Experimental Brain Research

, Volume 165, Issue 1, pp 125–131 | Cite as

Response to unexpected target changes during sustained visual tracking in schizophrenic patients

  • L. Elliot Hong
  • Matthew T. Avila
  • Gunvant K. Thaker
Research Article


Background: Evidence supports an association between liability to schizophrenia and smooth-pursuit eye movement (SPEM) abnormalities. Knowledge of the biological mechanisms of SPEM abnormalities may provide critical insights into the etiology of schizophrenia. SPEM is elicited by sensory motor information from the movement of the object’s image on the retina (retinal motion signal) and subsequent extraretinal motion signals. Previous studies suggest that a deficit in predictive responses to extraretinal motion signals may underlie the SPEM phenotype in schizophrenia. Data suggest that at-risk individuals for schizophrenia depend less on extraretinal and more on retinal motion signals to maintain pursuit than healthy individuals. Methods: We designed a pursuit task that employs unexpected changes in target direction during smooth pursuit. The unpredictable task is unique in that performance is expected to be better if the subject’s response is biased towards retinal motion. Results: The study included 23 schizophrenia patients and 22 normal controls. Results showed that schizophrenia patients showed significantly superior performance (i.e. higher smooth pursuit gain) for a brief period after an unexpected change in target direction compared with healthy subjects. Conclusion: Findings of superior performance by schizophrenic patients are interesting because they circumvent confounds of generalized deficits. These results provide further evidence of specific deficit in the predictive pursuit mechanism and over-reliance on retinal error signals to maintain pursuit in schizophrenia.


Smooth pursuit Schizophrenia Retinal Extraretinal Motion perception 


The etiopathophysiology of schizophrenia is incompletely understood. Evidence supports an association between genetic liability for schizophrenia and smooth-pursuit eye movement (SPEM) abnormalities (Levy et al. 1993; Trillenberg et al. 2004). Understandably, much attention has been focused on the application of SPEM to the study of etiologic risk. However, general enthusiasm for application of the SPEM phenotype in genetic studies has not been accompanied by necessary refinement of the phenotype, including thorough investigation of the specific neurophysiological mechanism(s) involved. Understanding the specificity of the biological basis of this phenomenon is likely to enhance our ability to identify disease-related brain regions and to refine the SPEM phenotypes in schizophrenia. In this study we intended to isolate a specific component in SPEM that may not be directly affected by schizophrenia.

The motion of an attended object’s image on the retina is the initial visual sensory signal (retinal motion signal) responsible for initiation of smooth-pursuit eye movements. This initial response is independent of visual feedback, hence called the open-loop response. In humans the oculomotor response latency during the open-loop condition varies from 60 to 150 ms, typically around 120 ms (Lisberger and Westbrook 1985; Barnes et al. 1987; Churchland and Lisberger 2001). Once the feedback loop (closed loop) is established, the target image is well-foveated with the eye moving at a speed that approximates that of the target, resulting in limited retinal motion. To maintain accurate pursuit during closed-loop condition, the visual system uses internal representations of the motor command and previous retinal motion information (i.e. extraretinal motion signals) to produce predictive pursuit eye movements (Barnes et al. 1987; Barnes and Asselman 1992; Turano and Massof 2001). The predictive component of the pursuit response can be assessed by briefly masking a target along its trajectory (removing retinal stimulation) and examining the resulting gain (eye speed/expected target speed in mask) (e.g. Thaker et al. 1998). Previous target-masking experiments have shown that patients with schizophrenia and their first-degree relatives show reduced predictive pursuit gain (Thaker et al. 1998, 1999). Behavioral modeling of sustained visual tracking in healthy controls has shown that pursuit eye velocity depends primarily on predictive pursuit. In comparison, performance among relatives of schizophrenia patients relies more on retinal signals—presumably to compensate for putative deficits in generating, storing, and/or utilizing extraretinal signals (Thaker et al. 2003).

On the basis of these findings one would predict that schizophrenia patients would actually perform better than unaffected individuals following an experimental manipulation that favored retinal motion-based performance over predictive pursuit. To test this hypothesis, we designed a pursuit task that employs unexpected changes in target direction during sustained visual tracking. Because patients presumably rely more on immediate visual feedback, they would have less difficulty responding to the unexpected change in target direction compared with healthy controls, whose reliance on prediction would interfere with performance under these conditions. We further hypothesize that the better performance in schizophrenia patients should occur only in a brief period around the response latency of the oculomotor system. This is because retinal motion signal from the unexpected target change cannot benefit pursuit performance before the system lag (~120 ms) is overcome; and better performance in patients based on this “new” retinal motion signal should also quickly diminish, because the closed loop regarding the new target motion will also be quickly re-established, at which time the healthy controls should regain their advantage. To test these predictions, we examined smooth pursuit response during the first 300 ms in 30 ms epochs after the unexpected change of target direction.

Methods and materials


All subjects gave informed written consent in accordance with University of Maryland Institutional Review Board guidelines. For patients an additional evaluation of the capacity to sign consent was administered to assess the subject’s understanding of the planned experiments (Carpenter et al. 2000). The study included 23 schizophrenia patients and 22 normal controls. Schizophrenia patients with deficit syndrome were not included because an earlier study suggested pursuit initiation abnormalities might mark the syndrome (Hong et al. 2003; more details are given in the Discussion section). Subjects were recruited and assessed using methods described in detail in earlier publications (Thaker et al. 1999). Briefly, all patients were recruited through the outpatient programs at the Maryland Psychiatric Research Center. Healthy subjects were recruited through newspaper advertisements. The Structured Clinical Interview for DSM-IV (SCID-IV) (First et al. 1997) and Schedule for Deficit Syndrome (Kirkpatrick et al. 1989) were used to screen all subjects. Healthy comparison subjects had no DSM-IV Axis I or II diagnoses, and no family history of psychotic illness. Ten patients were on first-generation antipsychotic medication, ten patients were on new-generation antipsychotic medication, and three were not on medication at the time of testing. Patients were clinically stabilized as defined by no changes in antipsychotic medication four weeks or more before testing. The average dose of antipsychotic medication as measured by daily CPZ equivalent was 636.7±406.8 mg. Clinical symptoms were assessed by using the brief psychiatric rating scale (BPRS; Hedlund and Vieweg 1980). Average BPRS total score was 32.5±7.6. Patients and controls were not different in age (33.9±8.7 and 37.5±7.4, respectively, t=1.5, P=0.14). There was a greater proportion of males in the patient group (five females:18 males among patients compared with 14 females:eight males among controls) (χ2=8.1, P<0.01). Accordingly, gender-adjusted analyses on the pursuit measures were reported (see Results section).

Experimental procedures

Testing was conducted in an enclosed, dark, and sound-attenuated room. The subject’s head was stabilized using chin rest and head abutments. Eye movement data were collected using an infrared technique (Eye-trackermodel 210, Applied Sciences), which has a sampling rate of 1000 Hz. The analogue data were digitized using a 16-bit analogue-to-digital converter and processed using a commercially available hardware/software system (MacPace TM, Model MP100 and Aqcknowledge, Biopac Systems, Goleta, CA, USA), which has a time constant of 4 ms. Data for horizontal eye position, target position, and blinks were collected and stored. The data were collected and simultaneously displayed to enable on-line monitoring. The target (a cross in a 0.25°×0.25° box) was displayed on a 20”, flat screen VGA monitor placed 68 cm away from a chin and head support frame. From a randomly positioned fixation point and after an unpredictable delay of 1–2 s, the target moved horizontally at a constant velocity. One sweep across the screen traversed 20° (±10°) of visual angle. Each trial consisted of 5–10 target sweeps.

In 75% of the trials, the target moved along a predictable trajectory for one to six sweeps; in the next sweep the target unexpectedly changed direction—instead of turning at the expected position of ±10°, the target turned within −3° to ±3° of the center fixation point (Fig. 1). In response to the unexpected change subjects typically maintained pursuit in the opposite direction of the now turned target at the same velocity for a short period (about 100 ms). The eyes then slowed, reaching zero velocity, making a corrective saccade, and began accelerating in the direction of the target motion (Fig. 1). Note that starting from the point of the target turn the eyes moved in the opposite direction of the target and thus the gain calculated for this period is negative (see Fig. 2). Slowing of the eyes in preparation to turn in response to the change in target direction results in the gain becoming “less negative”. Gain becomes positive at the point at which the eye has completed the turn and is moving towards the target.
Fig. 1

A sample trace that shows typical pursuit behavior during unexpected change of target direction. Note that at the unexpected turning of the target, the subject briefly maintained the prior eye velocity opposite to that of the new target velocity, followed by a slowdown, and then made a large saccade before moving toward the new target direction. Saccades are removed from the velocity trace

Fig. 2

A comparison of pursuit gain (mean, bars are standard errors) during the first 300 ms after the unexpected turn of target during maintenance smooth pursuit. *Significantly higher pursuit gain in patients compared with normal controls at the 120–150 ms time interval (P<0.01)

Note that eye velocity during sustained visual tracking depends upon:

  1. 1.

    predictive pursuit, which would tend to drive the eyes in the direction of the previous target motion, thus resulting in relatively less gain (more negative gain) after the turn;

  2. 2.

    immediate retinal motion signals, which would facilitate the slowdown and turning of the eyes and thus make the gain higher (less negative).


The number of sweeps before the change in direction, direction of the sweep when the change occurred, and the location of the change, were varied pseudorandomly across trials. After the change in direction, the target resumed a predictable ±10° motion for 1–3 sweeps and the trial ended. To further reduce the potential influence of anticipation, some of the trials (25%) did not have an unexpected change in target direction; and trials were presented at three different target speeds (9.4, 14.0, and 18.7° s−1) in a pseudorandom order. Previous target masking experiments have shown that deficits are restricted to pursuit in response to faster targets (Thaker et al. 1999; Avila et al. 2003a). Thus we tested our hypothesis by analyzing responses to change in direction for 18.7° s−1 targets. A total of 23 trials were presented to each subject. Several random target steps separated the trials.

Pursuit gain was obtained by dividing average artifact-free (i.e. saccades and blinks removed) eye velocity by target velocity during each epoch. To test the hypothesized transient group differences occurring around the time of response latency, pursuit gain to unexpected changes in target direction was measured in 30 ms epochs for 300 ms after the target direction change (Fig. 2). Maintenance pursuit gain (sometimes referred to as closed-loop gain) immediately before the unexpected target was obtained in the 60 ms epoch before the target direction change. The traditional maintenance pursuit gain was also obtained by averaged pursuit gain from ramps where no unexpected target change occurred. Scoring of maintenance gain did not include eye movement data 135 ms at the beginning and end of a half-cycle or 15 ms after a saccade. We obtained the change of velocity latency, which measures the time between unexpected target change and the point that the eyes start to decelerate. (This is similar to pursuit onset latency, in that the time between target onset and the point the eyes start to accelerate is measured.) We also obtained the change of direction latency, which measures the time between unexpected target change and the point the eyes turn and move in the same direction of the target. Eye data filtered by a 20 Hz low-pass filter were used for gain measures. Eye data filtered at 75 Hz were used in saccade analyses. Eye records were calibrated trial-by-trial using calibration steps (±10°) presented between trials. The scoring algorithm identified a saccade based on velocity (>35° s−1) and acceleration (>600° s2) criteria. Blinks were identified on the basis of characteristic morphology, and removed. All saccades and artifacts were identified as missing data points before the scoring of pursuit gain and latencies. Analysis of the oculomotor data was automated using algorithms developed in our laboratory in a commercially available software environment (Igor, Wavemetrics, Lake Oswega, OR, USA). The algorithms elicit the scorer’s active approval at critical junctures (e.g. identification of a saccade) before proceeding (interrater reliability for scoring routines are maintained above 0.90). Analysis of the eye movement records was done blind to diagnosis.


Pursuit gain was measured in epochs from −60 to 300 ms, including one 60 ms epoch before the target turn and 10, 30 ms epochs after the turn. Smooth-pursuit performance was analyzed using a mixed design ANOVA (group×epoch), where epoch was entered as a within-subjects factor and group was entered as a between-subjects factor. Latencies were compared using Student’s t-test.


Potential effects of antipsychotic treatment on eye movement were examined using maintenance pursuit gain. There was no significant difference in pursuit gain among patients on first-generation antipsychotic medications (Mean±SD: 0.75±0.15), patients on new-generation antipsychotic medications (0.71±0.21), and patients not on antipsychotic medications (0.79±0.13) (P=0.71). Eight patients were also on anticholigergic medications as a treatment for side effects from antipsychotic medications. Recent data suggested potential effects of anticholinergic medications on eye movement measures (Ettinger et al. 2003). In the current sample, the eight patients who were on anticholinergic medications (0.74±0.15) did not significantly differ from the rest of the patients (0.76±0.21) in maintenance pursuit gain (P=0.75).

During unpredictable change of target direction, there was a significant group by epoch interaction (F(10,41)=2.21, P=0.02). Because gender was significantly different between the two groups, the data were also analyzed using gender as a covariate. The results were essentially identical (F(10,40)=2.08, P=0.03). Analysis of simple effects revealed a significant patient-control difference in mean pursuit gain for the period 120–150 ms after target change (t(41)=3.0, P=0.005). As predicted, patients were briefly superior in their performance to control subjects in the 120–150 ms epoch (Fig. 2). The effect size was large (Cohen’s d=0.90). Levene’s test for equality of error variance of dependent variable between the two groups was not statistically significant (F(1, 41)=0.71, P=0.40). Introducing age as covariate also did not eliminate the significant difference in performance. There were no significant group differences in other time intervals. Maintenance gain (60 ms epoch) before the target turning was not significantly different between patients (0.82±0.21) and controls (0.92±0.18) (t(41)=1.71, P=0.10; effect size d=0.50). However, patients had reduced averaged (over all ramps) maintenance gain compared with controls (0.74±0.17 vs. 0.86±0.13, respectively; t(42)=2.5, P=0.02; effect size d=0.78). Change of velocity latency (in ms) was not significantly different between patients (103.0±53.7) and controls (93.0±44.8) (t=0.69, P=0.50). Change of direction latency was also not significantly different between patients (244.7±110.6, median 260.1, 95% confidence interval of the mean 207.4–283.9) and controls (245.7±66.3, median 267.5, 95% confidence interval 242.6–325.8) (t=0.03, P=0.98).


Using an unexpected target-change paradigm designed to favor the use of immediate retinal motion processing during pursuit, we found that schizophrenia patients showed transient, but significantly higher gain in response to the unexpected change compared with normal controls. Previously, we have demonstrated that patients and their unaffected relatives exhibit predictive pursuit deficits suggesting an abnormality in the brain’s ability to generate, store, and/or utilize extraretinal signals during pursuit (Thaker et al. 1998,1999). Subsequent modeling suggested that relatives of schizophrenia patients rely more on immediate retinal feedback to maintain pursuit in the presence of this predictive pursuit deficit (Thaker et al. 2003). Results of the present experiment provide independent evidence in support of the theory that individuals with schizophrenia rely more heavily on immediate sensory motion information to compensate for deficits in extraretinal motion processing, which under normal conditions is the primary means of maintaining pursuit. As a consequence they seem to have a slight advantage in tasks that favor retinal motion processing during pursuit. This inference is also consistent with recent functional imaging findings from our laboratory, which showed that smooth pursuit in schizophrenic patients is characterized by increased activation in the medial occipitotemporal region of the brain that is related to retinal motion processing; and decreased activation in the frontal eye field, supplemental eye field, anterior cingulate, and medial superior temporal cortex, regions that are engaged in extraretinal motion processing (Hong et al. 2005). Note that a recent fMRI study has suggested that several of the latter brain regions are associated with predictive pursuit in humans (Lencer et al. 2004).

The transient advantage found in patients for the period 120–150 ms after an unexpected change in target direction is compatible with the known ~120 ms delay or lag in the oculomotor system’s response to moving stimuli (Lisberger and Westbrook 1985; Barnes et al. 1987; Churchland and Lisberger 2001). That is, changes in the oculomotor response based on visual feedback are not expected to occur until ~120 ms after the change in retinal motion. That the advantage seen in patients was transient is also consistent with the expected integration of the new information into the extraretinal signal. That is, the unexpected change would produce a brief open-loop period favoring patients, but after new retinal motion information was integrated and the system returned to a closed-loop state, controls would quickly recover due to their ability to use the newly stored motion information (Fig. 2).

As expected, the maintenance pursuit gain was significantly lower in patients than in healthy control subjects when averaged over all ramps. We considered whether the transient advantage seen in patients could be a function of reduced gain before the unexpected turn. However, gain was not different between the groups for the period 60 ms before and 120 ms after the change in target direction. This lack of significant group differences in the pursuit gain for the period 60 ms before the change in target direction might be because of increased variability of gains from a brief 60 ms portion compared with gains obtained from averaging all ramps. The larger standard deviations of the gains using only the 60 ms epoch compared with the standard deviations of the gains using averaged epoch support this explanation. In addition, the 60 ms epoch is taken in the middle of the ramp (−3 to ±3° from the center), where subjects tend to perform better in gain (supported by higher gains in both groups compared with the gains using averaged epoch); this could have resulted in a ceiling effect, particularly in the healthy control group. Regardless, this should not detract from the significance of the findings during the 120–150 ms epoch, which were robust and detected even when we do not observe significant group difference in the maintenance pursuit from −60 to 0 ms preceding the target turning (effect size 0.5) or in any of the 30 ms epochs between 0 and 120 ms (all effect sizes 0.4 or less). To further evaluate whether group difference in the pre-turn eye velocity contributes to post-turn group differences, we repeated the analyses using the eye velocity value from the −60 to 0 ms epoch preceding the target turn as a covariate. The findings remained the same: the repeated measure ANCOVA showed a significant group by epoch interaction (P=0.032); and the post hoc analyses again showed group difference only in the 120–150 ms epoch after using the eye velocity from the 60 ms epoch preceding the target turn as a covariate (P=0.018). This suggests that after controlling for the pre-turn eye velocity the group difference in eye velocity during the 120–150 ms epoch remains.

We evaluated whether there were systematic differences in missing data points between the two groups during the window of measurement (0–300 ms) and found no significant differences in any of the epochs (all P>0.68). Results are also not explained by differences in response latency, as the two groups took about the same amount of time after the target direction change to begin decelerating (change of velocity latency, t=0.69, P=0.50), and for the eye position to change direction (change of direction latency, t=0.03, P=0.98). Similar response latencies suggest that both groups were paying attention to the task and responding to changes in the target velocity. This supports the argument that specific motion processing differences, rather than differences in non-specific factors, account for the group differences in pursuit gains.

As noted earlier, all patients were receiving antipsychotic medications. However, we do not think the results are due to the effects of these drugs. First, studies have shown that most antipsychotic medications do not affect position and velocity error, or saccadic activity during pursuit (Levy et al. 2000; Thaker et al. 1999; Hutton et al. 2001). Also, similar pursuit abnormalities are found in never medicated patients, and unaffected first-degree relatives of schizophrenia patients (Levy et al. 1993). The fact that patients actually performed better than controls also reduces the likelihood that the findings are an untoward effect of medications.

It has been debated whether retinal motion processing is impaired in schizophrenia. Traditionally, the integrity of retinal motion processing in smooth pursuit is assessed by examining smooth and saccadic components during the first ~100 ms of the response (i.e. the initiation or open-loop period before the integration of visual feedback). Several studies have found that patients performed more poorly during pursuit initiation (Clementz and McDowell 1994; Clementz et al. 1995), suggesting that retinal motion processing is impaired. This is consistent with psychophysical studies of motion perception showing that patients and their relatives have higher perceptual thresholds for velocity discrimination (Stuve et al. 1997; Chen et al. 1999). However, studies have found that saccades during initiation are normal in patients (Thaker et al. 1996; Clementz 1996; Sweeney et al. 1998). Because the accuracy of the initial saccade in response to target motion is based on retinal information, it has been argued that retinal motion processing is not impaired (MacAvoy and Bruce 1995; Clementz 1996; Sweeney et al. 1998).

There may be several reasons for conflicting findings. First, there is growing evidence that anticipation based on previously viewed targets can contribute to the open-loop response (Barnes and Schmid 2002). Thus, initiation deficits may reflect a deficit in anticipation and conflicting findings exist to the degree there are differences in how predictable target motion onset has been across studies. In fact, results of a recent study in our laboratory show that in response to novel/unexpected target motion patients perform similarly to controls during initiation, but when anticipation is introduced by making target onset predictable patients begin to show deficits relative to controls (Avila et al. 2003b). Similar to the concept of predictive pursuit, anticipation is thought to be based on the short-term storage of velocity information (Barnes and Donelan 1999). Thus a deficit in this domain, in addition to explaining eye-tracking findings, could also potentially explain increased speed discrimination thresholds which require subjects to hold velocity information “on-line” in order to make a comparative perceptual judgment for sequentially presented targets. Recent work by Turano and Massof (2001) offers another possible explanation for the motion perception findings—they noted that eye movements (which may occur during velocity discrimination tests) generate extraretinal signals that directly affect the perception of motion (Turano et al. 2001). Third, previous studies may have included both deficit and nondeficit schizophrenia patients (Kirkpatrick et al. 1989). We have found that impaired pursuit initiation is associated with patients with deficit schizophrenia (~15% of patients meet deficit criteria) and their relatives, but not patients with nondeficit schizophrenia or their relatives (Hong et al. 2003). Note that deficit patients were excluded from the present study.

Importantly, while initiation and motion perception tasks may provide an estimate of the integrity of retinal motion processing, it does not give an estimate of the degree to which retinal motion contributes to performance during closed-loop smooth pursuit. In this context, the results of this experiment add to the existing data and suggest that schizophrenia patients are adequately processing retinal motion during sustained visual tracking.


We attempted to expand upon previous findings suggesting that SPEM performance deficits in schizophrenia are related to poor extraretinal motion processing and that these deficits may in part be compensated by increased reliance on retinal motion signals in patients. We tested patients in a novel smooth pursuit paradigm that favors retinal motion processing. Consistent with our a-priori hypothesis, patients showed better smooth pursuit gain during a small but expected time period when retinal motion processing would favorably influence performance. Researchers have frequently (and rightly) questioned whether generalized deficits such as low motivation may contribute to performance deficits in schizophrenia patients across many neurophysiological and cognitive assessments. That patients perform better under conditions favoring retinal signal processing is compelling evidence that eye-tracking abnormality is a core impairment and not secondary to generalized deficits in this patient group.



Support was received from NIMH Grants MH67014, 68282, 68580, 49826, and 40279.


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Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • L. Elliot Hong
    • 1
  • Matthew T. Avila
    • 1
  • Gunvant K. Thaker
    • 1
  1. 1.Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreUSA

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