Positive event-related potentials to real and dummy rule-learning feedback and to perceptuomotor feedback

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Electroencephalography and clinical Neurophysiology 94 (1995) 60-79

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Positive event-related potentials to real and dummy rule-learning feedback and to perceptuomotor feedback Charles A. Warren *, Bruce E. McDonough School of Public Health (M / C 922), University of lllinois at Chicago, Room 310, EOHS, 2121 W. Taylor, Chicago, IL 60612, USA Accepted for publication: 27 July 1994

Abstract Amplitudes of 5 event-related potentials (ERPs) were recorded at 5 sites of 10 males to real (R-) and dummy (D-) feedback (FB) over two difficulty levels of a rule-learning task, with interspersed perceptuomotor (PM) task trials. Rule-learning R-FB for positive slow wave (PSW) and P3b was greater than for D-FB for both mean time-window and principal component factor score measures. FB effects varied by site for P2/P3a (mainly Fz-Ca-Pz) and for a late PSW (LPSW; mainly C4-C3-Fz). A new ERP, P508, showed the greatest topographic differentiation, but no FB main effect. The following ERPs may reflect different sources: PSW versus P3b; P2/P3a versus LPSW; R-FB versus D-FB P2/P3a; R-FB versus D-FB LPSW; and P508 versus all others. LPSW was greater to simple than complex task difficulty; while the "P508" factor score trended towards being greater for complex than simple. ERP interpretation is in terms of stimulus recognition classification, comparative evaluation and development elaboration of mental models. Rule-learning D-FB exceeded PM accuracy R-FB for all ERPs but P2/P3a. Strongly implicated in these differences are preparatory acts in the former task as reflected by the PSW and LPSW. Keywords: Event-related potentials; P300; Positive slow waves; Rule-learning feedback; Perceptuomotor feedback

I. Introduction Feedback may be defined as any signal to a learner which indicates the correctness or incorrectness of his previous response, or which of a number of possible responses were correct (Bourne et al. 1971, p. 271). A moderate number of studies have examined ERPs accompanying feedback (Jenness 1972; Poon et al. 1974; Johnson and Donchin 1978, 1982, 1985; Stuss and Picton 1978; Perrault and Picton 1980; Ruchkin et al. 1980, 1981, 1982; Stuss et al. 1980; DeSwart et al. 1981; DeLisle et al. 1986; Papakostopoulos et al. 1986). In a review of feedback ERP studies Johnson (1986) noted that the use of feedback information logically involves additional cognitive processing after stimulus categorization. Whereas traditional ERP tasks emphasize processing of information from a specific trial to indicate an immediate course of action (overt or covert), in which the well-known P300 (P3b) is

* Corresponding author. Tel.: (312) 996-0831; Fax: (312) 996-1374.

obtained at about 3 0 0 - 6 0 0 msec, feedback provides information relevant to past behavior which may be used to modify behavior on future trials. Thus, category evaluation (stimulus recognition classification), which seems to occur at about 300 msec, as well as multiple subsequent decisions must accompany feedback information. Ruchkin and Sutton (1983) have reviewed evidence for the differentiation of the P3b and the subsequent positive slow wave (PSW) in a variety of tasks, both feedback and non-feedback. The evidence from several studies (Stuss and Picton 1978; Ruchkin et al. 1980; Stuss et al. 1980) suggests that in feedback, relative to non-feedback tasks, the P3b and the PSW differ in terms of amplitude and topography. In this study we decided to focus on the effects of differential feedback utility on ERPs, rather than upon differences between ERPs elicited in feedback and nonfeedback tasks. The first major concern of this study was with the changes in specific ERPs, the P300 and PSW, plus any additional major ERP components, in response to high utility (real) versus zero utility (dummy) feedback. How are these ERPs differentiated in terms of functional-

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CA. W~rren,B.E. McDonough/ Electroencephalography and clinicalNeurophysiology 94 (1995) 60-79

ity and topography, and what do these ERPs imply as to underlying neural generators? Only 3 feedback ERP studies have compared real versus dummy feedback (Johnson and Donchin 1978; DeSwart et al. 1981; Chwilla and Brunia 1991), and they all had various confounds, which limited the comparisons between these two levels of feedback utility. Both Johnson and Donchin (1978) and Chwilla and Brunia (1991) used time-estimation tasks. Johnson and Donchin (1978) gave auditory feedback 200 m:~ec after the subject's time-estimation response, thus confounding the feedback ERP with after-potentials of the motor potential, and making comparison with other feedback ERP studies difficult. Both Johnson and Donchin (1978) and Chwilla and Brunia (1991) ran one or more series of trials with real feedback and other series with non-informative or dummy feedback. The problem with this appro~Lch was that the subject's prior expectation confounded tile comparison, since the type of feedback was always known in advance. DeSwart et al. (1981) avoided this problem by mixing one dummy feedback event and two real feedback events in the same series (i.e., two different tones indicating whether the just presented stimulus was or was not an exemplar of the concept). However, according to DeSwart et al., a distinct confound implicit in this arrangement, may have resulted in the unanticipated finding of a greater dummy than real feedback P300. Since all stimuli were equiprobable ( P = 0.33), subjects may have subjectively categorized both real feedback events as informative ( P = 0.67), and the dummy feedback as non-informative ( P = 0.33), thus producing an ,enhanced P300 to the latter, less frequent stimulus categour (oddball effect). The design of the present study sought to avoid the above-mentioned confounds. First, both the problems of differential prior expectation and of an unwanted oddball task effect were dealt with by intermixing an equal number of real and dummy fee,aback trials. The likelihood of frequency or oddball ERt' effects was presumably further reduced by several features of the task design: a very long intertrial interval was used, instead of the usual several seconds. As two studies have shown, use of such intervals can eliminate the classical oddball P300 effect (see Donchin (1981) and Polich (1990) who used 6 and 10 sec, respectively). Also, the demands of the task compelled attention to feedback in terms of utility, rather in terms of individual stimuli qualities, congruent with the work of Courchesne et al. (1977). Second, no motor response occurred in close proximity prior to or after the feedback, thus eliminating interference from accompanying motor responses, and the feedback was displayed figr 4 sec, providing ample opportunity for processing. A second major concern of this study was with feedback ERPs in the context of a higher-order cognitive task in which subjects had to carry information across trials in order to discover some underlying attribute or principle. Slightly more than half of the feedback ERP studies have

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used such cognitive tasks. This category includes tasks such as those requiring subjects to engage in the intentional detection of changes in sequence-generating rules (Johnson and Donchin 1982), or the identification of a concept (Poon et al. 1974; Stuss and Picton 1978; Perrault and Picton 1980; Stuss et al. 1980; DeSwart et al. 1981; DeLisle et al. 1986). Some evidence suggests that late positive-going ERP components are larger the greater the task demands (note the task demand-enhanced PSW of Jenness 1972; Kok and Looren de Jong 1980a,b; Friedman et al. 1981; Johnson and Donchin 1982; the P4 of Stuss and Picton 1978, and Stuss et al. 1980). Ruchkin and Sutton (1983) have observed that the P3b and PSW are differentially affected by processing requirements, the latter showing more marked variation than the former. We were interested in whether a task, which seemed to require a much greater degree of mental effort than tasks studied heretofore, would produce an increase in the magnitude of the feedback late positive complex (LPC), particularly in the P3b and PSW? We were also interested in whether this task would generate a greater variety of components in the feedback LPC, perhaps reflecting utilization of additional processing capabilities? We selected a principle-learning/problem-solving paradigm (Gagne 1965), which is much more demanding than the concept-identification paradigm, used previously, and seems to capture some elements of real-world, knowledge acquisition tasks. The present study is, to our knowledge, the first investigation of feedback-related ERPs using the former paradigm. Specifically, an arithmetic rule-learning task, using visual stimuli was employed. This task, adapted from Hammond and Summers (1965, 1972), constituted a reception paradigm with a successive mode of stimulus presentation (Bourne et al. 1971). This rule-learning task requires a greater degree of inductive and deductive reasoning than concept-identification tasks, which can be viewed as requiring simple pattern recognition and inductive reasoning. In this rule-learning task the subject was required to employ inductive reasoning to infer specific arithmetic correspondence rules between two cues and an underlying criterion variable, and deduce the usefulness of these rules. For this task, there were two novel aspects of the feedback, which appeared to require more processing than demanded in most previous ERP feedback studies, and, thus, might contribute to further enhancement of ERP variety and magnitude. First, most studies to date have used only response-oriented, binary feedback (e.g., your last response was correct or incorrect), while the task studied here delivered explicit outcome feedback (e.g., the correct value for the last trial was " 3 4 " ) . This type of feedback serves to inform the subject what his response should have been. Moreover, the multi-valued nature of the feedback (values ranging from 0 through 64) requires the subject to judge how close his response was to the

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C.A. Warren, B.E. McDonough / Electroencephalography and clinical Neurophysiology 94 (1995) 60-79

displayed numeric value, unlike the DeSwart et al. (1981) concept-identification study, which used only binary feedback. Second, the correspondence rules were probabilistic instead of deterministic. That is, the feedback consisted of the criterion variable contaminated by numeric "noise." This requirement thus made the feedback " f u z z y " or only approximate. A correct guess, then, rarely corresponded exactly to the displayed feedback value. The effect of using this type of feedback was to provide a small, abstract, increment of information over a long series of trials. This approach does not permit the subject to totally master the set of correspondence rules in a handful of trials, as in the Stuss type of task, but requires numerous trials, similar to the task used by DeSwart et al. (1981). Systematic manipulation of processing requirements offers an explicit path for garnering evidence for the distinctiveness of the P3b and PSW and other ERPs. In this study we sought to compare feedback ERPs across different levels of complexity in the rule-learning task. We knew of no study which had compared the effects on feedback-related positive ERPs of different levels of higher-order, task complexity, requiring different levels of mental effort in the same paradigm. It was reasoned that collection of ERP data from several levels of difficulty, administered to the same subjects over separate sessions, would permit such a comparison. The plan was to study ERPs across the various manipulations, rather than during the course of rulelearning, since many more artifact-free trials than we could reasonably collect would have been required to portray even a rudimentary learning curve. In addition to the arithmetic rule-learning task, which consisted of equal numbers of real and dummy feedback trials, a small number of trials (18% of total) from a second, independent task were also admixed in a random fashion. This was a similarly structured, but skill-oriented, perceptuomotor task, and was far less demanding than the rule-learning task. The former provided a further basis against which ERP feedback in the higher-order cognitive task could be compared. In perceptuomotor tasks the subject receives feedback, over successive trials, which assists in improving accuracy. Slightly less than half of the feedback ERP studies have used relatively simple, skill-oriented tasks, such as learning of a sensory discrimination (Jenness 1972), time estimation (Johnson and Donchin 1978; Ruchkin et al. 1981), and practice of a skilled performance task (Papakostopoulos et al. 1986). No studies appear to have contrasted the feedback ERPs in higher-order and perceptuomotor tasks within the same session. Processing this perceptuomotor accuracy feedback was expected to impose a very modest processing load, providing a reference feedback condition for stimulus events, fundamental memory recall, and comparison operations. The resulting feedback ERPs were expected to be of modest but positive amplitude. These control trials re-

quired only the carry-over of perceptuomotor accuracy information to subsequent control trials.

2. Methods

2.1. Subjects Ten right-handed, male students at the University of Illinois at Chicago (UIC), 18-25 years old, participated in this experiment, after first signing an informed consent form previously approved by the Institutional Review Board at UIC. The volunteers received course credit in an introductory psychology course, a n d / o r pay for participation. All had normal, or corrected to normal, eyesight, reported being right-handed and used their right hand in the experiment.

2.2. Experimental design This study manipulated 3 factors: task type, task difficulty, and type of feedback with the latter two factors being nested within one level of task type. Two types of task were employed: a rule-learning and a perceptuomotor (control) or "recall-paint" task. Nesting of task difficulty (simple and complex levels) and the type of feedback (either real or dummy) were within the rule-learning task. Feedback for the perceptuomotor (PM) task was always real. For the rule-learning task, real feedback (R-FB) consisted of the display of numeric information, which was useful for learning of relationships between a set of cues and a criterion variable; while dummy feedback (D-FB) consisted of a single stimulus which presented non-useful information. For the PM task the subject had to recall and register a numeric value displayed earlier in the trial by using two "paint" buttons. Here, feedback, given after registration of a prediction, consisted of the re-display of the value which had been presented earlier in the trial, allowing the subject to recall earlier information and judge the accuracy of his entry. The rule-learning task, adapted from a paradigm utilized by Hammond and Summers (1965, 1972), required subjects to learn relationships between two visually presented numeric cues (S 1 and S 2) and an underlying, numeric criterion value (Yc), which they predicted (Yp) on each trial. Subjects were ]nstrncted to combine the cues using estimation or mathematical calculation to arrive at Yp. Visual numeric feedback, the subject of analysis of this report, was given subsequent to each prediction. Two levels of rule-learning task difficulty (simple and complex) were administered to each subject during separate sessions. These levels varied in the complexity of the mathematical relationships between each cue and Yc; the order of each being counterbalanced over the two sessions over the 10 subjects. (Session separation > 5 days.) During each session, trials of the rule-learning task with R-FB and D-FB were administered along with PM trials

CA. Warren, B.E. McDonough /Electroencephalography and clinicaI Neurophysiology 94 (1995) 60-79 Thuab Press

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11.511.51 1o., I Fig. 1. Schematic trial initiation and stimulus diagram of a single trial (times in sec) for the rule-learning and PM tasks. A subject thumb press initiated the trial; for the rule-learning task, the first cue, S 1, was a value from " 1 " to " 9 " (for the PM task S 1 = " 0 " ) ; the second cue, S2, was also a value from " 1 " to " 9 " (fi~r the PM task S 2 was a value from " 0 " to " 6 4 " ) ; the third cue, S a, was " 3 2 , " the imperative stimulus or cue for subject to begin "painting.". For both tasks the feedback stimulus for R-FB was a value from " 0 " to " 6 4 . " For half of the rule-learning task trials, D-FB (always a " 9 9 " ) w~ts given.

employing real feedback. These 3 types of trial were presented in quasi-random order. Thus, 3 types of feedback were given: (a) rule-learning real, (b) rule-learning dummy, and (c) PM real. 2.3. Procedure During testing each subject sat alone in a recliner chair and listened to binaural 60 dB pink noise via headphones. Two-way communication to the subject was available. Background illumination was supplied by a 40 W indirect lamp. Numeric values ( " 0 " to " 6 4 , " " 9 9 , " " 0 0 " ) were presented to the subject o:a a 2-digit, light-emitting diode, display (each digit: 2.5 cm × 1.5 cm), at eye level 1 m in front of the subject. Trial structure. A schematic diagram showing the sequence and timing of eve~ts within a trial is shown in Fig. 1. One and one-half sec after the subject initiated each trial by pressing the trial-start button with his right thumb, a 500 msec duration numeric cue (S 1) was displayed and was followed 1.5 sec later by a second, equal duration, numeric cue ($2). Ten and one-half sec after S 2 onset a 3.5 sec duration, imperative ,;timulus (S 3) was presented. S 3 always began as the number " 3 2 " but the subject could increase or decrease the displayed value (change rate: 75 msec/value) by pressing the "paint-up" (right index finger) or "paint-down" (middle finger) buttons, respectively. The numeric value displayed at the end of the 3.5 sec "paint" period was accepted as the subject's numeric prediction (Yp) of Y~. (Pilot testing with other subjects showed the adequacy of the above paint period.) One and one-half sec after S 3 offset a 4 sec duration feedback stimulus was delivered (the meaning of the feedback is explained in the following 3 sections). Two seconds after the offset of the feedback stimulus " 0 0 " was repeatedly flashed on the display to signal the subject he could blink his eyes. Trial length was 22.5 sec, with about 2-5 additional seconds ehtpsing before the subject's initia-

Conditions and session structure. R-FB for both levels of task difficulty of the rule-learning task involved presentation of numeric values consisting of Y~, with numeric "noise" values added to make the relationships between cues and the criterion probabilistic. Subjects were never informed that numeric noise would be added to the criterion value on R-FB trials, but were told only that the correct answer would always be between 0 and 64. On D-FB trials, the value " 9 9 " was presented as feedback on each trial. PM trials only required a subject to carry PM accuracy information across trials. These trials were always heralded by an S 1 of " 0 . " The value of S 2 was a randomly selected number between 0 and 64. After the onset of S 3, subjects were required only to recall and enter the literal value given at S 2. Feedback was always real, consisting of a repetition of the S 2 value. The same numeric values were presented for PM trials in both sessions. No numeric noise was added to PM accuracy feedback. A trial block was defined as 22 trials, consisting of 9 real and 9 dummy, rule-learning feedback trials, and 4 PM feedback trials, presented in pseudo-random order. After each block, subjects completed a 1 min long questionnaire, regarding subjective experiences and performance strategies (not analyzed herein). Task mastery was defined for the rule-learning task as reaching a Pearson product-moment correlation between Yp and Y~ of 0.90 or more for 2 or more blocks. Several additional blocks were given as time permitted. Across subjects, the mean number of blocks administered was 5.9 (S.D. = 1.20) for the simple condition, and 6.6 (S.D. = 0.97) for the complex condition. Simple condition. On each rule-learning trial the subject generated a Yp, his prediction of the value of Yc, based on the specific cue values for that trial. At the end of the trial, R-FB or D-FB was presented. As explained to the subject, the goal was to learn by trial and error the relationship between the cues and Yc over the course of numerous trials; no algebraic equations expressing this relationship were given. For all rule-learning trials (R-FB and D-FB) the numeric cues, S 1 and S 2, were quasi-randomly selected integers between 1 and 9, inclusive. The cues were linearly related to the criterion (Yc) according to the equation: Yc = (4 X S1) + (4 X S 2) - 8. The value of Yc'varied from 0 to 64. The amount of numeric noise added to Y¢ to achieve R-FB for any trial was randomly drawn from an approximately normal distribution of integers, ranging from - 5 to + 5, with mean zero. Thus, a single trial could unfold as follows: If S l = 8 and S 2 = 9, the underlying Y~ = 60. The subject might, say, modify the initially displayed 32 (imperative stimulus)

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CA. Warren,B.E. McDonough/ Electroencephalography and clinicalNeurophysiology 94 (1995) 60-79

to predict a Yp of 40. If the randomly selected numeric noise value were - 5 , then R-FB = 55 ( 6 0 - 5). If this were to be a dummy trial, the subject would see the non-useful feedback, D-FB = 99. Complex condition. The following equation was used in the complex condition: Yc = (4 X $1) + (36 x sine (0.3491 X $2)) - 8. Thus, S 1 remained directly and linearly related to Yc, but S 2 was related to Yc according to an inverted-U function. The input values to the sine function were scaled to vary discretely over 9 equally spaced values between 0 and II radians, and the resultant function values were rounded to whole integers and equally weighted with those of the S 1 cue values. The complex condition also varied in several other ways from the simple condition: (a) the noise values added to each Y¢ to form R-FB were drawn from a zero mean, approximately normal distribution with a range of - 2 to + 2; (b) after the 3rd block of trials, the subject was shown graphs depicting the cue-criterion relationships for both cues. The graphs were presented and explained in a similar manner as in the practice task (see below). To insure that the cue information was assimilated, the subject was asked to "explain" the graphs to the experimenter, who then removed them before the start of the 4th block of trials. Again, no equations were presented. The need to impose this mid-session "graph tutorial" for the complex condition stemmed from pilot work demonstrating that substantial learning for this condition could be obtained in one session only by showing graphical cue-criterion relationships. By such an intervention the learning process could be telescoped into a single session, even though the precise comparability of the two difficulty levels was compromised by adding a visuo-spatial learning aid. Subject instruction and the practice task. At the beginning of the first session the subject was given a standardized set of task instructions, and training plus practice trials (2040). Schematic graphs depicting the relationships between each cue and the criterion for rule-learning trials were shown. For training and practice trials, the cues were linearly related to the criterion: Yc = (4 X S 1) - (4 X S 2) + 32. Here no noise was added, making R-FB = Yc. The subject was told that the relationships used during practice would not be applicable to subsequent experimental trials. 2.4. Recording procedure Sensormedics, non-polarizing, mg/mgC1 electrodes were applied, using Beckman conductive, electrolyte gel, to frontal (Fz), parietal (Pz), left and right central (C 3, C 4) scalp sites (international 10-20 system), and to the occipital site (O~). Electroencephalographic (EEG) leads were

referenced to linked earlobe leads, and a forehead lead was used as ground. Abrasive grit and alcohol were applied to lead sites to attain electrode impedances at or below 5 k ~ . Impedances of the ear reference electrodes were equalized by means of an adjustable resistor in series with the lower impedance electrode. The potential difference between the supraorbital ridge and the outer canthus of the right eye (electro-oculogram (EOG)) were used to detect eye-movement artifacts. Data were acquired using Grass (Model 7P122) amplifiers (output characteristics: 8 sec time constant; one half amplitude at 60 Hz, zero amplitude at 80 Hz, and a 60 Hz notch filter), displayed on a Grass (Model 78D) polygraph, and recorded on a Vetter (model A) FM tape recorder (DC to 3 dB down at 100 Hz). Pre-session square wave pulses (100 /xV), recorded on each channel, provided standard reference signals for calibrating all amplifiers to a common output scale. An eighth channel recorded stimulus and response events. 2.5. Performance measurement Each subject's performance error (PE) on the simple and complex rule-learning tasks was determined by taking the absolute value of the difference between the Yc and Yp for each trial. For the simple level, trials were then divided in half and averaged separately to provide scores for the first and second parts. For the complex level, single subject averages were created for the first 3 blocks (prior to the showing of the graphs), and for the subsequent blocks. The effects of task difficulty and change over the first and second " h a l v e s " of the session were tested using a repeated measures ANOVA (BMDP-2V; Dixon et al. 1990). All trials were included in each subject's average, without regard to EOG or EEG artifacts or to display of R-FB or D-FB. 2.6. ERP measurement and analysis The EEG data plus the EOG and event channel were digitized from tape at 125 Hz (8 msec per datum). An epoch length extending from 1500 msec prior until 4000 msec post-stimulus feedback onset was edited for artifact. The trials were extensively edited by computer in order to exclude trials contaminated by artifacts due to eye blinks and movements (eye artifacts were defined conservatively as voltage deviations greater than 30 /~V deviation from a running baseline on the EOG channel), as well as instances of excessive electrode drift, level shifts and clipped channels on all data channels. For each subject ERP averages of artifact-free trials were computed for each of 6 conditions: rule-learning R-FB or D-FB, and PM feedback for both the simple and complex levels of task difficulty. Across conditions and subjects, the mean percentage of surviving, artifact-free or " g o o d " trials was 62% (range: 31-90%) of the total. The mean number of good trials for R-FB and for D-FB = 33 (range: 11-57); the mean number for PM was 15 (range:

C.A. Warren,B.E. McDonough/ Electroencephalographyand clinicalNeurophysiology94 (1995) 60-79 7-28). The length of the feedback epoch selected for analysis consisted of 2500 msec of post-stimulus, average ERP data. The mean of a 200 msec, pre-stimulus baseline was used as a reference for subsequent measurements. The remaining 1500 msec of the 4000 msec feedback period was excluded from further analysis since, during that period, the ERP maintained an essentially asymptotic voltage level for both the PM and rule-learning tasks. Peak measurement. Prior to making ERP measurements, the single-subject averages were individually filtered using an ideal, discrete time, 8 Hz low-pass, digital filter in the frequency domain (Oppenheim and Willsky 1983; Cook and Miller 1992). This was done by first fast Fourier transforming the single-subject averages, and applying the frequency-domain filter by zeroing unwanted transform elements corresponding to 8 Hz and above. The inverse Fourier transform was then applied to the resultants to produce filtered (zero phase shift), time-domain averages. No high frequency noise due to ringing was apparent in the passed frequencies (see Attinger 1966). Note that this filter eliminated any 45-62 Hz activity, which the 125 Hz sampling rate theoretically could have induced through aliasing of any attenuated amplitude frequencies passing the amplifier filters in the range 80-63 Hz, and surviving averaging. A total of 5 ERP components were measured. The P2//P3a, P3b and PSW were measured as the baseline-referenced mean of all data points within specified latency windows 250-300 msec, 350-450 msec, and 600-900 msec post-stimulus onset, respectively, in close accordance with the latency ranges used by Ruchkin et al. (1990). The P2 and P3a were designated as a P2//P3a complex, since these two closely occurring components are difficult to dissociate when measured in response to an external stimulus (see Ruchkin et al. 1!987, 1990). A component was defined as a voltage deviation from baseline occurring in at least 3 identifiable peaks among the averages of Fz, Pz, and Oz for rule-learning R-FB and for D-FB trials (collapsing across simple and complex levels of task difficulty). In addition, two other positive components were observed: the P508 (peaks in the range: 487-526 msec), and the late positive slow wave (LPSW; range 952-1248 msec). Latency shifts in the LPC were reduced due to the use of highly readable numbers. Use of mean voltage values over specific latency ranges (mean time-window or MTW measures) circumvented effects of any small latency shifts.

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Statistical analysis. The statistical significance of the components was tested through an hierarchical set of repeated measures ANOVAs, similar to the approach used by Ruchkin et al. (1988). Repeated measures ANOVAs (BMDP-4V, Dixon et al. 1990) were used instead of MANOVAs, however, because of the slightly smaller number of available cases (Vasey and Thayer 1987). To reduce the likelihood of type I errors the correction for multiple repeated measures (Greenhouse and Geisser 1959) was applied to tests of electrode (E) and peak (P) main effects and their interactions, where df > 2. A global repeated measures ANOVA was first performed, using the positive MTW measured ERP peaks as a factor, along with feedback, task difficulty, and electrode as additional factors. Follow-up testing for specific ERP components was done using repeated measures ANOVAs. Finally, a set of ANOVAs was conducted at each of the 5 leads for each ERP peak. Where appropriate, ANOVA tests were Bonferonni corrected for multiple testing. Neuman-Keuls tests were used to test for differences in topography. Significance testing used 2-tailed critical values with P < 0.05. Where MTW ERP components proved significant, the same repeated measures ANOVA was also applied to the corresponding PCVA factor scores. Profile analysis. The profile analysis techniques, used by Ruchkin and his colleagues (cf., Glaser and Ruchkin 1976; Ruchkin et al. 1987, 1988) and by McCarthy and Wood (1985) were applied to the MTW ERP measures to determine whether the scalp-recorded ERP components might reflect operation of more than one population of neurons, or neural generators. This approach further tests initially significant interactions, by discounting any mean topographic or experimental condition effects through rms scaling away of mean ERP differences. Thus, comparisons are confined to profile shapes alone (i.e., of topography or of experimental conditions) (see McCarthy and Wood 1985). If an interaction remains significant, then different neural generators are possibly involved. But absence of a difference does not imply identical intracranial generators, since generators may overlap in space (radially and tangentially within the brain mass), so as not to be discriminable with a given array of scalp leads. Also, such analyses of scalp-recorded potentials were not assumed to reveal precise neuroanatomical loci.

3. Results

Principal component analysis. A principal component analysis of the covariance matrix with Varimax rotation (PCVA; BMDP-4M, Dixon et al. 1990) was applied to the low-pass filtered, averaged single-subject wave forms covering the first 157 data points following onset of the feedback stimulus (1-1256, msec). There were 10 subjects x 2 tasks X 2 feedback conditions X 5 electrode locations (200 cases).

3.1. Performance Mean PE was significantly greater for the first versus the second halves of the session (9.98 vs. 5.08; F ( 1 / 9 ) = 104.99, P < 0.00001), and greater for the complex (10.93) than the simple (4.13) levels ( F (1//9)= 120.15, P < 0.00001). Thus, significant learning took place for both difficulty levels, although error was greater for the com-

CA. Warren,B.E. McDonough/ Electroencephalography and clinical Neurophysiology 94 (1995) 60-79

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CA. Warren, B.E. McDonough/ Electroencephalography and clinical Neurophysiology 94 (1995) 60-79

67

plex than the simple level. Across session halves, the decline in error was significantly steeper for the complex (14.63-7.23 = 7.4; F ( 1 / 9 ) = 36.64, P < 0.0002) than for the simple level (5.32-2.93 = 2.39; F (1//9) = 15.42, P < 0.0035), as shown by the Complexity × Half interaction ( F ( 1 / 9 ) = 8.91, P < 0.015). However, the ratios of PE for first versus second halves were roughly comparable between difficulty levels (complex: 2.02 vs. simple: 1.82), indicating comparable degrees of mastery.

1500-2000 msec. The ERP positivity associated with PM feedback was found to be uniformly far less than that associated with rule-learning feedback. PM positivity also began to differ from both types of rule-learning feedback beginning around 250 msec, or earlier, and remained different throughout the epoch. The magnitude of the average EOG (eye) in Fig. 2 for all 3 conditions was far smaller than that seen for the ERPs, suggesting that eye activity was not responsible for these differences.

3.2. ERP wave forms Fig. 2 shows the plots of feedback ERPs for the rulelearning and the PM ("recall-paint") tasks averaged across task difficulty and subjects. The rule-learning ERPs are plotted in terms of whether R-FB or D-FB was given. It can be seen that, across all 5 EEG leads, the positivity for the R-FB began to exceed that of the D-FB starting at about 250 msec post stimulus and extended until about

3.3. Analysis of ERP components in the rule-learning task Mean time-window (MTW) ERP measures. Fig. 3 shows grand averages of ERP activity (over all electrodes and both levels of task complexity) which accompanied R-FB and D-FB levels of feedback for rule-learning. Also shown is the grand ERP average for accuracy feedback for the PM task. These plots illustrate the time windows, the mean

P3b

PSW

5 4

I

3

--

2

Z

1

q

0

0

4

<

3

F4'.

-

b_

2

P2/':

P508

.._

li 0

-1 I

0.0

0.2

,

,,

'1

I

0.4

0.6

,,,

I

I

0.8

1.0

i

I

1.2

SEC Fig. 4. Plots of factor loadings of six factors yielded by PCVA of the covariance matrix of rule-learning ERP averages, over subjects, difficulty levels, R-FB and D-FB, and sites. Order c,f factor extraction is indicated by number (e.g., FI: first factor). (Clustering of the first and second 3-factor loading plots on separate lines is for clarity.) Brackets accompanying each ERP label (e.g., P3b, PSW, etc.) indicate latency windows used for the MTW measurements of the 5 ERPs.

68

C.A. Warren, B.E. McDonough / Electroencephalography and clinical Neurophysiology 94 (1995) 60-79

voltages of which were used in measurement of the 5 ERP components, and the degree to which these windows encompass peaks apparent in the plots.

Table 1 Global repeated measures ANOVA of rule-learning task for 5 ERPs using the MTW measure

F

P

1/9

33.34

0.0003

1/9 1/9

3.20 0.46

0.1074 ns

vs. C 3 vs. C 4) F XE

4/36 4/36

DX E

4/36

DXFx E Peaks (P) (P2/P3a vs. P3b vs. P508 vs. PSW vs. LPSW) F× P D×P DXFXP E XP F x EXP D XE XP DXFXEXP

4/36

11.14 6.69 0.57 1.03

0.0001 0.0077 ns ns

4/36 4/36 4/36 4/36 16/144 16/144 16/144 16/144

15.28 5.39 0.38 1.14 5.26 1.12 1.10 1.09

0.0001 0.0098 ns ns 0.0058 ns ns ns

Effect

PCVA of time-varying voltages. A PCVA was conducted on all average ERP voltage values of the first 1256 msec (157 data points) following feedback onset, over all conditions and electrodes for all subjects. The factor loadings of PCVA are plotted in Fig. 4. The most striking finding was that three of the latency ranges of the MTW ERP measures, PSW, P3b, and LPSW, bracketed the maximum loadings of the first 3 factors, and fell completely within the strings of factor loadings which had values of 50% or more of the maximum loading for each of those factors (maxs0 loadings). Moreover, the max50 loadings for each of these factors showed very little temporal overlap. These 3 factors collectively accounted for 78.8% of the variance and individually represented 59.0% (F1); 13.1% (F2); and 6.7% (F3) of the total, respectively. The correspondence between the latency ranges of the MTW measures and the max s0 loadings suggests that the PSW, P3b and LPSW can be identified with F1, F2 and F3, respectively. The latency range of the P508 fell very near the maximum of F5 (2.6% of the total variance) and, thus, can be identified with this factor. The P2/P3a latency range was more problematic. It fell between the maximum loading of F4 (4.6% of the total variance) and F6 (2.1% of the total variance). Due to the ordering of the latency of the maxima of F4 (266 msec) and F6 (317 msec), it may be inferred that F4 corresponds to P2 and F6 with P3a. It seems reasonable to assume that the MTW P2/P3a measure reflected both the underlying P2 and P3a. These findings from the PCVA reinforce the idea that our MTW-measured components, PSW, P3b and LPSW, tap highly distinct sources of variance. However, this seems less true for P2/P3a and P508. The factor analog loadings for both of these ERPs are totally subsumed under those of F2 ( " P 3 b " ) , or F1 ( " P S W " ) and F2 ( " P 3 b " ) , respectively.

Analyses of MTW ERPs. The effects of the rule-learning task on MTW ERP measures were first analyzed by a global, repeated measures ANOVA (peaks (P2/P3a, P3b, P508, PSW, LPSW) × feedback (real, dummy) × difficulty (simple, complex)× electrode (Fz, Pz, Oz, C3, C4). The results are shown in Table 1. The feedback factor was highly significant, while the difficulty factor showed only a slight trend. The peaks and electrode main effects were also highly significant, and significant feedback x electrode, feedback × peaks, and electrode × peaks interactions were also found. Thus R-FB and D-FB exerted a general effect on ERP amplitude across peaks, and the different peaks and electrodes also had their own general effects, independent from the effects of the other factors. R-FB and D-FB also produced different overall amplitude topographies and had

Feedback (F) (real vs. dummy) Difficulty (D) (simple vs. complex) FxD Electrode (E)

df

(Fz vs. P~vs. Oz

different effects on amplitude depending on the ERP. Further, different ERPs showed different topographies. The functional and topographic behavior of the MTW ERP measures and of the PCVA-derived ERPs is displayed in Fig. 5. Considering only the MTW data plotted in the left-hand column of Fig. 5, the significant effects from the global ANOVA can be observed. From these plots it can be seen that the R-FB (solid lines) generally resulted in greater positivity than D-FB (dashed lines), and that successive peaks showed increasing amplitudes through P3b and P508 and declined thereafter. Also, the ERP amplitudes changed over electrodes, generally being largest at Pz, while tending to decline in the anterior direction, and at the occiput. Different peaks appeared to show topographic differences. Further, the overall R-FB versus D-FB amplitude differences tended to increase in an occipital to anterior direction, with this difference being greatest for PSW and P3b. To better delineate the above interactions of the peaks factor, repeated measures ANOVAs were performed on each of the 5 positive ERPs for feedback (real, dummy), electrode (Fz, Pz, Oz, C 3, C4), and difficulty (simple, complex). (Results for task difficulty will be presented later.) As seen in Table 2, the effect of feedback, while significant for all ERPs, was strongest for PSW and P3b, followed by P2/P3a, LPSW and, finally, by P508. The effect of electrode was significant for 4 out of the 5 ERPs. The electrode effects were strongest for P508 and P3b, followed by PSW and P2/P3a, while LPSW was nonsignificant. Both P508 and P3b showed a larger amplitude at Pz than the other ERPs, followed by a steep average

C.A. Warren, B.E. McDonough / Electroencephalography and clinical Neurophysiology 94 (1995) 60-79

decrease in the anterior direction (see Fig. 5). For the 2-way interactions between feedback and electrode only P2/P3a and LPSW attaine, d significance. However, several ERPs (i.e., P3b, P508, and PSW), which showed only feedback × electrode trends, evidenced changes in the same direction. Finally, as shown in Table 3, ANOVAs were performed for all 5 ERPs at each electrode to better delineate the size and locus of all feedback-by-electrode interactions, and to reveal significant, but isolated, single-electrode ERP effects. (Note the Bonferonni corrections for P3b, P508 and PSW, due to non-significant F X E (see Table 2).) The P 2 / P 3 a maximal effect showed a fronto-right central-posterior topography, while the maximal LPSW effect showed a centro (right and left)-frontal locus. Feedback affected P3b in a topographically extensive fashion, from parietal to right central to frontal, and also included the left central region. Feedback also broadly affected the PSW, but with a different profile of influence: the central electrodes, C 3 and C 4, were most affected, followed by Pz, and finally Fz. Feedback affected the P508 significantly only at Fz. These effects can be seen in Fig. 5. The topography of the MTW ERP measures, shown in Table 4, reveals similar profiles for P 2 / P 3 a and P3b: parietal, central and frontal areas all showed significantly greater amplitude than O z, but did not differ from each other. The P508 and PSW :~howed similar topographies in that Pz was more positive than most other electrodes (O~, C 4, and Fz). The P508, however, was somewhat more differentiated than PSW in that Pz was also significantly greater than C 3, and the latter was significantly greater than Fz.

Analyses of PCVA-derived ERP components. For each significant MTW ERP the same set of repeated measures, ANOVAs were performed for the scores of factors, F1, F2, F3, F5, and F6. Presumably, analysis of the orthogonalized set of ERP factor scores would permit greater differentiation of ERPs, which are intercorrelated. Where ANOVAs of the factor scores were significant at P < 0.05 or less,

69

the results are indicated in Table 2 by a c by the tabled P value. Both PCVA F 2 ( " P 3 b " ) and F I ( " P S W " ) factor scores were found to be significantly affected by feedback, with greater positivity for R-FB than for D-FB. These findings thus confirm the MTW findings for feedback. Plots of these and the other factor scores can be seen in the second column of Fig. 5. Further, the F2 ( " P 3 b " ) and F1 ( " P S W " ) factor scores also showed significant topographic changes, which generally paralleled the significant topographies seen for the MTW measures. The significant topographic change of the P508 was confirmed for the PCVA F5 factor score, the factorial analogue of the P508. The topography showed the same relative, posterior-to-anterior decrease in positivity as the MTW measure, but with a slight decrease in the Pz maximum, and a great decrease in the Fz positivity (see Fig. 5, col. 2). The topography of the F6 ( " P 3 a " ) score was significant, though its pattern was markedly different from that of P 2 / P 3 a as measured by MTW, as can be seen in Fig. 5. The F6 ( " P 3 a " ) score showed an obvious centro-frontal, instead of parietal maximum as did the MTW P2/P3a! The topography of the F4 ( " P 2 " ) factor score (not pictured) was also significant ( F (4/36) = 6.73, P < 0.0086) and was of the same pattern as the MTW measure, as seen in Fig. 5. The F3 ( " L P S W " ) showed a significant feedback by lead interaction, similar to the MTW measure, but did not confirm a feedback main effect. Notably, two PCVA ERP factors, F6 ( " P 3 a " ) and F5 ("P508"), failed to confirm the significant feedback main effects or feedback by lead interactions of the analogous MTW measures. The analysis of F4 ( " P2") also failed to show any feedback effects or feedback interactions. These findings suggest a reason for the significant feedback main effects and feedback by lead interactions shown by the MTW P2/P3a and MTW P508: they may have been contaminated by underlying slow wave activity related to the " P 3 b " and " P S W , " which were affected by feedback.

Table 2 Separate repeated measures ANOVAs for 5 ERP components using MTW a and PCVA-defined measures Effect

Feedback (F) Electrode (E) F× E b

df 1/ 9 4/36 4/36

P2/P3a (250-300 msec) P3b (350-450 msec)

P508 (487-526 msec)

PSW (600-900 msec)

LPSW (952-1248 msec)

F

P

F

P

F

P

F

P

F

P

11.47 9.01 9.16

0.0080 0.0026 c.d 0.0004

37.95 9.95 3.19

0.0002 c 0.0006 c 0.0682

5.94 10.55 2.81

0.0376 0.0005 c 0.0819

43.74 7.89 3.29

0.0001 c 0.0010 c 0.0733

10.81 2.73 6.89

0.0094 0.0948 0.0062 c

Note: results for the difficulty factor and its interactions are presented in Table 6. a The listed P values are for the ldTW measurements, with those values significant at P < 0.05 being underlined. b Since the interaction of the peaks factor with this interaction term was non-significant in the global ANOVA, to maintain a family-wise significance level of P < 0.05 over 5 ERPs, P = 0.01 (0.05/5) was used for each test of significance. The P values attaining family-wise significance are underlined. CAn ANOVA, conducted for the corresponding PCVA-derived component, using post-feedback onset voltages from times 1-1256 msec as data (157 values), was significant at P < 0.05. ANOVAs of PCVA factor scores were done only when ANOVAs of !he MTW values first showed P < 0.05. d The PCVA (see c above) factor, F6, corresponding to the P3a component, displayed a different topography from that yielded by the MTW ERP measure, P2/P3a (see Fig. 5).

C.A. Warren, B.E. McDonough/ Electroencephalography and clinicalNeurophysiology 94 (1995) 60-79

70

16

P3a

i -o.4

8 4 20

I'

L

I

1

I

I

I

I

I

I

f

Ca

F~

0.0776

0.0026

0.0021

0.0011

0.0002

0.0003

0.0621

0.0329

0.0038

0.0001

0.0004

0.0009

0.0034

0.0020

0.0057

P3b (350 450 msec)

°°f:

l

,

•

]

1.2

,

,

j

,

,

I

i

i

0.0434

0•0001

P508 (487-526 msec) 0.1359

PSW (600- 900 msec) 0.0125 a

0.0005

LPSW (949-1248 msec)

-0.8 {

I

I

1

I,

I

I

I

1.2

t6

I

l

I

I

I

0.5875

,

I

I

T

,

Note:

,..',

i

0.0

8

""

0.1166

for control of multiple testing effects and m a i n t e n a n c e of a family-wise P < 0.05, individual tests for P3b, P508, and PSW used P = 0.01 (0.05/5 electrodes) and P = 0.02 for evidence of a trend• The significant P values are underlined. a p value here reflects a weak trend.

0.4

12

,

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-0.8

4 20

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8

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--0.8 ~ "'/" '

I

i

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/

J

1.2

16

0.4 to.o

12

4

0•0062

C3

-0.4

8

4 2O

0•6488

I

0.0

P3b

PBW

P2/P3a (250- 300 msec)

i

I

0.4

Pz

-!

0.7300

12

P508

'-,:222."

O~

i

~-0.8

16

4 20

Table 3 Single electrode ANOVA main effect P values for feedback using MTW amplitude measures of 5 ERP components (dr = 1 / 9 for all entries)

PCVA : 1 - 1 2 4 8 m S Oz Pz C3 C4 Fz 1.2 [ j , , , , . 0.8 (Paa) / . . . . q{

MEAN TIME-WINDOWS Oz Pz C3 C4- gz 2O I I I l I

t

;:::"," ;::::;:::::Ti-o., Oz

Pz C3 C4 gz

FEEDBACK REAL

DUMMY

I

I

I

I

,

,

,

,

,

:-..

f I

"':'"'":"

I

I

,,

I

.--...

I

I

/

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]

i

3.

Oz Pz C3 C4 Fz

TASK DIFFICULTY SIMPLE' • COMPLEX - SIMPLE ......... COMPLEX ........

Fig. 5. Across-subject averages of MTW ("MEAN TIME-WINDOWS") and PCVA factor scores (FS) for each of 5 ERPs as a function of feedback level ( " R E A L " (solid lines), " D U M M Y " (dashed lines)), and task difficulty ("SIMPLE" (black circles), "COMPLEX" (no circles)). Note that .in the P2/P3a row for PCVA the F6 factor score was plotted, a factor identified as P3a.

Profile analyses of MTW ERP measures. Profile analyses were conducted on MTW measures by performing repeated measures ANOVAs on interactions involving the peaks and feedback factors, the peaks and electrode factors, and the feedback and electrode factors, after scaling to remove overall amplitude differences. Table 5 shows the results. Most notable was the finding, seen under the F x Prms, ERP functionality heading, that the PSW and P3b appeared

to be produced by different neural generators. Further, the neural generator for the PSW appeared to be distinct from those for the P2/P3a and P508. Finally, P508 seemed to reflect a different neural generator from LPSW. The PSW to R-FB was much larger than to D-FB (0.42 /zV rms difference), and this difference was greater than that for P2/P3a, P3b, or P508 (0.24, 0.16, or 0.14/xV rms difference, respectively). As shown under the E X P~ms, ERP topography heading, the neural generator for P508 appeared to be distinct from those of P2/P3a and P3b, and the P2/P3a neural generator seemed distinct from that of the LPSW. The P508 Table 4 Neuman-Keuls tests of topographic changes for 4 rule-learning MTW ERPs

Oz Pz P2 / P3a (250- 300 msec)

C3

C4

Fz

Oz

<

<

<

<

P3b (350- 450 msec) O~ <

<

<

<

P508 (487- 526 rased Oz

<

< a

Pz C3

<

C4 Fz

< <

a


<

<

PSW (600- 900 msec) Oz

ez C3 C4

Fz

< a

<

Note: the symbol < indicates P < 0.01, with family-wise error controlled for the tests performed for that ERP, except as otherwise indicated• a These tests are significant at P < 0.05.

C~A. Warren, B.E. McDonough / Electroencephalography and clinical Neurophysiology 94 (1995) 60-79

71

Table 5 Left column: significant profile analyses of MTW ERP amplitude interaction terms of repeated measures ANOVAs. Right column: corresponding rms differences in ERPs (Pros) and feedback (Frms) at 5 sites Effect

ANOVA

df ERP functionality FXP~, s

F

P

11.06 16.04 38.80 6.19

0.0089 0.0031 0.0002 0.0345

1/9

ERP P3b vs. PSW P2/P3a vs. PSW P508 vs. PSW P508 vs. LPSW

ERP topography EXP~m~ ERP column

4/36 ERP column A - ERP column B at sites a

A

B

P2/P3a vs. P2/P3a vs. P3b vs.

LPSW P508 P508

Feedback topography E × F,~s

4.58 6.25 15.69

0.0273 0.0120 0.0004

4/36

Pz

C3

C4

Fz

-0.27 -0.26 - 0.13

0.16 -0.07 - 0.07

0.01 0.02 - 0.02

0.12 0.09 0.01

0.06 0.15 0.15

R e a l - dummy at sites ~

ERP P2/P3a LPSW

Oz

4.80 6.28

0.0095 0.0065

Oz

Pz

C3

Ca

Fz

- 0.06 - 0.02

- 0.01 - 0.04

- 0.06 - 0.05

0.00 0.01

0.12 0.08

a Larger and largest values in boltd for emphasis.

g e n e r a t e d 0.15 /zV r m s le:ss p o s i t i v i t y t h a n the P 3 b at Fz, b u t 0 . 1 3 / ~ V r m s m o r e at O z. S i m i l a r l y , the P 5 0 8 g e n e r a t e d less p o s i t i v i t y t h a n P 2 / P 3 a at Fz, b u t m o r e at O z (0.15 ~ V r m s less a n d 0 . 2 6 / x V r m s m o r e , r e s p e c t i v e l y ) . T h e P 2 / P 3 a w a s m o r e p o s i t i v e t h a n L P S W at Pz a n d C 4 (0.16, 0.12 /xV r m s m o r e , r e s p e c t i v e l y ) , b u t 0 . 2 7 / ~ V r m s less at O z.

i n c r e a s e in p o s i t i v i t y r e l a t i v e to D - F B at Fz ( 0 . 1 2 /zV r m s m o r e ) , b u t less p o s i t i v i t y t h a n the P 2 / P 3 a to D - F B at C 3 a n d O z (0.06, 0 . 0 6 /xV r m s less, r e s p e c t i v e l y ) . F o r L P S W d i f f e r e n t g e n e r a t o r s w e r e also f o u n d to b e o p e r a t i v e u n d e r R - F B v e r s u s D - F B . C o n s i s t e n t w i t h the P 2 / P 3 a , the L P S W s h o w e d g r e a t e r p o s i t i v i t y to R - F B t h a n to D - F B at F z (0.08 /~V r m s difference), b u t less t h a n to D - F B at C 3 ( - 0 . 0 5 /~V r m s difference). Thus, when subjects experienced R-FB, a temporally early, n e u r a l g e n e r a t o r w i t h a l o c u s at F z p r o d u c e d p o s i t i v ity m e a s u r e d as P 2 / P 3 a ; w h e r e a s w h e n D - F B o c c u r r e d , a different neural generator was activated which had maxima

F i n a l l y , as s h o w n u n d e r the E × F~mS f e e d b a c k levelt o p o g r a p h y h e a d i n g , the P 2 / P 3 a itself w a s f o u n d to reflect o p e r a t i o n o f d i f f e r e n t n e u r a l g e n e r a t o r s u n d e r R - F B as c o m p a r e d to D - F B . T h i s w a s i n f e r r e d f r o m the f i n d i n g o f s i g n i f i c a n t t o p o g r a p h i c d i f f e r e n c e s as a f u n c t i o n o f feedb a c k level. T h e P 2 / P 3 a to R - F B s h o w e d the g r e a t e s t

Table 6 Task difficulty effects, interactiota~sand relationships for 3 ERP components: P values for separate repeated measures ANOVAs of MTW measures, plus P values for follow-up testing of factor scores from PCVA a Effect Difficulty (D) DXF×E

P508 (487--526 msec) MTW PCVA MTW

ns 0.0381

PCVA

0.0661

Relat. b

R: S > C Oz, Pz, C3 R: C > S C4, Fz RandD:C>S

PSW (600-900 msec)

Relat. b

LPSW (952-1248 msec)

Relat. b

0.0455 ns ns

S> C

0.0241 0.0422 ns

S>C S>C

-

Pz, C3 Note: MTW main effects of D for P2/P3a or P3b, and the MTW D × E and D × F for all 5 ERPs were non-significant ( P > 0.05). The df was 1 / 9 for D, and 4 / 3 6 for D × F × E . a Follow-up ANOVAs using the PCVA factor scores were done only where initial MTW measures attained P < 0.05. b Direction of relationship between simple (S) and complex (C) levels for real (R) or dummy (D) feedback. Specific sites listed, where relevant.

72

C~A. Warren, B.E. McDonough / Electroencephalography and clinical Neurophysiology 94 (1995) 60-79

at C 3 and O z sites. Later, nearing the midpoint of the 4 sec epoch, LPSW to R-FB feedback reflected another generator which, again, produced maximum positivity at FZ, whereas the D-FB LPSW resulted in yet another generator whose maximum occurred at C 3.

Task difficulty. Repeated measures ANOVAs showing the significant effects of task difficulty upon P508, PSW and LPSW are shown in Table 6. The ANOVAs of P 2 / P 3 a and P3b were non-significant and are not shown. Results are shown for the MTW measures and, where these were significant, for the corresponding PCVA factor scores. The MTW P508 was greater for the simple than for the complex condition but, surprisingly, the analogous PCVA factor score (F5) showed a reversed trend. Simple was greater than complex for the MTW PSW, as it was for the MTW LPSW, however, only the latter showed a significant factor score effect. These findings must be tempered by the fact, due to multiple analyses over 5 ERPs, capitalization on chance is possible, since no difficulty effect nor associated interactions were found significant in the global ANOVA (see Table 1). Here, maintenance of a family-wise P < 0.05 across all 5 ERPs would require a P < 0.01 (0.05/5) for a given ERP to be considered significant.

3.4. Analysis of P M R-FB versus rule-learning D-FB As shown in Table 7, ERPs to accuracy feedback in the PM task was contrasted to the D-FB in the rule-learning task with the task difficulty and electrode factors also included. The impact of task was greatest for P508 and PSW, followed by P3b and LPSW. Significant changes in topography were seen for all but LPSW, and task difficulty showed no significant effects for any ERP. Significant task X electrode interactions were found for a number of ERPs in the following order: P508, P3b, and P2/P3a. It is obvious from Fig. 2 that the differences between PM and rule-learning R-FB are larger by far for all ERPs.

4. Discussion 4.1. Overall ERP findings with mean time-window ERPs The positivities we found in the arithmetic rule-learning task represent an even greater diversity, as well as temporal separation of positive ERPs, than achieved by Stuss and Picton (1978), using a simpler, concept-identification task. Apparently the inherent complexity of our task, as well as the slow information accrual throughout the session, which was a hallmark of our task design, succeeded well in calling forth a variety of ERP components.

Table 7 Repeated-measures ANOVAs of task X difficulty X electrode for 5 MTW ERPs Effect

df

P2 / P3a Task (T) (PM accuracy FB vs. rule-learning D-FB) Difficulty (D) (simple vs. complex) Electrode (E) (Fz vs. Pz vs. Oz vs. C 3 vs. C 4) TXE

1/9 1/9 4/36 4/36

3.52 0.91 7.58 2.80

0.0935 ns 0.0002 0.0403

P3b Task (T) (PM accuracy FB vs. rule-learning D-FB) Difficulty (D) (simple vs. complex) Electrode (E) (Fz vs. Pz vs. O z vs. C 3 vs. C 4) TxE

1/9 1/9 4/36 4/36

22.66 0.08 7.18 4.98

0.0010 ns 0.0002 0.0027

P508 Task (T) (PM Accuracy FB vs. rule-learning D-FB) Difficulty (D) (simple vs. complex) Electrode (E) (Fz vs. P~ vs. Oz vs. C 3 vs. C 4) T×E

1/9 1/9 4/36 4/36

53.31 0.00 8.78 7.73

0.0000 ns 0.0000 0.0001

PSW Task (T) (PM accuracy FB vs. rule-learning D-FB) Difficulty (D) (simple vs. complex) Electrode (E) (Fz vs. Pz vs. Oz vs. C 3 vs. C 4) TXE

1/9 1/9 4/36 4/36

37.24 0.05 3.27 1.99

0.0002 ns 0.0218 0.1174

LPSW Task (T) (PM accuracy FB vs. rule-learning D-FB) Difficulty (D) (simple vs. complex) Electrode (E) (Fz vs. Pz vs. O z vs. C 3 vs. C 4) TXE

1/9 1/9 4/36 4/36

20.55 0.00 1.13 1.27

0.0014 ns ns ns

Note: interactions T X D, D X E and T X D X E were non-significant.

F

P

C.A. Warren,B.E. McDonough/Electroencephalography and clinicalNeurophysiology 94 (1995) 60-79

On the other hand, while there was an overall significant interaction of feedback with type of ERP, and significant topographic effects were found for all but the latest ERP, the LPSW, all 5 positive ERP components during feedback, from 250 to 12.50 msec, were significantly affected by feedback utility. Thus, while the diversity of positive ERPs was greater than in the simpler tasks used by previous investigators, the above results still seem consistent with the findings of a variety of non-feedback studies of the 1970s and early 1980s, showing a lack of differential functionality between the P300 and later positivities. Such findings include the numerous auditory stimulus counting studies using rare and frequent event probabilities (N. Squires et al. 1975; McCarthy and Donchin 1976; Duncan-Johnson and Donchin 1977; K. Squires 1977), and studies relating the P300 complex or LPC to cognitive capacity (Isreal et al. 1980a,b; Hoffman et al. 1986). 4.2. Multi-method assessment of ERP inter-relationships, functionality, and topography Profile analyses of our MTW-defined measures of ERPs and ANOVAs of PCVA-ctefined ERPs yielded results inconsistent with the above picture of a more or less undifferentiated LPC complex. Our PCVA of the time-varying voltage during feedback yielded a set of factors which corresponded remarkably well to the MTW measures of ERPs, but had an additional advantage of orthogonality across components. Repeated measures ANOVAs of the factor scores revealed a more selective impact of feedback upon ERPs, compared to analyses of MTW-defined ERPs; only the P3b and PSW were significantly affected. A series of profile analyses of the MTW ERP measures provided evidence of different possible generator sources for different ERPs, includiLng P3b, PSW, as well as others. Both the MTW and PCVA measurement modes, as well as profile analyses, suggested that two components, LPSW and P508, were related to task difficulty. Evidence further demonstrating the distinctive cognitive functionality and topography of the variety of ERPs will be presented and discussed. P 2 / P 3 a and P3b. In the rule-learning task, the subject had to recognize whether each stimulus represented R-FB or D-FB. The P 2 / P 3 a magnitude probably reflected this initial act of R-FB recognition. The topography of the MTW-defined P 2 / P 3 a varied with feedback level, being larger to R-FB than D-FB at frontal, right central, and parietal leads. These findings were further confirmed by profile analysis, suggesting that P 2 / P 3 a to R-FB reflected a different neural generator from that to D-FB. However, some ambiguity is introduced by the finding of a lack of a significant interaction between topography and feedback level for either the PCVA-defined P3a or P2. Ruchkin et al. (1990) reported that in processing visual

73

feedback information, regarding a subject's prior guess of "greater than" or "less than," a centro-frontally dominant P3a developed. This P3a distribution closely resembled the topography of the present R-FB P2/P3a. As in Ruchkin et al.'s experiment 1, R-FB here provided "memory information" and uncertainty-reducing "outcome information," while D-FB provided none, and the P2/P3a amplitude to the former exceeded the latter. The D-FB here seems similar to Ruchkin et al.'s (1990) no information condition, where "procedural" and "memory information" were delivered (at S1), but differs in that no "memory information" was presented. The lack of a memory requirement in the D-FB condition probably enhanced the differences in feedback ERPs. (The memory requirement in Ruchkin et al.'s task probably accounts for their failure to find a difference between their partial vs. no information conditions; it simply swamped out differences between conditions.) Thus, our study appears to have distinguished between the outcome-plus-memory information and purely procedural information functions of the P2/P3a. Further, the R-FB condition in the present experiment may have elicited P3a plus P2 generator activity; while the D-FB condition may have activated mostly the P2 generator. A similar situation seemed present in the Ruchkin et al. (1990) study, wherein the P2/P3a complex to the full information condition showed a longer latency to stimuli than the no information condition (2~90 vs. 247 msec). The P3b showed an enhancement to R-FB, as compared to D-FB, and our ANOVAs and profile analyses failed to demonstrate separate P3bs to R-FB and D-FB, thus suggesting (but not proving) operation of a single, underlying generator source. The P3b showed a parietal maximum, like that typically found in the traditional oddball task (e.g., N. Squires et al. 1975; Duncan-Johnson and Donchin 1977). However, this P3b additionally showed frontal and central amplitude enhancement, especially for R-FB, which is suggestive of a different process than that reflected in the oddball P3b. Rather, the R-FB P3b, here, appears analogous to the P3b found by Ruchkin et al. (1990) to stimuli which delivered "outcome information." The second stimulus in their task, under conditions varying from neutral, and low to high expectancy, appears to serve a similar function as the R-FB in our task. The time-estimation task of Chwilla and Brunia (1991) also showed enhanced P3b amplitude to feedback at central and frontal leads. Enhancement of frontal P3b to feedback has also been shown with higher-order cognitive tasks (see later section on P3b and PSW to R-FB in higher-order cognitive tasks). The above evidence, showing the P3b as reflecting the same process across feedback level, and the P 2 / P 3 a as reflecting different processes with changes in feedback, suggests that these two ERPs may represent different processes. Further evidence in this regard comes from the finding of significant feedback P3b main effects across both measurement modes, while the P2/P3a effect failed

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to confirm using the PCVA measure. This evidence is consistent with that of Ruchkin et al. (1990) who also found the P3a and P3b to reflect different sources in their prediction/feedback task. However, an apparent problem arises in that profile analysis showed neither functional feedback nor topographic differences between the P2/P3a and the P3b, suggesting that their generator sources were too closely allied for separation. Perhaps the P2/P3a, in response to R-FB, represents a generator closely coupled with that of the P3b (R-FB or D-FB), and the greater relative amplitude of the former ERP to R-FB resulted in a failure to find differences between the two ERPs. P508. The P508 emerged as an apparently new compo-

nent, being functionally distinguishable from the PSW and LPSW, and topographically distinguishable from P2/P3a and P3b. The P508, however, seems functionally lackluster with respect to R-FB and D-FB; the significant MTW main effect was not confirmed in either of the other two measurement modes. As discussed later, the P508 did seem to be influenced by task difficulty. However, the separate existence of this component must be considered highly tentative, pending replication, and identification of its functionality relative to feedback. Perhaps some previous studies, measuring the P3b over a long latency range, may have inadvertently picked up the P508 riding on top of the P3b, thereby confounding and clouding the functionality attributed to the P3b. and P3b. The PSW was shown to be a separate entity from both P2/P3a and P3b, despite the fact that the present rule-learning task resulted in positive covariation between the PSW and the latter two ERPs. Substantial evidence indicates that the P3b and PSW reflect separate, if related, processes. The PCVA showed that, structurally, the P3b and PSW were the most distinct and nonoverlapping of components. Moreover, profile analyses suggested that these two ERPs reflected independent neural generators, with the magnitude of each reflecting variations in strengths of each source. Our evidence, across two measurement modes, clearly showed that both ERPs were modulated by type of feedback. An additional profile analysis suggested a slightly stronger effect for PSW (R-FB > D-FB by 0.42 /xV rms) than P3b (0.16 /zV rms, same direction difference). Our findings, thus, complement those reviewed by R6sler (1986) and Ruchkin and Sutton (1983), showing the PSW and P3b to be separate entities. Our study appears to answer to the question raised by Ruchkin and Sutton (1983), who wondered whether the Stuss and Picton's (1978) auditory P4 component (seen at 647 msec), and Stuss et al.'s (1980) visual P4 (590 msec), corresponded to the PSW. We found a peak within the MTW latency range at 638 msec, and a peak loading in the P S W versus P 2 / P 3 a

PCVA PSW (F1) at 697 msec (see Fig. 4). These two maxima within the PSW range would seem to correspond well to the P4. Thus, the " P 4 " to feedback in this task appears to reflect the same process as PSW. A correspondence in latency range exists between our PSW and the Pc (725 and 900 msec) observed in children and adults by Courchesne (1978) in response to non-expected, non-precategorized stimuli, relative to expected, precategorized stimuli. Both ERPs are also enhanced at frontal scalp; Pc being maximal there. Courchesne speculates that Pc may reflect collection of information for refinement of existing hypotheses, or generation of new ones. The information-rich R-FB, used in the rule-learning task, may have prompted the evolution and refinement of a variety of new hypotheses. The PSW, which we find to be synonymous with P4, may reflect such development. L P S W versus P2 / P3a, P3b and P S W . Profile analyses of

the MTW measures showed that the LPSW and P2/P3a reflected different processes. The LPSW showed a flatter topographic distribution than the P2/P3a. However, these two ERPs appeared to covary across R-FB and D-FB. Both showed a similar tendency towards greater amplitude for R-FB than D-FB at the frontal area, and slightly less amplitude at central and parietal areas. These LPSW findings were consistent with the significant feedback by electrode interactions and the significant follow-up profile analysis, thus suggesting that the LPSW reflected two sources, one for R-FB and another for D-FB. However, this interaction was not confirmed with the PCVA-defined P3a. It is uncertain whether this failure can be attributed to misallocation of variance (Wood and McCarthy 1984). The LPSW may reflect processes different from either of those underlying the P3b and PSW. Profile analyses indicated each of the latter ERPs reflected separate single sources across feedback levels, whereas LPSW appears to reflect two processes. Moreover, the LPSW showed only a main effect of feedback for the MTW measure, whereas, the P3b and PSW showed feedback main effects across both MTW and PCVA modes. Perhaps, future profile analyses might provide evidence as to source differences between R-FB LPSW versus P3b and PSW. The latency separation found between the PCVA-defined P3b, PSW and LPSW may be theoretically significant. Johnson and Donchin (1985) and Johnson (1986) noted a minimum separation of about 300 msec latency separation between multiple single-trial "P300s." Here, a similar temporal separation was apparent between the maximum loadings of the above 3 components. From Fig. 4 it can be seen that the latency separation between the maximum loading of " P 3 b " (F2) and the maximum loading of " P S W " (F1) was 292 msec, while the separation between " P S W " and the " L P S W " (F3) was 345 msec. It is possible, though not established here, that for R-FB the PSW and the LPSW may represent triggering of additional "P300s." Such additional " P 3 0 0 " triggering was pro-

C~A. Warren, B.E. McDonough /Electroencephalography and clinical Neurophysiology 94 (1995) 60-79

posed by Johnson (1986) and Johnson and Donchin (1985) as reflecting further analysis of data from earlier stages of processing. These later components might reflect the operation of a coupled series of context-updating mechanisms. Role of P S W and LPSW in cognitive processing. Our slow positivities, PSW and LPSW, seem functionally consistent with the slow positivities isolated by others. The PSW may reflect the further proce,ssing associated with subjects' utilization of feedback information, as proposed by Ruchkin et al. (1980). A slow, frontally maximum, positive ERP, starting at about 600 msec, has been tied to elaborative processes or complex rrmemonic strategies, reported to occur among selected subjects when performing verbal memorization (Karis et al. 1984). Such a later positive ERP in other studies appeared in response to words previously appearing in a semantically congruous context (Neville et al. 1986; Friedman 1990). Neville et al. (1986) found that, during a word recognition test, words, which subjects had previously judged as appearing in a semantically congruous context, produced a larger P650 than words appearing in an incongruous context. In our task, subjects presumably found that R-FB could be placed within a congruent context to a much greater extent than the D-FB. Our PSW to R-FB may reflect this congruency. That is, R-FB conformed more to subjects currently activated mental model than did the D-FB. The degree of fit of a presented stimulus to a representation in long-term, rather than short-term, memory seems to be the crucial requirement for elicitation of these positivities both in this study as well as in the Neville et al. (1986) study. Note that Neville et al. (1986) found no difference in positive ERPs to words which were congruous with the immediately preceding phrases (which were presumably registered only in short-term memory) compared to those which had been judged as non-congruous; rather, the latter resulted only in enhancement of a late negativity at about 400 msec, the N400. Friedman (1990), using visually displayed words in a continuous performance recognition task, reported a number of late slow waves which bear great similarity to those we found. In his task the subject classified words as having been seen previously (old) or not (new). Certain information had to be retrieved and compared to current information to correctly recognize an old stimulus as old, and these acts seemed to be reflected by an enhanced, parietally dominant, " P 6 0 0 " component (400-800 msec). His P600 overlaps greatly with our PSW (600-900 msec). Neville et al. (1986) similarly found correctly recognized old words to show a larger P650 than correctly recognized new words. For Friedm~Ln's task, certain elaborative processing (Mandler 1980; Graf and Mandler 1984) had to be executed upon the initial[ presentation of a word in order for a subject to correctly evaluate, categorize and encode it, so that subsequent correct recognition could be achieved. This appeared to be reflected by an enhanced, anteriorly

75

prominent, positivity which encompassed the P600 and extended broadly onward from 900 until about 1200 msec post stimulus. This latter positivity would seem to correspond to our LPSW. In the present rule-learning task, similar retrieval and comparison acts first had to be performed, as in the Friedman task, in order for the subject to evaluate how close the R-FB stimulus was to the value which had been predicted. Second, this closeness had to be evaluated in terms of what it meant for the learning of the correspondence rules, and this probably involved an elaborative operation, as in the Friedman task. We speculate that our PSW, which substantially overlaps Friedman's P600 (400800 msec), reflected the evaluation of closeness of the predicted (Ps) to the R-FB value. Unlike the Friedman task, the rule-learning task additionally required the subject to carry out a continual type of elaboration, leading to the subject's development of a cognitive model of the relationship of the cues to the value of the criterion variable. We hypothesize that this specialized sort of elaboration, probably propositional in nature, is reflected directly in the variation seen in the LPSW. The LPSW, which is similar in time course to Friedman's 900-1200 msec slow wave, as well as to Ruchkin et al.'s (1981) late positive slow wave (1100-1600 msec), may reflect a trans-event (across-trial), elaborative process. 4.3. P3b and PSW to R-FB in a higher-order cognitive task A second question posed at the outset was whether the magnitude of feedback ERPs would be increased by collectively boosting the processing demands of the task in various ways? The rule-learning task seemed to require a high order of cognitive processing relative to previously used, simpler tasks. Moreover, explicit, but probabilistic, multi-valued outcome feedback was used, instead of deterministic binary, response-oriented (correct/incorrect) feedback in order to further increase the subjects' mental work. How do the amplitudes of the feedback P3b and PSW at parietal, central and frontal areas in this higherorder task compare to those seen in previous work? The 3 most relevant ERP studies of higher-order cognition, which may serve as a reference to the R-FB ERPs of our rule-learning task, are the studies by Stuss and Picton (1978), DeSwart et al. (1981) and Johnson and Donchin (1982). The first two studies permitted comparisons at the parietal, central and frontal areas. Only the parietal area could be compared across all 3 studies for the Johnson and Donchin study. (Measurements in the latter study were made directly from average plots (Fig. 7, p. 193). Biases due to equating of bilateral lead placements with those along the midline were considered to be relatively negligible, though the former tend to show smaller amplitudes.) For rule-learning R-FB the P3b was 1.30, 1.02, and 1.65 times larger at parietal, central, and frontal areas, respectively, than comparable mean values of the above 3

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studies. The rule-learning R-FB PSW was, respectively, 2.24, 3.75, and 3.94 times larger than comparable brain region PSW values of these previous studies. A relevant concern, however, is that some proportion of these ERP differences across studies may be modality-related: the previous 3 studies all employed auditory feedback, while the present, rule-learning task used visual feedback. More study of feedback ERPs is needed using the same modality across a range of tasks of varying complexity to verify our above comparisons. 4.4. Task difficulty manipulation and ERPs The task difficulty manipulation produced a trend over all MTW ERPs taken together, showing greater positivity for simple than complex task difficulty. This trend might reflect only chance differences but, alternatively, may reflect a heterogeneity of direct and inverse effects between difficulty and the various components, indexing operation of different processes. This issue is explored below on a post hoc basis. Three of the 5 MTW ERPs (P508, PSW, and LPSW) were significantly related to task difficulty. The PSW and LPSW were greater for simple than complex task difficulty. However, the PCVA-defined LPSW was also significant, thus confirming the effects found with the MTW measure; while the PCVA-defined PSW was not significant. Thus, the LPSW-difficulty effect appears robust, while the PSW effect is not. The MTW PSW difficulty effect is perhaps the result of pickup of early LPSW activity. The complex condition involved the mid-sesssion use of a visuo-spatial aid, in addition complex rule-learning. Past ERP studies requiring cognitive operations upon visuo-spatial designs (e.g., for example, Stuss et al.'s (1983) study of mental rotation of spatial designs) have shown slow ERP negativity, rather than positivity. These different polarity wave forms suggest that different processes may be invoked, where visuo-spatial processing is required. Since graph usage in this study did not produce negativity like that appearing during the Stuss et al. (1983) study, one might conclude that use of this aid did not change the processes mediating the effects of difficulty upon LPSW and P508. Perhaps, the visual graph in our complex condition was translated into verbal rules, which were then used to evaluate the feedback, similar to the strategy used by one subgroup in a study by Johnson et al. (1987). That subgroup (group. 1), which made same-different judgments of visuo-spatial designs, appeared to follow a verbal rules strategy, which utilized a 2-stage, serial identification classification process. Group 1 showed a large late positivity; while group 2, which may have used visuo-spatial strategy, showed a large late negativity. For the LPSW, a reasonable question is why it was greater for the simple than the complex condition? It is speculated that development of a cognitive model for the simple condition took the form of an iterative elaboration

of a single, simple, generic rule which covered the cuecriterion relationships of both cues (both relationships were direct and linear). The result of such iterative elaboration would be a larger amount of positivity for the simple than for the complex condition. For the complex condition, a less focussed strategy may have been required. Whether multiple, iterative (serial) utilization of a single rule can directly affect FB LPSW will require a study utilizing a range of like-rule cues. Another issue is why differential processing of information associated with task difficulty would be observable at such a prolonged latency as was shown by the LPSW? Again, the work of Neville et al. (1986) is instructive. They found maximum late positivity to occur later to the initial presentation of words appearing in an incongruous context than to those appearing in a congruous context. The matching of feedback to an abstract, internal cognitive model would seem to involve association of information in a similar, relatively incongruous, context. The P508 component showed modest, though inconsistent, evidence of variation with task difficulty across the two measurement modes. As measured by the PCVA, the P508 to R-FB showed greater magnitude in the complex than in the simple condition, especially at Fz and C a. On the other hand, the MTW P508 to R-FB showed an inverse relationship to task difficulty. This reversal in relationship across measurement modes may be valid, since the PCVA does serve to remove contamination from nearby components, like the PSW and LPSW, which did show direct relationships to task difficulty. However, such a reversal in our view dictates great caution in acceptance of the findings. The PCVA results for the P508 to R-FB (but not to D-FB) suggest a functionality related to response equivocation and associated with processing differing levels of task complexity. It is speculated that the P508 at frontal and right central areas may reflect the effort required to integrate new information into a cognitive model. Greater effort may have been required for the complex than the simple condition, since two qualitatively different rules related each cue to the criterion value (one direct and linear; the other non-linear) in the complex condition, whereas the same rule could be applied to both cues in the simple condition. However, the P508 to parietal and left central areas may be related to differences in arousal between the two conditions, rather than to stimulus processing, since P508 showed no differences between R-FB and D-FB. 4.5. Exclusion of certain confounds Systematic differential preparation prior to the R-FB versus the D-FB in the rule-learning task can be ruled out in this study, since subjects had no way of knowing which type of feedback would be coming. Consistent with this lack of differential preparation, visual inspection of the contingent negative variation (CNV) during the 1500 msec

C.A. WtIrren,B.E. McDonough/ Electroencephalography and clinical Neurophysiology 94 (1995) 60-79

prior to feedback between the two feedback conditions (not pictured in results) ievealed no sizeable differences. Moreover, visual inspection showed no differences in the CNV between the rule-learning and PM feedback conditions. This lack of CNV differences also rules out the possibility these feedback positivities might be due to a return to baseline of differential negativity preceding feedback. In addition, these results were free of contamination from motor responding, unlike some earlier studies of slower ERP activity (e.g., the Johnson and Donchin (1978) time-estimation task, as well as the slow wave data from Benson and Teas (1972), N. Squires et al. (1975), and Snyder and HiUyard (1976)). Thus, an interpretation framed in terms of the mental operations involved becomes more credible. It is possible to argue tlhat, although the R-FB and D-FB stimuli were equiprobable, the design was confounded due to the use of the same stimulus ( " 9 9 " ) for the D-FB, and a variety of other stimuli for the R-FB condition. Thus, subjects, exercising "subject option" (Sutton 1969), may have classified feedback stimuli based on the relative rareness of occurrence of each of the R-FB stimuli, rather than into the equiprobable categories of real and dummy. This could result in a session-wide oddball or probability effect, evoking a rare stimuli P300 larger than that to the frequent stimuli. This effect would occur in addition to enhancement of the LPC due to the task relevance of the R-FB stimuli. Only further study equating the number of R-FB and D-FB stimuli can definitively rule out the possibility of an oddball effect. Two aspects of this study's design, however, would seem to reduce chances of this happening. First, the mental set requilred of subjects, trying to solve a problem, would seem to compel their attention to feedback in terms of the utility of the information (R-FB vs. D-FB), rather than to individual stimuli, congruent with the work of Courchesne et al. (1977). Second, the intertrial interval used here (24.5-28.5 sec) was much longer than that used by any study reporting an oddball effect (typically ranging from one to seven sec), and may have attenuated any oddball effect. Oddball ERP effects have been reported for feedback stimuli, but these studies also used fairly short intertrial intervals (6-10 sec, Campbell et al. (1979); 5-20 sec, Ruchkin et al. 1980). In the classical oddball paradigm, Polich (1990) found the oddball effect to disappear when 10 sec intertrial intervals were used (although the relevant vs. irrelevant P3b amplitude effect was still found). Similarly, Donchin (1981) reported that, with subjects performing a primary tracking task, the P300 oddball effect to probe stimuli disappeared when a 6 sec interstimulus interval was used.

4.6. PM versus rule-learning feedback As expected, the ERPs to rule-learning, R-FB were much larger than those t,a the R-FB in the PM task (Fig.

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2). ERPs to the PM feedback trials were also expected to be larger than to the rule-learning D-FB, since information was being processed in the former case. Surprisingly, then, that significant differences in the opposite direction were found. These post-stimulus changes cannot be attributed to modulation due to differences in the pre-stimulus CNV, since no differences appeared (see previous section). Note that any P300 frequency effect confounding, due to the relative rareness of the PM trials, would have increased ERP amplitudes to accuracy feedback, thereby reducing the differences in magnitude we observed. How could these LPC components on rule-learning, D-FB trials be so much larger than those to R-FB on the PM trials? An explanation for P3b turns on its well-known sensitivity to a priori event probabilities (e.g., Tueting et al. 1971; Duncan-Johnson and Donchin 1977). The P3b may have been smaller to the R-FB in the PM task, relative to D-FB in the rule-learning task, due to the complete feedback predictability in the former, and the probabilistic occurrence of the latter ( P = 0.50), even though it had zero utility (but see 4.5). The explanation for differences in the later positive components is much less obvious. The PSW has also been found to covary directly with the P3b with changes in event probability (N. Squires et al. 1975; K. Squires et al. 1977), but other work has shown PSW to change little or to even decrease as P decreases from high to near 0.50 (Duncan-Johnson and Donchin 1977; Ruchkin and Sutton 1983). Apparently, in the rule-learning task the subjects' cognitive state (expectancy) prior to feedback onset indirectly influenced the size of the LPC to the feedback stimulus regardless of whether D-FB or R-FB was given. A partial post hoc explanation of this second finding, based on the findings of R/Ssler and his co-workers (RiSsler 1986; R/Ssler et al. 1986), is as follows: subjects in the rule-learning task had to prepare to perform additional operations which were unnecessary in the PM task. In the current experiment, it appears that the subjects' strategy during rule-learning trials, as dictated by the task's structure, was directed at always preparing for assimilation of feedback information. Possibly, such a continual preparation strategy developed, because the cost of such preparation, if a D-FB trial ensued, was zero, whereas the net gain from such preparation, if R-FB ensued, was positive. R~Ssler and his group found that a substantial PSW was elicited if a stimulus initiated operations, such as comparison of information or decision making, even though the execution of such operations might not be performed until later, after encoding and storage had taken place. Thus, D-FB on rule-learning trials, led the brain to generate a PSW, even though it had no stimulus information to process. R/Ssler and co-workers have demonstrated that a PSW is not triggered if a preceding stimulus can be encoded easily, and if information is only stored in short-term

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memory. In the PM task, the preceding prediction value could be encoded readily, held in short-term memory and easily compared to the feedback stimulus. Thus, no PSW was elicited. These results also seem consistent with R6sler's (1986) view which identifies the PSW with "effortful, controlled processing" and proposes that PSW magnitude reflects the amount of cognitive effort or capacity needed to perform a sequence of operations. R6sler and co-workers (R6sler 1986; R6sler et al. 1986) noted that a stimulus, signalling which attentional set would be appropriate subsequently in the trial, showed a significantly larger PSW than another stimulus, explicitly directing the subject's attention. In our situation, the large PSW on D-FB trials seemed to reflect the act of preparation for cognitive operations to be required at some future point in time, and seems to extend to the LPSW as well.

Acknowledgments This research was supported by a series of grants from the Kairos Foundation, Inc. Grateful appreciation is expressed to Norman S. Don, Ph.D., for his generous assistance, and to Ms. Selvi Dayabaran, Mr. Tom McHale, Mr. Henry Pak, and Mr. Nabil Dajani for their technical expertise. Thanks are given to Patricia Tueting, Ph.D., for helpful comments on an earlier manuscript version.

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