Working memory (WM), the ability to briefly retain and manipulateinformation in mind, is central to intelligent behavior. Here we takeadvantage of the high temporal resolution of electrophysiologicalmeasures to obtain a millisecond timescale view of the activityinduced in distributed cortical networks by tasks that imposesignificant WM demands. We examined how these networks areaffected by the type and amount of information to be remembered,and by the amount of task practice. Evoked potentials (EPs)were obtained from eight subjects performing spatial and verbalversions of a visual n-back WM task (n = 1, 2, 3) on each of threetesting days. In well-trained subjects, WM tasks elicited transientresponses reflecting different subcomponents of task processing,including transient (lasting 0.02–0.3 s) task-sensitive and load-sensitive EPs, as well as sustained responses (lasting 1–1.5 s),including the prestimulus Contingent Negative Variation (CNV), andpost-stimulus frontal and parietal Slow Waves. The transientresponses, with the exception of the P300, differed between theverbal and spatial task versions, and between trials with differentresponse requirements. The P300 and the Slow Waves were notaffected by task version but were affected by increased WM load.These results suggest that WM emerges from the formation of adynamic cortical network linking task-specific processes withnon-specific, capacity-limited, higher-order attentional processes.Practice effects on the EPs suggested that practice led to thedevelopment of a more effective cognitive strategy for dealing withlower-order aspects of task processing, but did not diminishdemands made on higher order processes. Thus a simple WM task isshown to be composed of numerous elementary subsecond neuralprocesses whose characteristics vary with type and amount ofinformation being remembered, and amount of practice.

IntroductionWorking memory (WM) refers to the limited, attention-

demanding capacity to hold and manipulate information in mind

for several seconds in the context of cognitive activity (Baddeley

and Hitch, 1974). This faculty is intimately involved in language

comprehension, reasoning and learning (Baddeley, 1992), and it

appears to be a central component of intelligent behavior in

general (cf. Kyllonen and Christal, 1990). Once thought of as a

single, unitary system, WM has come to be regarded as a

multicomponent process. Based on behavioral and lesion data,

Baddeley and Hitch (1974) proposed a trivariate model of WM,

in which WM is composed of a central executive and two

distinct storage buffers: the visuospatial sketch pad and the

articulatory loop. Others, however, have argued for a less

structured view of WM. For example, Just and Carpenter (1987,

1992) describe WM as a limited pool of nonspecific neural

activation that is necessary for task-related manipulation of

information and for the maintenance of that information in an

accessible state. Alternatively, Schneider and Detweiler (1988)

picture WM as a shifting coalition of interacting but

independent process- specific subsystems.

Much progress has been made in the past two decades in

characterizing the neural substrate of WM. Evidence from

human lesions studies has suggested that WM depends on the

activity of a number of cortical regions, primarily the prefrontal

cortex (Petrides and Milner, 1982; Frisk and Milner, 1990; Owen

et al., 1996). Neuroimaging studies have also shown that the

prefrontal cortex, as well as other areas of association cortex, are

active during WM tasks (Jonides et al., 1993; Paulesu et al., 1993;

McCarthy et al., 1994; Smith et al., 1995; Courtney et al., 1996;

Owen et al., 1996; Smith et al., 1996; Braver et al., 1997; Cohen

et al., 1997; Jonides et al., 1997; Manoach et al., 1997; Courtney

et al., 1998). These studies demonstrate that WM relies on

distributed activity in a number of cortical areas. However, the

relatively poor temporal resolution of neuroimaging methods

makes it difficult to track the time course of activation in dif-

ferent cortical areas as attention is allocated to each successive

stage of task processing.

This information can be provided by the high temporal

resolution of electrophysiological measures. In nonhuman

primates, invasive electrophysiological recordings have shown

that the neural representation of information retained over short

delays is associated with transient activation of neurons in

widespread association areas. This neuronal activation is

sensitive to momentary within-task demands, and is modulated

by the allocation of attention to different stimulus attributes and

task requirements (Fuster and Jervey, 1981; Fuster and Jervey,

1982; Miyashita and Chang, 1988; Funahashi et al., 1989; Koch

and Fuster, 1989; Chelazzi et al., 1993; Wilson et al., 1993; Miller

and Desimone, 1994; Vaadia et al., 1995; Rao et al., 1997). These

data are consistent with the notion that WM emerges when

distributed activity is recruited into a functional network by the

effortful attention required to meet task demands (cf. Gevins et

al., 1983; Crick, 1984; Gevins et al., 1987; Schneider and

Detweiler, 1988; Bressler et al., 1993; Gevins and Cutillo, 1993;

Kimberg and Farah, 1993).

In humans, noninvasive electrophysiological measures have

been used to track the subsecond timecourse and distribution of

WM processes in a variety of paradigms (e.g. Starr and Barrett,

1987; Gevins et al., 1990; Ruchkin et al., 1990, 1992, 1995; Lang

et al., 1992; Gevins and Cutillo, 1993; Raney, 1993; King and

Kutas, 1995). In a recent study, Gevins et al. (1996) compared

the evoked potentials (EPs) elicited in tasks that imposed a high

WM demand with those elicited in tasks that had the same

stimulus and response requirements, but that placed minimal

demands on WM. Both verbal and spatial versions of the WM task

were associated with subsecond changes in electrical signals

over frontal and parietal cortex. Although there were some

differences in the EPs between the spatial and verbal versions,

the similarity in both waveshape and topography between the

EPs elicited by the two task versions was even more notable. This

suggested that WM is a function of a distributed system with

Cerebral Cortex Oct/Nov 1998;8:563–574; 1047–3211/98/$4.00

Dynamic Cortical Networks of Verbal andSpatial Working Memory: Effects ofMemory Load and Task Practice

Linda K. McEvoy, Michael E. Smith and Alan Gevins

EEG Systems Laboratory and SAM Technology, 101 Spear

Street, Suite 204, San Francisco, CA 94105, USA

both task-specific and task-independent components, and led to

the suggestion that the functional networks that arise during the

performance of specific WM tasks interact with a system which

may be common to all attention demanding tasks.

The current study aims to replicate and extend these findings

in two ways. The first goal of this study is to further investigate

the subsecond dynamics of WM networks by incrementally

increasing WM load in verbal and spatial versions of a WM task.

Task-specific increases in activation with increased WM load

could indicate that an area is involved in maintaining internal

representations of the task relevant stimulus features. Non-

specific increases in activation with increased WM load would

be expected from areas involved more with the higher order

attentional demands of the task than with task-specific

processing.

A second purpose of this study is to investigate the effects of

practice on the dynamic networks underlying WM. If WM is a

complex process, some of the subsecond neural processes of a

WM task should change with practice while others remain

constant. In related studies (Gevins et al., 1997; Smith et al.,

1998), we have found that the ongoing EEG is sensitive to

practice effects in WM tasks. For example, practice resulted in a

relative increase in activation of right parietal areas in spatial

tasks and a relative decrease in activation of these areas in verbal

tasks, indicating the formation of task-specific neurocognitive

strategies. Practice also resulted in increased power of the

frontal midline theta component, which was interpreted as

ref lecting increased activation of the anterior cingulate, an

important node in the anterior attentional network.

The presence of practice effects in the ongoing EEG suggests

that practice is also likely to affect the EPs. EPs and ongoing EEG

ref lect different, but related aspects of neurophysiological

function. The ongoing EEG ref lects the state of functional

networks underlying task performance (cf. Lopes da Silva,

1991), whereas EPs index specific operations being performed

on internal representations and provide a more temporally

fine-grained view of cortical function. Thus, while the task- and

practice-related effects show that the EEG is sensitive to both

changes in attentional state and to the consolidation of

task-specific skills, changes in the EPs could demonstrate which

of the many subprocesses involved in task performance change

as a function of practice. Therefore, in addition to examining

how load and content manipulations affect the functional neural

networks involved with WM task performance, this study also

seeks to characterize the changes in such networks that occur

with task practice.

Materials and Methods

Subjects

Eight healthy young adults (22–28 years, mean age 24 years; three

women) participated as subjects. All participation was fully informed and

voluntary. Subjects received an honorarium at the end of the experiment,

a portion of which depended on the accuracy of their task performance.

Cognitive Tasks

Subjects performed a continuous matching task that required them to

indicate whether the current stimulus matched that presented on a

previous trial. Full details of the task can be found in Gevins et al. (1996).

Brief ly, task stimuli consisted of single capital letters, drawn randomly

from a set of 12. At the beginning of each trial, a warning cue appeared in

the center of the screen for 0.2 s. The stimulus occurred 1.3 s after the

onset of the warning cue in one of 12 possible locations. Stimuli were

presented for 0.2 s once every 4.5 s. The identity of the letter and its

spatial position varied randomly from trial to trial. A small fixation dot was

continuously present at the center of the screen.

Subjects performed two versions of the task at each of three difficulty

levels. In a verbal version, subjects were required to remember the

identity of the visual stimulus presented; in a spatial version, they were

required to remember its position on the screen (see Fig. 1). In the low

load (LL) difficulty level, subjects were required to compare the current

stimulus with that presented on the previous trial and to respond by

pressing a microswitch with their right index finger if the current

stimulus matched that presented on the previous trial (i.e. if, in the verbal

version, it was the same letter as that presented on the previous trial

regardless of its position on the screen, or, in the spatial version, if it

occurred in the same position as on the previous trial, regardless of what

the letter was). A ‘non-match’ decision was indicated by a right middle

finger response. In the moderate load (ML) difficulty level, subjects were

required to compare the current stimulus with that presented two trials

ago, and in the high load (HL) difficulty level, to compare the current

stimulus with that presented three trials ago. Match stimuli occurred

randomly on 50% of the trials. Since trials were 4.5 s long, subjects were

required to remember the identity or location of each stimulus, as well as

its sequential order, for 9 s in the ML level and for 13.5 s in the HL level.

Subjects participated in three sessions, each spanning a 6–8 h period.

The first two sessions occurred on two consecutive days. Subjects then

had one day off before the final session. On the first day, subjects began

by performing one block (23 trials) of each of the six task conditions in

order of increasing difficulty. Subjects then performed seven blocks of

each condition. Task conditions were presented in groups of six, with

each condition occurring once within the group. Condition order within

each group was randomized to eliminate any order effects. Subjects were

given a lunch break and rest periods as needed. During the second and

third sessions, subjects performed eight blocks of 23 trials of each task

condition. The six test conditions were presented randomly within each

block. The first block on each day served as warm-up exercises and was

omitted from further analyses.

To maintain motivation, subjects were rewarded 5 cents for each

correctly detected ‘match’ stimulus, and lost 10 cents for each incorrect

match response. Subjects were instructed to respond as quickly and

accurately as possible.

Recordings

EEG was recorded continuously from 27 scalp locations (Fp1, Fp2, AF3,

AF4, FT9, F7, F3, FZ, F4, F8, FT10, T7, C3, CZ, C4, T8, P9, P7, P3, PZ, P4,

P8, P10, O1, OZ, O2, Iz) during task performance, using electrically

linked mastoids as reference. Electrooculographic (EOG) activity was

recorded from electrodes located in the center of the supraorbital ridge

Figure 1. Spatial and verbal versions of the high load (HL) level of the WM task. Inseparate blocks subjects were required to compare either the location (Spatial task) orthe identity (Verbal task) of the current stimulus with that presented one (low load, LL),two (medium load, ML) or three trials ago (high load, HL). Match decisions (50%) wereindicated by pressing a switch with the middle finger of the right hand; nonmatchdecisions were indicated by pressing a switch with the index finger of the right hand.

564 Evoked Potentials and Working Memory • McEvoy et al.

above each eye, referenced to an electrode at the outer canthus of each

eye. Physiological signals were band-pass filtered at 0.01–100 Hz and

sampled at 256 Hz. Automated artifact detection was followed by

application of adaptive eye movement artifact decontamination filters (cf.

Du et al., 1994). The data were then visually inspected and segments

containing possible residual artifacts were eliminated from subsequent

analyses. In one subject, excessive frontal low-frequency artifact

necessitated high-pass filtering the data at 2 Hz. The data from this subject

were excluded from all analyses involving EP components with

significant low-frequency contributions.

Analysis

After artifact removal, data from trials with correct responses were

averaged into 12 separate categories based on Task Version (Spatial and

Verbal), Load Level (LL, ML, HL), and Stimulus Type (Match or Nonmatch

stimuli). EPs were calculated over the period 1.7 s prior to the onset of the

stimulus (0.4 s prior to the warning cue) to 2.0 s after stimulus onset.

Amplitude measurements were made relative to the average value in the

pre-cue interval. To isolate components of interest, the EPs were digitally

filtered with a zero phase-shift digital filter. Low-frequency EPs were

examined after low-pass filtering the data at 7 Hz. To measure the

post-stimulus transient EPs, the data were digitally filtered with a

band-pass from 2 to 20 Hz to attenuate overlapping low-frequency activity

and higher-frequency muscle noise. An exception to this was the parietal

P300 response, which has significant energy in the low-frequency band.

Prior to measuring this component, the data were band-pass filtered from

0.5 to 20 Hz.

EP component overlap was further minimized by spatially enhancing

the data. After temporal filtering, a nearest-neighbor, planar current

source density derivation (CSD) was computed using realistic head-shape

information based on the measured electrode positions and an optimal,

least-squares estimate of the CSD operator using 5–10 surrounding

electrodes (Le et al., 1994). The CSD reduces the high degree of spatial

overlap of the EPs that would otherwise be observed in the scalp data,

and removes the effect of the reference electrode. Thus, the CSD

produces a waveform topography that emphasizes local changes in EP

amplitude and is relatively insensitive to contributions from remote

generators. Peripheral sites for which the CSD cannot be accurately

calculated were eliminated from analysis.

Statistical analyses were performed using repeated measures analyses

of variance (ANOVAs), with Greenhouse–Geisser corrections for

violations of the assumption of sphericity. Prior to statistical analyses, data

were standardized within subjects by converting the data to z-scores

across all conditions. When significant effects were observed as a

function of electrode site, the data were standardized separately at each

site to remove the possibly confounding effects of overall amplitude

difference between sites (cf. McCarthy and Wood, 1985).

To differentiate brain areas specifically involved in WM tasks from

those activated by novel or difficult tasks in general, it is important to

examine the EPs in subjects who have practiced the tasks to the level that

behavioral variables have stabilized (cf. Gevins et al., 1997). Therefore,

the effects of type of information held in WM, verbal or spatial, and the

effect of increasing WM load were examined in EPs obtained in the third

session, after subjects had practiced the tasks sufficiently that the

behavioral responses had stabilized. To examine the effects of practice on

the EPs, data recorded in the first and third session were compared. Since

the behavioral data showed that the greatest practice-related changes

occurred in the HL level, analysis of the practice effects was restricted to

this subset of the data.

Results

Effect of Task Type and WM Load on Behavioral and

Electrophysiological Measures

Behavior

Behavioral performance in the third session systematically

differed between the three WM load levels but not between the

two task versions (Table 1). Separate Task Version (Verbal,

Spatial) by Load Level (LL, ML, HL) repeated-measures ANOVAs

were performed on accuracy (assessed using d ′ scores) and

reaction time (RT) measures. No effects of Task Version for

either RT or accuracy were obtained, indicating that the spatial

and verbal versions of the task were well matched for difficulty.

There were highly significant effects of Load for both accuracy

[F(2,14) = 23.27; P < 0.001] and RT [F(2,14) = 12.78; P < 0.001].

Accuracy decreased and RT increased with increasing load level.

Neither accuracy nor RT differed across the seven test blocks

within the session, indicating that the asymptotic level of

performance achieved during the two prior sessions was

maintained throughout this session.

To determine whether selectively rewarding correct match

responses resulted in a response bias, we examined β values in

each of the three load levels. β values ranged from 2.0 (±1.2) in

the HL level to 2.5 (±3.0) in the LL level, suggesting a small

tendency towards a conservative response bias (i.e. subjects

were less likely to make false ‘match’ responses). However, βvalues did not significantly differ from 1.0 (indicating no

significant response bias) at any load level.

Electrophysiology

Figure 2 shows CSD EPs, averaged across subjects, from a subset

of the channels for each of the three load levels in the verbal and

spatial WM tasks. Both tasks elicited a series of topographically

distinct responses beginning with the EP peaks over parieto-

occipital regions elicited by the visual warning cue. This was

followed by the low-frequency contingent negative variation

(CNV) over the central midline region associated with

anticipation of the stimulus. The onset of the stimulus elicited a

series of EP peaks over parietal, central and frontal regions,

labeled P250, P280, P300 and P390 on the figure. (EP def lections

are commonly named according to their polarity and latency.

This convention is followed here, where current emerging from

the scalp is represented as positive polarity, and current entering

the scalp is represented as negative polarity.) Sustained

responses (‘Slow Waves’), lasting several hundred milliseconds,

were elicited over the midline central region in the prestimulus

period (the CNV), and over the left frontal (frontal slow wave,

FSW) and right parietal (parietal slow wave, PSW) regions in the

post-stimulus period. The sustained responses showed long-

lasting amplitude differences as a function of WM load. Some of

the transient responses (e.g. P250, P300) were also affected by

WM load.

Figure 2 shows that the EP waveforms and topography in the

two tasks are very similar. There are, however, some task-

specific effects. For example, a central positive-negative series of

peaks in the interval between ∼200 and ∼450 ms (which is

largest at Cz but can also be seen at Fz) is more prominent in the

spatial than the verbal task. In contrast, a peak occurring at ∼390

Table 1Average accuracy (d ′) and reaction time scores in the low (LL), moderate (ML) and high (HL)working memory load conditions for the Verbal and Spatial tasks. Both accuracy and reaction timesignificantly differed as a function of working memory load, but not as a function of task type

Accuracy (d′) Reaction time (ms)

Verbal Spatial Verbal Spatial

LL 4.12 4.10 589 559ML 3.97 3.96 733 674HL 3.06 3.15 796 781

Cerebral Cortex Oct/Nov 1998, V 8 N 7 565

ms is larger in the verbal task than in the spatial task (this effect

is less visible in Fig. 2 due to the overlapping low-frequency

activity, but see Fig. 3). A Slow Wave difference between verbal

and spatial tasks is apparent in Figure 2 at electrode site C3;

however, this difference was not significant. Since this

waveform does not show any significant effects of task or load

(F‘s < 1), it will not be considered further. The Slow Waves and

EP peaks that were significantly affected by the task and/or load

manipulations will be discussed in turn.

Slow Waves

CNV. Following the warning cue, a CNV developed over the

midline central region reaching maximum amplitude at stimulus

onset. Associated def lections were also observed over left (F3),

central (Fz) and, with opposite polarity, right (F4) frontal areas

(Fig. 2). The amplitude of this response was measured at

electrode site Cz, where the response was largest. The CNV was

measured in a 100 ms band, beginning 100 ms prior to stimulus

onset. CNV amplitude decreased as load level increased

[F(2,12) = 17.45; P < 0.001; Fig. 5]. It was also significantly larger

in the verbal task than in the spatial task [F(1,6) = 9.7; P < 0.05;

Fig. 4]. The Task Version by Load Level interaction was not sig-

nificant (F < 1).

Frontal Slow Wave. Responses over the left frontal region

(electrode site F3) differentiated between load level from ∼800

ms prior to stimulus onset until ~1300 ms post-stimulus (Fig. 2).

This sustained activity appears to ref lect at least two spatially

overlapping components. Based on the similarity in waveshape

and task effects, the prestimulus negativity is likely associated

with the CNV. Following the overlapping transient response at

∼400 ms, a slow positive wave emerged in the higher load

conditions and lasted until ∼1300 ms. The amplitude of this

positive slow wave (called the Frontal Slow Wave or FSW) was

measured in subject-specific 100 ms bands at electrode site F3.

The center latency of these bands ranged from 650 to 950 ms,

with an average center latency of 760 ms. The FSW increased

significantly with increasing WM load [F(2,12) = 5.5; P < 0.05;

Fig. 5] but did not differ as a function of Task Version [F(1,6) =

1.88; P > 0.05; average amplitude of –0.08 (±0.17) versus –0.01

(±0.18) µV/cm2 in Verbal and Spatial tasks respectively].

Parietal Slow Wave (PSW). A low-frequency negative wave

emerged over the right parieto-temporo-occipital (PTO) region,

at electrode site P8, beginning at ∼400 ms post-stimulus and

lasting until ∼1400 ms post-stimulus (Fig. 2). This response was

also measured in subject-specific 100 ms bands, at electrode site

P8. The center latency of these bands ranged from 850 to 1150

ms, with an average center latency of 950 ms. The PSW showed

a significant increase in amplitude as load level increased

[F(2,12) = 7.2; P < 0.02; Fig. 5] but did not differ as a function of

Task Version [F(1,6) = 1.2; P > .05; average amplitude of –0.05

(±1.0) versus –0.03 (±0.09) µV/cm2 in Verbal and Spatial tasks

respectively].

EP Peaks

PTO P280. Over the PTO regions, at electrode sites P7 and P8,

a positive def lection with an average latency of ∼280 ms discrim-

inated between verbal and spatial tasks. This response was

significantly larger over the right hemisphere than over the left

Figure 2. Spatiotemporal distribution of CSD EPs in the three load levels of the verbal (left) and spatial (right) WM task. Grand mean EPs over subjects are shown for the epochextending 0.4 s prior to the warning cue to 2.0 s after stimulus onset. The stimulus occurred at time 0. Data are averaged across match and nonmatch stimulus types. A similar patternof EP peaks and Slow Waves were elicited in both tasks. Three Slow Waves (FSW, CNV and PSW) varied as a function of load, as did the P300 response. The EP peaks, including theP280, the P250 and the frontal P390 differed between spatial and verbal task versions.

566 Evoked Potentials and Working Memory • McEvoy et al.

[F(1,7) = 7.43; P < 0.05], and was significantly larger in the verbal

than in the spatial task [F(1,7) = 14.41; P < .01; Fig. 4]. The

significant Hemisphere by Task version interaction [F(1,7) = 7.0;

P < 0.05] indicated that the amplitude difference between the

verbal and spatial tasks was significant over the right hemisphere

only. A significant Task Version by Stimulus Type interaction

[F(1,7) = 22.01; P < 0.01] indicated that for the verbal task,

responses to Nonmatch stimuli were significantly larger than

those to Match stimuli, whereas for the spatial task, responses to

Match and Nonmatch stimuli did not differ (Fig. 3).

Central P250. A biphasic response developed over the

midline central region with a positive def lection occurring at

∼250 ms, and a negative def lection at ~400 ms (Fig. 2). Both

aspects of this response showed the same topography and were

affected in the same way by the experimental manipulations.

Therefore, for simplicity, this complex of responses will be

referred to simply as the P250. The P250 was larger in the spatial

than in the verbal task [F(1,7) = 13.44; P < 0.01; Figs 3 and 4] and

larger to Nonmatch than to Match stimuli [F(1,7) = 13.74; P <

0.01; Fig. 6]. The Stimulus effect interacted with Task Version

[F(1,7) = 45.5; P < 0.001], in that the difference between Match

and Nonmatch responses was significant in the spatial task only

(Fig. 3). The Load Level by Stimulus Type interaction [F(2,14) =

4.82; P < 0.05] showed that responses to Nonmatch stimuli only

were affected by load level: response amplitude decreased as

load level increased.

Parietal P300. The WM tasks elicited a broad positive parietal

complex, or P300 response, between 300 and 500ms. This

response was measured in a 100 ms band centered at the

response peak (353 ms, on average) at electrode sites P3 and P4.

The P300 was significantly larger over right parietal cortex

(electrode site P4) than over the left (electrode site P3) [F(1,6) =

10.18; P < 0.02]. Response amplitude did not differ as a function

of Task Version, but was significantly attenuated by increased

WM load [F(2,12) = 10.25; P < 0.01; Fig. 5]. The Hemisphere by

Stimulus Type interaction was significant [F(1,6) = 9.12; P <

0.05], with larger responses to Match stimuli than to Nonmatch

stimuli over the left hemisphere (P3; Figs 3 and 6).

Frontal P390. A bilateral transient response over the frontal

regions was superposed on the low-frequency activity at these

sites. After filtering out the overlapping low frequency response

with a band-pass filter of 2–20 Hz, this response was larger over

the left hemisphere than over the right, and peaked at ~390 ms.

The P390 was significantly later in the verbal task than in the

spatial task [by an average of 18 ms; F(1,7) = 9.29; P < 0.02]. It

was also significantly later to Nonmatch than to Match stimuli

[by an average of 8 ms, F(1,7) = 9.4; P < 0.02]. The P390 was

significantly larger over the left hemisphere than over the right

[F(1,7) = 11.88; P < 0.02], and significantly larger in the verbal

than in the spatial tasks [F(1,7) = 11.31; P < 0.02; Fig. 4]. There

was a significant Hemisphere by Stimulus Type interaction

[F(1,7) = 12.82; P < 0.01]: over the left hemisphere, responses

were significantly larger to Nonmatch than to Match stimuli (Fig.

3); over the right hemisphere, responses to Nonmatch and

Match stimuli did not differ.

Summary: Well-practiced Tasks

In summary, the analysis of the EP data obtained after subjects

had extensively practiced the tasks showed that WM tasks

elicited both sustained Slow Waves and more transient EP peaks

over widespread cortical areas. Figures 4–6 summarize the

effects of Task Version, WM Load and Stimulus Type on these

responses. Since the functional implications of the task effects

on these potentials can be further understood by considering the

effects of practice, we will defer discussion of the task correlates

of these findings until we have considered the effects of task

practice.

Practice Effects on Behavioral and Electrophysiological

Responses in Difficult Verbal and Spatial WM Tasks

The analysis of practice effects focuses on the EPs in the HL

Spatial and Verbal WM tasks since the behavioral data indicated

that performance in this load level changed most dramatically as

a function of practice. Thus, EPs in the HL level were compared

between the first and third sessions.

Behavior

For the HL tasks, accuracy significantly increased [F(1,7) = 23; P

< 0.01] and RT significantly decreased between the first and

third sessions (F(1,7) = 11.2; P < 0.05; Table 2). Response

accuracy did not differ as a function of Task Version, but RT

showed a significant Task Version by Test Session interaction

Figure 3. Differences in CSD EPs to match and nonmatch stimuli. Data are shownaveraged across load level in the verbal (left) and spatial (right) tasks, for the channelshowing the largest effect for each component. (A) Responses to nonmatch stimuliwere larger than those to match stimuli for the P390, P250, P280 responses. Data havebeen band-pass filtered at 2–20Hz. (B) Responses to match stimuli were larger thanthose to nonmatch stimuli for the P300. Data are filtered at 0.5–20Hz.

Cerebral Cortex Oct/Nov 1998, V 8 N 7 567

[F(1,7) = 6.4; P < 0.05]: responses were significantly faster in the

Spatial task than in the Verbal task in the first session; in the third

session, there were no significant differences in RT between the

two task versions. Analysis of β values from data in the high load

level of the first session showed no significant response bias (β =

0.91 ± 0.16).

Electrophysiology

Slow Waves. The CNV increased significantly as a function of

practice [F(1,6) = 16.1; P < 0.01; Fig. 7], but did not differ as a

function of Task version. Neither the FSW nor the PSW were

significantly affected by practice [FSW: F(1,6) = 0.38; P > 0.05;

PSW: F(1,6) = 0.45, P > 0.05].

EP Peaks. The P280 over the PTO regions was not signi-

ficantly affected by task practice. The central P250 showed a

significant three-way interaction involving Task Version, Stimu-

lus Type and Practice [F(1,7) = 10.0 ; P < 0.02]. The Stimulus

Type by Task Version interaction, caused by larger responses to

Nonmatch than to Match stimuli in the Spatial task only, was

significant in the third session but not in the first. Both the

parietal P300 and the frontal P390 showed significant Practice by

Stimulus Type interactions [Parietal P300: F(1,6) = 12.7; P < 0.02;

Frontal P390: F(1,7) = 14.45; P < 0.01]. Response amplitude did

not differ as a function of Stimulus Type in the first session for

either response, but did in the third. For the parietal P300,

responses to Match stimuli were significantly larger than those to

Nonmatch stimuli, whereas for the P390 responses to Nonmatch

stimuli were significantly larger than those to Match stimuli (Fig.

7). The parietal P300 also showed a Practice by Hemisphere

interaction [F(1,6) = 11.28; P < 0.02]. Response amplitude did

not change over the left hemisphere across sessions, but

significantly increased from the first to third session over the

right hemisphere.

Figure 7 summarizes the effects of task practice on the EPs in

the WM tasks. Practice increased CNV amplitude and increased

the difference between responses to Match and Nonmatch

stimuli for the P250, P300 and P390 responses. Practice did not

have any significant effects on the frontal or parietal slow waves.

DiscussionThis study was designed to characterize the subsecond dynamics

of cortical networks involved in WM task performance. This was

accomplished by examining activity elicited while subjects

performed verbal and spatial versions of a continuous perfor-

mance WM task, each of which was expected to activate

somewhat different WM processes. An important aspect of the

experimental design is that the stimulus and response

requirements were the same for both task versions. This allows

us to attribute any differences in the EPs between the two task

versions to attentional strategy (i.e. focusing on either the verbal

or spatial attributes of the stimulus) rather than to differences in

stimulus properties. A potential drawback of this design is that

some differences between spatial and verbal WM processes may

be masked if subjects are unable to ignore the irrelevant task

attribute. While it is probable that subjects encoded both verbal

and spatial attributes of the stimuli in all task versions, it is

unlikely that subjects performed the memory comparison on

both attributes simultaneously. Both subjective reports and

task-related differences in the EPs suggest that subjects were able

to selectively focus on the relevant task attribute. The lack of any

task version by load level interactions also supports this view,

since an attempt to perform the memory comparison on both

attributes simultaneously might be expected to produce smaller

between-task differences in the low load levels, in which such a

dual-attribute comparison would be easier. Taken together, these

findings suggest that subjects were able to selectively focus on

the relevant stimulus attribute.

In addition to exploring differences between spatial and

verbal WM processes, this experiment was designed to

determine which aspects of task processing are affected by

increasing the WM demands of the task, and to determine which

aspects of task processing change as subjects become more

expert in task performance. The results showed that the verbal

and spatial versions of the WM task elicited both sustained and

transient EPs over widespread cortical areas. These responses

can be broken down into four broad categories: prestimulus

preparatory Slow Wave (CNV), task-sensitive EP peaks (P280,

P390, P250), task-insensitive EP peaks (P300), and task-

insensitive Slow Waves (FSW, PSW).

Pre-stimulus Activity: The CNV

The WM task elicited a slow negative wave, the CNV, in the

period between the warning cue and the stimulus. The

topography of the CNV, and its inverse relationship with RT and

WM load, suggests that it ref lects response preparatory

processes (cf. Rohrbaugh et al., 1976). CNV amplitude

decreased and RT increased as WM load increased. This is

consistent with the results from our prior EP study (Gevins et al.,

1996) in which CNV amplitude was significantly attenuated in

high load WM tasks as compared with control tasks with

minimal WM requirements. The CNV has long been known to

decrease in amplitude when subjects are distracted in the S1–S2

interval, either by intervening stimuli (Teece, 1972), or by the

demands of a secondary task (Teece and Hamilton, 1973). In the

WM tasks, rehearsal of the order and contents of the information

being remembered may have interfered with subjects’ ability to

focus attention on the upcoming stimulus, and thus may have

suppressed the response preparatory processes.

The practice effects on CNV amplitude are in agreement with

this interpretation. Practice served to increase CNV amplitude,

as well as to decrease RT and increase response accuracy. This

suggests that with practice, subjects become better able to

attend and prepare for the upcoming stimulus and required

Figure 4. EPs showing a main effect of task version. Grand average topographs in the verbal and spatial tasks are shown for each EP component affected by Task Version. Data areshown averaged across load level. For the prestimulus Contingent Negative Variation (CNV), responses to matching and nonmatching stimuli are averaged together. For the remainingcomponents, responses to nonmatching stimuli are shown. The CNV was larger in Verbal than Spatial tasks, as was the P280 and P390. The P250 response was larger in Spatial thanin Verbal tasks. The same scale is used for responses in the Verbal and Spatial task for each component. Scales differ across the different components. All topographs in this paperare based on values derived from the 18 electrode sites that remain after performing CSD analysis.

Figure 5. EP components showing a main effect of WM load. Data are collapsed across task version and stimulus type. Grand mean topographs in the low load and high load levelsare shown for each component affected by increased WM load. The CNV, P250 and P300 were all attenuated by increased WM load. The parietal (PSW) and frontal (FSW) SlowWaves increased as function of increased WM load. The same scale is used for the Low and High Load levels for each component. Scales differ across the different components.

568 Evoked Potentials and Working Memory • McEvoy et al.

Cerebral Cortex Oct/Nov 1998, V 8 N 7 569

response, while simultaneously maintaining the necessary

information in WM.

The CNV also showed amplitude differences between the

verbal and spatial versions of the task. It was larger in the verbal

tasks despite the lack of difference in RT or accuracy scores

between the two task versions. This difference was unexpected

and is in need of replication.

Task-sensitive EP Peaks

Three brief EP peaks were recorded which varied as a function

of task version: the P280, the P250 and the P390. The P280 and

P390 were both larger in verbal than in spatial tasks, whereas the

P250 was larger in the spatial tasks. All these responses were

sensitive to the match–nonmatch stimulus distinction, with

larger responses to nonmatching than to matching stimuli.

The P280 recorded bilaterally over the PTO region was larger

in the verbal task than in the spatial task, and was larger to

nonmatching than to matching stimuli in the verbal task only.

This response is similar in latency and topography to the ‘visual

memory potential’ described by Begleiter et al. (1993). The

visual memory potential is a right predominant response over

the PTO regions which is larger to figures that do not match

previously presented pictures than to those that do. This

potential was interpreted as ref lecting visual short-term memory

for objects (Begleiter et al., 1993). It is possible that the P280

observed in this study also ref lects activation of a visual object

short-term memory system.

The P390, recorded bilaterally over the frontal areas, but with

larger amplitude over the left hemisphere, was larger in the

verbal than in the spatial task. Over the left hemisphere, this

response was larger to nonmatching than to matching stimuli.

Practice increased this difference. The finding that the P390 was

larger in verbal than spatial tasks and larger over the left

hemisphere than the right may indicate that left frontal cortex is

more strongly engaged in verbal than in spatial WM tasks. Similar

conclusions have been drawn from functional neuroimaging

studies (e.g. Smith et al., 1996).

Functional imaging studies have indicated an important role

for the left frontal cortex in subvocal articulation (Sergent et al.,

1992; Paulesu et al., 1993; Awh et al., 1996; Fiez et al., 1996) and

in sequencing operations (Petrides and Milner, 1982; Shallice,

1982; Shimamura et al., 1990; Petrides et al., 1993). Both of

these processes are likely to be more involved in verbal than in

spatial WM rehearsal strategies. Introspective reports indicated

that subjects used a sequential, subvocal rehearsal strategy to

perform the verbal WM task, whereas most subjects reported

using some type of analog, or moving spatial image strategy in

the spatial task.

However, the transient nature of the P390, and the lack of any

load-related effects, suggests that this response does not index

WM maintenance operations. Rather, the sensitivity of this

response to the matching dimension of the stimulus suggests

that it may be involved with a memory comparison process or

with WM updating. When a nonmatching stimulus is presented,

the subject is required to add a new stimulus to the list of those

currently held in memory and to delete from memory the

stimulus that is no longer required. In the case of matching

stimuli, WM updating can be performed by rearranging the

order of which the stimuli currently held in memory are to be

compared to future stimuli. The larger EPs to nonmatching than

to matching stimuli may indicate that more resources are

required for WM updating when new content must be added.

The larger response in the verbal than spatial task suggests that

the process ref lected in the P390, whether a comparison process

or an updating process, is more active in response to verbal than

to spatial information. The practice-related increase in P390

sensitivity to the matching/nonmatching dimension shows that

this process becomes more efficient with task practice.

The biphasic midline central response, the P250, which was

apparent over approximately the same interval as the P280 and

P390 responses, may ref lect activity in areas involved with

processing spatial stimulus attributes. The P250 was larger in the

spatial than in the verbal task and was larger to nonmatching

than to matching stimuli in the spatial task. Like the P390, the

difference between responses to matching and nonmatching

stimuli was enhanced with task practice. These task correlates

suggest that this response ref lects activity in areas involved with

the comparison of spatial attributes of a stimulus with those

represented in WM, and/or with activity related to spatial WM

updating. Like the P390, this response also showed a practice-

related increase in the difference between matching and

nonmatching stimuli.

Figure 6. EP components modulated by match versus nonmatch decision requirements. Grand mean topographs are shown for responses to matching and nonmatching stimuli, forthe task version showing the largest stimulus differences. Data are averaged across load level. For the P250 grand mean responses in the spatial task are shown. For the P280 grandmean responses in the verbal task are shown. Both the P250 and P280 were larger to nonmatching than to matching stimuli. For the P300, grand mean responses averaged over bothverbal and spatial task are shown since this response was larger to matching than to nonmatching stimuli in both task versions. For the P390, responses in the verbal task are shown.Responses were larger to nonmatching than to matching stimuli. The same scale is used for the responses to Match and Nonmatch stimuli for each component. Scales differ acrossthe different components.

Figure 7. EP components affected by task practice. Grand mean topographs for each component differing between the first and third sessions are shown. Data from the high loadlevel only are shown. The CNV is averaged across verbal and spatial tasks, since it increased with practice for both task versions. The P250, P300 and P390 showed increaseddifferences between responses to matching and nonmatching stimuli with practice, thus for these responses, the topography of the difference waveforms are shown. The P250 waslarger to nonmatching than to matching stimuli in the spatial task only. Thus for this response, responses to matching stimuli were subtracted from those to nonmatching stimuli inthe spatial task. The P300 was larger to matching than to nonmatching stimuli so responses to nonmatching stimuli were subtracted from those to matching stimuli. The P390 waslarger to nonmatching than to matching stimuli, so responses to matching stimuli were subtracted from those to nonmatching stimuli. Since the practice effects for the P300 andP390 did not differ as a function of task version, these responses are shown averaged across spatial and verbal conditions. The same scale is used for the responses in the First andThird session for each component. Scales differ across the different components.

Table 2Average accuracy (d′) and reaction time scores in the Verbal and Spatial tasks in the first andthird testing session. Accuracy increased and reaction time decreased between the first and thirdsession. There were no differences in response accuracy as a function of verbal and spatial tasktype. Responses were significantly faster in the Spatial than in the Verbal task in the first sessiononly

Accuracy (d′) Reaction time (ms)

Verbal Spatial Verbal Spatial

Session 1 2.17 2.05 1111 1006Session 3 3.06 3.15 796 781

570 Evoked Potentials and Working Memory • McEvoy et al.

Cerebral Cortex Oct/Nov 1998, V 8 N 7 571

Load-sensitive but Task-insensitive P300 Response

In the latency range of 0.3–0.5s, a load-sensitive but task-

insensitive response (P300) occurred over parietal cortex. The

parietal P300 response observed in this study is one of a broad

category of positive parietal def lections occurring to task-

relevant stimulus, collectively referred to as the P3 or P300 (for a

review, see Picton, 1992). Although the P300 is one of the most

studied of all EP def lections, the cognitive significance of this

potential is not fully understood. Since the P300 is typically

elicited by task stimuli requiring a decision, and since its

amplitude increases with subjective confidence in the response

choice, it appears to be related to an aspect of cognitive proces-

sing involved with stimulus classification. The insensitivity of

this response to task version suggests that it represents a

higher-order cognitive component than that ref lected in the

P390 and P250 responses. Whereas the latter responses may

ref lect task-specific comparison or updating processes, the P300

may ref lect a decision-making component common to both

tasks.

The results of both our current and prior study (Gevins et al.,

1996) indicated that increasing the WM demands of the task

attenuated P300 amplitude. Similar results have been observed

in numerous memory studies using versions of the Sternberg

paradigm (Sternberg, 1969): as memory set size increases, the

P300 to probe stimuli decreases (e.g. Okita et al., 1985; Kramer

et al., 1986; Strayer and Kramer, 1990; Mecklinger et al., 1992).

This effect can be interpreted in terms of a resource allocation

theory of P300 amplitude. That is, the P300 has been concep-

tualized as indexing a pool of processing resources that can be

allocated among tasks. The P300 amplitude in a given task will be

attenuated to the extent that additional tasks utilize the same

resources (Natani and Gomer, 1981; Isreal et al., 1980; Kramer et

al., 1986). Thus the inverse relationship of P300 amplitude to

WM load suggests that the demand to retain information in WM

requires the reallocation of resources from the stimulus

classification and response decision-making component of the

task to the WM maintenance, or rehearsal, component (cf.

Mecklinger et al., 1992).

P300 amplitude in this task was also found to vary according

to whether the current stimulus matched or did not match the

target stimulus being remembered, being larger in the match

trials. This is consistent with results from other studies showing

that P300 amplitude is larger to probes that match a stimulus in

the memory set than to those that do not (e.g. Kramer et al.,

1986; Mecklinger et al., 1992). The difference in response

amplitude to matching and nonmatching stimuli increased with

practice, suggesting that with practice subjects became more

skilled in the stimulus classification and response decision-

making component of the task.

Late Post-stimulus Activity: Frontal and Parietal Slow

Waves

The requirements to maintain focused concentration on the task

and to constantly remember and manipulate information can be

expected to elicit sustained activation over various cortical areas

(Lang et al., 1992; Mecklinger and Pfeifer, 1996; Ruchkin et al.,

1990, 1992, 1998). For example, the analysis of the ongoing EEG

in both verbal and spatial WM tasks showed that frontal midline

theta increased as load level increased (Gevins et al., 1997,

1998). This was interpreted as ref lecting increased activation in

the anterior cingulate, an important node in the anterior

attentional network (Posner and Peterson, 1990; Posner and

Rothbart, 1992). The EP results in both this and our prior study

(Gevins et al. 1996) provide evidence of sustained activation of

left frontal and right parietal cortex, in the form of load-sensitive

but task-insensitive Slow Waves.

These frontal and parietal Slow Waves occurred after stimulus

evaluation and response preparation, in an interval in which

verbal or spatial information was being maintained in WM

between successive stimulus presentations. Both responses

systematically increased as a function of WM load but did not

differ between spatial or verbal versions of the tasks. This

suggests that these responses do not ref lect modality-specific

WM maintenance, but may instead be related to the higher-order

attentional demands of the task.

Support for the notion that the FSW is involved in nonspecific,

higher-order aspects of task performance comes from a recent

electrophysiological investigation of WM in the monkey (Rao et

al., 1997). In monkey frontal cortex neuronal firing in the delay

interval of a delayed matching-to-sample task was alternately

associated with the maintenance of either stimulus location or

object identity based upon the changing demands of the task.

Neuroimaging studies have also supported the role of the

prefrontal cortex in general executive and higher-order control

functions (eg. D’Esposito et al., 1995). Similarly, WM for any

form of information requires sustained attention, which has

been shown to asymmetrically activate right parietal regions

(Roland, 1982; Gevins et al., 1983, 1985; Deutsch et al., 1988;

Pardo et al., 1991). Modality nonspecific activation of inferior

right parietal cortex has also been reported in a recent PET

investigation of WM (Klingberg et al., 1996).

Thus the load-sensitivity and task-insensitivity of the frontal

and parietal Slow Waves suggest that these responses ref lect

higher-order aspects of task processing that are common to both

versions of the WM task employed here. These Slow Waves may

be related to the functional association between frontal and

parietal cortices necessary for performance of WM tasks. Studies

of lesion patients have indicated that electrical fields generated

in posterior cortex are modulated by inputs from frontal regions

(e.g. Knight et al., 1989; Yamaguchi and Knight, 1990). Studies

of nonhuman primates have also demonstrated the importance

of information transmission between frontal cortex and

posterior parietal regions for performance of WM tasks (e.g.

Friedman and Goldman-Rakic, 1994).

Activity in this frontal-parietal network did not change with

practice. This suggests that although subjects became more

skilled in task performance, and developed more efficient

preparatory and task-specific processing strategies, the higher-

order demands of the task were not diminished by practice.

Conclusions

This study has demonstrated that the mental operations engaged

by simple WM tasks are composed of numerous elementary

subsecond neural processes. Some of these processes, especially

those ref lected in the brief EP peaks, varied with type of

information (verbal or spatial) being retained. Other responses,

especially those ref lected in the frontal and parietal Slow Waves,

were affected by the amount of information being retained but

not by the type of information. This suggests that, in addition to

task-specific activation related to perceptual processes and to

task-specific WM processes, the functional neural networks that

arise during the performance of different types of WM tasks also

draw upon a limited-capacity system common to all

attention-demanding tasks (cf. Kahneman, 1973). Increasing

WM load not only systematically increased the sustained

activation of frontal and parietal regions involved in higher-order

572 Evoked Potentials and Working Memory • McEvoy et al.

attentional processes, but also suppressed response-related

processes (CNV and P300). Thus, as the WM demands of the task

increased, fewer resources were available for trial-specific

stimulus evaluation and response processing.

The results further showed that practice increased the

efficiency of stimulus evaluation and response preparatory

processes, although it did not decrease the higher-order

attentional demands of the task. The practice-related increase in

the prestimulus CNV suggests that response preparatory

processes became more efficient with practice, while the

practice effects on the P250, P300 and P390 potentials show that

stimulus evaluation and comparison processes also improved

with practice. In contrast, the absence of practice effects on the

frontal and parietal Slow Waves indicates that practice did not

globally decrease the attentional demands of the task. Rather,

these results indicate that practice-related improvements in

performance were primarily due to an increased efficiency in

preparing for and responding to stimuli on a trial-by-trial basis

while concurrently maintaining information in WM.

NotesWe thank Sue Whitfield and Georgia Rush for assistance with data

acquisition and preprocessing. This research was supported by grants

from the National Institute of Mental Health and the Air Force Office of

Scientific Research. Parts of this research were presented at the Annual

Meeting of the Society for Neurosciences, New Orleans, 1997.

Address correspondence to Linda McEvoy, EEG Systems Laboratory

and SAM Technology, 101 Spear Street Suite 203, San Francisco, CA

94105, USA.

ReferencesAwh E, Jonides J, Smith E, Schumacher EH, Koeppe RA, Katz S (1996)

Dissociation of storage and rehearsal in verbal working memory:

evidence from positron emission tomography. Psychol Sci 7:25–31.

Baddeley A (1992) Working Memory. Science 255:556–559.

Baddeley A, Hitch G (1974) Working memory. In: Recent advances in

learning and motivation (Bower G, ed.), Vol. 8, pp. 47–90. New York:

Academic Press.

Begleiter H, Porjesz B, Wang W (1993) A neurophysiological correlate

of visual short-term memory in humans. Electroenceph Clin

Neurophysiol 87:46–53.

Braver TS, Cohen JD, Nystrom LE, Jonides J, Smith EE, Noll DC (1997) A

parametric study of prefrontal cortex involvement in human working

memory. Neuroimage 5:49–62.

Bressler SL, Coppola R, Nakamura R (1993) Episodic multiregional

cortical coherence at multiple frequencies during visual task

performance. Nature 366:153–155.

Chelazzi L, Miller EK, Duncan J, Desimone R (1993) A neural basis for

visual search in inferior temporal cortex. Nature 363:345–347.

Cohen JD, Peristein WM, Braver TS, Nystrom LE, Noll DC, Jonides J, Smith

EE (1997) Temporal dynamics of brain activation during a working

memory task. Nature 386:604–607.

Courtney SM, Petit L, Maisog J, Ungerleider LG, Haxby JV (1998) An area

specialized for spatial working memory in human frontal cortex.

Science 279:1347–1351.

Courtney SM, Ungerleider LG, Keil K, Haxby JV (1996) Object and spatial

working memory activate separate neural systems in human cortex.

Cerebl Cortex 6:39–49.

Crick FHC (1984) Function of the thalamic reticular complex: the

searchlight hypothesis. Proc Natl Acad Sci USA 81:4586–4590.

D’Esposito M, Detre JA, Alsop DC, Shin RK, Atlas S, Grossman M (1995)

The neural basis of the central executive system of working memory.

Nature 378:279–281.

Deutsch G, Papanicolaou AC, Bourbon T, Eisenberg HM (1988) Cerebral

blood f low evidence of right cerebral activation in attention

demanding tasks. Int J Neursci 36:23–28.

Du W, Leong H, Gevins AS (1994) Ocular artifact reduction by adaptive

filtering. Paper presented at the Seventh IEEE SP Workshop on

Statistical Signal and Array Processing, Quebec City, CA.

Fiez JA, Raife EA, Balota DA, Schwarz JP, Raichle ME, Petersen SE (1996)

A positron emission tomography study of the short-term maintenance

of verbal information. J Neurosci 16:808–822.

Friedman HR, Goldman-Rakic PS (1994) Coactivation of prefrontal cortex

and inferior parietal cortex in working memory tasks revealed by 2DG

functional mapping in the Rhesus monkey. J Neurosci 14:2775–2788.

Frisk V, Milner B (1990) The relationship of working memory to the

immediate recall of stories following unilateral temporal or frontal

lobectomy. Neuropsychologia 28:121–135.

Funahashi S, Bruce C, Goldman-Rakic P (1989) Mnemonic coding

of visual space in the monkey’s dorsolateral prefrontal cortex.

J Neurophysiol 61:331–339.

Fuster JM, Jervey JP (1981) Inferotemporal neurons distinguish and retain

behaviorally relevant features of visual stimuli. Science 212:952–955.

Fuster JM, Jervey JP (1982) Neuronal firing in the inferotemporal cortex

of the monkey in a visual memory task. Occup Med J Neurosci

2:361–375.

Gevins AS, Cutillo BA (1993) Spatiotemporal dynamics of component

processes in human working memory. Electroenceph Clin Neuro-

physiol 87:128–143.

Gevins AS, Schaffer RE, Doyle JC, Cutillo B, Tannehill RS, Bressler SL

(1983) Shadows of thought: shifting lateralization of human brain

electrical patterns during brief visuomotor task. Science 220:97–99.

Gevins AS, Doyle JC, Cutillo BA, Schaffer RE, Tannehill RS, Bressler SL

(1985) Neurocognitive pattern analysis of a visuospatial task:

rapidly-shifting foci of evoked correlations between electrodes.

Psychophysiology 22:32–43.

Gevins AS, Morgan NH, Bressler SL, Cutillo BA, White RM, Illes J, Greer

DS, Doyle JC, Zeitlin GM (1987) Human neuroelectric patterns predict

performance accuracy. Science 235:580–585.

Gevins AS, Bressler SL, Cutillo BA, Illes J, Fowler-White RM, Miller J, Stern

J, Jex H (1990) Effects of prolonged mental work on functional brain

topography. Electroenceph Clin Neurophysiol 76:339–350.

Gevins AS, Smith ME, Le J, Leong H, Bennett J, Martin N, McEvoy L, Du R,

Whitfield S (1996) High resolution evoked potential imaging of the

cortical dynamics of human working memory. Electroenceph Clin

Neurophysiol 98:327–348.

Gevins A, Smith ME, McEvoy L, Yu D (1997) High resolution EEG

mapping of cortical activation related to working memory: effects of

task difficulty, type of processing, practice. Cereb Cortex 7:374–385.

Gevins A, Smith ME, Leong H, McEvoy L, Whitfield S, Du R, Rush G (1998)

Monitoring working memory load during computer-based tasks with

EEG pattern recognition methods. Hum Factors 40:79–91.

Isreal JB, Wickens CD, Chesney GL, Donchin E (1980) The event-related

brain potential as an index of display-monitoring workload. Hum

Factors 22:211–224.

Jonides J, Smith EE, Koeppe RA, Awh E, Minoshima S, Mintun M (1993)

Spatial working memory in humans as revealed by PET. Nature

363:623–625.

Jonides J, Schumacher EH, Smith EE, Lauber EJ, Awh E, Minoshima S,

Koeppe RA (1997) Verbal working memory load affects regional brain

activation as measured by PET. J Cogn Neurosci 9:462–475.

Just MA, Carpenter PA (1987) The psychology of reading and language

comprehension. Newton, MA: Allyn and Bacon.

Just MA, Carpenter PA (1992) A capacity theory of comprehension:

Individual differences in working memory. Psychol Rev 99:122–149.

Kahneman D (1973) Attention and effort. Englewood Cliffs, NJ: Prentice

Hall.

Kimberg DY, Farah MJ (1993) A unified account of cognitive impairments

following frontal lobe damage: the role of working memory in

complex, organized behavior. J Exp. Psychol 122:411–428.

King JW, Kutas M (1995) Who did what and when? Using word- and

clause-level ERPs to monitor working memory usage in reading.

J Cogn Neurosci 7:378–397.

Klingberg T, Kawashima R, Roland PE (1996) Activation of multi-modal

cortical areas underlies short-term memory. Eur J Neurosci 8:

1965–1971.

Knight RT, Scabini D, Woods DL (1989) Prefrontal cortex gating of

auditory transmission in humans. Brain Res 54:338–342.

Koch K, Fuster J (1989) Unit activity in monkey parietal cortex related to

haptic perception and temporary memory. Exp Brain Res

76:292–306.

Kramer AF, Schneider W, Fisk AD, Donchin E (1986) The effects of

practice and task structure on components of the event-related brain

potential. Psychophysiology 23:33–47.

Cerebral Cortex Oct/Nov 1998, V 8 N 7 573

Kyllonen PC, Christal RE (1990) Reasoning ability is little more than

working memory capacity?! Intelligence 14:389–433.

Lang W, Starr A, Lang V, Lindinger G, Deecke L (1992) Cortical DC

potential shifts accompanying auditory and visual short-term

memory. Electroenceph Clin Neurophysiol 82:285–295.

Le J, Menon V, Gevins A (1994) Local estimate of the surface Laplacian

derivation on a realistically shaped scalp surface and its performance

on noisy data. Electroenceph Clin Neurophysiol 92:433–441.

Lopes da Silva F (1991) Neural mechanisms underlying brain waves: from

neural membranes to networks. Electroenceph Clin Neurophysiol

79:81–93.

Manoach DS, Schlaug G, Siewert B, Darby DG, Bly BM, Benfield A,

Edelman R, Warach S (1997) Prefrontal cortex fMRI signal changes are

correlated with working memory load. NeuroReport 8:545–549.

McCarthy G, Wood CC (1985) Scalp distributions of event-related

potentials: an ambiguity associated with analysis of variance models.

Electroenceph Clin Neurophysiol 62:203–208.

McCarthy G, Blamire AM, Puce A, Nobre AC, Bloch G, Hyder F,

Goldman-Rakic P, Shulman RG (1994) Functional magnetic resonance

imaging of human prefrontal cortex activation during a spatial

working memory task. Proc Natl Acad Sci USA 91:8690–8694.

Mecklinger A, Pfeifer E (1996) Event -related potentials reveal

topgraphical and temporal distinct neuronal activation paterns for

spatial and object working memory. Cogn Brain Res 4:211–224.

Mecklinger A, Kramer AF, Strayer DL (1992) Event related potentials

and EEG components in a semantic memory search task. Psycho-

physiology 29:104–119.

Miller EK, Desimone R (1994) Parallel neuronal mechanisms for

short-term memory. Science 263:520–522.

Miyashita Y, Chang HS (1988) Neuronal correlate of pictorial short-term

memory in the primate temporal cortex. Nature 331:68–70.

Natani K, Gomer FE (1981) Electrocortical activity and operator

workload: a comparison of changes in the electroencephalogram and

in event-related potentials (MDC-e2427). St Louis: McDonnell Douglas

Astronautics Company.

Okita T, Wijers AA, Mulder G, Mulder LJ M. (1985) Memory search and

visual spatial attention: an event-related brain potentials analysis. Acta

Psychol 60:263–292.

Owen AM, Evans AC, Petrides M (1996) Evidence for a two-stage model of

spatial working memory processing within the lateral frontal cortex:

a positron emission tomography study. Cereb Cortex 6:31–38.

Pardo JV, Fox PT, Raichle ME (1991) Localization of a human system for

sustained attention by positron emission tomography. Nature

349:61–64.

Paulesu E, Frith CD, Frackowiak RS J. (1993) The neural correlates of the

verbal component of working memory. Nature 362:342–344.

Petrides M, Milner B (1982) Deficits on subject-ordered tasks after frontal-

and temporal-lobe lesions in man. Neuropsychologia 20:249–262.

Petrides M, Alivisatos B, Meyer E, Evans AC (1993) Functional activation

of the human frontal cortex during the performance of verbal

working memory tasks. Proc Natl Acad Sci USA 90:878–882.

Picton TW (1992) The P300 wave of the human event-related potential. J

Clin Neurophysiology 9:456–479.

Posner ME, Peterson SE (1990) The attentional system of the human brain.

Annu Rev Neurosci 13:25–42.

Posner MI, Rothbart MK (1992) Attentional mechanisms and conscious

experience. In: The neuropsychology of consciousness (Milner AD,

Rugg MD, eds), pp. 91–111.San Diego, CA: Academic Press.

Raney GE (1993) Monitoring changes in cognitive WM-load during

reading: an event-related brain potential and reaction time analysis. J

Exp Psychol Learn Mem Cogn 19:51–69.

Rao SC, Rainer G, Miller EK (1997) Integration of what and where in the

primate prefrontal cortex. Science 276:821–824.

Rohrbaugh JW, Syndulko K, Lindsley DB (1976) Brain wave components

of the contingent negative variation in humans. Science 191:

1055–1057.

Roland PE (1982) Cortical regulation of selective attention in man: a

regional cerebral blood f low study. J Neurophysiol 48:1059–1078.

Ruchkin DS, Johnson RJ, Canoune H, Ritter W (1990) Short-term memory

storage and retention: an event-related brain potential study.

Electroenceph Clin Neurophysiol 76:419–439.

Ruchkin DS, Johnson RJ, Grafman J, Canoune H, Ritter W (1992)

Distinctions and similarities among working memory processes: an

event-related potential study. Cogn Brain Res 1:53–66.

Ruchkin DS, Canoune HL, Johnson R, Ritter W (1995) Working memory

and preparation elicit different patterns of slow wave event-related

brain potentials. Psychophysiology 32:399–410.

Ruchkin DS, Berndt RS, Johnson RJ, Ritter W, Grafman J, Canoune H

(1997) Modality-specific processing streams in verbal working

memory: evidence from spatio-temporal patterns of brain activity.

Cogn Brain Res 6:95–113.

Schneider W, Detweiler M (1988) A connectionist/control architecture

for working memory. In: The psychology of learning and motivation

(Bower GH, ed.), pp. 53–119. San Diego, CA: Academic Press.

Sergent J, Zuck E, Levesque M, MacDonald B (1992) Positron emission

tomography study of letter and object processing: empirical findings

and methodological considerations. Cereb Cortex 2:68–80.

Shallice T (1982) Specific impairments in planning. Phil Trans R Soc

London B 298:199–209.

Shimamura AP, Janowsky JS, Squire LR (1990) Memory for the temporal

order of events in patients with frontal lobe lesions and amnesic

patients. Neuropsychologia 28:803–813.

Smith EE, Jonides J, Koeppe RA, Awh E, Schumbacher EH, Minoshima S

(1995) Spatial versus object working memory: PET investigations.

J Cogn Neurosci 7, 337–356.

Smith EE, Jonides J, Koeppe RA (1996) Dissociating verbal and spatial

working memory using PET. Cereb Cortex 6:11–20.

Smith ME, McEvoy L, Gevins A (1998) Neurophysiological changes during

skill acquisition: EEG measures of focussed attention, cortical

utilization, neurocognitive strategy development. Brain Res Cogn

Brain Res (in press).

Starr A, Barrett G (1987) Disordered auditory short-term memory in man

and event-related potentials. Brain 110:935–959.

Sternberg S (1969) Memory scanning: mental scanning: mental processes

revealed by reaction time experiments. Am Scient 57:421–457.

Strayer DL, Kramer AF (1990) Attentional requirements of automatic and

controlled processes. J Exp Psychol Learn Mem Cogn 16:67–82.

Teece JJ (1972) Contingent negative variation (CNV) and psychological

processes in man. Psychol Bull 77:73–108.

Teece JJ, Hamilton BT (1973) CNV reduction by sustained cognitive

activity (distraction). Electroenceph Clin Neurophysiol 33(suppl):

229–237.

Vaadia E, Haalman I, Abeles M, Bergman H, Prut Y, Slovin H, Aersten A

(1995) Dynamices of neuronal interactions in monkey cortex in

relation to behavioral events. Nature 373:515–518.

Wilson FA W., Scalaidhe SPO, Goldman-Rakic PS (1993) Dissociation of

object and spatial processing in primate prefrontal cortex. Science

260:1955–1958.

Yamaguchi S, Knight RT (1990). Gating of somatosensory inputs by

human prefrontal cortex. Brain Res 521:281–288.

574 Evoked Potentials and Working Memory • McEvoy et al.