P300 and slow wave: The effects of reaction time quartile

Bmlogtc:rcal Psychologv

49

18 (1984) 49-71

North-Holland

P300 AND SLOW WAVE: THE EFFECTS OF REACTION TIME QUARTILE * David

FRIEDMAN

Depurtment

Accepted

of Medical

** Genetics, New York State Psychmtric

for publication

Institute.

New York, NY 10032, U.S.A.

26 July 1983

Event-related potentials (ERPs) were recorded from 25 adolescents in a modification of the odd-ball paradigm. On alternate blocks subjects were required to detect either a missing stimulus or a change in pitch, each of which occurred 17% of the time and was embedded in a series of background tone pips (66% occurrence). The study was designed to assess the relationship between P300 and Slow Wave elicited by two infrequent targets which differed in the amount of temporal uncertainty (and thus, equivocation) associated with them. Principal components analyses (PCAs) were used in an attempt to reduce overlap among components. Stimulus-synchronized (SSA) and response-synchronized (RSA) averages were computed for correct trials only in association with the first (Ql) and fourth (44) reaction time (RT) quartiles. Measurement of the SSAs replicated the results of Roth, Ford and Kopell (1978): P300 amplitude was larger in Ql than in 44. while Slow Wave amplitude increased in 44 relative to Ql, In the RSAs for Slow Wave, only the frontally negative aspect remained larger in 44 than in Ql, while the parietally positive component did not differ between quartiles. The PCA basis waves showed that the major portions of P300 and Slow Wave followed response execution, thus precluding their involvement in the discrimination process per se. These results support the functional dissociation of P300 and Slow Wave and the functional independence of the frontal and parietal aspects of Slow Wave.

1. Introduction Several investigators have demonstrated that in some experimental paradigms P300 and Slow Wave are related in the same fashion to behavioral variables * I would like to thank Mr. Charles Brown, Jr. for technical assistance and computer programming; Drs. Walter Ritter and Dan Ruchkin for providing invaluable suggestions and productive criticism on an earlier draft of this manuscript. I give special thanks to Dr. Samuel Sutton who read through several drafts of this paper and provided productive critical commentary. I would also like to thank two anonymous referees for their helpful suggestions and criticism. However, I take sole responsibility for any errors or misinterpretations. This research was supported in part by grants HD14959, MHl9560 and by the New York State Department of Mental Hygiene. Clinical research center grant MH30906-05 provided support for the use of the Psychiatric Institute’s Computer Center. ** Address requests for reprints to: Dr. David Friedman, Department of Medical Genetics, New York State Psychiatric Institute, 722 West 168 Street, New York, NY 10032, U.S.A.

0301-0511/84/$3.00

0 1984, Elsevier Science Publishers

B.V. (North-Holland)

50

D. Friedman

/ P300 and slow waoe

(e.g. Squires, Squires and Hillyard, 197.5; Squires, Donchin. Herning and McCarthy, 1977) while in others they are related differently to experimental variables (e.g. Roth, Ford and Kopell, 1978; Ruchkin. Sutton and Stega, 1980a; Ruchkin, Sutton, Kietzman and Silver, 1980b; Ruchkin, Munson and Sutton, 1982). Because of the spatial and temporal overlap of these two components, caution must be exercised when drawing conclusions from baseline to peak measures as it is often not clear whether it is P300 or Slow Wave that is sensitive to the behavioral variable in question (see Ruchkin and Sutton, 1983 for a discussion of this issue). In fact, Ruchkin et al. (1980a) reported that while their baseline to peak measures showed an effect of task on the P300 component, when principal components analysis (PCA) was used in an attempt to reduce overlap, the factor scores showed that the experimental variable affected Slow Wave and not P300. Roth et al. (1978) were the first to report a dissociation between P300 and Slow Wave: P300 diminished in amplitude as reaction time lengthened, while Slow Wave increased in amplitude. However, Roth et al. (1978) did not measure Slow Wave in their response-locked waveforms, so they did not determine if the dissociation between P300 and Slow Wave was related to stimulus- or response-processing. To the extent that either component is more a reflection of processes initiated by the stimulus or processes initiated by the reaction time response, then the relationship between P300 and Slow Wave might be expected to differ in the response-synchronized and stimulus-synchronized waveforms. In addition, Roth et al. (1978) did not estimate to what extent the amount of overlap between P300 and Slow Wave may have influenced their results. Inspection of their fourth quartile waveforms (see their fig. 1) shows them to be almost entirely composed of Slow Wave with little evidence of P300 activity. Thus, if measurement of P300 and Slow Wave in those waveforms were confounded by overlap between them. its greatest effect might have been expected in their fourth quartile ERPs. Ruchkin and Sutton (1983) have postulated that experimental situations where the stimulus is difficult to perceive or where task requirements are more complex lead to ‘further processing’ of the stimulus and increments in Slow Wave amplitude. In their formulation, equivocation plays a major role in influencing Slow Wave amplitude. Temporal uncertainty increases equivocation (cf. Ruchkin and Sutton, 1978a). In experimental situations where interstimulus-intervals are constant, detection of stimulus presence does not involve variations in the subject’s time-keeping ability (see Klemmer, 1956) and thus has no temporal uncertainty associated with it. By contrast, since the stimulus does not occur in the case of a missing stimulus, the subject has only his/her sense of time judgement to determine if stimulus absence has taken place, thus leading to greater temporal uncertainty in the missing stimulus situation (cf. Ruchkin and Sutton, 1978b). Since temporal uncertainty (which has its effect on latency jitter in single trials) due to equivocation induced by the stimulus is

D. Friedman / P300 and slmv waue

51

reduced when averages are computed with respect to the reaction time (RT) response, it was expected that this variable would have differential effects on P300 and Slow Wave depending upon whether they were measured in the stimulus- or response-locked waveforms. Therefore, the use of a target in which the stimulus is always present (pitch change) and a target in which the stimulus is always absent (missing stimulus), allowed an assessment of the effects of two levels of temporal uncertainty on Slow Wave and P300 amplitudes. Fitzgerald and Picton (1981) have suggested that Slow Wave is most likely comprised of two components, one negative at frontal scalp, the other positive at parietal scalp. In the data of Roth et al. (1978) their finding that the frontally negative aspect of Slow Wave became larger in 44 relative to Ql, while the parietally positive aspect became larger in 44 relative to Q2, led to a quartile by electrode interaction and indicated (though not discussed by Roth et al.) that each aspect of Slow Wave was differentially affected by the quartile variable. In the current study, the possibility that Slow Wave is composed of two functionally distinct components was investigated by performing separate ANOVAs at Fz and Pz to determine if the variables of reaction time quartile and type of target stimulus and/or their interaction had similar or different effects on the frontally negative and parietally positive aspects of Slow Wave. Another question has to do with the temporal course of Slow Wave relative to reaction time and decision processes, and whether the frontal and parietal aspects have different onsets and/or durations. Due to the overlap of late positive components, it is difficult to measure Slow Wave onset in the conventional average. One method for dealing with overlap (although others exist - e.g. subtraction) has been PCA (see Friedman et al., 1981 and Ruchkin and Sutton, 1983 for rationales for the use of PCA in this context). In the current study, separate cross products PCAs were computed for each quartile (Ql/Q4), target stimulus (pitch change/missing stimulus) and type of average (stimulus-synchronized (SSA)/response-synchronized (RSA)). In addition, separate PCAs of the ERPs recorded at frontal and parietal scalp sites were performed on the stimulus-triggered and response-triggered averages. This allowed an estimate of the onset and duration of Slow Wave relative to RT and decision processes and the determination of the effect of temporal uncertainty on Slow Wave amplitude, after an attempt to reduce overlap between P300 and Slow Wave via PCA.

2. Method This report will deal only with the infrequent stimuli when they were targets. The ERPs to the infrequent non-targets and background events are dealt with in a separate report (Friedman, Brown, Vaughan, Cornblatt and Erlenmeyer-Kimling, in press).

52

2. I. Subjects Subjects were a sub-sample (N = 25) of adolescents (consisting of 2, 12 year olds; 6, 13 year olds; 6, 14 year olds; 2, 15 year olds; 5, 16 year olds; 3, 17 year olds; and 1, 18 year old) who were part of a normal control group in a longitudinal study of children at risk for schizophrenia (Erlenmeyer-Kimling, 1970). 2.2. Task and procedure All tones were 64dB SPL, 50 msec in duration, with 2.5 msec rise and fall times. The background stimulus (66% occurrence) was a 1000 Hz tone pip which was randomly replaced either by the absence of stimulation (the ‘missing stimulus’) or a pitch change to 700 Hz. Each of these infrequent events occurred with a probability of 17%. There were 4 blocks of trials with a total of 300 stimuli per block and an inter-stimulus-interval of 800 msec. Subjects were instructed to respond as quickly as possible to targets with a finger lift (which activated a reaction time key of local design). Each infrequent event served as target on alternate blocks of trials. A finger lift within the interval of 200 to 1000 msec after the onset of each target was considered a correct response, while a lift at any other time was considered an error. 2.3. Data collection and recording procedures EEG was recorded with Beckman miniature biopotential electrodes located at midline frontal, central, parietal and occipital scalp sites, and EOG was recorded from an electrode located at the supraorbital ridge of the right eye. All leads were referred to the right earlobe. The physiological data were recorded on a Beckman Dynograph (Type RM) recorder with 1 set time constant and high frequency cut-off at 30 Hz. Data acquisition and stimulus presentation were under the control of a PDP ll/lO computer which digitized the EEG at 4 msec intervals and recorded it, along with reaction time (RT) on digital tape, for a 100 msec pre- and a 700 msec post-stimulus epoch. Averages of the target ERPs were computed separately in association only with correct Ql and 44 RTs. Stimulus- as well as response-locked averages were computed for both target stimuli and both quartiles, resulting in 4 averages per subject. Each subject’s individual records were inspected for blinks, eye movements and other artifacts, and roughly one-quarter to one-third of the records for a given subject was discarded to produce an artifact-free average. Although these rejection rates are relatively high. in a recently completed study (Friedman et al. in press) with this same task, there were no significant differences between reaction times for ERP epochs which contained artifacts and ERP epochs which did not (i.e. those included in the averages).

53

2.4. Data una&sis 2.4. I. Baseline to peak measures Baseline to peak measures were obtained by computer program with the latency criteria for a given component established using the latency of the associated PCA-derived basis waves. These measures consisted of 100-200 msec of averaged amplitude referred to the pre-stimulus baseline (for the SSAs), or referred to the 100 msec epoch beginning 400 msec prior to the motor response (for the RSAs). Roth et al. (1978) measured their RSAs using the pre-stimulus baseline obtained from their SSAs. However, since the two types of averages (SSA/RSA) could produce components which reflect different brain events, and/or similar components with different amplitudes, the decision was made to refer the RSA measurement to its own baseline. 2.4.2. Principal components analysis (PCA) There were 8 PCAs performed (2 stimuli (missing stimulus/pitch change) by 2 quartiles (Ql/Q4) by two types of averages (SSA/RSA)). The number of waveforms per PCA was 100 (25 subjects by 4 electrodes), with the 200 points in the original waveforms reduced to 66 (by averaging every 3 time points), resulting in 12 msec per point in these ‘new’ averages. PCAs were also performed on the averages pooled across Ql and 44. There were 4 of these PCAs (2 stimuli by 2 average types with 200 waveforms per analysis). Additional PCAs were performed separately at Fz and Pz in order to obtain basis waves corresponding to the frontal negative and parietal positive aspects of Slow Wave. The data for these PCAs were pooled across the 44 pitch change and missing stimulus target ERPs. There were 4 of these PCAs (Fz and Pz. recording sites by 2 average types), with 50 waveforms (2 target stimuli by 25 subjects) per analysis. PCA with Varimax rotation was performed on the time point by time point cross products matrix. Since no transformations are performed when using this matrix, the basis waves retain the original FV scale and the factor scores reflect the correct polarity (cf. Donchin and Heffley, 1978; Glaser and Ruchkin, 1976). The BMDP (Dixon, 1979) statistical package was used to perform PCA (BMDP4M). 2.4.3. Analyses of variance (A NO VAs) ANOVAs (BMDP2V) were performed on the baseline to peak and factor score measures of ERP activity. These were stimulus (pitch change/missing stimulus) by quartile (Ql/Q4) by electrode (Fz, Cz, Pz, Oz) ANOVAs performed separately for SSA and RSA data sets. For all ANOVAs, degrees of freedom were reduced, using the epsilon correction, according to the method outlined by Jennings and Wood (1976), in order to correct for the likelihood of unequal variance-covariance matrices. The factor scores were converted to E_IV (by multiplying the factor loadings associated with the peak of a given basis

54

D. Friedman

/ P300 and slow wuue

wave by the factor scores associated with that basis wave), and the data were pooled across quartiles, in order to perform the above-described stimulus by quartile by electrode ANOVAs.

3. Results 3.1. ERP waveforms

and baseline to peak measurement

The top half of fig. 1 depicts the grand mean ERPs averaged across subjects for each target stimulus, quartile, and type of average. The bottom half of fig. 1 depicts the potentials averaged across the first and fourth RT quartiles. In the SSAs, for both Ql and 44, the pitch change ERPs consist of the exogenous components, NlOO (Cz maximum) and P200 (Cz maximum), which overlaps with and is followed by both N250 (Fz maximum) and P350 (Pz maximum) components. This overlap could be one of the factors accounting for the sharpness of the P350 component, although the missing stimulus P300 component is also quite sharp and is not overlapped by any exogenous activity. For the pitch change ERPs, a negativity at about 500 msec with a frontal maximum follows P350 and, in turn, is followed by Slow Wave activity (negative at Fz and positive at Pz). The ERPs elicited by the background, frequent event (not presented here ~ see Friedman et al., in press) contained smaller-amplitude NlOO, P200 and N250 components, with little evidence of P300 activity. Thus, for the pitch change ERPs, the large-amplitude P200 component seen in fig. 1 at the parietal scalp site and the above-baseline N250 component seen at the central scalp site are most likely the result of overlap of P200, N250 and P350. For the missing stimulus, a broad negativity (with a frontally-dominant distribution) can be seen to precede late positive activity, which consists of P300 (parietally dominant) and Slow Wave components (negative at Fz and positive at Pz) in Ql, while in 44 the early negativity and P300 are not prominent, but Slow Wave activity is. Both P350 (pitch change ERPs) and P300 (missing stimulus ERPs), although of different latencies will be referred to as ‘generic P3OOs’, since they have the same topographies and relate to the quartile variable (in the SSAs) in the same fashion. For Ql, the SSA P300 elicited by the missing stimulus appears to have an earlier latency to peak than the Ql P300 elicited by the pitch change (fig. 1). However, when measured subject by subject, 17 out of the 25 subjects showed a longer latency to peak for P300 elicited by the missing stimulus than for the pitch change. Thus, the across-subject average for P300 latency in the Ql SSAs (table 3) is longer (with a larger standard deviation) for the missing stimulus then for the pitch change ERPs. Table 1 presents the latency windows used to obtain baseline to peak measures of ERP activity. These windows were obtained from the PCA basis

55

D. Friedman / P300andslow~ wme RSA

SSA Missing Stimulus

Pitch Change

c,

.‘,

P ‘, . .,:

Pitch Change

Missing Stimulus

-

GP350

If+ ‘\

Pz.. oz


,-.I, ‘, _., I_:

Fz -hL~o

P

CZ

---

.

-P350

._

;“--

pz YJ Oz-,

h

/SW

Ql

+ Q4

1 I I I

200

msec

600

200 200, RT

Fig. 1. Grand mean ERPs averaged across subjects. The two columns on the left depict the stimulus-synchronized averages (SSAs), while the two columns on the right depict the responsesynchronized averages (RSAs). In the SSAs, arrows mark stimulus onset, whereas in the RSAs arrows mark reaction time (RT) onset. Time lines every 100 msec. The top two rows represent the data separated into Ql and 44 SSA and RSA waveforms. For the SSAs, the insets depict the cumulative percentile histograms for RT separated into Ql and Q4 distributions (the solid horizontal line represents 100%; vertical bars mark mean RT; and horizontal bars depict plus and minus the mean within-subject standard deviation of RT). The bottom two rows depict the ERP averages pooled across Ql and 44. SW = Slow Wave.

waves associated with P300 and Slow Wave components (see PCA section below). Table 2 presents the significance of the trends depicted in fig. 1, as assessed by ANOVAs of the baseline to peak measures of ERP activity. As can be seen from fig. 1 and table 2 for the SSAs, P300 was larger in Ql than in 44, while Slow Wave was larger in 44 than it was in Ql. For Slow Wave, the frontally negative aspect showed a greater difference between Ql and Q4 (greater negativity in Q4) than did the parietal positive aspect (greater

56 Table 1

Criteria (in msec) used to obtain latency and amplitude measures for P300 and Slow Wave ‘I Pitch change ERPs

Q1 QJ Ql Q4

Missing stimulus

ERPs

P3OO 250- 350 215-375

Stimulus synchronized Slow Wave 60% 700 600-700

P300 300-500 300-500

Slow wave 600- 700 600-700

400-500 400-500

Response 600-700 600-700

hJ 400-500 375-475

600-700 600-700

synchronized

‘*I Based on peak latencies of PCA basis waves. h’ 100 msec baseline beginning 400 mbec prior to RT.

positivity in Q4), while the effect of quartile on P300 was greatest at Pz. Depicted below each of the SSAs is the cumulative distribution of RTs as well as mean RT and mean within-subject standard deviation of RT. Mean RT has a very restricted range in Ql and can be seen to precede the peak of P300,

Table 2 ANOVAs

of baseline

Source

to peak measures Components

red df

Slow Wave

l/24 7.62 ‘I’ 21.81 a1 I,‘24 6.17 eJ l/24 2,‘55 43.07 %I’ 2/55 19.04 *I’ 2/55 10.61 *’ 2,‘55 0.46 epsilon = 0.76

l/24 l/24 l/24 2/52 2/52 2/52 2/52

3.32 0.66 0.52 12.92 “I 0.72 25.25 ‘I’ 1.18 0.72

0.96 I/24 37.84 a> I/24 9.35 a) l/24 2/50 42.93 ” 2/50 0.39 2/50 7.85 a’ 2/50 6.06 .‘I epsilon = 0.70

l/24 l/24 l/24 2,‘50 2/50 2/50 z/so

2.3X 2.96 7.59 *’ 15.02 a1 1.67 9.95 ‘I) 2.34 0.70

df

red df

I /24 l/24 l/24 3/72 3/72 3/72 3/72

l/24 l/24

Strmulus s,whruniaxJ Stimulus (S) Quartile (Q)

SQ Electrode SE

of ERP activity

(E)

QE SQE

P300

Response synchrorzrzed

Stimulus (S) Quartile (Q)

l/24

SQ Electrode SE

QE SQE

(E)

3/12 3/72 3/72 3/72

‘) p < 0.05 after df reduction;

F(1/24)

= 4.28; F(2/40)

= 3.23.

D. Friedman

/ P300

and slow wuuc

57

while in 44 the range of RT is roughly 5 times that of Ql and its mean follows the peak of P300. These results essentially replicate those of Roth et al. (1978). As can be seen in fig. 1 for the RSAs, both the pitch change and missing stimulus ERPs consist of a negativity which precedes the RT response, followed by a mix of P300 and Slow Wave activity. The negative component. with a frontally preponderant topography (which is identified with N250 of the SSAs) is more prominent in the Ql than in the 44 pitch change ERPs, and can be seen in the missing stimulus ERPs, where it overlaps with the frontal aspect of Slow Wave. In addition, for the Ql pitch change ERPs, there appears to be an additional positive component, with a central-maximum topography, occurring at about 100 msec post-RT, This may be the equivalent of the P + 90 component of the motor potential (e.g. Shibasaki, Barrett, Halliday and Halliday, 1980a; Gerbrandt, Goff and Smith, 1973) but its absence in the missing stimulus ERPs suggests that it is the P2 component of the auditory ERP, which in Ql is tightly coupled with the generation of the subsequent P300 component. These differences between Ql and 44 RSA waveforms could be due to the much greater RT variability in 44 compared with Ql which could have caused a reduction in the amplitude of the stimulus-locked components seen in the RSAs. Reference to fig. 1 and table 2 shows that, in the RSAs, P300 amplitude is reduced in 44 relative to Ql for the pitch change ERPs only, while for the missing stimulus ERPs, Ql and 44 produced equivalent P300 amplitudes at Pz, with smaller P3OOs in 44 than in Ql at Fz and Cz. For both the pitch change and missing stimulus ERPs, the negative aspect of Slow Wave was larger at Fz and Cz in 44 than in Ql, but of equal amplitude at Pz for Ql and 44, and this effect was greater for the missing stimulus ERPs. 3.2. Peak latency and reaction time Table 3 presents mean P300 latency, mean RT and mean P300 latency minus RT as measured in the SSAs. As can be seen in table 3, P300 increased in latency from Ql to 44 for both the pitch change (F(1.24) = 5.92. p < 0.05) and the missing stimulus (R(1.24) = 20.60, p < 0.001). Reaction time increased from Ql to Q4 for both the pitch change (F(1,24) = 181.50, p < O.OOOl), and the missing stimulus (F(1,24) = 285.70, p < 0.0001). In the same fashion, within-subject RT variability increased from Ql to 44 for both the pitch change ( F(1,24) = 104.42, p < 0.0001) and the missing stimulus ( F(1.24) = 91.65, p < 0.0001). There were systematic relationships (table 3) between the latency of P300 and reaction time. There was a significant effect of quartile on the difference between P300 latency and mean reaction time (F(1,24) = 76.85, p < 0.001). Averaged across missing stimulus and pitch change, RT preceded P300 latency by a mean of 71 msec in Ql, whereas in 44 P300 latency preceded RT by a

58

D. Friedman / P300 and slow wac~e

Table 3 P300 peak latencies, mean reaction time, mean within-subject standard deviations of reaction time and P300 latency minus reaction time difference measures in Ql and 44 for the stimulus-synchronized averages Pitch change

Missing stimulus

P300 latency (Pz) 0) 300.12 (32.1) 44 312.28 (26.7)

Q1

336.16 (55.9) 446.16 (52.9)

Reuction time hi

Q1 44

262 (30.08) 594 (91.36)

232 (19.08) 448 (91.00)

P300 latency minus reaction tune u’ 68.12 (31.13) - 135.72 (94.53) ‘) Standard deviations h, Mean within-subject

74.16 (57.94) - 147.84 (134.40)

in parentheses. SD in parentheses

RSA

SSA

PITCH CHANGE

MISSING STIMULUS

PITCH CHANGE

MISSING STIMULUS

P300 SW

-

Ql Q4

Ql

+ Q4

Fig. 2. Varimax-rotated basis waves corresponding to P300 and Slow Wave obtained from the cross-products matrix (8 PCAs: 2 stimuli by 2 average types by 2 quartiles). SSAs appear in the left two columns, RSAs in the right two columns. The separate Ql and 44 basis waves appear in the top two rows (mean RT = vertical bars; plus and minus mean within-subject standard deviation of RT = horizontal bars). The first and second bottom two rows depict respectively P300 and Slow Wave basis waves obtained from PCAs performed on the ERPs pooled across Ql and 44. SW = Slow Wave.

59

D. Friedman / P3UO and slow wme

Table 4 Percentage

of variance

of P300 and Slow Wave (after rotation)

Pitch change

ERPs

Missing stimulus

ERPs

Stimulus synchronized P300 35 Q1 12 44

Slow Wave 23 33

P300 43 16

Slow Wave 38 60

Response synchronmd 52 56 44

16 15

49 45

18 34

Ql

mean of 142 msec. There were no stimulus these difference measures. 3.3. Principal

components

an+ses

or stimulus

by quartile

effects

on

(PCA)

It is not clear from the above described baseline to peak analyses, the extent to which these measurements of P300 and Slow Wave are contaminated by overlap. This is dealt with by using PCA of the averaged waveforms. 3.3.1. Basis waves Fig. 2 presents

the Varimax-rotated

basis waves extracted

from the cross

products matrices for each target stimulus, quartile and type of average (top half of fig. 2) as well as the basis waves extracted by the PCAs performed on the pooled Ql and 44 waveforms (bottom half of fig. 2). PCAs of the ERPs elicited by the frequent event (not presented here), produced separate NlOO, P200 and N250 basis waves. In addition to the P300 and Slow Wave factors, PCAs of the SSA pitch change ERPs produced a basis wave corresponding to NlOO and one which appeared to be a combination of P200 and N250 activity, since it spanned the original

the time interval

ERPs.

Thus,

degree of overlap between

when these two components

taken together,

this indicates

were active in

that there was a large

P200 and N250 for the pitch change ERPs.

Table 4

presents the percentage of energy accounted for by each P300 and Slow Wave component for the 8 separate PCAs. For the SSAs, as can be seen from table 4, it is clear that these data corroborate the dissociation of P300 and Slow Wave: there is more energy in the P300 region for the Ql potentials than for the 44 potentials, with the opposite the case for Slow Wave. For the RSAs, there is more energy in the P300 than the Slow Wave region for both RT quartiles. As can be seen in fig. 2, it is quite clear that each PCA produced both a P300 and Slow Wave basis wave and that each is related to RT in the same manner as was the component with which it is associated in the original

60

D. Friedman

,’ P300

and slow M‘UW

waveforms (depicted in fig. 1). While the temporal course and duration of Slow Wave is unclear from the raw averages depicted in fig. 1, when RT response variability is eliminated, fig. 2 clearly demonstrates that the major portion of Slow Wave follows the motor response in both quartiles, whereas some portion of P300 precedes response execution in Q4 (evident in both the pitch change and missing stimulus RSA basis waves). In order to determine the relationship of the two aspects of Slow Wave to the stimulus and the RT response, PCAs were performed on the fourth quartile ERPs (where Slow Wave was the predominant component) pooled across target stimuli separately at frontal and parietal recording sites. Fig. 3 presents the basis waves that resulted from these PCAs. As can be seen, the frontal and parietal Slow Waves have similar time courses and durations, thus accounting for their lack of differentiation when the PCA is performed across electrodes. However, frontal negative Slow Wave is larger in the RSAs than in the SSAs, whereas the reverse is the case for parietal positive Slow Wave.

RSA

SSA

-

Fz

.-__-

Pz

\ \

I.


---l 600

200

5Irv

400

RT

400 +

msec Fig.

3. Basis

products stimuli that mark

waves

matrix. separately

each

basis

stimulus

corresponding

The

PCAs

at frontal wave

onset

reflects

were and

to frontal

and

performed

on

parietal

recording

the polarity

for the SSAs

and

parietal the

sites.

of the ERP

RT onset

Slow

fourth

In this

component

for the RSAs.

Waves

quartile

computed ERPs

fig. the polarities which Time

lines

using

pooled

the cross

across

it approximates. every

target

are adjusted 100 msec.

so

Arrows

D. Friedmm

/

P300

61

and slow waue

3.3.2. Factor score anabses Fig. 4 presents the grand mean factor scores (in pV) for all conditions and, for comparison purposes, the previously described baseline to peak measures. Table 5 presents the significance of the factor score trends depicted in the right half of fig. 4. As can be seen, there is a relatively good correspondence between the baseline to peak and factor score measures of ERP activity. As can be seen for the SSAs in fig. 4, the effect of the PCA was to reduce the frontal negative activity seen for P300 in the 44 baseline to peak measures. Thus, the PCA attributes this negativity to Slow Wave. Apart from this, the factor score analyses of P300 corroborated those performed with the baseline to peak measures. For Slow Wave (with the exception of a stimulus by quartile by electrode interaction) there were no differences between the baseline to peak and factor score data sets. In the RSAs for P300, there was little difference (baseline to peak ANOVAs showed a stimulus by quartile by electrode interaction, the PCA measures did not) between factor score and baseline to peak measures. For Slow Wave,

Baseline Pitch Change

to Peak

Factor

Results

Missing Stimulus

Score

Results

Pitch Change

Missing Stimulus

P300

P300

SW

measures

(both

SSA

RSA

4-

: n

P300 Fig. 4. Baseline activity

averaged

the bottom Wave.

row.

SW to peak across Data

P300

SW

(on the left) and

factor

subjects.

for the SSAs

are depicted

Scores

score

for the pitch

SW

(on the right) are depicted

change

and

in the top row,

missing

stimulus

in pV) of ERP RSA

ERPs.

scores

in

SW = Slow

62

D. Friedman / P300 and slow wave

however, a significant stimulus main effect (not present in the baseline to peak ANOVAs) which was modulated by significant stimulus by quartile (also seen in the baseline to peak data) and stimulus by quartile by electrode interactions (not seen in the baseline to peak ANOVAs), indicated a differential effect of quartile on the frontal and parietal aspects of Slow Wave. In view of this and the recent report by Fitzgerald and Picton (1981) pointing to the functional independence of the two aspects of Slow Wave, stimulus by quartile ANOVAs were performed separately at Fz and Pz. As depicted in the right hand portion of fig. 4 for the SSAs, both the parietally positive and the frontally negative aspects were larger in 44 than in Ql (Fs(1,24) > 8.31, p < 0.001). However, for the parietal component. a stimulus by quartile interaction (F(1,24) = 4.71, p < 0.05) indicated that the missing stimulus elicited greater positivity than the pitch change in Q4 but not in Ql. For the RSAs, only the frontally negative aspect of Slow Wave showed any significant effects. The missing stimulus elicited greater frontally negative Slow Wave activity than did the pitch change (F(1,24) = 10.26, p < 0.001). with this effect modulated by a stimulus by quartile interaction (F(1.24) = 12.14, p <

Table

5

ANOVAs

of factor score measures or ERP activity Component

Source

P300

df

red df

red df

Slow Wave

Stimulus (S)

l/24

l/24

6.54 ‘0

l/24

0.04

Quartile (Q)

l/24

I,‘24

1.63

l/24

0.00

SQ

l/24

l/24

2.61

l/24

0.63

3/72

2,‘40

3/72

2/40

E

3/72

2/40

SQE

3/72

2/40

Sfrmulus synchronrzed

Electrode

(E)

13.83 a’

2/50

x.99 ill

2,‘50

0.86

21.04 *’

2,‘50

27.39 ”

4.58 ”

2/50

2.98

epsilon =

3.69 ‘I’ 0.69

0.55

Response synchronized Stimulus (S)

l/24

l/24

l/24

4.52 .”

Quartile (Q)

l/24

l/24

11.32 ‘I’

l/24

0.25

SQ

l/24

l/24

7.92 ”

l/24

5.67 ‘I)

3/72

2/51

36.97 ”

2/49

2.84

SE

3/72

2/51

1.24

2/49

2.35

QE SQE

3/12

2/51

4.53 ‘I’

2/49

3/12

2,‘51 epsilon =

0.99

2/49

Electrode

(E)

” p < 0.05 after df reduction;

&l/24)

0.35

0.71

= 4.28: F(2/40)

1.65 11.95 ‘I’ 0.68

= 3.23

D. Friedman / P300 and skw WCIW

63

0.001). As can be seen in the lower right hand portion of fig. 4, this was due to the fact that while at Ql the missing stimulus and pitch change ERPs had equivalent frontal negative amplitudes, at 44 the frontal aspect of Slow Wave was positive for the pitch change but negative for the missing stimulus.

4. Discussion 4. I. Dissociation of P300 and Slow Wave

This study has replicated for the SSAs the finding, originally reported by Roth et al. (1978), of a behavioral dissociation between P300 and Slow Wave. In the current study, Slow Wave amplitude increased whereas P300 amplitude decreased as RT lengthened. New findings were highlighted by the measurement of P300 and Slow Wave in the RSA waveforms. In these averages, only the frontally negative aspect of Slow Wave was larger in 44 than in Ql, while the parietally positive aspect did not differ between RT quartiles; P300 retained its reduced amplitude in 44 for the pitch change, but for the missing stimulus its amplitude in 44 was enhanced and was equivalent to that of Ql. Roth et al. (1978) did not measure Slow Wave in their RSA waveforms so that they could not comment on the relationship between P300 and Slow Wave in those averages. In addition, they used a pre-stimulus baseline obtained from their SSAs rather than a baseline obtained from the RSAs in their measurement of P300 in their RSA waveforms. This led to their analysis showing no effect of RT quartile on P300 in the response-locked waveforms when, in fact, it is quite clear from their fig. 6 that P300 decreases markedly in amplitude from Ql to Q4, a result in consonance with our finding for the pitch change ERPs (Roth et al. did not use a missing stimulus). The timing of Slow Wave relative to RT was elucidated by an analysis of the PCA-derived basis waves. In the stimulus-locked waveforms, Slow Wave onsets as early as 250 msec post-stimulus for the pitch change ERPs, about 350 msec for the missing stimulus ERPs, reaches its maximum as P300 activity is decreasing, and occurs after or during the RT response interval. Eliminating RT response variability by averaging with respect to the motor response showed that the major portion of both P300 and Slow Wave followed RT, indicating that these components cannot reflect decision processes per se, but must index mental events related to the outcome of the discrimination process. However, some portion of P300 was seen to precede RT in the 44 RSA basis waves (in both pitch change and missing stimulus ERPs), thus indicating that P300, but not necessarily Slow Wave, is related in part to response requirements (Friedman, Vaughan and Erlenmeyer-Kimling, 1978 reported the same phenomenon for P300 using visual ERPs), adding additional evidence for the functional dissociation of P300 and Slow Wave.

64

4.2. Dissociation

D. Friedman

of Slow

/ P300

Wave into frontal

and slow H’UOP

and parietal

component3

In reviewing the data on endogenous negativities. Naatanen and Michie (1979) concluded that the ‘frontal nonspecific negative shift’ which was usually associated with a slow positive shift over parietal scalp (together these comprise the Slow Wave originally reported by Squires et al., 1975) was a unique component, associated with the processing of intensity and the significance of the stimulus. In a later paper, Picton and Stuss (1980) also concluded that Slow Wave was most likely not a single component, but consisted of frontally negative and parietally positive waveforms, each with its own potential cognitive correlate. The current data lend support to that contention. In our SSAs, both aspects of Slow Wave were larger in 44 than in Ql, but only the frontally negative Slow Wave remained larger in 44 than in Ql in the response-locked waveforms. Thus, a tentative conclusion that could be drawn is that parietal Slow Wave is sensitive to processes initiated by the stimulus, whereas the frontally negative component is tied to processes initiated by the response. Enhanced positive Slow Wave in the 44 SSA waveforms relative to those of Ql may indicate an effect of equivocation since longer RTs (with increased variance) and diminished P300 amplitude indicate that these stimuli may have been more difficult to process than those in Ql (cf. Ruchkin and Sutton, 1978a). This could have been due to momentary lapses of attention, confusion as to whether it was the high- or low-pitched tone that had been presented, and to the subject’s expecting one infrequent but receiving the other. In the SSAs. frontally negative Slow Wave did not differ in amplitude in Q4 whether elicited by the missing stimulus or the pitch change, while parietal Slow Wave was greater for the missing stimulus than for the pitch change. This may have been due to temporal uncertainty which exists only for the missing stimulus and not for the pitch change, since the subject has only his/her sense of time judgement to determine if stimulus absence has occurred (cf. Ruchkin and Sutton, 1978b). Temporal uncertainty has its effect on the latency jitter of the missing stimulus’s ERP components by affecting the time it takes the subject to decide that a stimulus has not occurred. Since there is no temporal uncertainty associated with stimulus presence, the only process that can affect the latency jitter of the ERP components elicited by the pitch change is the time it takes to decide that a change in pitch has occurred. Averaging with respect to the RT response should reduce these effects on ERP components by eliminating single-trial latency variability correlated with RT as the source of the amplitude modulations seen for P300 and Slow Wave in the SSA waveforms. Any residual amplitude differences between Ql and Q4 remaining after application of the RT trigger would be evidence that other sources (e.g. single trial latency variability uncorrelated with RT; lowered amplitudes in the single trial), in addition to latency variability correlated with RT, had affected P300 and Slow Wave amplitudes (cf. Ruchkin and Sutton,

D. Friedman

/ P300 and slow waoe

65

1978b). Averaging with respect to the RT response had the effect of eliminating, for both the pitch change and missing stimulus ERPs. the greater amplitude parietal positive Slow Wave in 44 that was seen in the SSAs. In addition, it eliminated the greater amplitude parietal Slow Wave favoring the missing stimulus over the pitch change seen in the SSAs. Thus, the findings suggest that single-trial latency jitter correlated with RT accounted for these parietal Slow Wave amplitude differences between the Ql and 44 SSA data sets. In contrast to positive Slow Wave, P300 amplitude for the pitch change in the RSAs remained smaller in 44 than in Ql, while for the missing stimulus ERPs there were no P300 amplitude differences between 44 and Ql. This pattern of findings indicates that equivocation had more of an effect on detection of the pitch change than it did on detection of the missing stimulus. Thus, aside from its effect on latency jitter, stimulus absence appears more discriminable than a change in frequency. That is, there is no equivocation for the missing stimulus (both P300 and parietal Slow Wave amplitudes were equivalent between Ql and 44 in the RSAs in contrast to the SSAs), whereas there is relatively more uncertainty about whether the pitch in fact did change, resulting in reduced P300 amplitude in both the SSA and RSA Q4 pitch change waveforms. Additional evidence for behavioral dissociation of the two Slow Wave components comes from several sources. Depending upon the experimental paradigm, the effect of experimental conditions may be greater for one or the other (frontal versus parietal) component. Fitzgerald and Picton (1981) reported that while frontal negative Slow Wave differentiated target and standard stimuli at all levels of stimulus probability, parietal Slow Wave distinguished targets and standards only at very low levels of probability. Inspection of fig. 5 of Ruchkin et al. (1980b) reveals that for ERPs to hits in a signal detection paradigm only parietal Slow Wave differentiated their three levels of accuracy. There are virtually no differences amongst the three levels for frontal Slow Wave. In a recent report from our laboratory (Friedman et al., in press), only frontally negative Slow Wave showed an effect of relevance of the eliciting event (larger to the relevant infrequent), and showed a modest decrease in amplitude with increments in chronological age. In the data of Naatanen, Simpson and Loveless, (1982) their frontally negative Slow Wave was prominent under all conditions including reading, whereas parietal positive Slow Wave was not prominent during reading. Parietal Slow Wave was of greater amplitude to extremely deviant stimuli than to moderately deviant stimuli, whereas frontal negative Slow Wave was of equal amplitude to all deviant stimuli. In addition, Naatanen et al. (1982) reported that their parietal Slow Wave had an earlier onset than did the frontal negative component. It is difficult at our current state of knowledge to determine the functional role of frontally negative Slow Wave. Both aspects of Slow Wave appear to have the same temporal relationship to the stimulus and the RT response but

66

D. Frmiman

/ P300

cmd slow WILT

appear to differ in amplitude depending upon whether they are elicited by stimulus or response processing. The data presented in fig. 3 support the notion that frontal negative Slow Wave is associated more with mental events related to the RT response than to the stimulus, whereas the opposite is the case for parietal positive Slow Wave. Fitzgerald and Picton (1981) have suggested that positive Slow Wave reflects ‘information load’ and Ruchkin and Sutton (1983) have suggested that it represents ‘further processing’. Neither of these formulations appear to fit all the wrinkles seen in the current data. 4.3. Effect

of motor potentials

It is likely that pre- and post-movement potentials interacted with the potentials reflecting cognitive activity in these tasks. However, inferences from a few sources suggest that the contribution of motor potentials to the observed effects was minimal. Ritter, Simson and Vaughan (1972) using the identical finger movement as used here showed that the activity associated with self-paced movements was of much smaller amplitude and of different morphology than the brain activity elicited under vigilance conditions where RT was required. The finger movement used in the current study was deliberately chosen to minimize motor potentials, which are much greater when subjects perform vigorous movements, such as hand dorsiflexion. The topographies of the components seen in the current investigation also mitigate against their interpretation as motor potentials. N250, which is tightly coupled to the RT response, displayed a frontal-maximum topography in the SSAs and RSAs, as did negative Slow wave, while P300 displayed a parietal maximum topography. Shibasaki et al. (1980a), and Shibasaki et al. (1980b) recorded from a large array of electrodes including Fz, Cz and Pz and, with middle finger extension, found that the primary negative and positive components (N - 90; N + 50; P + 300 ~ times are from the EMG burst) had central maximum distributions. The only component that had a central maximum topography (see fig. 1) occurred 100 msec after RT in the pitch change ERPs, but was not seen in the missing stimulus ERPs, arguing against its interpretation as a motor potential. Similarly, that portion of P300 seen to precede the RT response (see fig. 1) is unlikely to be motor-related positivity as its topography can be clearly seen to be parietally-maximum. Interpretation of Slow Wave activity as similar to Bereitschaftspotential and/or CNV-like activity can also be ruled out on the basis of topography (Shibasaki et al., 1980a, 1980b; Simson, Vaughan and Ritter, 1977) as well as on the timing of this component relative to RT as demonstrated by the PCA-derived basis waves. 4.4. Topographic

distributions

of P300 and Slow

Wave

For the SSAs, P300 was large and its topography was parietally dominant in Ql, while in 44 Slow Wave was predominant and was negative frontally and

D. Frredmm

/ P300 and slow wow

b7

positive parietally. In the RSAs, the same pattern held, with the exception of P300 elicited by the missing stimulus which was of equal amplitude in Ql and 44. When these two components were small (Slow Wave in Ql and P300 in Q4), their topographies were indistinct with considerably lessened amplitude variation across the scalp. The usual interpretation for differential topographies is that the potentials emanate from different intracranial generators (e.g. Picton and Stuss, 1980; Vaughan, 1974). In the current study, however, this conclusion does not seem warranted as what appears to have occurred is that when either P300 or Slow Wave was large, measurement of the other component was rendered less reliable, possibly due to the large temporal and spatial overlap between the two. In spite of this fact, PCA was able to resolve these two components in both Ql and 44 although it could not accurately resolve their scalp distributions. 4.5. Contribution

of the PCAs

4.5.1. Correspondence between baseline to peak and factor score meusures An important issue in the use of PCA in the attempt to reduce overlap among components is the degree of correspondence between the baseline to peak and factor score indices of ERP activity. The fact that, under certain conditions, PCA can misallocate variance and lead to artifactual findings (as assessed by simulations - Wood and McCarthy, personal communication), argues for measurement using both baseline to peak and PCA measures of ERP activity. Some reassurance may be gained by noting that the baseline to peak and factor score measures yield identical findings. However, since both methods of measurement can misallocate variance, the correspondence between indices derived from each does not overcome the problem. Different experimental paradigms produce different degrees of component overlap. In the Roth et al. (1978) and the current data, the degree of overlap between P300 and Slow Wave was not great (as can be clearly seen from the onsets and time courses of the basis waves presented in fig. 2). Thus. amplitudes for both P300 and Slow Wave could be obtained relatively free of overlap by choosing a measurement window where the two components were differentially active. In this situation, the PCA findings were essentially redundant, and either the baseline to peak or factor score measures could have been used to report the results. In the data from other experiments, however, the amount of overlap between P300 and Slow Wave was so great (e.g. Ruchkin et al., 1980a) that baseline to peak measures for at least one of the components could not be obtained relatively free of overlap, no matter what latency window was chosen to make the measurement. In this kind of situation, some method for dealing with overlap must be employed in order to decide which of the two components is affected by a particular experimental variable (see Ruchkin and Sutton, 1983 for a discussion of this issue).

68

D. Friedman / P300 and slow vm~e

In the current report, there was a good correspondence between the baseline to peak and factor score measures of P300 and Slow Wave activity. The PCA had its greatest effect in reducing the overlap between P300 and Slow Wave at frontal scalp. It can be seen from the baseline to peak plots depicted in fig. 4 that P300 has a substantial negative amplitude at Fz. With the exception of the pitch change RSA factor score measures, the effect of the PCA was to markedly reduce that negative manifestation, indicating that frontal negative Slow Wave was responsible for pulling P300 in a negative direction at the anterior electrode sites. This occurred only for the 44 averages where Slow Wave was the predominant component. By contrast, parietal Slow Wave shows virtually no effect on P300. This can be seen in the fig. 4 plots. where the relationship between Ql and 44 parietal Slow Waves is the same for the factor scores as it is for the baseline to peak measures. 4.5.2. Slow Wave as a longer-latency P300 One of the assumptions of PCA of ERP waveforms is that components must occur with constant latency (e.g. Donchin and Heffley, 1978). The fact that P300 latency tends to increase with increments in RT (e.g. Ritter et al., 1972; Ritter, Simson, Vaughan and Friedman, 1979) is a violation of that assumption. PCAs of the ERPs pooled across RT quartiles produced both P300 and Slow Wave basis waves. Since pooling the ERPs associated with Ql and 44 RTs results in a broad range of RTs (and possibly a broad range of P300 latencies), the possibility exists that Slow Wave in these pooled averages is a P300 occurring at longer latencies. However, PCAs performed separately on each quartile’s ERPs produced both P300 and Slow Wave basis waves. If Slow Wave were simply a later and broader P300, PCA of the Q4 waveforms (where Slow Wave is the dominant component) should not have produced a P300 basis wave. In fact, all eight PCAs produced basis waves corresponding to P300 and Slow Wave. A more compelling argument against interpreting Slow Wave as a longer latency P300 is its amplitude relationship to RT quartile. A characteristic of P300 is that latency jitter decreases its amplitude. However, in 44 where ERP latency jitter is assumed to be large (see RT data, table 3) there is substantial Slow Wave activity. If Slow Wave were simply a P300 shifted in latency due to jitter in single trials, one would expect its amplitude to be smaller than depicted in fig. 1. In the Q4 missing stimulus ERP waveforms, in the face of extreme RT variability (and associated time-jittered P3OOs) which moved P300 and Slow Wave into the same measurement window, PCA was able to tease apart P300 and Slow Wave components. The fact that PCAs of the RSA waveforms, where RT variability (and P300 latency variability associated with RT ~ Ritter et al.. 1972) is reduced to zero, produced P300 and Slow Wave components in both Ql and Q4, argues against a longer-latency P300 interpretation of Slow Wave. However. it would have been more conclusive had

D. Frredman / P300 and slow wnue

69

distinct topographies been obtained for P300 and Slow Wave (especially evident in the Ql and 44 SSA missing stimulus ERPs - fig. 4). In the data of Friedman et al. (in press), separate PCAs of target and non-target infrequent ERPs yielded basis waves corresponding to P300 and Slow Wave. In those data, the distinction between P300 and parietal Slow Wave was much clearer than in the current data, both on the basis of topography and differential relationship to the experimental variables. Apparently, PCA can handle some violation of the fixed latency assumption, and latency variation evidently can be more easily tolerated the longer the time constant of the ERP component. Ruchkin et al. (1980b) demonstrated the existence of P300 and Slow Wave basis waves in separate analyses of high, medium and low intensity ERPs recorded during a signal detection paradigm. If Slow Wave were simply a P300 occurring at longer latencies, PCA of their low intensity ERPs (where latency jitter can be expected to be more of an influence) should have yielded only a Slow Wave basis wave. In a more recent report, Ruchkin et al. (1982) also ruled out the P300 latency shift explanation of Slow Wave on the basis of its amplitude relationship to amount of equivocation.

5. Conclusions The relationship between P300 and Slow Wave is complex and depends upon which aspect of Slow Wave one is dealing with. The temporal course of the two Slow Wave components as well as P300 link these brain potentials to mental events occurring after the decision to respond has been made. The timing of Slow Wave relative to P300 indicates that P300 reflects an early evaluative stage of information processing whereas the two Slow Wave components reflect longer-duration stages which can occur in parallel to that represented by P300. The current data add to the evidence for the existence of two Slow Wave components, allowing more degrees of freedom in attempting to define cognitive functions for these long-latency components. Cognitive events previously ascribed to a single Slow Wave must be reconsidered in terms of the mounting evidence for the functional independence of these two aspects of Slow Wave. It is evident that the method of determining Slow Wave onset and timing from the PCA-derived basis waves is an important one since, if the two aspects of Slow Wave represent distinct cognitive processes their latencies and durations should be affected by the complexity of those processes. It is likely that, due to the differences in polarity at frontal and parietal sites, these slow potentials derive from different intracranial sources. Thus, the data point to the importance of distinguishing between the frontal and parietal aspects of Slow Wave both on the basis of their potentially distinct cognitive correlates and their physiological underpinnings.

70

D. Frredman / P3OO and slow waue

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potentials in guessing and detection tasks. Electroencephalography and Clinical Neurophysiology, 49, I-14. Ruchkin, D.S., Sutton, S.. Kietzman, M.L. and Silver, K. (1980b). Slow wave and P300 in signal detection. Electroencephalography and Clinical Neurophysiology, 50, 35-47. Shibasaki, H., Barrett, G., Halliday, E. and Halliday, A.M. (1980a). Components of the movement-related cortical potential and their scalp topography. Electroencephalography and Clinical Neurophysiology, 49, 2133226. Shibasaki, H.. Barrett, G.. Halliday. E. and Halliday, A.M. (1980b). Cortical potentials following voluntary and passive finger movements. Electroencephalography and Clinical Neurophysiology, 50, 201-213. Simson, R., Vaughan, H.G. Jr. and Ritter. W. (1977). The scalp topography of potentials in auditory and visual go/no-go tasks. Electroencephalography and Clinical Neurophysiology. 43, 8644875. Squires, K.C., Donchin, E., Herning, R.I. and McCarthy, G. (1977). On the influence of task relevance and stimulus probability on event-related potential components. Electroencephalography and Clinical Neurophysiology, 42, 1-14. Squires, N., Squires, K.C. and Hillyard, S.A. (1975). Two varieties of long-latency positive waves evoked by unpredictable auditory stimuli in man. Electroencephalography and Clinical Neurophysiology, 38. 387-401. Vaughan, H.G. Jr. (1974). The analysis of scalp-recorded brain potentials. In: Thompson, R.F. and Patterson, M.M. (Eds.). Bioelectric Recording techniques: Part B. Electroencephalography and Human Brain Potentials. Academic Press: New York. 158-207.