Spectral analysis of electroencephalogram during perceptual-motor learning

Biological Psychology 16 ( 1983) I- 13 North-Holland Publishing Company

SPECTRAL ANALYSIS PERCEPTUAL-MOTOR

OF ELECTROENCEPHALOGRAM LEARNING

Jeffrey A. GLINER,

* Patricia

Institute

of Environmental

Stress, University of California, Santa Barbara, CA 93106, U.S.A.

Accepted

for publication

26 October

M. MIHEVIC

DURING

and Steven M. HORVATH

1982

demands Spectral analysis of the electroencephalogram was utilized to determine attention in the task which during learning of a perceptual motor task. Fifteen subjects participated consisted of 15 trials of the mirror star learning task. EEG was monitored bilaterally from the occipital and parietal lobes during each trial and analyzed for intensity and mean frequency components within five different bandwidths. All subjects rapidly improved performance in terms of both time and error reduction although the former measure appeared to be a better fit of the supradiagonal form of matrix typically found for this type of learning. Mean alpha frequency increased slightly during the task while delta frequency decreased.

1. Introduction Behavioral assessment of the role of attention in motor learning and perceptual-motor performance has become the focus of research within the field of information processing (Stelmach, 1976, 1978). Neurophysiological indicators of attention have also been studied through the use of event related potentials (Hillyard, Picton and Regan, 1978) and electroencephalogram (EEG) spectral analysis (Creutzfeld, Grunewald, Simoneva, and Schmitz, 1969). These EEG studies focused primarily on attention to perceptual demands of the input and not on the attention demands of the effector or movement phase of learning. Kantowitz and knight (1978) demonstrated that effector processes in relatively overlearned skills like reciprocal tapping, still required attention as measured by a time-sharing procedure. The purpose of the present experiment was to investigate the neurophysiological changes of attention as measured by spectral analysis of the EEG during learning of a perceptual-motor task. EEG correlates of attention have primarily involved the alpha rhythm, the frequency band between 8 and 13 cycles per second (Hz). Two different

* Please address correspondence to: Dr. Jeffrey A. Gliner, Colorado State University, Fort Collins, Colorado 80523.

0301-05 1 l/83/0000-0000/$03.00

Department

0 1983 North-Holland

of Occupational

Therapy,

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J.A. Glint-r et al. / EEG and motor learning

interpretations have been provided. Classical studies had found increases in arousal to correlate with decreases in alpha (alpha blocking) and increases in the higher frequencies (beta) (Duffy, 1962). Following this interpretation, an investigation of the relationship between alpha responsivity and attention was performed by Creutzfeld et al. (1969). They had subjects perform eight different tasks ranging from drawing a simple wavy line to maze drawing while seeing the maze in a mirror, with the latter task being scaled by the subjects as that which required the most attention. Maze drawing without the mirror was scaled as less attention demanding than using the mirror. Their group results suggested that there were small but significant decreases in occipital alpha between a resting eyes open condition and other tasks, but no differences between tasks. Individual records showed that there was almost an even division between those subjects that increased alpha during the task and those that decreased alpha during the task. This failure to find differences between tasks may have been due to the relatively short time that each task was studied (90 set). Creutzfeld et al. (1969) did not use spectral analysis to characterize EEG information and focused solely on the alpha band during task performance. A different interpretation of alpha activity (Wertheim, 1974), suggested that alpha blocking was related to whether use of sensory information in occulomotor control was present, and not due to the quality of visual perception. This hypothesis, while counter to classical EEG-attention formulations, supported the concept of attention in motor performance. More specifically, the idea that increased attention is related to visual monitoring of motor activity. If the Wertheim (1974) hypothesis was correct, it might be predicted that alpha would desynchronize early in learning of a motor task due to the increased visual monitoring demands. As the task becomes learned, less attention (visual monitoring) is required and alpha blocking should disappear. Pollen and Trachtenberg (1972) found this to happen for a mental task (chess playing) which lasted 1 hour. Wertheim (1974) was not certain what would happen to the EEG during motor activity, due to the role of kinesthetic sensory input, and did not speculate on the role of occipital alpha, but rather on the role of central alpha. However, the visual system is intimately involved in motor activity. Furthermore, Wertheim’s predictions only involved alpha blocking, and not changes within the alpha activity itself. The present study investigated changes in the EEG power spectrum during repeated performances of the same task to determine whether neurophysiological correlates of attention changed as a function of learning the task. The mirror star tracing task was chosen because of its uniqueness for most subjects during motor learning and its rapid acquisition in learning. Although the present study was exploratory in nature, we predicted that performance on early trials would require greater attention demands which would be reflected by increased energy at the higher end of the power spectrum, and decreases

3

J.A. Gliner et al. / EEG and motor learning

within the alpha band. With repeated attention demands lessened.

trials, these shifts would be reversed,

as

2. methods 2.1. Subjects Fifteen male paid volunteers between the ages of 18-24 served as subjects for this experiment. For all subjects the right hand was the dominant hand. 2.2. Apparatus The subject was seated at a desk within a 1.85 X 1.85 X 2.46 m double-walled acrylic chamber. The mirror star was located directly in front of the subject. The mirror star tracing task (Lafayette Instrument Company) consisted of a metal star embedded in a wood base, and a foldable mirror attached to the back of this base which could be adjusted to the proper visual angle for each individual subject. A metal stylus was utilized by the subject which recorded errors electronically. After any trial, the subject pushed a reset button which cleared the digital readout. EEG was recorded from Gress (E5) platinum needle electrodes placed bilaterally at positions 0,-P, and 0,-P, according to the international lo-20 system (Jasper, 1958). The bipolar method was utilized because it provided a more localizing record (Lindsley and Wicke, 1974). The signal from these leads was amplified by Grass (7P3B) wide band a.c. preamplifiers and recorded for visual inspection on a Grass (7) polygraph. The output from the preamplifiers was also recorded on FM tape (Hewlett Packard 3960) for subsequent spectral analysis at the Computer Science Laboratory. 2.3. Procedure The nature of the task was explained fully to each subject prior to the task the experiment and a consent form was signed. ’ For the experimental subject entered the experimental chamber and was seated in a chair facing the apparatus. After EEG leads were attached, the subject was instructed to perform the following sequence of responses utilizing the dominant hand as fast and as accurately as possible, with no specific emphasis on either one. The

’ The nature and purpose of this study and the risks involved were explained verbally and given on a written form to each subject prior to his voluntary consent to participate. The protocol and procedures for this study have been approved by the Committee on Activities Involving Human Subjects, of the University of California, Santa Barbara.

4

J.A. Gliner et al. / EEG and motor learning

first trial involved tracing the star in full vision without the mirror. Trials 2 through 16 were the mirror star task with subjects tracing the star with vision of the star only through the mirror. A trial ended when the subject completed one full trace of the star. At this point he voiced ‘stop’, and the elapsed time and number of errors were recorded. 2.4. Data quantification

and analysis

Performance during the mirror star task was analyzed for both number of errors per trial and time taken to complete each trial for the 15 trials. A correlation matrix for successive trials was computed for each of these two measures averaged over all subjects using a Pearson Product Moment Correlation. The pattern most typically found which demonstrates that learning has taken place during a continuous task of this type is called the superdiagonal form (Jones, 1972). A correlation matrix of this type shows the highest correlations lying along the main diagonal, and progressively lower correlations moving either vertically or horizontally away from this diagonal. In order to determine whether either matrix fit this pattern, a structural matrix was made up using a perfect supradiagonal form and compared to each of the two obtained matrices (time and errors) separately. The time and errors matrices were also directly compared to each other. The method of comparison of these matrices was the Quadratic Assignment paradigm (QA) (Hubert and Levin, 1976). Briefly, the QA paradigm provides a confirmatory technique to compare the structure between two matrices. Specifically, the QA paradigm tests for the significance of the correlation between two matrices. An index F defined as the sum of the products of corresponding elements in both matrices is computed. The value r is then compared to a sampling distribution for all possible values of r based on all equally likely permutations of the rows and corresponding columns of one of the matrices. If the probability of the obtained F is sufficiently small (0.05 or 0.01 as in the standard hypothesis testing model) then the structure of both matrices are considered similar. It should be noted that F is merely an ‘ unnormalized’ correlation coefficient between the two matrices. Electroencephalogram (EEG) data were analyzed by spectral analysis (Gliner, Horvath, Sorich and Hanley, 1980 for more detailed information). In order to perform spectral analysis, the data were digitized at the rate of 128 samples/set (dictated by sampling theory, Nyquist, 1924) and then analyzed for spectral parameters within the conventional bandwidths of O-3 Hz; 4-7 Hz; 8-13 Hz; 14-19 Hz; and 20-32 Hz. These bandwidths included points through the halfway point of each interval. For example, for the interval 4-7 Hz, we included the points from 3.5 to 7.5 Hz. For each trial, one 10 set continuous sample was analyzed for mean power (energy) and frequency. The mean frequency measure was determined for each bandwidth by multiplying

J.A. Glineret al. / EEG and motor

learning

5

the intensity by the frequency for each Hz unit in the bandwidth, summing these results and then dividing by the sum of the intensities. These 10 set epochs were judged by two different judges experienced in electroencephalography to be artifact free prior to spectral analysis. The 15 trials with the mirror were blocked into groups of three yielding five trial blocks (referred to as trial blocks 2-6) with the mirror. A sixth trial (referred to as trial l), tracing the star without the mirror prior to the use of the mirror, was also included in the EEG analysis yielding six trials. EEG data were also analyzed for hemisphere differences, but only on the measure of frequency. For each dependent measure (energy or frequency within each bandwidth) a two factor analysis of variance (hemisphere x trials) was performed with repeated measures on both factors. On the measure of intensity, main effects were disregarded because the bipolar method of recording EEG can result in power changes at either one or both of the lateral electrode pair. However, interaction effects were interpretable, since changes over trials within one hemisphere would be important if these same changes were not observed within the other hemisphere. Where significant interaction effects were found, simple main effects analyses were performed. Post hoc comparisons using the Newman-Keuls procedure were performed on all significant main effects and simple main effects where appropriate. 3. Results 3.1. Behavioral

measures

Tables 1 and 2 show the correlation matrices obtained for the time taken to perform each trial and the number of errors for each trial over 15 trials, respectively. A comparison of the structure of the time matrix with the perfect structure matrix yielded a correlation of 0.59, with a corresponding z value of 4.55 (p < 0.01). A similar comparison was made between the errors matrix (table 2) and the structure matrix. The correlation between these two matrices was somewhat lower (r = 0.36), but still significant (z = 3.24, p < 0.01). However, when the errors matrix was compared to the time matrix, the correlation was not significant (r = 0.19). Therefore, although both matrices obtained from changes in behavior from trial to trial fit a typical learning pattern, the two measures appeared independent of each other. Observations of individuals’ performances for both time and error data showed that while all subjects consistently decreased time as a function of trials, the error data demonstrated three different patterns. For eight of the subjects, errors were minimal throughout the 15 trials, but no consistent decrease was seen (mean = 1.32 errors per trial), The other six subjects showed a high error rate during the early trials which steadily decreased, but they had rarely eliminated errors by the last trial (mean, 13 errors per trial). The

15

14

13

12

11

10

9

8

7

6

5

4

3

2

1

Trial No.

1

0.56

3

0.50

2

0.14

6

0.67

0.54 0.74 0.79

0.86

I

0.41 0.92

0.52 0.71 0.64

0.46

0.41

0.59 0.51

’

0.13

0.06

0.90

/

0.88

0.76

0.85

I

0.77

0.87

0.81

0.85

0.11

0.84

0.71

0.87

0.42

0.50

0.65

0.14

0.23

0.42

0.38

’

/ 11

0.53

0.57

0.48

0.08

-0.01

0.07

0.66

10

9

0.01

8

0.43

0.15

0.07

0.61

0.19

0.25

0.24

0.54

5

0.34

4

Table 1 Correiation matrix between trials using time as the dependent variable

0.58 0.74

1 0.64 / 0.85 / 1 0.96

0.82 0.66 0.88

0.72

0.14

0.65

0.7 1

0.83

0.42

0.36

0.58 0.55

0.64

0.14

0.79

0.27

13 0.28

12 0.21

0.78

0.46

0.51

/ 0.68

0.97

0.91

’ 0.89

1 0.60 1 0.83 ’ j 0.85 I 0.96 / 0.96 1 0.90

0.73

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0.44

0.69

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t m, %

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Trial No.

Correlation

Table 2

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1

0.53

2

matrix between

0.43

0.43

3

0.49

0.48

0.76

4

0.64

0.67

0.84

0.58

5

0.65

0.78

0.55

0.76

0.70

0.58

I

variable.

1

0.52

0.5

0.78

0.40

0.34

6

trials using errors as the dependent

0.63

0.48

0.88

0.75

0.62

0.55

0.60

8

0.56

0.77 0.66

0.57 0.34

0.34 0.81

0.44 0.62

0.55 0.67

0.18

1

I o.72 I 0.59

0.85

0.9 1

0.63

0.50 0.78

0.64 0.87

0.49

0.66

1

0.59

0.53

0.78

0.8

0.32

0.42

1

0.85

0.65

0.33

0.35

0.46 0.87

0.88

0.64

0.07

0.39 0.71

0.59

0.47

0.8

0.75

0.43

0.21

0.55

0.30 0.51

0.80

0.24

0.16

0.52

I

15

0.26

0.12

0.26

0.38

I

0.39

0.47 0.26

14

’

13

0.30

12

0.32

11

0.53

0.48

0.17

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0.69

0.61

0.36

0.25

0.42

0.71

0.73

0.27

0.16

0.28

0.69 0.25

10

9

’

5964 729

6015 813

5989 736

5993 797

1SO8 0.019

1.501 0.022

1.501 0.027

0.494 0.023

1.502 0.025

1.481 0.025

1.479 0.025

1.478 0.025

1.481 0.022

1.480 0.024

2Jr s.d.

3x s.d.

414

5x

s.d.

611 s.d.

Sd.

615.5 647

1.511 0.070

1.484 0.024

IX s.d.

5965 747

(L hem)

intensity

5.31 I 0.019

6742 801

5701 834

5720 118 5.313 0.020

5.307 0.020

s.310 0.018

5.315 0.02 I

5643 813

568 1 814

5.315 0.021

(L hem)

Theta (4-7

and hemisphere

5693 840

(R hem)

for each bandswidth

(R hem)

Frequency

Delta (O-3Hz)

and mean frequency

(L hem)

Trial

Table 3 EEG intensity

5.321 0.013

5.316 0.012

5.322 0.015

5.319 0.013

5.325 0.019

5.322 0.019

(R hem)

Hz)

493s 848

4914 768

4940 881

4904 776

4977 853

5151 727

(L hem)

intensity

per the six trial blocks

4933 855

4844 763

0.351 0.035

10.352 0.03 I

10.349 0.038

10.346 0.030

4899 778 4895 851

10.331 0.043

10.333 0.024

4941 703 5061 927

(L hem)

(R hem)

frequency

Alpha (8-13

10.341 0.032

10.357 0.035

10.349 0.043

10.348 0.043

10.334 0.060

10.344 0.029

(R hem)

Hz)

6715 1221

6616 1106

6697 1232

6643 112.5

i 260

6683

6861 948

(R hem)

intensity

6788 1109

6122 1044

6760 1052

7646 940

6831 1237

6814 817

(R hem)

16.315 0.035

16.322 0.042

16.309 0.040

16.308 0.052

16.313 0.043

16.308 0.043

2x s.d.

3x s.d.

4x s.d.

5x s.d.

6X s.d.

16.317 0.036

16.308 0.028

16.313 0.041

16.311 0.027

16.313 0.034

16.322 0.03 1

5961 693

5977 651

6007 648

5923 698

5901 758

6074 611

5960 633

5965 539

5997 589

5973 569

5984 694

6066 614

25.392 0.178

25.411 0.159

25.388 0.160

25.387 0.164

25.409 0.187

25.353 0.159

(L hem)

(L hem)

(L hem) (R hem)

frequency

intensity

frequency

(R hem)

Beta (20-32

Sigma ( 14- 19 Hz)

1X s.d.

Trial Hz)

25.412 0.139

25.436 0.161

25.414 0.158

25.423 0.144

25.423 0.165

25.398 0.104

(R hem)

10984 551

11105 643

10977 621

11076 643

10988 602

11171 800

(L hem)

intensity

11062 548

11135 606

11156 750

11124 662

11048 618

I1272 794

(R hem)

10

J.A.

Gliner ef al. / EEG

and motor

learning

remaining subject showed almost 50 errors on the first trial, but then gave only one error for the remaining trials, not fitting either pattern. These different error patterns may represent different cognitive styles of performance. 3.2. EEG data The average electroencephalogram intensities and mean frequency for each bandwidth and hemisphere for the six trial blocks are presented in table 3. Analysis of the mean frequency data showed significant trial effects for the alpha band and the delta band. A significant trials effect for the alpha band width [F = 3.22, df = 5,70, p < 0.051 showed that the fifth trial block (mirror trials 9- 12) had a significantly higher frequency than the second trial block (mirror trials l-3). No significant differences between other trials were observed even though the trend was toward an increase in alpha frequency as trials increased. A significant trials effect for delta activity [F = 3.75, df = 5,70, p -C 0.011 found trial blocks 1 (non-mirror trial) and 2 (mirror trials l-3) to have a significantly larger mean frequency than trial block 5 (mirror trials 9- 12), but there appeared to be no trend in this direction. There were no significant trial effects for any bandwidth on the intensity variable. There were significant differences with respect to hemisphere on the mean frequency variables. Significantly lower mean frequencies were found for both theta [F= 6.37, df = 1,14, p < 0.051 and delta [F= 16.85, df = 1,14, p < 0.011. There were no significant hemisphere X trial interactions for either mean frequency or intensity variables. Thus for the most part, the EEG variables yielded few significant changes due to learning on this type of task. 3.3. EEG and behavioral correlates One purpose of this research was to examine the temporal relationship between either behavioral measure and EEG. It appeared that the only EEG measure to show a general temporal trend was the alpha frequency. Unfortunately, the wide variability on this measure coupled with the three different behavioral patterns resulted in low, non-significant correlations. In order to determine whether EEG changes could be used to separate the differences in cognitive style as determined from the overall errors per trial data, a stepwise discriminant function analysis was performed using all of the EEG dependent variables. This analysis selected only one variable, left mean sigma frequency [F to force tolerance = 7.561, df = 1,12). This variable classified 75% of the cases correctly in the low error group and 67% of the cases correctly in the high error group. An attempt was also made to determine whether there might be a relationship between subjects who increased or decreased alpha and cognitive style, but the variability between left and right hemisphere on this parameter made it difficult to determine what constituted

J.A. Gliner et al. / EEG and motor learning

11

an increase in alpha. For example, some subjects showed a decrease in alpha from the pre-trial to the first mirror star trial and then an increase from the first to the fifteenth trial. This was not always the case for the combined left and right hemisphere. Therefore, further analysis was not undertaken. 4. Discussion The utilization of EEG as an index of attention during learning of a perceptual motor task was considered in this experiment. The mirror star tracing task was selected as an appropriate task for this experiment because of its novelty and relative ease in acquisition. The analysis of both time to complete each trial and number of errors per trial indicated that both followed the typical pattern of learning on this type of task (Jones, 1972; Noble, 1970) even though both measures appeared independent of one another during the learning. This replicated the findings of Drowatzky (1969) although using a different statistical method of evaluation. Even though the EEG within the alpha band increased slightly in frequency over trials, the results of the EEG spectral analysis were not generally in support of our hypothesis suggesting a shift from the higher frequencies to the lower frequencies as learning took place. Instead, it appeared that as long as visual monitoring was taking place, alpha remained desynchronized, supporting Wertheim (1974). The need for visual monitoring on this task, even when well learned, was substantiated when we had two subjects who had plateaued at a high level of performance attempt the task with eyes closed. Neither subject could complete the task. Another possible explanation for the failure to find the systematic EEG changes over trials, especially within the alpha band, could have been due to the changes in the speed of movement as the task became well learned. We had assumed that increases in performance would result in decreases in attention, but it was certainly possible that movement speed could increase without changes in attention. The dichotomy that was observed between subjects who decreased their error scores over trials as opposed to those who did not was tested utilizing parameters of the EEG spectral analysis. The subsequent discriminant function analysis picked only one variable, and that was EEG frequency in the 14-19 Hz range. This rhythm, often referred to as high alpha or beta 1, has not been investigated thoroughly, although there have been some reports relating it to cognitive actiivty. Doyle, Ornstein and Galin (1974) found that when investigating the relationship between cognitive tasks and EEG frequency, the Beta 1 and Beta 2 bands showed similar acitivity to the alpha band. This similarity in the beta bands was interpreted as a ‘spill over’ from the alpha band. On the other hand these authors pointed out the possible contribution from motor activity in this band. The motor aspect of our study could account for the discriminatory properties in this band.

12

J.A. Ghner el al. / EEG and motor learning

Hemispheric differences were observed on the frequency measured at the lower frequencies. However, there were no trials effects that indicated these differences were due to changes in learning or attention. Also, it indicated that hemispheric shifts were probably not due to switching from a non-novel (mirror star without the mirror) to a novel task. More than likely, these hemispheric shifts were due to gross changes, perhaps from non-motor to motor task. Gevins, Zeitlin, Dolce, Yingling, Schaffer, Callaway and Yeager 91979) attributed EEG asymmetries to limb movements and non-cognitive aspects of the tasks. This study attempted to show a relationship between EEG frequency changes and perceptual motor practice. The EEG changes observed were quite small and quite variable. Busk and Galbraith (1975) pointed out that skilled movements were not likely to be reflected in the EEG. Furthermore, we recorded only from the occipital-parietal areas because of our primary hypothesis concerning attention shifts with practice. Perhaps other areas of recording might have yielded more systematic results.

Acknowledgements The help and cooperation of many individuals at the Institute of Environmental Stress are appreciatively acknowledged, including particularly James Allen and Lovette Weir. This work was supported in part by the Environmental Protection Agency Grant No. EPAR 808177010.

References Busk, J. and Galbraith, G.C. (1975). EEG correlates of visual motor practice in man. Electroencephalography and Clinical Neurophysiology. 38, 415-422. Creutzfeld, O., Grunewald, G., Simoneva, L. and Schmitz, H. (1969). Changes in the basic rhythms of the EEG during the performance of mental and visomotor tasks. In: Evan, C.R. and Mulholland, T.B. (Ed%). Attention in Neurophysiology. Butterworth: London, 148-168. Doyle, J.C., Ornstein, R., and Galin, D. (1974) Lateral specialization of cognitive mode: II, EEG frequency analysis. Psychophysiology, 11, 567-578. Drowatzky, J.N. (1969). Evaluation of mirror tracing performance measures as indicators of learning. Research Quarterly, 40, 228-230. Duffy, E. (1962). Activation. In: Greenfield, N.S. and Steinbach, R.A. (Eds.). Handbook of Psychophysiology. Holt, Reinhart and Winston: New York. Gevins, A.S., Zeitlin, G.M., Dolce, J.C., Yingling, C.D., Schaffer, R.E., Callaway, E. and Yeager, C.L. (1979). Electroencephalogram correlates of higher cortical function. Science, 203, 655-668. Gliner, J.A., Horvath, SM., Sorich, R.A. and Hanley, J. 91980). Psychomotor performance during ozone exposure: spectra1 and discriminant function analysis of EEG. Aviation, Space and Environmental Medicine, 5 1, 344-35 1,

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Hillyard, S., Picton, T. and Regan, D. (1978) Sensation, perception and attention: analysis using ERPs. In: Callaway, E., Tueting, P. and Koslow, S.H. (Eds.). Event-Related Brain Potentials in Man. Academic Press: London, 223-321. Hubert, L. and Levin, J. (1976). Evaluating objects set partitions; free sort analysis and some generalizations. Journal of Verbal Learning and Verbal Behavior, 15, 459-470. Jasper, H.H. (1958). The lo-20 electrode system of the International Federation. Electroencephalography: Clinical Neurophysiology, 10, 271. Jones, M.B. (1972). Individual differences. In: Singer, R.N. (Ed.). The Psychomotor Domain: Movement Behavior. Lea and Febiger: Philadelphia. Kantowitz, B.H. and Knight, J.L. (1978). Testing tapping and time-sharing: attention demands of movement amplitude and target width. In: Stelmach, G. (Ed.). Information Processing in Motor Control and Learning. Academic Press: New York, 205-227. Lindsley, D.B. and Wicke, J.D. (1974). The electroencephalogram. In: Thompson, R.F. and Patterson, M.M. (Eds.). Bioelectric Recording Techniques Part B. Academic Press: New York, 20-23. Noble, C.E. (1970). Acquisition of pursuit tracking skill under extended training as a joint function of sex and initial ability. Journal of Experimental Psychology, 86, 360-373. Nyquist, H. (1924). Certain factors affecting telegraph speed. Bell Systems Technology Journal, 3, 324-346. Pollen, D.A. and Trachtenberg, M.C. (1972). Some problems of occipital alpha block in man. Brain Research, 41, 303-314. Stelmach, G. (Ed.). (1976) Motor Control: Issues and Trends. Academic Press: New York. Stelmach, G. (Ed.). (1978) Information Processing in Motor Control and Learning. Academic Press: New York. Wertheim, A.H. (1974). Oculomotor control and occipital alpha activity: a review and a hypothesis. Acta Psychologica, 38, 235-256.