High resolution evoked potential imaging of the cortical dynamics of human working memory

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Electroencephalography and clinical Neurophysiology 98 (1996) 327-348

High resolution evoked potential imaging of the cortical dynamics of human working memory Alan Gevins *, Michael E. Smith, Jian Le, Harrison Leong, Jeffrey Bennett, Nancy Martin, Linda McEvoy, Robert Du, Sue Whitfield EEG Systems Laboratory and SAM Technology, One Rincon Center, 101 Spear Street, No. 204, San Francisco, CA 94105, USA Accepted for publication: 11 January 1996

Abstract

High resolution evoked potentials (EPs), sampled from 115 channels and spatially sharpened with the finite element deblurring method, were recorded from 8 subjects during working memory (WM) and control tasks. The tasks required matching each stimulus with a preceding stimulus on either verbal or spatial attributes. All stimuli elicited a central P200 potential that was larger in the spatial tasks than in the verbal tasks, and larger in the WM tasks than in the control tasks. Frequent, non-matching stimuli elicited a frontal, positive peak at 305 msec that was larger in the spatial WM task relative to the other tasks. Irrespective of whether subjects attended to verbal or spatial stimulus attributes, non-matching stimuli in the WM tasks also elicited an enhanced P450 potential over the left frontal cortex, followed by a sustained potential over the superior parietal cortex. A posterior P390 potential elicited by infrequent, matching stimuli was smaller in amplitude for both spatial and verbal WM tasks compared to control tasks, as was a central prestimulus CNV. These results indicate that WM is a function of a distributed system with both task-specific and task-independent components. Lesion studies and coarse temporal resolution functional imaging methods, such as PET and fMRI, tend to paint a fairly static picture of the cortical regions which participate in the performance of WM tasks. In contrast, the fine-grain time resolution provided by imaging brain function with EP methods provides a dynamic picture of subsecond changes in the spatial distribution of WM effects over the course of individual trials, as well as evidence for differences in the activity elicited by matching and non-matching stimuli within sequences of trials. This information about the temporal dynamics of WM provides a critical complement to the fine-grain spatial resolution provided by other imaging moralities. Keywords: High resolution evoked potentials; Imaging; Finite element deblurring method; Working memory; Verbal task; Spatial task

1. Introduction

" P r i m a r y " (James, 1890) or " w o r k i n g " (Baddeley, 1992) memory refers to the attention-demanding capacity to store information for several seconds while it is being utilized in the context of cognitive task performance. Early models depicted W M as a type of small capacity buffer, the content of which was subject to extremely rapid decay without continuous conscious rehearsal (e.g. Miller, 1956; Waugh and Norman, 1965). This view was elaborated by Baddeley and Hitch (1974), who characterized W M as a type of mental workspace composed of 3 subsystems: a

* Corresponding author. Tel.: (415) 957-1600; Fax: (415) 546-7121; E-mail: [email protected].

central executive involved in control and selection processes; a buffer responsible for maintaining acoustically coded information; and, a buffer responsible for maintaining visual and spatial information. Some investigators in recent years have argued for a more amorphous view of WM, wherein it is construed as a shifting coalition of interacting but independent process-specific subsystems (e.g. Schneider and Detweiler, 1988), or as a limited pool of neural "activation" (e.g. Just and Carpenter, 1987, 1992) that is necessary for manipulating information and for maintaining that information in an immediately accessible state. The neural substrate of W M does not appear to depend on the cortico-limbic circuitry that is necessary for retrieval of information when a delay of more than a few seconds is interposed between study and test trials of a

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memory task (e.g., Wickelgren, 1968; Cave and Squire, 1992). Rather, neuropsychological studies of the effects of lesions on the human brain have indicated that the frontal lobes often play a critical role in WM (e.g. Shallice, 1988; Frisk and Milner, 1990). Several recent studies have used positron emission tomography (PET) or functional magnetic resonance imaging (fMRI) to measure regional cortical activity associated with WM (Roland, 1984; Jonides et al., 1993; Pautesu et al., 1993; Petrides et al., 1993a,b; Cohen et al., 1994). These studies have demonstrated metabolic increases in regions of the frontal lobes and in other areas of the association cortex. However, lesion studies provide little insight into the dynamics of WM processes, and metabolic methods lack the subsecond temporal resolution necessary for examining the course of activation in different cortical regions as attention is allocated to the successive stages of performing a task. In contrast, recordings of neuronal activity in primates have indicated that the neural representation of information over short delays is associated with transient activation of widespread populations of association cortex neurons. This neuronal activation is sensitive to momentary within-task events, and it is modulated by the allocation of attention to different stimulus attributes and task requirements (e.g., Fuster and Jervey, 1981, 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). Such 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 the demands and contingencies of specific behavioral contexts (cf., Gevins et al., 1983, 1987; Crick, 1984; Schneider and Detweiler, 1988; Bressler et al., 1993; Gevins and Cutillo, 1993; Kimberg and Farah, 1993). Several studies have utilized EP measures in the context of a wide variety of task paradigms to examine the time course of WM processes (e.g. Starr and Barrett, 1987; Ruchkin et al., 1990a, 1992, 1995; Lang et al., 1992; Gevins and Cutillo, 1993; Raney, 1993; King and Kutas, 1995). Most studies of scalp-recorded EPs have had insufficient spatial resolution to relate observed effects to regions of functionally specialized cortex. In the present study, an attempt was made to characterize the regionalization of WM-related modulation of scalp-recorded EP measurements by improving EP spatial resolution and integrating physiological and anatomical images. This was accomplished by utilizing a recently devised spatial deblurring method that makes it possible to combine scalp-recorded EEG with realistic, subject-specific anatomical models to derive a computational estimate of the topography of potential fields as they would appear just above the exposed superficial cortical surface (Gevins et al., 1991, 1994; Le and Gevins, 1993). The "finite element deblurring" method is an inverse solution of Poisson's equation using finite element models of volume conduction through

the skull and scalp that are constructed from anatomical information derived from each subject's MRI; here we refer to the product of this process as a "computed cortical potential." The anatomical and electrical modeling is combined with high density electrode arrays in order to prevent aliasing of high spatial frequencies; spatial sampling of 2.5 cm or better is required for most EP components (Gevins et al., 1991). EPs were collected while subjects performed 4 different versions of a continuous matching task. The versions differed in their WM requirements and in the type of information to which subjects were required to attend and remember (the verbal name versus the spatial position of visually presented letters), but were identical in terms of their perceptuomotor requirements. It was anticipated that EPs would be modulated in a regional fashion by differences in WM requirements and by differences in the stimulus attributes (verbal or spatial) to which subjects were attending.

2. Methods

2.1, Subjects Eight (4 female) fully informed, healthy, right-handed (1 ambidextrous) volunteers (mean age 27 years) participated in the study.

2.2. Cognitive tasks Participants were comfortably seated facing a videomonitor at a distance of approximately 60 cm. They performed 4 versions of a continuous matching task (Fig. 1). Matches occurred on 30% of the trials. Equivalent stimuli were presented and equivalent behavioral responses were required in each condition, with type of processing and WM requirements varying across conditions. Each stimulus item was drawn from a set of 12 capital letters. The letter on a given trial appeared at 1 of 12 positions, each of which was on 1 of 6 equidistant radii of an imaginary circular array 1 or 3.5 cm from the screen's center (the inner and outer circles of this array respectively subtended approximately 1.5° and 4.5 ° visual angle). Letter name and position were counterbalanced across conditions so that each letter and position occurred with equal probability. Stimuli were presented for a duration of 250 msec once every 4.5 sec. A fixation point appeared at the center of the screen 1.5 sec before the stimulus. In 2 task conditions that imposed a heavy load on WM, the participants had to decide if a stimulus on each trial matched a stimulus occurring 3 trials previously. In different blocks this matching was performed on different stimulus attributes, that is, either the name or the spatial location of the stimulus was matched to prior stimuli. Subjects were

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Fig. I. Schematic diagram of the course of cvcnts in cach trial(A) and each block (B) for the differenttask conditions. Lcttcr stimuli wcrc presented at l of 12 screen positions,illustratedby the points of intcrscctionin the top part of Fig. IA (the circular grid displayed in this figure did not actually appear on the screen), according to the within-trialschcdulc illustratedat the bottom of Fig. A. As depicted in B, in control conditions subjects wcrc required to match thc stimulus on each trialwith the stimulus which occun~cd on the firsttrialof the block, and in W M conditions subjects wcrc required to match the stimulus on each trialwith the stimulus which occurred 3 trialspreviously.

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informed of which attribute t o attend to at the initiation of each block of trials. These WM task conditions thus required concurrent maintenance of a sequence of 3 letter names or 3 spatial locations. Since each trim lasted 4.5 sec, accurate performance in these WM conditions required that stimuli be retained for about 13.5 sec. Subjects had to update the sequence on each trial by retaining the most recent stimulus and could drop the stimulus from 4 trials back. Further, given that simply detecting that the current stimulus was a repetition of some previous event was not sufficient to insure accurate performance; both the precise sequence and the name or location of a letter had to be remembered. In 2 corresponding control tasks, only the verbal name or spatial location of the first stimulus of each block had to be remembered and compared to subsequent stimuli. Subjects were also informed at the initiation of each block whether it was to be a WM task block or a control task block. In all 4 conditions subjects were required to respond by pushing a microswitch with the right-hand index finger for non-matching stimuli (those that did not match the name or spatial location of the comparison stimulus) and a second microswitch with the

right-hand middle finger for matching stimuli (those which had the same name or spatial location as the comparison stimulus). Subjects were requested to respond as quickly and accurately as possible after the stimulus appeared. Stimuli in each of the 4 conditions were presented in blocks of 53 trials. Since for WM conditions no matches could appear in the first 3 trials of a block, data from those trials were excluded from analyses in all conditions. On the day before the experimental session, participants performed about 200 training trials for each condition. On the day of the recording they performed 7 blocks of each task condition (i.e., 350 trials in each of 4 conditions). The order of these blocks was randomized for each subject. Blocks were self-paced and several breaks were taken over the course of the session.

2.3. Recordings EEGs were recorded from a 115-channel array of electrodes placed in an extended 10-20 system montage (Fig. 2, see also Sharbrough et al., 1990; Gevins et al., 1994), referenced to linked mastoids, amplified 9.24 K, bandpass

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A. Gevins et al. / Electroencephalography and clinical Neurophysiology 98 (1996) 327-348

filtered at 0.05-100 Hz and sampled at a rate of 256 Hz. Vertical and horizontal eye movements were also recorded. The 115-electrode recording array provided average interelectrode distances of about 2.25 cm. The 3-D positions of each scalp electrode and fiducial anatomical reference points (including the nasion, the tragus of each ear, the external comer of each eye, and the tip of the nose) were measured with a Polhemus magnetic digitizer for subsequent registration of the recording array with subjectspecific MRI-derived anatomical models.

2.4. Magnetic resonance image processing MRIs for all subjects were recorded with a Siemens Magnetron scanner in a 3-D acquisition mode to obtain Tl-weighted images of contiguous 1.5 × 1.0 × 1.0 mm voxels. Using these MRI images, each subject's scalp, skull and cortical surface layers were contoured (cf., Gevins et al., 1994), using an automated surface extraction program, and then visually inspected and manually edited using an interactive 3-D graphics program. The resulting surfaces were used for construction of finite element models, registration of EEG electrode positions with MRIs and 3-D display of electrical field potential topography. Details of the procedures involved have been extensively described elsewhere (Gevins et al., 1991, 1994; Le and Gevins, 1993).

2.5. Analysis Single trials were manually edited for artifacts. Trials with instrumental artifacts, amplifier saturation, visually detectable vertical and horizontal eye movement potentials, and muscle activity over approximately 80 /zV peak-topeak were eliminated, as were trials for which behavioral performance was inaccurate. After rejecting approximately 30% of the trials because of artifactual contaminants or incorrect responses, and collapsing data across blocks, stimulus locked EPs were averaged over an epoch beginning 200 msec before the stimulus and extending 1200 msec following the stimulus. Averages included around 70 trials for matching stimuli and around 170 trials for nonmatching stimuli in each of the 4 task conditions for each subject. Prestimulus EPs were also calculated, collapsing across matching and non-matching conditions (since subjects did not know whether a matching stimulus would be occurring prior to its presentation). These prestimulus EPs encompassed an epoch that began 1700 msec prior to stimulus onset and which extended for 100 msec following stimulus onset. Preliminary visual inspection of the resulting averages was performed prior to any application of spatial enhancement techniques. Exploratory data analysis was also performed after computing a Laplacian derivation (LD) for each EP. The LD is computed as the second derivative in space of the potential field at each electrode.

331

It is thought to be proportional to the current entering and exiting the scalp at each electrode site and is independent of the location of the reference electrode used for recording (Nunez, 1981). The LD was calculated over the actual shape of a subject's head using the measured 3-D positions of the electrodes and 3-D spline functions (Leet al., 1994). Formal data analyses were performed after finite element deblurring was applied to the original EPs. As noted in the Introduction, this method provides a computational estimate of the electrical potentials which would be recorded near the cortical surface by using an MRI-derived realistic mathematical model of volume conduction through the skull and scalp to downwardly project scalp-recorded signals (for details of this procedure see Le and Gevins, 1993; for a more general overview of the application of finite element techniques to electrical field problems, see Chaff and Silvester, 1980). In initial applications, this method has been shown to be reliable and more accurate than the LD (Gevins et al., 1994), of course, at the expense of obtaining and processing each subject's structural MRI. Although the deblurring method can substantially improve the spatial detail provided by scalp recorded EEGs, it does not provide conclusive information about the location of generating sources. Nevertheless, the improved spatial detail facilitates formation of more specific hypotheses about the distribution of active cortical areas during cognitive tasks than was previously possible. (Although an electrical field maximum does not necessarily imply the existence of a local generator, brain electrical field strength decreases proportionally to the square of distance from its source. As a result, contributions from radially oriented generators in superficial areas of association cortex near the recording electrodes would be expected to dominate measurements of electrical field topography once the effects of reference electrode and deblurring due to volume conduction through the skull and scalp are corrected, cf., Nunez, 1981, 1989; Le and Gevins, 1993.) As noted in the previous section, each subject's MRIs were used to construct a realistic model of his or her head in the form of many small tetrahedral elements representing the tissues of scalp, skull and brain. By assigning each tissue a conductivity value, it is possible to calculate the potential at all finite element vertices using Poisson's equation. The finite element modeling of tissue below the scalp surface was limited to structures above a horizontal plane that bisected the head of each subject just above the sinus cavity. Several factors influenced the decision to limit the size of the region modeled in this fashion. First, the spatial resolution yielded by the finite element modeling approach is determined by the number of nodes in the model, and at a given density of nodes the number of nodes increases with the extent of the region included in the model. However, if too many nodes are employed, the inverse solution to Poisson's equation becomes unstable (Huebner and Thornton, 1982). In the approach used here, the size of the deblurred region reflects a compromise

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between an effort to obtain as high a spatial resolution as permitted by the electrode sampling density, and the requirement that any solution obtained be a stable one. Effectively, this compromise limits the absolute size of the modeled region (this constraint can be somewhat circumvented by employing multiple models specific to different regions of the head, a procedure beyond the scope of the current study). Second, the current generation of anatomical modeling techniques is not yet adequate to accurately model such areas as the sinus cavities and the inner ear, which limited the anterior and temporal extent of the models. Further, preliminary analyses based on consideration of the scalp waveforms (both unenhanced and following the LD transform) indicated that most of the task-related modulation of EPs occurred in the frontal, central and parietal regions (see Results below). This observation was consistent with a priori assumptions made on the basis of other physiological studies of WM utilizing similar task paradigms (see below). This data-driven constraint limited the posterior extent of the models. Given that the actual conductivity value of each finite element is unknown, a constant value was used for the ratio of scalp to skull conductivity (80:1); the conductivity of each finite element is set by multiplying this constant by the local tissue thickness as determined from the MRI. Thus, even though true local conductivity is unknown, the procedure is well behaved with respect to this source of uncertainty, because it successfully accounts for relative conductivity variation due to regional differences in scalp and skull thicknesses. Further, simulation studies in which the conductivity ratio was varied over wide limits have demonstrated that relative spatial topography and field maxima are not appreciably affected by changes in this parameter up to + / 50% (Le and Gevins, 1993), and hence this source of uncertainty would not be expected to affect the regional localization accuracy reported here. Similarly, since the same conductivity ratio was utilized to estimate the cortical electrical fields in all task conditions, relative differences in potential amplitude between conditions are not influenced by this parameter. After the deblurring procedure, a spatial low-pass Gaussian filter (16 dB at the Nyquist frequency conservatively assuming a mean projected inter-electrode distance at the cortical surface of 1.5 Cm) was applied to all of the averages in order to reduce spatial high frequency noise (cf., Le et al., 1994). This conservative spatial filtering strategy is more than adequate to compensate for spatial noise introduced by undersampling the potential field at the scalp and by other potential sources of spatial noise (e.g. electrode position measurement error, slight movement of the electrode positioning hat over the course of the testing session, etc.), but it also serves to reduce some of the true spatial details that would otherwise be produced by applying the deblurring process to brain signals with a high spatial signal-to-noise ratio. (Methods are currently under development for identifying optimal spatial filters

based on the observed spatial signal-to-noise ratio of a given brain signal.) Estimated cortical electrical field voltages for stimulus locked EPs were measured relative to the average voltage of a 200 msec baseline period immediately preceding the stimulus; timing measurements were made relative to stimulus onset. In order to compare results across subjects, points corresponding to the scalp electrode positions were projected downward to the cortical surface, and the value of the estimated cortical electrical field was interpolated from the finite element vertices to those positions. Statistical tests assessing the reliability of waveform changes associated with the task manipulations were performed by comparing peak amplitudes of the waveforms within small regions of interest (ROI) encompassing subsets of these locations. Prior electrophysiological work in non-human primates (Goldman-Rakic, 1987, 1988), as well as functional imaging studies in humans (Jonides et al., 1993; Paulesu et al., 1993; Petrides et al., 1993a,b; Cohen et al., 1994; Smith and Jonides, 1995) during spatial and non-spatial tasks have provided convergent evidence that dorsolateral prefrontal cortex and posterior parietal cortex are activated by conditions that impose a high WM load. Statistical tests of the hypothesis that these areas would also show task-related modulation of brain electrical activity were thus mainly defined in terms of EP measures made over specific anatomically defined regions of interest. In addition

Fig. 3. The shaded forms on this image represent the approximate boundaries of the right-hemisphereregions of interest (ROD over frontal and parietal cortices. Homologousregions on the left hemisphere were also included in the analyses. Estimates of the cortical extent of these regions were derived from an anatomical atlas in conjunction with prior studies of the position of electrodes in the 10-20 system relative to cortical anatomy(e.g. Homan et al., 1987), and in consideration of prior results concerning the regions of association cortex activated in tasks similar to the tasks employed here (e.g. Cohen et al., 1994; Smith and Jonides, 1995). The actual position of electrode cortical surface projections relative to the ROIs was confirmed in the manner illustrated in Fig. 4 and described in the text.

A. Gevins et a L / Electroencephalography and clinical Neurophysiology 98 (1996) 327-348

to providing an opportunity to test a specific hypothesis about the functional neuroanatomy of WM, this approach also served to help avoid the unacceptable loss of statistical power that would normally occur when using conventional repeated measures statistical tests to evaluate data simultaneously obtained from a large number of electrodes (cf., Oken and Chiappa, 1986). Further, given that many so-called cognitive EP components are undoubtably generated by multiple cortical areas and relatively widespread populations of neurons, this approach (of collapsing measures across nearby recording sites) implicitly recognizes the limits of functional localization that can be reasonably expected with scalp-recorded cognitive EPs. Finally, electrode placement can reasonably be expected to vary with respect to cortical anatomy, because of individual differences in head size and shape as well as brain morphology. Defining analyses in terms of broad ROIs serves to accommodate these individual differences.

333

ROIs were defined as groups of electrodes centered over frontal positions F3 and F4, and parietal positions P355 and P255 (depicted on Fig. 2). The extent of the region was defined by the group of 5 - 6 electrode positions immediately adjacent to this center position, producing a region of approximately 4.5 × 4.5 cm. The added constraint was imposed that all of the electrodes included in a region had to fall within the subset of electrodes demarcated by the common extent of the finite element model across subjects (as indicated by the interior boundary on Fig. 2). These regions (depicted in Fig. 3) were selected to include electrodes which would be expected to approximately cover (cf.,~ Homan et al., 1987; Lagedund et al., 1993; Towle et al., 1993) the areas of frontal (esp. areas in and around the principal sulcus including Brodmann areas 45, 46, and 9) and parietal association cortices (esp. areas 7 and 40), previously implicated in performance of tasks such as those employed in this study. In order to confirm

ANATOMICAL LOCALIZATION OF ESTIMATED CORTICAL VOLTAGE MAXIMA

Fig. 4. Anatomical localization of cortical projections of electrode positions and local voltage maxima of computed cortical potentials. This illustration shows the computedcortical topographyof an EP (see Fig. 7) peak in one subject and identification on her 3-D MRI of the cortical region associated with cortical projection of a scalp electrode near the maximaof that peak.

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the cortical anatomy associated with each region, each electrode included in an analysis was localized in terms of its intersection with horizontal, sagittal and coronal MRI

slices (Fig. 4), and the associated gyral anatomy was identified by comparison to a stereotaxic brain atlas (Talairach and Tournoux, 1988).

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Fig. 5. Grand average computed cortical potential waveforms (across subjects) for correctly performed non-matching (A) and matching (B) stimuli in each task condition (the electrode names correspond to scalp electrode positions downwardly projected to the conical surface). Waveforms are plotted after subtraction of the average amplitude in a 200 msec prestimulus interval. Y-axis units are p.V, x-axis units are sec.

A. Gevins et al. /Electroencephalography and clinical Neurophysiology 98 (1996) 327-348

One approach to measurement of EP peaks within an ROI is to measure the average voltage across electrodes within the region (cf., Curran et al., 1993). However, this approach ignores the possibility of polarity inversions across the ROI. In the extreme, electrodes near a source/sink configuration may yield amplitudes of opposite polarity, thus effectively canceling each other. To avoid this, EP peak amplitudes for each task-sensitive modulation of the waveforms were measured in each subject at the electrode in the ROI for which that peak was largest for the subject (cf., Gevins et al., 1995). Thus, for each EP peak and each subject, the value at the point of maximum amplitude within the region (and in the case of hemispheric analyses, the value at that point's homologue on the opposite side of the head), was included in the statistical analyses. Latencies for measurement were selected based on the latency to peak amplitude in each individual subject within the ROI that demonstrated the largest task-related modulation in grand average (group) waveforms, and then measurements in all ROIs and all task conditions for a subject were made at that latency. The experiment was designed to permit repeated-measures analysis of variance (ANOVA) with a maximum of two levels per factor in order to avoid violations of the sphericity assumption in repeated measures designs (cf., Cohen, 1987).

3. Results

3.1. Behavior The higher cognitive demands of the WM conditions were reflected in behavioral performance. Specifically, reaction times (RTs) to correctly classified stimuli (Table l) were slower in the WM conditions than in the control conditions ( F (1, 7 ) = 54.90, P < 0.001), but no significant difference in RT was observed between matching and non-matching stimuli, or between the spatial and verbal tasks, and there was no WM load vs. task-type interaction ( F s < 1). There was a WM load vs. matching status (correctly identified match versus non-match trials) interaction ( F (1, 7) -- 7.01, P < 0.05), and the 3-way interaction was also significant ( F (1, 7) = 5.98, P < 0.05). This 3-way interaction reflects the fact that in the verbal WM

Table 1 Mean (S.D.) reaction time for correct responses in each task Spatial control (msec) Matches

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task matching stimuli were classified faster than nonmatching stimuli, and in the verbal control task, matching stimuli were classified slower than non-matching, whereas RTs were virtually identical for matching and non-matching stimuli in the spatial tasks. Target detection accuracy was lower in the WM tasks (average d ' = 2.5 for verbal and d' = 2.4 for spatial) than in the control tasks (average d' = 4.4 for verbal and d' = 4.35 for spatial; F (1, 7 ) = 134.03, P < 0.0001), but there was no significant effect of task type ( F (1, 7 ) = 1.8, P < 0.221), and no significant WM load vs. task type interaction ( F (1, 7 ) = 3.1, P < 0.122). This failure to find any overall significant difference in the speed or accuracy of performance between the spatial and verbal versions of the tasks indicates that the task types were well balanced with respect to difficulty.

3.2. Electrophysiology A subset of grand average waveforms for the computed cortical potentials produced by the deblurring procedure are shown in Fig. 5A and B. Preliminary analysis of the data involved visual inspection of both raw EP waveforms and LD waveforms from the whole head. Early P1/N1 deflections did not discriminate between task conditions and were isolated primarily to posterior occipital and occipito-temporal regions. The equivalence of these potentials across task conditions suggests that the 4 tasks were well matched on perceptual attributes. The amplitudes of the early peaks (particularly the P1) were very small. This likely reflects the fact that stimuli were small and presented eccentric to the point of fixation (on half of all trials the small letter stimuli occurred on the outer circle of the stimulus array, i.e., outside of foveal vision). These potentials were followed by a P200 deflection that was maximal at the vertex and narrowly distributed around Cz. The onset of the P200 was the first point following the stimulus at which the waveforms begin to reflect the task manipulations. The P200 was largest in the spatial WM condition and it did not discriminate between matching and nonmatching stimuli. Preliminary analyses of this effect at the predefined ROIs did not reveal any reliable differences. Given that the P200's distribution was largely isolated to the vertex, an ROI with Cz as its center and extending to one electrode from Cz in every direction was constructed. Measures of P200 peak amplitude from this ROI were subjected to a WM load by task-type repeated measures ANOVA (data were collapsed across the matching/nonmatching dimension). A significant effect of task type was obtained ( F (1, 7 ) = 18.92, P < 0.005), with the P200 being larger in the spatial tasks than in the verbal tasks. A significant effect of WM load was also obtained ( F (1, 7) = 7.48, P < 0.05), with the P200 being enhanced in the WM tasks relative to the control tasks. There was no significant interaction between the factors. Following the P200, several EP deflections over frontal and parietal association cortices discriminated between the different

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W M tasks, and a posterior P3b-type late positive component discriminated the infrequent, matching from the frequent, non-matching stimuli. In order to disambiguate the topography and task effects of other EP potentials from spatiotemporal overlap with the P3b-type component, task effects on EPs to non-matching stimuli are analyzed separately from EPs to matching stimuli. 3.3. EPs to frequent, non-matching stimuli

At both anterior and posterior sites the EP waveforms for non-matches were characterized by a series of transient positive and negative peaks, in some cases followed by a n d / o r superposed on slower potential shifts (Fig. 5A). Following the P200 peak, another more widely distributed positivity reached maximum amplitude (mean + S.D.) at 305 + 15 msec and was enhanced at frontal sites in the spatial W M condition relative to the other conditions. The P305 was followed by a left lateralized, frontal, slow positive potential, which reached m a x i m u m amplitude at around 450 + 110 msec. This P450 potential was also

largest in the spatial W M condition and enhanced in both W M conditions relative to their respective control conditions. At posterior sites a broad positive slow wave (PSW) with an onset of approximately 200 msec post stimulus dominated the waveforms. The amplitude of this slow wave discriminated between the WM conditions and the control conditions beginning about 500 msec post stimulus. That is, after approximately 500 msec the potential quickly returned to baseline in the control conditions, yet it was sustained for a much longer period in the W M conditions. For each of these effects a repeated measures A N O V A was performed for measurements at the frontal and parietal ROIs separately, with task type (spatial vs. verbal), W M load (WM vs. control tasks), and hemisphere (left vs. right) as factors. Where significant interactions were obtained, one-tailed paired sampled t tests were performed for post hoc comparisons of means within each ROI, and a conservative P < 0.005 criterion was used to identify significant differences between means. Prior studies have indicated that a 2 - 9 Hz band is optimal for dissociating transient late positive potentials

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from overlap with slow potentials (Pritchard et al., 1986; Farwell et al., 1993). Thus, for analysis of the relatively sharp P305 peak, a zero phase-lag, infinite impulse response, 2 - 9 Hz bandpass filter was applied to the EPs before cortical potentials were calculated. This filtering had the effect of reducing the absolute amplitude of the response and disambiguated its spatial topography and task correlates (Fig. 6). The resulting maxima of the P305 task-related modulation occurred in the region of the frontal ROIs, that is, over dorsolateral frontal cortex in the region of the superior and middle frontal gyri (Figs. 4 and 7). Repeated measures ANOVA at the frontal ROIs revealed a main effect of task-type, with EP amplitude highest in the spatial tasks ( F (1, 7) = 25.39, P < 0.001), a main effect of WM load, with amplitude higher in the WM conditions ( F (1, 7) -:- 8.62, P < 0.03), and a main effect of hemisphere, with higher amplitude on the right side ( F (1, 7)---6.7, P < 0.04). There was also a significant interaction between task type and WM load ( F (1, 7 ) = 9.71, P < 0.02), with the P305 being relatively enhanced exclusively in the spatial WM task. No other interactions reached significance. In post hoc analyses at the left frontal ROI, the amplitude of the P305 peak was larger in the spatial WM task than in both the verbal WM and verbal control tasks, but not in the spatial control task. At the right frontal ROI, the amplitude of the P305 peak was larger in the spatial WM task than in each of the other tasks. No significant differences were observed between conditions or hemispheres in a repeated measures ANOVA performed on data from the parietal ROIs. Thus, the enhancement of the P305 in the spatial WM task was isolated to the frontal ROIs and was most pronounced at the right frontal ROI.

The largest task-related amplitude differences for the P450 (Fig. 8) potential were also observed to occur over the superior and middle frontal gyri, in the area of the left frontal ROI. Repeated measures ANOVA performed at the frontal ROIs revealed a significant main effect of WM load, with the WM tasks eliciting increased EP amplitude for this peak relative to control conditions ( F (1, 7) = 20.1, P < 0.003). There was no significant main effect of task type and no significant main effect of hemisphere, but the WM load by hemisphere ( F (1, 7) = 15.9, P < 0.005), and the 3-way WM load by task type by hemisphere ( F (1, 7) = 8.8, P < 0.03) interactions were significant. In post hoc analyses at the left frontal ROI, the P450 in the spatial WM was larger in amplitude than that for either the spatial or verbal control task. No significant differences in the P450 between the spatial and verbal WM tasks were observed at this ROI. No post hoc comparisons of the P450 between task conditions at the fight frontal ROI reached the criterion significance level. The absolute amplitude of the P450 was largest over the parietal ROIs. This distribution likely reflects overlap with the large amplitude sustained posterior positivity that can be seen in the waveforms in Fig. 5A. Repeated measures ANOVA performed on measurements of the P450 at the parietal ROIs produced no significant main effects of WM load, task type or hemisphere. In sum, the P450 was enhanced in the WM task conditions, and this enhancement was isolated to the left frontal ROI. Finally, as noted above, a sustained positive potential appeared within 200 msec of stimulus onset over posterior cortical regions to stimuli in non-matching task conditions. Beginning approximately at 500 msec the evoked cortical

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electrical fields for both the spatial and verbal WM tasks diverged from those elicited by the corresponding control conditions (Fig. 9). This difference related to WM was maximal in the 600-900 msec interval, and at some locations and in some subjects it was sustained throughout the rest of the averaging epoch (one subject was eliminated from these analyses because of a large number of movement-related artifacts produced after the response, which would contaminate low frequency brain potentials). The PSW was inverted in polarity at more central and anterior sites, and the largest task-related differences in signal amplitude for PSW occurred over the parietal cortex in the area of the supramarginal gyrus and the inferior portion of

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the superior parietal lobule (i.e., in the area of the parietal ROIs depicted in Fig. 4). Repeated measures ANOVA performed on measurements made at the frontal ROIs revealed a significant main effect of task type ( F (1, 6)--13.76, P < 0.02), and a 3-way interaction between the WM load, task type, and hemispheric factors. In post hoc comparisons of means at the left frontal ROI, the PSW in the spatial WM was larger in amplitude than that for the verbal WM task, but no other comparisons reached significance. This enhancement of the PSW in the left frontal ROI in the spatial WM task appeared as a continuation of the enhanced P450 in that condition in that location rather than as a separate phenomenon. At the parietal ROIs, a

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main effect of WM load was obtained ( F (1, 6) = 21.56, P < 0.005), with amplitudes larger for the WM tasks. The main effects for task type and hemisphere did not approach significance, nor were there any significant interactions. Thus, at parietal sites the PSWs in the WM tasks were enhanced relative to the control conditions bilaterally and independently of task type. In sum, at the left frontal ROI, EPs in the spatial WM task continued to be elevated in amplitude in a sustained positive shift that began early in the epoch (i.e., with the onset of the P305 peak) and continued past 600 msec. At parietal ROIs, EPs in both WM tasks diverged in a positive direction from the control tasks during the SW interval, with this divergence being most reliable at the fight parietal ROI.

3.4. EPs to infrequent, matching stimuli The EPs to infrequent, matching stimuli were characterized primarily by a large amplitude, predominantly right parietally distributed, P3b-type P390 ( + 7 0 msec) peak (Fig. 5B). Collapsing across ROIs and task conditions, repeated measures ANOVA performed on amplitude measures made at 1)390 peak latency revealed a significant matching effect ( F (1, 7) = 6.01, P < 0.05), with matching stimuli eliciting larger amplitude potentials. In order to identify how EP activity related to the matching process differed between experimental conditions, difference waveforms were computed (Fig. 10) wherein EPs to nonmatch trials (i.e., those depicted in Fig. 5A) were subtracted from EPs to match trials (i.e., those depicted in 5B). At the frontal ROIs, repeated measures ANOVA performed on peak amplitude measures derived from these difference waveforms revealed a main effect of WM load ( F (1, 7 ) = 10.68, P < 0.02), a significant task type by WM load interaction ( F (1, 7 ) = 9.26, P < 0.02), and a significant WM load by hemisphere interaction ( F (1, 7) = 20.7, P < 0.005). In post hoc comparisons of means at the left frontal ROI, the P390 in the verbal control task was larger in amplitude than that for all of the other 3 task conditions, but no significant differences were found in post hoc compatistms for the right frontal ROI. Repeated measures ANOVA performed on peak amplitude measures derived from these difference waveforms at the parietal ROIs yielded a main effect of WM load ( F (1, 7) = 28.56, P < 0.001), with peak amplitude significantly reduced in the WM tasks relative to the control tasks, but no other effects approached significance (all Fs < 2). In sum, the P390 in difference waveforms was selectively enhanced in the verbal control condition at the left frontal ROI. This enhancement might partly reflect an effect of subtracting out temporally overlapping sustained positivities (such as the P450) that were relatively larger to non-matching stimuli in the WM tasks. It might also be partly due to differences in the number of trials included in the average EPs elicited by matching versus non-matching stimuli. The P390 was also larger in amplitude at the parietal ROIs for

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the control tasks relative to the memory tasks, a difference that was apparent even in the non-subtracted waveforms (Fig. 11).

3.5. Pre-stimulus potentials Visual inspection of grand average (across subjects) prestimulus waveforms (Fig. 12) revealed a small amplitude, slowly ramping contingent negative variation (CNV) that was largely limited to central sites and that showed maximum differences between the 4 task conditions near the vertex. The CNV was larger in the control conditions than in WM conditions and it did not discriminate between verbal and spatial task conditions. Preliminary analyses at the frontal and parietal ROIs did not reveal any significant effects. Thus, as with the P200 described above, given that its distribution was largely isolated to the vertex, an ROI was defined with Cz as its center and extending to one electrode from Cz in every direction; measures of CNV amplitude at stimulus onset were submitted to a WM load by task type repeated measures ANOVA. A significant effect of WM load was obtained ( F (1, 7) = 26.99, P < 0.001), with the CNV being enhanced in the control tasks relative to the WM tasks. No effects of task type or WM load by task type interaction were observed (both Fs < 1).

4. Discussion

Low temporal resolution functional imaging methods such as PET and fMRI paint a fairly static picture of the cortical regions which participate in WM tasks (cf. Jonides et al., 1993; Paulesu et al., 1993; Petrides et al., 1993a,b;

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Cohen et al., 1994; Smith and Jonides, 1995). The finegrain temporal resolution and improved topographic detail provided by high resolution electroencephalography make it possible to measure the subsecond changes in the activity induced in distributed regions of cortex by tasks which impose significant WM demands. This information about the temporal dynamics of WM provides a critical complement to the fine-grain spatial resolution provided by other imaging modalities. For example, in the current study, the higher time resolution provided by imaging brain function with computed cortical potentials provided evidence for dynamic changes in the spatial distribution of task WM load effects over the course of individual trials, as well as evidence for differences between activity elicited by matching and non-matching stimuli within blocks of trials. These observations and their possible significance are discussed below.

4.1. Task-related modulation of early "visual" EPs In the present study, early posterior components of the visual EP (P1/N1) did not discriminate between the various task conditions. In prior studies in which visual attention has been cued to particular spatial locations, stimuli appearing in those locations have been observed to elicit enhanced P1/N1 potentials relative to stimuli appearing in other spatial locations (e.g. Mangun and Hillyard, 1989). In the current study, although stimuli appeared in different spatial locations in all task conditions, no cue was provided to indicate what particular spatial location an upcoming stimulus would appear in. Thus, performance in all conditions of the current study required relatively diffuse spatial attention rather than the orientation of attention to a particular location. The lack of any differences in the P1/N1 in this study might be viewed as evidence that the 4 tasks employed were well matched on basic perceptual requirements. In contrast, the tasks clearly differed in their cognitive requirements, as evidenced by the fact that by the time of the P200 the waveforms had begun to reflect the task manipulations. The task-related modulation of P200 amplitude was maximal near the vertex, and it was sensitive both to the spatial vs. verbal dimension of the tasks, and to the WM vs. control dimension. In particular, its amplitude was larger in the spatial tasks than in the verbal tasks and, within each task type, its amplitude was larger in the WM tasks than in the control tasks. It did not, however, discriminate between matching and non-matching stimuli, which suggests that the process it represents is not involved with match detection per se. Prior studies of memory for visual stimuli have reported a similar central P200 peak that was sensitive to differences in task conditions that were equivalent on perceptual parameters (e.g. Van Petten et al., 1991; Smith, 1993), indicating that the amplitude of this peak is mainly related to endogenous factors. It has previously been suggested (Smith, 1993) to be related to the engage-

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merit of a neural system involved with allocating attentional or WM resources. In the context of the current study, this would suggest that the spatial tasks (and in particular the spatial WM task) placed a heavier demand on such resources than did the verbal tasks. This hypothesis would be consistent with the fact that the longer latency EPs to non-matching stimuli were typically somewhat larger in amplitude for the spatial WM task than for the other tasks. However, it is at odds with the fact that behavioral performance did not greatly discriminate between verbal and spatial tasks. An alternative explanation for the P200 effect is that its amplitude is sensitive to differences in neurocognitive strategy, and that subjects were employing different neurocognitive strategies for performing the two types of tasks. For example, during post-test debriefing, most subjects reported using a conventional rote rehearsal strategy in the verbal WM task, in which the current sequence of letter names were cycled until a new stimulus occurred. In contrast, many subjects reported developing some form of analogue or visuospatial strategy in the spatial WM task. From this perspective, the smaller amplitude of EPs in the verbal conditions would suggest that the familiar rote rehearsal strategy involved different neural mechanisms than any novel analogue or visuospatial strategy that subjects might have employed in the spatial tasks.

4.2. Working memory-related spatiotemporal modulation of EPs to frequent, non-matching stimuli The sequencing information provided by the subsecond temporal resolution obtained in this study permits inferences about the role of different cortical regions in the effortful process of representing information in WM. For example, the P305 peak that reached maximum amplitude over right dorsolateral frontal cortex occurred shortly after stimulus presentation and several hundred milliseconds before a response had been executed. Given this time course (i.e., it appears during an interval when the stimulus was presumably being evaluated and a decision formulated), this signal might reflect encoding and analysis processes rather than activity specifically related to the maintenance of temporary representations in WM. On the one hand, the right-sided asymmetry of the response and its specificity to the spatial WM task can be interpreted as being consistent with results from imaging studies which have interpreted increases in activity in the right hemisphere during spatial tasks as evidence for functional specialization for spatial information (e.g. Jonides et al., 1993; Petrides et al., 1993a). However, the amplitude difference between hemispheres that was observed for this potential was relatively small, and although the signal was largest in the spatial WM condition, the asymmetry of the response did not interact with the verbal versus spatial dimension of the task. This lack of an interaction suggests that the increase could instead be due to the relative

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unfamiliarity of the task of continuously monitoring a sequence of spatial locations for the occurrence of matching stimuli. Indeed, other evidence suggests that the lateral regions of the frontal lobe, with some weak right hemisphere asymmetry, play an important general role in an attentional system necessary for maintaining vigilance in matching detection tasks (Posner and Rothbart, 1992). The P450 potential over left dorsolateral frontal cortex in both verbal and spatial WM conditions more strongly indicates that to some degree similar neural processes were engaged by both tasks. Other neuroimaging studies have demonstrated left frontal activation occurring in tasks that require sustained attention to sequences of information (Gevins et al., 1987, 1989; Sergent et al., 1992; Paulesu et al., 1993; Petrides et al., 1993b; Cohen et al., 1994), and lesions to this region of the brain disrupt the ability to remember sequences or to hold plans for sequential behavior in WM (Petrides and Milner, 1982; Shallice, 1982). Thus, the common pattern of left frontal activation in both the verbal and spatial WM conditions in this study may be associated with cognitive processes involved with the mental tracking of stimulus sequences. The sustained PSW over parietal cortex followed stimulus evaluation and response preparation, and it occurred during an interval when representations of spatial and verbal information were being maintained during the delay between stimuli. Given the difference in distribution between this potential and the prestimulus CNV, it appears unlikely to be associated with preparatory processes. Posterior slow potentials (both negative and positive) have also been observed in other types of WM tasks (e.g. Ruchkin et al., 1990a, 1995), where they have been observed to vary with WM load and where they have been suggested to reflect storage processes rather than more general preparatory processes. In the current study, the common distribution of this effect for both spatial and verbal WM suggests that it was not produced by modality-specific storage of representations, although it may reflect some more general process related to the persistence of information in WM. One possibility is that PSW is closely related to attention rather than being specific to memory per se. This interpretation is consistent with other results indicating that networks engaging this region of the brain are critical for the deliberate and sustained allocation of attention to task performance. Working memory 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). Similarly, the ability to selectively allocate attention and to maintain a state of vigilance is frequently observed to be impaired after right hemisphere lesions (Posner and Peterson, 1990). Lesion studies in humans have indicated that electrical fields generated in posterior cortex are modulated by inputs from the frontal cortex (presumably in conjunction with the strategic allo-

cation of attention, cf., Knight et al., 1989; Yamaguchi and Knight, 1990). Similarly, studies of transient cryogenic suppression of prefrontal neurons in non-human primates have provided evidence for feedback from prefrontal to posterior parietal regions during performance of WM tasks (e.g. Goldman-Rakic and Chafee, 1994). Thus, it is possible that the PSW is produced by sustained task-related input from association fibers originating in anterior cortical regions. Alternatively, thalamo-cortical circuits involved with attentional processes (cf., Crick, 1984) might be responsible for the effect.

4.3. Working memory and "P300' '-type evoked potentials These data have implications for widely held views about the relationship between the "P300" potentials and WM. For example, there have been many claims in the literature that the P300 reflects a process invoked when an internal model of the environment that is being held in WM is updated (Fabiani et al., 1986), and further that the larger the amplitude of the P300, the larger the change made to the internal model (Donchin and Coles, 1988). In the current study, the posterior P390 potential elicited by infrequent matching events was smaller in the WM conditions. This was true even though the WM tasks specifically required that an internal representation be updated on each trial and no such memory updating was necessary in the control conditions. Thus, these data provide little support for the notion that the amplitude of the "classical" probability-sensitive, posterior P300 potential necessarily reflects a WM updating process. For the most part, these proposals about the relationship of the P300 and WM processes were made with the more or less explicit assumption that this potential represents a unitary phenomenon, and they were derived primarily from studies of the central/parietal P300 to infrequent matching events that made little or no attempt to assess task-specific topography. However, a wide variety of compelling evidence suggests that the P300 is better construed as a whole class of potentials that share family resemblances, but that vary in their regional distribution and task correlates (e.g. Knight, 1984; Halgren et al., 1986, 1995a,b; Johnson, 1989a,b; Knight et al., 1989; Ruchkin et al., 1990b; Smith et al., 1990; Yamaguchi and Knight, 1991; Gevins and Cutillo, 1993, 1995; Rogers et al., 1993; Verleger et al., 1994; Baudena et al., 1995; Gevins et al., 1995). In the current study, consistent with neuropsychological (e.g. Goldman-Rakic, 1987, 1988; Fuster, 1989) and computational (e.g. Kimberg and Farah, 1993) theories of the role of frontal cortex in maintaining accurate temporary representations in WM, the P450 component elicited by frequent, non-matching stimuli was enhanced at left frontal recording sites in the WM conditions relative to the control conditions (this was also true for the right frontal P305 in the spatial WM condition). Thus, while these data suggest

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that some positive peaks (with a morphology and latency that would justify labeling them as P300s or late positive components) are enhanced by task conditions that require updating representations in WM, these task correlates are not generalizable to all members of this class of EP component. In particular, an example has been shown of a case in which the classical posterior P300 component is not augmented by context updating in a well-controlled WM task. In the context of studies of task difficulty and the P300, the claim has frequently been made that P300s elicited by discrete events embedded within a primary task in dual task contexts increase in amplitude with increases in primary task difficulty (Kramer et al., 1987), whereas the amplitude of the P300 occurring in a secondary task has been shown to be inversely related to primary task difficulty (e.g. Donchin et al., 1986; Gopher and Donchin, 1986). In the current study, the P390 potential was smaller in amplitude in the WM tasks than in the control tasks, and behavioral data provided unambiguous evidence that the WM tasks were more difficult than the control tasks. However, the implications of this finding with respect to proposals about the relationship of the matching P300 to primary task difficulty (cf. Kramer et al., 1987) are ambiguous, because in the WM task employed here subjects were required to meet both a memory maintenance/memory updating task demand and a simultaneous match/nomatch task demand, and it is unclear which of these demands a subject might weight as "primary."

4.4. Working memory and preparatory processes In addition to the stimulus-following EPs, a prestimulus CNV potential was also observed in the current study. As noted above, although it was sensitive to differences in WM load between task conditions, its distribution was different from that of other WM-sensitive slow potentials such as the PSW. Consistent with other studies of slow potentials during WM tasks (cf. Ruchkin et al., 1995), the central distribution of the CNV in this study suggests that it may reflect differences in preparatory processes between the WM and control tasks. In particular, the finding that the amplitude of the CNV was reduced in the WM conditions, in combination with the observation that responses were slowed in these conditions, suggests less attention was allocated to response preparation in the WM tasks than in the control tasks. This finding is in contrast with results from a prior study of the electrophysiology of WM reported by Gevins and Cutillo (1993), where the prestimulus CNV was increased in a WM task relative to a control task. This difference is likely due to large differences in response requirements from those in the present experiment. Specifically, in the prior study, rather than a simple y e s / n o decision, responses took the form of a graded pressure

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response exerted on a force transducer, and they followed stimuli which took the form of a single digit number indicating the force to be applied. In that WM task, the response force was to correspond to the stimulus number which occurred two trials previously, whereas in the control task the response force was to correspond to the immediately preceding stimulus number. Thus, in the previous experiment, in the WM task it was possible to prepare the response in advance of perceiving the stimulus number, but this condition was not true of the control task. In contrast, in the current study, it was necessary to wait until the stimulus was fully analyzed before initiating a response, and this analysis was significantly more difficult in the WM conditions than in the control conditions.

4.5. Working memory and task-specific distributed functional networks As noted in the Introduction, it is becoming increasingly common to view WM as a fluid, distributed process that is closely related to attentional functions (cf., Schneider and Detweiler, 1988; Just and Carpenter, 1992). Although studies of brain lesions or studies of brain activation that utilize low temporal resolution imaging methods can provide evidence of the importance of particular cortical regions to WM, studies of individual neurons in nonhuman primates reveal the dynamic, task-specific character of WM-related changes in the activity of local cell populations. That is, studies of the electrophysiology of WM reveal that the neuronal activation associated with the internal representation of information over short delays is sensitive to momentary within-task events and is modulated by the allocation of attention to different stimulus attributes and task requirements (e.g. Fuster and Jervey, 1981, 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). The task specificity of WM modulation of regional cortical activity appears also to be reflected in studies of scalp recorded EPs. For example, in addition to the CNV differences in the current study and those noted above in the prior EP study of WM reported by Gevins and Cutillo (1993), other task-specific differences were also apparent. Specifically, on match trials that required responses to be inhibited, Gevins and Cutillo (1993) observed a decrease in the latency and an anterior shift in the distribution of a P3 potential in a WM task relative to a control task, although no such effect was observed in the present study. Although the Gevins and Cutillo (1993) study and the present one shared the requirement of continuously maintaining and updating representations in WM, the type of information being represented in the two studies was very different, as were the decision and response requirements. Such task-specific regional modulation of EPs suggests that the diverse observed effects of WM loading reflects

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the recruitment of distributed activity into task-specific functional networks by the effortful attention required to meet the demands and contingencies of particular behavioral contexts (cf., Gevins et al., 1983, 1987, 1991; Bressler et al., 1993; Gevins and Cutillo, 1993; Fuster, 1995). Even so, there appear to be limits to this task specificity. For example, in the current study, it was possible to compare conditions where perceptual and response characteristics were kept constant while the type of information being maintained in WM (spatial vs. verbal) was varied. Under such conditions, although some differences between spatial and verbal tasks were observed, the data were striking in the amount of similarity of waveforms (and their topography) between the two types of tasks. This similarity suggests that the functional networks that arise during performance of specific WM tasks interact with a system common to all attention-demanding tasks.

Acknowledgements We thank Don DuRousseau, Vinod Menon, Paulo Raffaelli and Jenny Zhang for their contributions to the research. Brian Cutillo, Terry Allard and Patricia GoldmanRakic provided advice on the design of the experimental task. This research was supported by grants from the Office of Naval Research, the Air Force Office of Scientific Research, and the National Institute of Mental Health of the U.S. Government.

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