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The effects of caffeine (250 mg) and placebo on healthy controls were studied in a double-blind, cross over study on 24 healthy subjects who performed a working memory n-back task. Reaction time and accuracy levels were tested using the n-back working memory measure in cognitive neuroscience. An experimental study tested on the 1, 2 and 3-back tasks under the placebo/coffee condition. Based on the empirical results obtained in this study it can be concluded that changes produced by caffeine ingestion support the hypothesis that caffeine acts as a stimulant. However, it cannot be proven that the stimulant translates into enhanced motor processes with an improvement in performance.
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Effects of Caffeine on Cognitive Tasks
Lorraine Valladares (Lorraine.Valladares@RMIT.edu.au)
School of Electrical and Computer Engineering, RMIT University
GPO Box 2476V, Melbourne, Victoria, Australia 3001
Irena Cosic (Irena.Cosic@RMIT.edu.au)
College of Science, Engineering, and Technology, RMIT University
GPO Box 2476V, Melbourne, Victoria, Australia 3001
Anthony Bedford (Anthony.Bedord@RMIT.edu.au)
Sport Statistics Group, RMIT University
PO Box 71, Bundoora, Victoria, Australia 3083
Abstract
The effects of caffeine (250 mg) and placebo on healthy
controls were studied in a double-blind, cross over study on
24 healthy subjects who performed a working memory n-back
task. Reaction time and accuracy levels were tested using the
n-back working memory measure in cognitive neuroscience.
An experimental study tested on the 1, 2 and 3-back tasks
under the placebo/coffee condition. Based on the empirical
results obtained in this study it can be concluded that changes
produced by caffeine ingestion support the hypothesis that
caffeine acts as a stimulant. However, it cannot be proven that
the stimulant translates into enhanced motor processes with
an improvement in performance.
Keywords:. Caffeine, placebo, mean response time (MRT),
accuracy (ACC) n-back, cognition, cognitive tasks, working
memory (WM).
Introduction
The aim of this research was to determine whether caffeine
enhances cognition in healthy subjects. Prior to this research
work healthy subjects have not been assessed in sufficient
detail. To this end it assesses (i) the effect of 250 mg of
caffeine on mean response time (MRT) and (ii) accuracy in
normal healthy human controls.
Numerous studies have examined the
psychopharmacological and electrophysiological effects of
caffeine on the human brain and heart (Bruce et al., 1986).
Caffeine has been tested to assess effects on sleep patterns,
arousal, and its enhancement effectiveness in enhancing the
effects of analgesics (Richardson et al., 1995).
Drinking a cup of coffee is a daily pleasure for millions of
people around the world with an average individual
consumption estimated at around three cups per day.
Caffeine has been found to enhance mental performance,
mood, and vigilance (Barry et al., 2005). Research findings
also present a great body of evidence on the medical aspects
of caffeine enhancement on patients suffering from bi-polar
disorder, schizophrenia, and depression (Coffey et al., 1990;
Callicott and Ramsey, 1998; and Callicott et al., 2003).
However, there is comparatively little literature available
on the effects of caffeine on healthy subjects with no
medical impediments. Hence, this research proposed to
answer the following question: Can a certain dosage of
caffeine ingestion measurably enhance cognitive functions?
Few studies have examined the effects of caffeine on
cognition on healthy individuals.
Caffeine is widely consumed throughout the world for a
variety of reasons, including its stimulant-like effects on
mood and cognitive performance (Fredholm et al., 1999 and
Liberman et al., 1987). The purpose of this study was to
investigate the possible effect of caffeine on cognitive
neural function in healthy human volunteers.
Caffeine absorption from the gastrointestinal tract is rapid
and reaches 99% in humans in about 45 minutes after
ingestion (Marks and Kelly, 1973). Peak plasma caffeine
concentration is reached between 15 and 120 minutes (mins)
after oral dosage, and therefore, it can be estimated that
peak concentration is reached after 30 mins of ingestion.
One effect of caffeine is the ability to manifests itself in
lengthening the post firing duration in the hippocampus; this
effect lasts longer than the changes induced by caffeing on
the EEG (Kenemans and Lorist, 1995).
Working Memory
Working memory (WM) refers to a system which enables
temporary storage and manipulations of information within
the context of cognitive activity (Baddeley and Hitch,
1974). Baddeley and Hitch characterised WM as a type of
mental workspace composed of 3 sub-systems:
(a) Central executive involved in control and selection
process
(b) A buffer responsible for maintaining acoustically-
coded information
(c) A buffer responsible for maintaining visual and
spatial information.
The present study attempted to clarify whether WM is
improved or enhanced in any way with ingestion of a
controlled amount of caffeine. The fundamental
characteristic of WM, are well known. Working memory
capacity to handle information is limited; the physiological
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basis of this limitation has not been explained and is still
being explored extensively.
N-back
Figure 1: An example of a trial illustrating the schematic
representation of the 3WM (n-back task). Each subject
performed 20 practice trials, before performing 90 trials in
the test, re-test sessions.
The n-back was used to test WM. The task involved a
number of stimuli that must be held in the mind at any one
time, to be varied parametrically (Owen et al., 2005). Figure
1 outlines a series of stimuli, in the present case letters, and
participant had to match and identify the stimuli 1, 2, or 3
previously seen
.
Methodology
Twenty four healthy (non smoking) volunteers aged
between (19-38 years) participated in this experiment with a
mean age of (26.5), with no history of psychiatric disease.
All subjects gave written informed consent to take part in
the study, which was approved by the Human Research
Ethics Committee, Swinburne University of Technology.
Study Design
A double blind, counter-balanced, placebo controlled, cross-
over was used. Each participant was tested under two
different drug conditions [placebo and caffeine (250mg)]
separated by a seven-day ‘wash-out’ period. The doses
selected were based on previous research that found
significant behavioral effects at this dose (Barry et al.,
2005), but being low enough to minimize the possibility of
side-effects, such as nausea, which could confound the
results. Upon arrival, participants were provided with a
standard lunch to reduce the possible nausea caused by
caffeine administration
.
N-back task was a stimuli, which in this study was a
single white consonant presented for 500 ms each every 3s
in the middle of a black computer screen (Koivisto, Krause
et al., 2000). The letter case was alternated at each
appearance of each particular letter of the alphabet (e.g. z-b-
Z-B). Letter case was treated as irrelevant, e.g., “g” and “G”
were defined as matching. The rationale for alternating
letter-case is to force participants to remember letters by
their meaning rather than their shape (Levin et al., 2002).
The 1, 2 and 3-back tasks were also administered in a
counterbalanced order, so that the effects of memory load
were not confounded by caffeine/placebo condition.
Results
Mean reaction time and accuracy data were collected and
analysed by a two-way analysis of variance (ANOVA)
testing the effects of ‘groups’ (A and B). Group A consisted
of participants consuming placebo first and coffee in the
second session. Group B involved participants who
consumed coffee first in session one and placebo in session
two. The term ‘drug’ will refer to coffee or placebo.
ANOVA testing was conducted to determine any
significant differences between MRTs for the groups, n-
backs, treatments, and their interactions. The data was split
into the four groups (placebo first, coffee first, placebo
second, coffee second), with all passing the Kolmogorov-
Smirnov normality tests (p > 0.05), meeting one of the
underlying assumptions of the ANOVA test. Posthoc
analysis was conducted utilising Tukey’s Honestly
Significant Difference (HSD) test. Significant differences
were found amongst all 3 n-back task comparisons (1 versus
2: p<.012, 1 versus 3: p<.001, 2 versus 3: p<.01).
Figure 2: Behavioural data presented for visual comparison:
95 % Confidence Interval for the mean response time MRT
for placebo and coffee ingestion, estimated by n-back: (1-,
2-, and 3- back). Data was collapsed across the different
treatment conditions (coffee or placebo for all 24
participants over 2 sessions = 48 experiments). The error bar
range suggests that reaction time increased with working
memory load.
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Figure 3: Percentage increase in response time. MRTs for
all 3 trials: 95% confidence interval for the MRT for coffee
first vs. placebo first by n-back: 1-back, 2-back, and 3-back,
across task conditions. Participants taking coffee first (green
bar) had a significantly lower MRT for all n-back
experiments. This does not account for whether or not the
group ingested coffee or placebo, Group A or B, rather
provides the MRT of all their experiments. This indicates
that being in the coffee first group significantly impacts
MRT, irrespective of treatment.
Figure 4: Activity data for n-back MRT of performance
across the levels of the n-back task for all 4 sessions
.
These results
depict the trend irrespective of coffee
ingestion, first or second. However, the placebo plot shows
that the placebo first group exhibited a markedly higher
MRT compared to the coffee first group. Placebo first and
Placebo second, exhibits an inverse result with placebo
being quicker than coffee, (a lower MRT). This weakness
was identified in the previous figure (Fig. 2).
Figure 5: Behavioural data presented for visual comparison:
95% confidence interval of the mean response time for all
subjects (grouped) by n-back: 1-back, 2-back, and 3-back.
Data displayed across task conditions and collapsed across
working memory task.
Figure 6: Average mean percentage accuracy levels of
Group A and B (4 sessions) by n-back task. Accuracy Data
for the 3 levels of task difficulty. This provides a pictorial
image where the coffee second group performed better than
coffee first.
MRT gradually increased with WM load and 3-back
proving the most difficult task, with the longest MRT.
Retrieval decreased as n increased in all variants of the n-
back task.
As previously plotted n-back 1, has the highest level of
accuracy. Figure 6 illustrates the mean percentage accuracy
for each of the four sessions by n-back. The coffee second
group had the highest mean accuracy for n-back 1, whereas
the placebo second had the highest accuracy for n-back 2
and 3.
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An ANOVA was conducted on mean accuracy levels to
determine if there was any significant difference between
the groups. A full interaction model was utilised. The only
significant difference occurred in relation to the 3-back F(2,
143) = 64.241, p= .001 indicating, as shown in the Fig. 7.
Treatment (coffee or placebo) and Group (1 or 2) showed no
significant differences in mean accuracy level F(1,143) =
2.834, p =.095; F(1,143) = 2.437, p = .121.
Figure 7: Plots the n-back by groups. Visual complexity of
accuracy levels within the groups. Average accuracy for
each of the groupings presented and the proportion of drop
in accuracy levels whilst performing 3-back which had the
greatest difficulty.
Discussion
Caffeine was associated with a significant increase in
alertness. However, there was no significant enhancement
on cognition. There was no significant relationship between
the intake of caffeine and cognitive task. Analyses between
caffeine/placebo conditions, found significant results in 1-
back and 2-back. On the other hand, results were not
significant with n-back 3. This performance decrement
could be due to familiarity of content, and counteracted by
caffeine (Deslandes et al., 2005). Briefly summarising,
across subjects, accuracy was higher, and RT faster, in the
low-load WM task compared with the high-load WM tasks.
Optimal level of performance was achieved with caffeine,
when comparing the two groups in Fig. 2, providing support
for the hypothesis, that caffeine improved response time.
Whereas MRT was slightly higher for the coffee group in
the 3-back task, this could be attributed to memory load or
other variables. It can be clearly observed that MRT
increases with memory load of n-back 3 in both group
conditions. As the task difficulty and memory load
increased, reaction time also increased. The 3-back task
required judging whether an item matched any item up to
and including 3-back. Reduced MRT suggests that three
items could not be effectively maintained in focal attention.
These results indicate that focal attention has a much
smaller capacity than has typically been assumed (Cowan,
2008).
The ANOVA results supports earlier findings which
indicated that taking coffee first, then a placebo had some
effect in the second test. Although the task was
counterbalanced across subjects so as to control for task
practice effects, it still seemed that group B fully or partially
were alert in session one to enable familiarity in Session 2.
This however did not occur when the group ingested
Placebo first. There was no speed-accuracy trade off and
accuracy since all subjects found the 3-back task difficult in
all 4 sessions and RTs were slower. Data shows that the n-
back judgements are in part mediated by a search process,
and that the complexity of the search depends on ‘n’. Inter-
subject variability poses a different problem, in that no
standard method has emerged for reliably comparing
activity across subjects (Braver et al., 1997).
A possible explanation of these results could be due to
testing bias, that is, exposure to the n-back test originally
leads to better results the second time. This could be due to
the nature of the test, rather than any treatment effects.
Therefore, it is reasonable to conclude that having practised
the n-back task over 90 trails, thrice, familiarity of content,
enables participants to improve MRT in group having
placebo second, rather than the effects of treatment? Perhaps
the measures are not reflective of arousal rather indicators of
task related difficulty.
As expected, increasing WM load was associated with
declining accuracy and performance tended to decrease as
memory load increased. Performance declines continuously
with increased task load. The behavioural findings also
indicate that both accuracy and speed declined
monotonically with increases in task load. Note that the 3-
back task differs considerably. The statistical analysis of
Smith and Jonides stated that there are 22 significant sites of
activation in both 2-back and 3-back tasks, but only 2
significant areas in 0, and 1-back (Smith and Jonides, 1997).
These results support previous studies that as memory load
increases, more areas in the brain are recruited to perform
the task.
As was mentioned above, the primary purpose of this
study was to determine whether caffeine improved
cognition. The empirical results obtained did not support a
strong correlation. The second aim was to test whether
caffeine improved accuracy and this objective was
accomplished by comparing the behavioural data obtained
during the WM task performance (Gevins et al., 1996 and
Gevins et al., 2000). With respect to the performance data,
significantly shorter response times were recorded for the
caffeine rather than the placebo condition.
Koppelstaetter et al. (2008) concluded that the
modulations seen in specific cortical regions suggest an
effect on brain areas engaged in specific cognitive processes
rather than a general effect due to the influence of caffeine
on the vasculature. The Koppelstaetter study used functional
magnetic resonance imaging (fMRI) with a focus on
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caffeine users. The most relevant aspect of Koppelstaetters’
study was the confirmation that “caffeine had no significant
effect on cognitive performance,” which matched our
experimental results. Our study differs from the
Koppelstaetter study in that it used healthy subjects and an
increased dosage of 250 mg (as against 100 mg).
Conclusions
Based on the empirical results obtained in this study it can
be concluded that changes produced by caffeine ingestion
support the hypothesis that caffeine acts as a stimulant.
However, it cannot be proven that the stimulant translates
into enhanced motor processes with an improvement in
performance. Improved performance through ingestion of
caffeine may be evident in a fatigue situation. However, to
verify this assumption additional studies are needed to better
understand the mechanisms of how caffeine influences WM,
as the underlying fundamental processes are still unclear. In
the present work, caffeine showed little effect on
performance and it can be suggested that caffeine had no
large effects on cognitive tasks.
Future Work
Coffee as a beverage and its popularity in society definitely
warrants additional investigation. Consequently, large scale
studies need to be undertaken to affirm caffeine’s possible
effectiveness on specific cognitive functions and working
memory. Testing would require replication, and inclusion of
a third session that would result in a broader range of scores.
Investigators would have to be mindful that the dual-task
nature of the n-back, such as encoding, matching,
responding, updating, storing and rehearsing demands, vary
greatly between individuals due to (demographics,
education and social status). This perhaps may pose as a
potential problem. To date, little is known about the
sequence of events or neural pathways whilst performing
the WM task. Although calculation of response times and
accuracy levels assist to a degree, further studies are
required to account for subtleties. This thesis offered one
more account to add to its underlying processes.
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... Owing to its ability to permeate all biological membranes, including those of the blood-brain barrier and the placental barrier, caffeine has the ability to affect the body speedily: peak plasma concentration occurs approximately 15 -45 minutes after ingestion Snel, Lorist, and Tieges, 2004). Other researchers contend that peak plasma caffeine concentration is reached between 15 and 120 minutes after oral dosage (Valladares, Cosic, and Bedford, 2009).Further support comes from Lorist & Tops (2003), who state that peak plasma concentrations are reached in about 30-60 minutes after consumption. Once caffeine is ingested, it is equally distributed in total body water. ...
... This allowed sufficient time for the stimulant to take effect before alcohol was consumed. This criterion was based on the works of Maisto et al., (1991) who contend that caffeine takes approximately 15 -45 minutes to affect the central and peripheral nervous systems and this criterion is further supported by (Marks & Kelly, 1973;Valladares, Cosic, and Bedford, 2009). Upon the completion of test battery 1 (pure caffeine), participants from the caffeine+alcohol group were required to consume the alcohol dose. ...
Thesis
Full-text available
Despite extensive research into the effects of alcohol consumption, there is no clear understanding into the mechanisms underlying human information processing impairment. The acute consumption of alcohol was investigated to determine the implications for human information processing capabilities, and to identify the extent to which these implications were stage-specific. Further aims included the investigation and quantification of caffeine-induced antagonism of alcohol impairment. Moreover, the aforementioned relationships were investigated in morning versus evening conditions. A test battery of six resource-specific tasks was utilised to measure visual perceptual, cognitive and sensory-motor performance, fashioned to return both simple and complex measures of each task. The tasks implemented were: visual perceptual performance (accommodation, visual detection, visual pattern recognition); cognition (memory recall- digit span); and motor output (modified Fitts’ and a driving simulated line-tracking). Performance measures were recorded by the respective computer based tasks. Physiological variables measured included heart rate frequency, heart rate variability (RMSSD, High and Low Frequency Power) and body temperature. Saccade speed, saccade amplitude, pupil size and fixation duration were the oculomotor parameters measured. Three groups of participants (alcohol, caffeine+alcohol and control) n=36 were studied, split evenly between sexes in a mixed repeated/non-repeated measures design. The control group performed all test batteries under no influence. The alcohol group performed test batteries one and two sober, and three and four under the influence of a 0.4 g/kg dose of alcohol. Group caffeine+alcohol conducted test battery one sober, two under the effect of caffeine only (4 mg/kg), and three and four under the influence of both caffeine and alcohol (0.4 g/kg). The third test battery demonstrated the effects of alcohol during the inclining phase of the blood alcohol curve, and the fourth represented the declining phase. Morning experimentation occurred between 10:00 - 12: 45 and 10:30 -13:15 with evening experimentation between 19:00 - 21:30 and 19:30 - 22:00. Acute alcohol consumption at a dose of approximately 0.4 g/kg body weight effected an average peak breath alcohol concentration of 0.062 % and 0.059 % for the alcohol and caffeine+alcohol groups respectively. Task-related visual perceptual performance demonstrated significant decrements for simple reaction time, choice reaction time and error rate. Cognitive performance demonstrated no significant performance decrements, while motor performance indicated significant decrements in target accuracy only. Physiological parameters in response to alcohol consumption showed significantly decreased heart rate variability (RMSSD) in the modified Fitts’ task only. A significant decrease in saccade amplitude in the memory task was the only change in oculomotor parameters. Prior caffeine consumption demonstrated limited antagonism to task-related alcohol impairment, significantly improving performance only in reduced error rate while reading. Caffeine consumption showed stimulating effects on physiological parameters, significantly increasing heart rate and heart rate variability when compared to alcohol alone. The design of the tasks allows for comparison between complex and simple task performance, indicating resource utilisation and depletion. Complex tasks demonstrated higher resource utilisation, however with no statistical performance differences to simple tasks. Physiological parameters showed greater change in response to alcohol consumption, than did the performance measures. Alcohol consumption imposed significant changes in physiological and oculomotor parameters for cognitive tasks only, significantly increasing heart rate frequency and decreasing heart rate variability, skin temperature and saccade amplitude. Caffeine consumption showed no antagonism of alcohol-induced performance measures. Physiological measures showed that caffeine consumption imposed stimulating effects in only the neural reflex and memory tasks, significantly increasing heart rate frequency and heart rate variability. Prior caffeine consumption significantly decreased fixation duration in the memory task only. The time of day at which alcohol was consumed demonstrated significant performance and physiological implications. Results indicated that morning consumption of alcohol imposes greater decrements in performance and larger fluctuations in physiological parameters than the decrements in evening experimental sessions. It can be concluded that alcohol consumption at a dose of 0.4 g/kg affects all stages in the information processing chain. Task performance indicates that alcohol has a greater severity on the early stages of information processing. Conversely, under the influence of alcohol an increased task complexity induces greater effects on central stage information processing. In addition, caffeine consumption at a dose of 4 mg/kg prior to alcohol does not antagonise the alcohol-induced performance decrements. Key Words: alcohol, visual perception, cognition, motor output, human resource utilisation, caffeine antagonism.
... Fig. 1. Example of the n-back task using letters [31]. ...
... To elucidate the psychometric properties of eye blink rates and microsaccades as measures of cognitive load, Christian and Philipp suggest a repeated-measuresstudy according to the "n-back" paradigm (described and applied in Bedford et al. 2009). Here, participants are presented with digits one after another and need to recall elements that came earlier. ...
Chapter
Neurodesign is a novel field of research, education and practice that emerges as a cross-disciplinary initiative. In 2019, the Hasso Plattner Institute (HPI) offered for the first time a neurodesign curriculum. The objective of neurodesign as we pursue it is to explore synergies at the intersection of (i) neuroscience, (ii) engineering and (iii) design thinking · creativity · collaboration · innovation. In this chapter, we share insights into the development of a curriculum that quickly became more comprehensive than we had anticipated for this initial implementation phase. Neurodesign evolves serendipitously driven by the passions of numerous protagonists who contribute their expertise, ideas and work results in a uniquely collaborative fashion. The chapter briefly summarizes input provided by neuroscientists and creative engineers from several countries and different continents, who contributed guest expert talks at the HPI to help build up a joint knowledge base. The major part of the chapter is a review of neurodesign projects that have emerged, often in collaboration with guest experts of the program. Overall, these projects indicate how intersections of neurodesign (i)–(ii)–(iii) open up cornucopias of opportunities. Especially the integration of engineering expertise has introduced many favourable dynamics. In terms of strategic reflections, this chapter shares “missions” we pursue in the development of neurodesign. These directions for further initiatives also commence a brief outlook on upcoming neurodesign developments.
... Additionally, overall stimulant effect of caffeine could possibly enhance motor processes that cause the improved performance [43]. This latter point is, however, less likely since previous studies showed no effect of caffeine on human motor functioning [51]; although see [14] for a different view. Further Electroencephalographic (EEG) experiment would be necessary to identify the stage of visual processing that is influenced by caffeine: attention allocation i.e., N2PC (N200 component of posterior contralateral) [52]. ...
Article
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Background Black Tea is a widely consumed drink in the world. Evidence suggest Black Tea has stimulatory effect on humans. We investigated the effect of Black Tea on cognition using a cognitive test battery. Methods Participants (n = 32) were fasted overnight for 10 h and restrained from caffeine and other stimulant drugs for 14 days prior to participation. We randomly assigned participants into either an experimental (n = 16) or a control (n = 16) group. Experimental group consumed 250 ml of Black Tea (BT) while control group was received equal volume of water (W). Participants were tested on the following cognitive tasks: executive function, sustained attention, memory (memory span, immediate, delayed, working memory) and arithmetic calculation task. Results We found that BT group performed significantly (p < 0.05) faster in the executive function task (BT: M = 1671, SD = 319; W: M = 1935, SD = 372); simple reaction time task (BT: M = 333, SD = 87; W: M = 361, SD = 101), identification of target location in the visual search task (BT: M = 925, SD = 50; W: M = 972, SD = 115). We also showed that BT group forgotten significantly (p < 0.05) lower number of words in the delayed memory recall test (BT: M = 1.12, SD = 0.15; W: M = 1.37, SD = 0.33) and made significantly (p < 0.05) fewer errors in the trail making task (BT: M = 0.31, SD = 1.01; W: M = 1.31, SD = 1.66). Conclusions BT consumption speeded the performance, improved memory, reduced number of errors in the various cognitive tasks. Our results further showed that even in small volume of BT consumption can speed up cognitive processing.
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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 course 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 modalities.
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