doorn, 1987). Indeed, MST cells are often selective for particular headings (Bradley et al., 1996; Duffy and Wurtz, 1997) and their responses are at least partially compensated for smooth pursuit eye movements (Bradley et al., 1996). These features suggest that MST may be a substrate for the recovery of heading from optic flow information.

To test the hypothesis that macaque MST is involved in heading tasks, we trained two monkeys to discriminate their perceived heading from random dot patterns simulating trajectories toward three-dimensional clouds. Here we report that the perceptual performance of monkeys on this task appears to resemble that of human subjects on similar tasks. Furthermore, we tested the involvement of area MST by using electrical micro-stimulation to perturb its activity during task performance. The general approach is similar to that used in other experiments testing the role of MT and MST in the perception of translational dot motion (Salzman et al., 1992; Celebrini and Newsome, 1994b). In our experiment, we applied microstimulation during smooth pursuit eye movements or during fixation. When applied to a region of MST preferring one heading alternative, micro-stimulation frequently induced biases, which correlated well with the heading preferences of neurons at the stimulation site. In addition, bias frequently depended on the pursuit condition. This pattern of results supports the hypothesis that MST is involved in recovering self-motion from optic flow and compensating heading perception for pursuit eye movements.

Parts of this work have appeared in short form (Britten and van Wezel, 1998). This paper additionally analyzes the normal psychophysics of our monkey subjects and relates stimulation site tuning to the effects of microstimulation.

Materials and Methods

Subjects and Surgery

Two female rhesus macaques (Macaca mulatta) were used in this study. Each was implanted with a head restraint post and a scleral search coil following the previously described method (Judge et al., 1980). The hardware was implanted under surgical anesthesia using sterile techniques in a dedicated primate surgical suite (California Regional Primate Research Center, UC Davis). After several months of training on the psychophysical task, each monkey was additionally implanted with a chronic recording cylinder over occipital cortex. This cylinder (Crist Instruments Inc.) was oriented parasaggitally, 17 mm lateral to the mid-saggital plane and elevated 20° above the horizontal plane, allowing posterior access to extrastriate cortex in the superior temporal sulcus. All animal procedures were approved by the UC Davis Animal Care and Use Committee and fully conformed to ILAR and USDA guidelines for the treatment of experimental animals.

The monkeys were initially trained on easy (for human subjects) versions of the task, with central fixation and large heading angles. They learned the basic task rapidly, typically within two sessions. Next, smaller heading angles were included, until the animals' psychometric functions allowed us to estimate their heading thresholds. Next, the monkeys were trained to generalize across a range of fixation locations and to report heading during pursuit eye movements. Lastly, the monkeys were presented different conditions in rapid alternation, emulating experimental conditions. When thresholds were asymptotically low, monkeys were deemed ready for the stimulation experiment.

Recording and Stimulation

Monkeys were seated in a primate chair with their heads restrained. Eye movements were measured with a scleral search coil system (David Northmore Inc.) and sent to a PC running the public domain experimental control software REX (Hays et al., 1982). On recording days, the cap covering the cylinder was removed and an electrode (glass-insulated Pt–Ir, 0.5–1.0 MΩ, FHC Inc.) was introduced into occipital cortex via a transdural guide tube. Initial mapping penetrations located the superior temporal sulcus (STS) and identified approximate boundaries of the motion-sensitive areas in its depths. MST was identified according to previously published methods (Celebrini and Newsome, 1994a). To identify the STS, we used a combination of anatomical and physiological landmarks, including the depth from the brain surface, grey matter/white matter transitions, sulcus crossings and response properties. Within the STS, we located and mapped MT on the posterior bank, using its well-understood and consistent retinotopy and responses for physiological confirmation. MST was encountered after crossing the STS to its anterior bank and was identified by large RFs that often included the fovea or extended substantially into the ipsilateral hemifield. In addition, cells on the anterior bank often showed MST-like stimulus selectivities, preferring rapidly moving stimuli and complex optic flow stimuli. All experiments reported here came from penetrations in which the lumen of the STS was crossed and thus most likely from the dorsal subdivision (MSTd). Furthermore, the neurons we recorded typically preferred large stimuli over smaller ones, a hallmark of MSTd. Histological verification of recording sites has not yet been obtained, as both monkeys are still being used in related experiments.

Heading selectivity was measured for multiunit sites at ~100 μm intervals. When a region of clear and consistent heading selectivity was found, quantitative heading-tuning measurements were made at more frequent intervals. Sites were deemed acceptable if they maintained clear and consistent selectivity for a distance of 250 μm or more. The electrode was then positioned in the middle of this region and the microstimulation experiment initiated.

The receptive field of each site was established using a mixture of hand-and computer-presented stimuli. The fixation point was adjusted to bring the center of the range of headings into the receptive field and then to maximize heading selectivity. The site's tuning to heading stimuli was then measured under the pursuit conditions used in the stimulation experiment. A range of headings was chosen to span the expected threshold at that location. During the experiment, a block of trials containing 15 or 20 trials for each condition, was presented. Typically, there were eight or 10 heading angles, three pursuit conditions and two microstimulation conditions, presented interleaved in a fully crossed, block-random design.

The microstimulation consisted of 200 Hz pulse trains delivered through the recording electrode from a multichannel pulse generator (AMPI) and linear stimulus isolator (FHC Inc.). Pulses were biphasic, 20 μA in amplitude, cathodal leading. Each phase was 200 μs in duration, and 100 μs intervened between phases. The pulse train was 1 s in duration, exactly simultaneous with the visual stimulus motion upon which the monkey's decision was based.

Data Analysis

Psychophysical performance was measured both with and without microstimulation, and all resulting psychometric functions were similarly treated. Data consisted of the proportion of rightward choices as a function of heading angle (distance of the simulated heading from straight ahead). Such functions were fit with probit functions (cumulative Gaussians) expressed as follows:

(1)
$P(r)=\frac{1}{{\sigma}\sqrt{2{\pi}}}{{\int}_{{-}{\infty}}^{h}}\mathrm{e}^{\frac{{-}(x{-}u)^{2}}{2{\sigma}^{2}}}\mathrm{d}x$

In this expression, P(r) is the proportion of rightward choices, h is the heading angle, μ is the mean of the Gaussian and σ is the standard deviation or width. The data were fit with an iterative method called ‘Stepit’ (Chandler, 1965) using maximum-likelihood fitting, assuming binomially distributed choices. In this application, the mean (μ) estimates the bias of the monkey and is zero if the monkey's subjective ‘dead ahead’ point is veridically in the center of the screen. The width parameter (σ) captures the monkey's sensitivity to heading and is the heading angle required to support 84% correct performance. For the psychophysical data (without microstimulation), each individual function (from the three interleaved pursuit conditions) was fit separately. This approach does not allow statistical testing of individual effects, but provides a simple, unbiased estimate of the fit parameters across the range of conditions tested, which was all we desired to extract from these data.

For the microstimulation and pursuit data, the probit model was elaborated to include additional terms, so that we could test the significance of effects on an experiment-by-experiment basis. If we simplify the above expression (1) as probit(μ,σ), then for any micro-stimulation condition, i(0,1) and pursuit condition j(0,1,2), we get:

(2)
$\mathit{P}_{\mathit{i,j}}(\mathit{r})\ {=}\ probit({\mu}\ {+}\ {\mu}_{\mathit{i}}\ {+}\ {\mu}_{\mathit{j}}\ {+}\ {\mu}_{\mathit{ij}},{\sigma}\ {+}\ {\sigma}_{\mathit{i}}\ {+}\ {\sigma}_{\mathit{j}}\ {+}\ {\sigma}_{\mathit{ij}})$
where the single-subscript terms describe the main effects of pursuit or microstimulation and the double-subscript terms describe interaction effects. The significance of these additional terms was tested using likelihood-ratio testing, as described in the text.

Results

Psychophysical Performance

Heading performance has been extensively studied in humans, but little described in monkeys. We measured heading discrimination in two adult female monkeys during their last month of training. During this period, the monkeys' measured thresholds were asymptotically low, with no trend toward improved performance. Thresholds were measured in half-hour blocks of trials, consisting of 24–30 conditions and 15 trials per condition. In each block, heading eccentricity (distance in degrees from fixation point to the center of the range of headings) was held constant and three different pursuit conditions were used—left, right and no pursuit. All conditions were pseudorandomly interleaved.

In humans, sensitivity is largely invariant to heading eccentricity across a range from 0 to ~30° (Crowell and Banks, 1993). We found a similar result in monkeys (Fig. 2A). Monkey C (open circles) showed reliably higher average thresholds (σ from probit fits) than monkey B (t = 4.4, P < 0.0001), but in neither animal was the relationship with eccentricity statistically significant (linear regression; monkey C, P > 0.17; monkey B, P > 0.45). Heading thresholds, therefore, were consistent across our range of eccentricities.

In general, our monkeys' psychophysical performance appeared similar to that of human subjects on similar tasks (Warren and Hannon, 1988, 1990; Royden et al., 1992; Banks et al., 1996), in terms of threshold and effects of eye movements. This lends credence to the idea that the monkeys were performing a true heading task, rather than discriminating some local cue in the stimulus.

Microstimulation Effects

The primary goal of these experiments was to determine the role of area MST in heading perception by perturbing its activity with electrical microstimulation. We first mapped the heading selectivity of multiunit recording sites along oblique penetrations through area MST to find regions of consistent heading selectivity, as MST is organized in a clustered or columnar manner by optic flow preference (Tanaka et al., 1986; Geesaman et al., 1997; Britten, 1998). When a region with consistent heading tuning was identified, we positioned the electrode tip in its center, measured the heading tuning of neurons there and commenced a microstimulation experiment. Figure 6 shows schematically the distance traversed by the electrode with respect to the landmark of the entry into grey matter. The tuning functions below each site show the multiunit tuning for horizontal heading. The hatched region on the penetration denotes the boundaries of the region that showed consistent preference for left headings. This site was ~300 μm in extent, a typical value. The results reported here derive from 67 such experiments, conducted in the same two monkeys whose psychophysical data appear in the previous section. In 16 of these experiments, we did not include multiple pursuit conditions due to lack of time during the session.

Effects in the Presence of Pursuit

To explore the prevalence of both main effects and of interactions, we used a hierarchical series of tests—nested analysis (Hoel et al., 1971). The six sets of psychometric data resulting from each experiment were modeled with probit functions with progressively increasing numbers of free parameters. In the most limited case, a single mean and sigma were used to describe all the data. Then, systematic effects of microstimulation were assessed by fitting the stimulation and control data with different means (to detect changes in bias), different values of σ (to detect changes in threshold), or both. Lastly, interactions were tested by including cross terms between pursuit and microstimulation in the model. To test the statistical significance of the terms in the model, we employed likelihood ratio testing. This approach compares the value of two models using the difference of their log-likelihoods. This difference, which is distributed approximately as chi-square, is well behaved. Adding free parameters to a model will always improve the fit and the actual improvement can be compared against the value expected under the null hypothesis that the data are really not different in a way captured by the added parameter. As progressively more relaxed models incorporating more free parameters are fit to the data, the improvement required to achieve significance rises as well. This effectively ‘penalizes' models with too many free parameters, because the expected increase in fit quality grows–even if just fitting noisy data otherwise well described under the null hypothesis.

(3)
$heading\ turning\ index\ {=}\frac{right\ response\ {-}\ left\ response}{right\ response\ +\ left\ response}$

This index is bounded in a range from –1.0 to 1.0; the former indicates strong left heading tuning, whereas the latter indicates equally strong right heading tuning. Values near zero imply no tuning. This tuning index was significantly related (r = –0.25, P < 0.05) to the bias induced by microstimulation, captured by the average shift of the psychometric function under micro-stimulation (Fig. 12). Recall that the shift of the function is to the left if right choices are increased and to the left if right choices are increased. Therefore, the negative slope indicates that the relationship agrees with intuition—biases tended to be stronger in a given direction when the neurons at the stimulation site were more strongly tuned in the same direction. Furthermore, this plot shows that effects that were opposite to the tuning were observed when we stimulated sites where the tuning was relatively weak.

Discussion

In this paper we have demonstrated that selective activation of heading-selective regions within area MST can produce substantial effects on the perceived direction of self-motion when this is simulated visually. Thus, MST signals directly influence decisions on a heading discrimination task. Effects of microstimulation were heterogeneous in sign and magnitude and interacted with the presence of ongoing smooth pursuit eye movements in many cases. This suggests that MST signals are important in the process of correcting heading perception for optic flow field distortions produced by eye movements.

Technical Issues

The first important question to consider is the task itself. It is important to the interpretation of these results to consider whether the monkeys were perceiving the global pattern of motion, or simply performing a linear (even local) direction or speed discrimination. In a complex, cue-rich stimulus such as ours, it is difficult to know unequivocally what cue was being used. However, the overall similarity of the monkey psychophysics with published human psychophysical work provides one strong indication. Human observers trained on heading tasks attempt to use global cues rather than local ones; this maximizes performance. If our monkeys were using local cues instead, their performance would probably have been much worse. In addition, local cues change substantially and systematically with heading eccentricity and with pursuit, yet our monkeys showed little systematic effect of these manipulations on their performance. Therefore, we believe it likely that our monkeys and human observers use similar global cues. Of course, the cues in use are impossible to know for certain, especially in nonhuman primates.

Microstimulation is an artificial perturbation of the complex local circuits of the cortex and it is important to think carefully about potential pitfalls in interpretation. The most obvious concern is the extent of current spread, which we cannot directly measure. Based on similar experiments in the better-understood architecture in MT, Newsome and colleagues (Salzman et al., 1992; Murasugi et al., 1993) have estimated that currents such as ours should spread ~150 μm from the electrode tip. This is also consistent with 2-deoxyglucose labeling measurements made in the smooth cortex of the owl monkey (Tootell and Born, 1991) and with dual electrode experiments in motor cortex (Asanuma, 1981). This dimension is below the typical dimensions of clusters of similarly tuned neurons in MST (Britten, 1998), suggesting that direct current spread is largely within a column. We were also concerned about whether current would spread to underlying white matter and that this might produce unpredictable effects. To address this, we performed a small number of experiments with the electrode intentionally lowered into the white matter underlying MST, ~250 μm from the exit from grey matter. In none of these four experiments were any effects of microstimulation observed. Therefore, the effects reported here are likely due to the direct activation of the local circuit surrounding the electrode.

Another concern for the experiments involving pursuit is whether microstimulation influenced the pattern of eye movements, which might spuriously introduce interactions. Indeed, Komatsu and Wurtz showed pursuit gain changes after stimulating MST, but their stimulation currents were much larger than ours (Komatsu and Wurtz, 1989). Nonetheless, to test this possibility, we measured eye movements in a subset of four experiments in which significant microstimulation effects were seen. We removed saccades from eye-movement records using a velocity criterion, and compared eye velocities on stimulation trials to those on control trials. In no case did we see the slightest trend toward a change in velocity during microstimulation. This analysis was sufficiently sensitive that 1% changes in pursuit gain would have been detected. Therefore, we believe that direct effects of microstimulation on eye movements did not substantially influence our results.

Heterogeneity of Results

Approximately a third of our experiments revealed effects of microstimulation opposite to those that were expected based on the preferences of neurons at the stimulation site. Although such a pattern of results is still consistent with MST signals being used in the judgement of heading, interpretation becomes more difficult. Such mixed results were almost never seen by Celebrini and Newsome in their microstimulation experiment exploring a discrimination between opposite directions of uniform, linear motion (Celebrini and Newsome, 1994b). Several possibilities remain open to explain this apparent difference.

One possible explanation lies in the task—ours is a ‘just noticeable difference’ task, where the alternatives are much closer to each other along the relevant stimulus dimension. This task design should, in principle, change the ‘readout’ from MST, perhaps making the results of microstimulation less predictable from neuronal preferences.

Another, more likely possibility lies in the nature of the architecture in MST. Tuning for complex optic flow patterns is not as common in MST as tuning for direction of linear motion (Duffy and Wurtz, 1991). Our data are consistent with this: most of the sites that reached criterion length preferred linear motion with a horizontal flow component (making them tuned for extreme headings). Therefore, the local heterogeneity of single cell signals might be greater along the dimension of heading than along the dimension of linear motion direction. This in turn could cause the net effect of stimulating a column to be less predictable from the multiunit measurements of tuning. This possibility is supported by the relationship shown in Figure 13. The more weakly tuned the site, the more likely we were to get a ‘backward’ result. This could result either from activation of a subset of signals within the column being activated, or because activation of regions outside the column overwhelmed the within-column effects. At the very least, better neuronal tuning to linear motion might alone have produced the more consistent results in earlier experiments (Celebrini and Newsome, 1994b).

Lastly, it is likely that some columns of MST neurons are involved in other perceptual roles besides heading. For example, the percept of structure from motion involves many of the same signals as does heading; both require the extraction of three-dimensional depth structure from the optic flow pattern. If, for example, a column of neurons more affiliated with structure-from-motion were activated, the resulting percept might have led to unpredictable reports in our heading task. The possibilities we have discussed are not exclusive; many factors may contribute to the heterogeneity of our results.

The Role of MST in Heading Perception

These experiments have provided positive evidence for the use of MST signals in heading tasks and in the compensation of performance on such tasks for the effects of eye movements. The sign of the interaction effects in our experiments was such that, on average, the effect of microstimulation produced larger biases in the direction of the pursuit movement. This is the sign of bias expected if eye movements were incompletely compensated, as retinal image flow opposite the pursuit direction would indicate headings to that side. From the psychophysical measurements, we have seen that monkeys are able to compensate for pursuit and one of ours even overcompensated. The finding that activation of MST signals led to less compensation for pursuit suggests that MST signals are signaling the direction of retinal image flow, rather than being transformed into head-or body-centered coordinates. This is consistent with results from Andersen and colleagues (Bradley et al., 1996; Shenoy et al., 1999), which indicate that compensation of MST tuning for ongoing head or eye movements is incomplete.

The effects of microstimulation were larger and more consistent under pursuit than when the monkey was merely fixating. This suggests that the representation in MST is more engaged in heading perception when the animal is actively compensating for ongoing eye movements. MST contains extraretinal signals of pursuit eye movements (Newsome et al., 1988) and the tuning of MST cells is at least partially compensated for the distortions produced by such movements (Bradley et al., 1996; Shenoy et al., 1999). Therefore, if heading perception is guided by signals in multiple areas, for instance both MT and MST, the signals in MST might be more influential while pursuit is underway. In turn, this suggests fairly sophisticated gating of the outputs of such high-order sensory areas. Evidence for such gating has recently been uncovered in area MT (Seidemann et al., 1998). In general, this notion is consistent with the idea that ‘vision for action’ is an important function of the dorsal extrastriate area (Goodale, 1998). Although the strongest versions of this hypothesis are clearly challenged by the substantial body of work connecting signals in dorsal extrastriate areas to perception, hybrid versions of the idea are attractive.

Although this work strongly supports the use of MST in heading tasks, microstimulation experiments do not provide much information as to the mechanism of heading perception. The results of the present experiment are consistent with a variety of models relating MST physiology to perception and more traditional methods of quantitative physiology will be required to understand further the cortical mechanisms of self-motion perception.

Table 1

Frequencies of significant effects of microstimulation on heading psychophysics

Main stimulation effects Pursuit interactions
Any Mean Slope Both Any Mean Slope Both
Experiments with pursuit included substantially more trials, raising sensitivity to small effects of microstimulation.
Cases with pursuit (51) 36 (71%) 32 (63%) 19 (37%) 13 (25%) 29 (57%) 29 (57%) 13 (25%) 10 (20%)
Cases w/o pursuit (16) 4 (25%) 4 (25%) 2 (13%) 2 (13%)  –  –  –  –
All cases (67) 40 (60%) 36 (54%) 21 (31%) 15 (22%)  –  –  –  –
Main stimulation effects Pursuit interactions
Any Mean Slope Both Any Mean Slope Both
Experiments with pursuit included substantially more trials, raising sensitivity to small effects of microstimulation.
Cases with pursuit (51) 36 (71%) 32 (63%) 19 (37%) 13 (25%) 29 (57%) 29 (57%) 13 (25%) 10 (20%)
Cases w/o pursuit (16) 4 (25%) 4 (25%) 2 (13%) 2 (13%)  –  –  –  –
All cases (67) 40 (60%) 36 (54%) 21 (31%) 15 (22%)  –  –  –  –
Figure 1.

Stimulus schematic and task timing. (A) Top view of the simulated geometry of the task. The arrow shows the trajectory with respect to the stationary dot fields, which represents a cube in three-dimensional space. The vertical dashed line shows straight ahead and the angled dashed line illustrates the heading angle for the particular trajectory. For reasons of graphical clarity, the illustrated angle is larger than most that were used. The dotted lines indicate the limits of the field of view on the projection on the monitor. (B) Timing of events in the task. For the fixation location lines, upward on the page indicates right locations. The two dashed lines for the microstimulation portrayal indicate the two types of trial that are possible—some trials lack microstimulation.

Figure 1.

Stimulus schematic and task timing. (A) Top view of the simulated geometry of the task. The arrow shows the trajectory with respect to the stationary dot fields, which represents a cube in three-dimensional space. The vertical dashed line shows straight ahead and the angled dashed line illustrates the heading angle for the particular trajectory. For reasons of graphical clarity, the illustrated angle is larger than most that were used. The dotted lines indicate the limits of the field of view on the projection on the monitor. (B) Timing of events in the task. For the fixation location lines, upward on the page indicates right locations. The two dashed lines for the microstimulation portrayal indicate the two types of trial that are possible—some trials lack microstimulation.

Figure 2.

Dependence of heading threshold and bias on heading eccentricity. Heading eccentricity is defined as the distance between the fixation point (or center of pursuit sweep if pursuit is included on the trial) and the center of headings, which was directly ahead of the monkey, in the center of the screen. Sensitivity is estimated by the sigma parameter from the best-fit probit function. For both panels, data were taken from the trials in which the fixation point remained stationary. (A) Thresholds did not depend on eccentricity. The weak and inconsistent trends seen in each monkey's data were probably the result of unequal training in different locations and were not statistically significant. (B) Bias, estimated from the mean of the best-fit probit function, did not systematically depend on the horizontal heading eccentricity. Monkey C (open circles) was only trained for fixation points to the left of the center of headings.

Figure 2.

Dependence of heading threshold and bias on heading eccentricity. Heading eccentricity is defined as the distance between the fixation point (or center of pursuit sweep if pursuit is included on the trial) and the center of headings, which was directly ahead of the monkey, in the center of the screen. Sensitivity is estimated by the sigma parameter from the best-fit probit function. For both panels, data were taken from the trials in which the fixation point remained stationary. (A) Thresholds did not depend on eccentricity. The weak and inconsistent trends seen in each monkey's data were probably the result of unequal training in different locations and were not statistically significant. (B) Bias, estimated from the mean of the best-fit probit function, did not systematically depend on the horizontal heading eccentricity. Monkey C (open circles) was only trained for fixation points to the left of the center of headings.

Figure 3.

Dependence of threshold (sigma) on pursuit for each monkey. While the monkey was trained on a range of pursuit speeds, most experiments were carried out using a pursuit speed of 10°/s. This speed slightly reduced the sensitivity to heading for both monkeys, but this difference was modest. Where error bars are not visible, they are within the dimensions of the plot symbol.

Figure 3.

Dependence of threshold (sigma) on pursuit for each monkey. While the monkey was trained on a range of pursuit speeds, most experiments were carried out using a pursuit speed of 10°/s. This speed slightly reduced the sensitivity to heading for both monkeys, but this difference was modest. Where error bars are not visible, they are within the dimensions of the plot symbol.

Figure 4.

A single experiment in which pursuit influenced heading perception. Data resulting from the three pursuit conditions are illustrated by the three different symbols: solid circles denote fixation, open circles show left pursuit and open diamonds show right pursuit. The three independently fit probit functions are illustrated. To describe the induced bias in such experiments, we measured the horizontal distance between the reference curve (fixation) and each of the others. This shift is graphically depicted by the horizontal distance between the vertical dashed lines, which intersect each curve where it crosses the 50% point (horizontal dashed line). Fit parameters for these functions were as follows: static, μ = –0.42, σ = 1.09; left pursuit, μ = 0.83, σ = 1.33; right pursuit, μ = –0.53, σ = 1.29.

Figure 4.

A single experiment in which pursuit influenced heading perception. Data resulting from the three pursuit conditions are illustrated by the three different symbols: solid circles denote fixation, open circles show left pursuit and open diamonds show right pursuit. The three independently fit probit functions are illustrated. To describe the induced bias in such experiments, we measured the horizontal distance between the reference curve (fixation) and each of the others. This shift is graphically depicted by the horizontal distance between the vertical dashed lines, which intersect each curve where it crosses the 50% point (horizontal dashed line). Fit parameters for these functions were as follows: static, μ = –0.42, σ = 1.09; left pursuit, μ = 0.83, σ = 1.33; right pursuit, μ = –0.53, σ = 1.29.

Figure 5.

The relationship between pursuit and average induced bias. The induced bias expresses the horizontal shift of the psychometric function under pursuit, compared to that measured under fixation. Both monkeys were largely able to compensate for the distortions in the optic flow produced by pursuit. If uncompensated, the induced bias from the change in retinal optic flow would be ~20°.

Figure 5.

The relationship between pursuit and average induced bias. The induced bias expresses the horizontal shift of the psychometric function under pursuit, compared to that measured under fixation. Both monkeys were largely able to compensate for the distortions in the optic flow produced by pursuit. If uncompensated, the induced bias from the change in retinal optic flow would be ~20°.

Figure 6.

Map of a typical microstimulation site in MST. The Xs mark multiunit recording locations and the corresponding tuning functions show multiunit responses to a range of headings at each site. Vertical scale on each is arbitrary, since the spike threshold could not be set precisely relative to the amplitude of neuronal signals. The bold line on the track record denotes the region in which the tuning was sufficiently clear to pass our criterion and be included in the site.

Figure 6.

Map of a typical microstimulation site in MST. The Xs mark multiunit recording locations and the corresponding tuning functions show multiunit responses to a range of headings at each site. Vertical scale on each is arbitrary, since the spike threshold could not be set precisely relative to the amplitude of neuronal signals. The bold line on the track record denotes the region in which the tuning was sufficiently clear to pass our criterion and be included in the site.

Figure 7.

Example of a typical MST microstimulation experiment. The filled symbols and bold-line curve show performance with microstimulation; open symbols and fine lines depict the interleaved control trials. The site in this case preferred left headings, and microstimulation produced significant decreases in rightwards choices. The best-fit curves were allowed to be fully independent, so that each curve could have its own mean and slope. Nonetheless, the stimulation data are close to a horizontally shifted replica of the control data. Fit parameters for this case were: stimulated, μ = –1.58, σ = 2.47; control, μ = –0.26, σ = 2.53.

Figure 7.

Example of a typical MST microstimulation experiment. The filled symbols and bold-line curve show performance with microstimulation; open symbols and fine lines depict the interleaved control trials. The site in this case preferred left headings, and microstimulation produced significant decreases in rightwards choices. The best-fit curves were allowed to be fully independent, so that each curve could have its own mean and slope. Nonetheless, the stimulation data are close to a horizontally shifted replica of the control data. Fit parameters for this case were: stimulated, μ = –1.58, σ = 2.47; control, μ = –0.26, σ = 2.53.

Figure 8.

Effects of MST microstimulation in the absence of smooth pursuit. For each experiment, we calculate the difference in the means (A) and sigmas (B) between the stimulation and control data. For the analysis of bias, the sign of the resulting difference was assigned relative to the preference of the neurons at the stimulation site.

Figure 8.

Effects of MST microstimulation in the absence of smooth pursuit. For each experiment, we calculate the difference in the means (A) and sigmas (B) between the stimulation and control data. For the analysis of bias, the sign of the resulting difference was assigned relative to the preference of the neurons at the stimulation site.

Figure 9.

Example of microstimulation in the presence of smooth pursuit. We now separately plot the animal's choices and fit functions for each of the three pursuit conditions. Conventions are as in Figure 7. The neurons at this site preferred left headings.

Example of microstimulation in the presence of smooth pursuit. We now separately plot the animal's choices and fit functions for each of the three pursuit conditions. Conventions are as in Figure 7. The neurons at this site preferred left headings.

Figure 10.

Schematic illustrating different microstimulation effects. In all panels, the dashed line illustrates the control, unstimulated psychometric function, and the solid line illustrates the function with microstimulation. (A) A single experiment with an effect only on bias, which was an increase in left choices. (B) A case where microstimulation had no effect on bias, but did have an effect on sensitivity. (C) A case with effects on both bias and sensitivity. (D) Interaction of induced bias effects with pursuit. The magnitude of the function shift is different for the different pursuit conditions, but the sensitivity is unaffected throughout. (E) Interaction of induced sensitivity effects with pursuit. In this case, the effect of microstimulation is on the sensitivity to heading (as in B) and this sensitivity effect varies with pursuit.

Figure 10.

Schematic illustrating different microstimulation effects. In all panels, the dashed line illustrates the control, unstimulated psychometric function, and the solid line illustrates the function with microstimulation. (A) A single experiment with an effect only on bias, which was an increase in left choices. (B) A case where microstimulation had no effect on bias, but did have an effect on sensitivity. (C) A case with effects on both bias and sensitivity. (D) Interaction of induced bias effects with pursuit. The magnitude of the function shift is different for the different pursuit conditions, but the sensitivity is unaffected throughout. (E) Interaction of induced sensitivity effects with pursuit. In this case, the effect of microstimulation is on the sensitivity to heading (as in B) and this sensitivity effect varies with pursuit.

Figure 12.

The relationship between site tuning and magnitude of stimulation effect. The heading tuning index is a contrast index expressing the difference between the average responses to left and right headings. The solid line shows the results of linear regression analysis. This indicates that stimulation of more selective sites usually produced bigger perceptual effects. The correlation was significant with either parametric or non-parametric testing. Four cells were omitted from this analysis because quantitative tuning data were not obtained.

Figure 12.

The relationship between site tuning and magnitude of stimulation effect. The heading tuning index is a contrast index expressing the difference between the average responses to left and right headings. The solid line shows the results of linear regression analysis. This indicates that stimulation of more selective sites usually produced bigger perceptual effects. The correlation was significant with either parametric or non-parametric testing. Four cells were omitted from this analysis because quantitative tuning data were not obtained.

Figure 13.

The interaction between smooth pursuit condition and the effects of MST microstimulation. In each panel, we plot the effect without pursuit (x-axis) against the effect in the same experiment on the interleaved trials with pursuit. Solid symbols denote experiments performed in right-tuned sites; open symbols show those with left-tuned sites. The dashed line indicates equal effect under both conditions. The correlation coefficients under both conditions were significant (left pursuit, 0.75; right pursuit, 0.71) and the slopes in both relationships were significantly greater than one (regression assuming equal variance on x and y: left pursuit, 2.55; right pursuit, 1.45).

Figure 13.

The interaction between smooth pursuit condition and the effects of MST microstimulation. In each panel, we plot the effect without pursuit (x-axis) against the effect in the same experiment on the interleaved trials with pursuit. Solid symbols denote experiments performed in right-tuned sites; open symbols show those with left-tuned sites. The dashed line indicates equal effect under both conditions. The correlation coefficients under both conditions were significant (left pursuit, 0.75; right pursuit, 0.71) and the slopes in both relationships were significantly greater than one (regression assuming equal variance on x and y: left pursuit, 2.55; right pursuit, 1.45).

1
Current address: Functional Neurobiology, Helmholtz Institute, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands

The authors wish to thank A.L. Jones for writing software, and E.A. Disbrow, R.E. Tarbet and J.L. Moore for excellent technical assistance. We gratefully acknowledge the assistance of M.S. Banks in the design of the stimuli. We also appreciate the help of P.I. Harness, H.W. Heuer, K.J. Huffman and K.A. McAllister, who read earlier drafts of the paper. Thanks also to S.M. Amodt for expert editorial help. Supported by NIH grant EY10562 to K.H.B. and NEI Vision Core Center grant EY192576A. R.v.W. was partly supported by the Netherlands Organization for Scientific Research (NWO).

References

Asanuma H (1981) Microstimulation technique. In: Electrical stimulation research techniques (Patterson MM, Kesner RP, eds), pp. 61–70. New York: Academic Press.
Banks MS, Erlich SM, Backus BT, Crowell JA (
1996
) Estimating heading during real and simulated eye movements.
Vision Res

36
:
431
–443.
Bradley DC, Maxwell M, Andersen RA, Banks MS, Shenoy KV (
1996
) Mechanisms of heading perception in primate visual cortex.
Science

273
:
1544
–1547.
Britten KH (
1998
) Clustering of response selectivity in the medial superior temporal area of extrastriate cortex of the macaque monkey.
Vis Neurosci

15
:
553
–558.
Britten KH, van Wezel RJA (
1998
) Electrical microstimulation of cortical area MST biases heading perception in monkeys.
Nature Neurosci

1
:
1
–5.
Celebrini S, Newsome WT (
1994
) Neuronal and psychophysical sensitivity to motion signals in extrastriate area MST of the macaque monkey.
J Neurosci

14
:
4109
–4124.
Celebrini S, Newsome WT (
1994
) Microstimulation of extrastriate area MST influences perceptual judgements of motion direction.
Invest Ophthalmol Vis Sci

35
:
1828
.
Chandler JP (1965) STEPIT. University of Indiana Quantum Chemistry Program Exchange.
Crowell JA, Banks MS (
1993
) Perceiving heading with different retinal regions and types of optic flow.
Percept Psychophys

53
:
325
–337.
Duffy CJ, Wurtz RH (
1991
) Sensitivity of MST neurons to optic flow stimuli. I. A continuum of response selectivity of large-field stimuli.
J Neurophysiol

65
:
1329
–1345.
Duffy CJ, Wurtz RH (
1995
) Response of monkey MST neurons to optic flow stimuli with shifted centers of motion.
J Neurosci

15
:
5192
–5208.
Duffy CJ, Wurtz RH (
1997
) Planar directional contributions to optic flow responses in MST neurons.
J Neurophys

77
:
782
–796.
Geesaman B, Born R, Andersen R, Tootell R (
1997
) Maps of complex motion selectivity in the superior temporal cortex of the alert macaque monkey: a double-label 2-deoxyglucose study.
Cereb Cortex

7
:
749
–757.
Gibson JJ (1950) Perception of the visual world. Boston, MA: Houghton-Mifflin.
Goodale MA (
1998
) Vision for perception and vision for action in the primate brain.
Novartis Found Symp

218
:
21
–34.
Hays AV, Richmond BJ, Optican LM (
1982
) A UNIX-based multiple process system for real-time data acquisition and control.
WESCON Conf Proc

2
:
1
–10.
Hoel P, Port S, Stone C (1971) Introduction to statistical theory. Boston, MA: Houghton-Mifflin.
Judge SJ, Richmond BJ, Chu FC (
1980
) Implantation of magnetic search coils for measurement of eye position: an improved method.
Vision Res

20
:
535
–538.
Koenderink JJ (
1986
) Optic flow.
Vision Res

26
:
161
–180.
Koenderink JJ, van Doorn AJ (
1987
) Facts on optic flow.
Biol Cybern

56
:
247
–254.
Komatsu H, Wurtz RH (
1989
) Modulation of pursuit eye movements by stimulation of cortical areas MT and MST.
J Neurophysiol

62
:
31
–47.
Murasugi CM, Salzman CD, Newsome WT (
1993
) Microstimulation in visual area MT: effects of varying pulse amplitude and frequency.
J Neurosci

13
:
1719
–1729.
Newsome WT, Wurtz RH, Komatsu H (
1988
) Relation of cortical areas MT and MST to pursuit eye movements. II. Differentiation of retinal from extraretinal inputs.
J Neurophysiol

60
:
604
–620.
Regan D, Beverley KI (
1982
) How do we avoid confounding the direction we are looking and the direction we are moving?
Science

215
:
194
–196.
Royden CS, Banks MS, Crowell JA (
1992
) The perception of heading during eye movements.
Nature

360
:
583
–585.
Royden CS, Crowell JA, Banks MS (
1994
) Estimating heading during eye movements.
Vision Res

34
:
3197
–3214.
Saito H, Yukie M, Tanaka K, Hikosaka K, Fukada Y, Iwai E (
1986
) Integration of direction signals of image motion in the superior temporal sulcus of the macaque monkey.
J Neurosci

6
:
145
–157.
Salzman CD, Murasugi CM, Britten KH, Newsome WT (
1992
) Micro-stimulation in visual area MT: effects on direction discrimination performance.
J Neurosci

12
:
2331
–2355.
Seidemann E, Zohary E, Newsome WT (
1998
) Temporal gating of neural signals during performance of a visual discrimination task.
Nature

394
:
72
–75.
Shenoy KV, Bradley DC, Andersen RA (
1999
) Influence of gaze rotation on the visual response of primate MSTd neurons.
J Neurophys

81
:
2764
–2786.
Tanaka K, Hikosaka H, Saito H, Yukie Y, Fukada Y, Iwai E (
1986
) Analysis of local and wide-field movements in the superior temporal visual areas of the macaque monkey.
J Neurosci

6
:
134
–144.
Tootell RBH, Born RT (
1991
) Architecture of primate area MT.
Soc Neurosci Abstr

17
:
524
.
Warren WH (1998) The state of flow. In: High-level motion processing (Watanabe T, ed.), pp. 315–358. Cambridge, MA: MIT Press.
Warren WH, Hannon DJ (
1988
) Direction of self-motion is perceived from optical flow.
Nature

336
:
162
–163.
Warren WH, Hannon DJ (
1990
) Eye movements and optical flow.
J Opt Soc Am A

7
:
160
–169.
Warren, WH, Morris, MW, Kalish, M (
1988
) Perception of translational heading from optical flow.
J Exp Psychol

14
:
646
–660.