Does Event Related Desynchronization reveal anticipatory attention in the somatosensory modality?

DOES EVENT RELATED DESYNCHRONIZATION

REVEAL ANTICIPATORY ATTENTION

IN THE SOMATOSENSORY MODALITY?

O. ROMIJN

Does Event Related Desynchronization reveal anticipatory attention in the somatosensory modality?

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Does Event Related Desynchronization Reveal Anticipatory Attention

in the Somatosensory modality?


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Table of contents

Abstract 5

Introduction 6

Anticipatory attention and motor preparation 6

What is attention? 7

A taxonomy of attention 8

The phenomenon of anticipatory attention 11

A neurophysiological model for anticipatory processes 12

The Reticular Nucleus 12

Rhythmic activity and the thalamocortical network 16

Synchronization and desynchronization 18

On the existence of different rhythms 19

Physiological measures in attention research 24

EEG 25

Preparatory processes: slow potentials and ERD 28

Motor preparation 28

Anticipatory attention 30

Aims of the present study 32

Methods 33

Subjects 33

Experimental design & Procedure 33

Experimental design 33

Procedure 35

Apparatus and KR stimuli 36

Apparatus 36

Stimuli 37

Electrophysiological recordings 37

EEG recordings 37

EOG recordings 38


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Data reduction and statistical analysis 38

Artifacts 38

ERD computation 39

Statistical analysis 40

Results 45

Behavioral data 45

Physiological data 45

Premovement data 45

Prestimulus data 48

Discussion 55

Conclusions 61

Recommendations 62

References 64

Acknowledgements 82

Appendix A 83

Appendix B 95

Appendix C 97


5

Abstract

Attention that is directed at an upcoming stimulus is termed anticipatory attention. The extended

thalamocortical gating model (Brunia, 1999) addresses the processes underlying anticipatory

attention. According to this model, both the thalamic relay (TCR) nuclei and the reticular nucleus

(RN) are involved in the selection (i.e. gating) of the relevant sensory modality. The TCR nuclei

can fire in two modes. The tonic mode is associated with the transmission of afferent and

subcortical input to the cortex and leads to desynchronization of 10 Hz rhythmic activity in the

cortical projection area of the TCR nucleus. The burst mode is associated with a disruption in this

transmission and results in the occurrence of 10 Hz rhythmic activity at the cortical projection

area. This implies that event-related changes in 10 Hz activity in the scalp recorded EEG may

give insight into the firing mode of the TCR nuclei and thus into the process of anticipatory

attention. Event Related Desynchronization (ERD, Pfurtscheller & Aranibar, 1977) can quantify

such changes. The extended thalamocortical model states that anticipatory attention is manifest as

a prestimulus activation of the sensory cortex corresponding to the modality of the anticipated

stimulus. Anticipatory attention to somatosensory stimuli would therefore be manifest as a 10 Hz

ERD over the postcentral cortex, whereas anticipatory attention to visual stimuli would be

manifest as a 10 Hz ERD over the occipital cortex. To test this hypothesis 9 subjects performed a

time-estimation task. They received a Knowledge of Results (KR) stimulus 2 seconds after their

manual response. ERD was recorded in the 10 Hz and 20 Hz frequency bands. An occipital ERD

was present preceding visual KR stimuli, whereas no significant postcentral ERD was present

prior to somatosensory KR stimuli. Nonetheless, the statistical analyses indicated that these

differences between conditions were not significant. Therefore, these results do not support the

extended thalamocortical gating model. It can be hypothesized that the postcentral ERD

preceding somatosensory stimuli is masked by a postmovement Event Related Synchronization

(ERS).


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Introduction

Anticipatory attention and motor preparation

How convenient yet unchallenging life would be if man always knew what was going to happen

next, completely destitute of the element of surprise. This is not the case, however, and the

unforeseen allows us to gain new experiences throughout life. In general, knowing what is going

to take place and when this is about to occur does have certain advantages. Imagine an athlete

taking his starting position for the 100 m dash finals. “Now is the time for the months of

preparation to pay off”, he whispers, while his feet meet the starting blocks. One last check. Is

everything in place? A glance at his running shoes makes him feel somewhat uneasy. As he is

still in doubt whether he should tie the laces more firmly, the starting pistol is fired. The starting

shot takes him completely by surprise and when he raises his head he realizes that he must drag

himself to his inevitable defeat.

This is not a desirable situation for an athlete. How then can this athlete improve his

performance? Attention seems so be the answer. According to Laberge (1995) attention can

increase the accuracy (and speed) of perceptual judgments by selecting information flow on the

input side of cognitive processing, and increase the accuracy (and speed) of actions on the output

side of cognitive processing by selecting information flow in the organizing and planning of both

internal and external actions. Therefore, the athlete would benefit from paying attention to the

starting gun (input side) and from preparing for the action that must be initiated by its firing

(output side).

Preparing for events, that will take place in the near future and call for action, is often referred to

as anticipatory behavior. As the example of the athlete indicates, anticipatory behavior

encompasses at least two factors (see figure 1): anticipatory attention and motor preparation. By

enhancing perceptual processes anticipatory attention may result in a better performance: the

sooner the athlete becomes aware of the gunshot, the better he is able to commence running at the

right moment. For the actual running to take place in a coordinated and appropriate manner,

however, another process comes into play: the process of motor preparation. Motor preparation

centers round planning and organizing internal and external actions.


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Anticipatory attention Motor preparation

Anticipatory behavior

Figure 1. Two components of anticipatory behavior.

Although motor preparation will be touched upon, this paper focuses on anticipatory attention

related to visual and somatosensory events. For a more detailed description of the purpose of this

investigation, the reader is referred to the section “Aims of the present study”.

The ensuing section provides an introduction to the concept of attention followed by a section on

the classification of attentional processes in order to elucidate the work domain of this thesis. For

a more thorough understanding of the concept of attention, the interested reader is referred to

Appendix A, which provides a concise history of the concept of attention.

What is attention?

“Everyone knows what attention is. It is the taking possession by the mind in clear and vivid form

of one out of what seem several simultaneous objects or trains of thought ... It implies

withdrawal from some things in order to deal effectively with others…”(William James, 1890, p.

403-404).

Perhaps the opening sentence of this chapter should have been “Nobody knows what attention is”.

Being familiar with the manifestations of attention does not define attention itself. Describing

attention somewhat resembles explaining the meaning of the word love to an alien. Experiencing

love is one thing, putting your finger on it is another. The main difficulty with defining attention

is that attention is not a single concept, but a term for a wide variety of psychological phenomena

(e.g. Styles, 1994, p. 1).


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It seems that the goals of attention are less disputed than its definitions. Laberge (1995) states that

being able to pay attention has three major benefits for an individual: accuracy, speed and

maintenance of mental processing. Accuracy in making a perceptual judgment is ordinarily not a

problem for the individual when the object is the only item in the perceptual field. Difficulties

sharply increase when other objects are in the vicinity, because information arising from

distractors can confuse one’s judgment of the target object. By selecting information flow on both

the input side and the output side (behavior) of attention one can respectively increase accuracy in

perception and accuracy of (future) actions. Attention also increases the speed with which

perceptual judgments and the planning and performance of actions take place.

In other words, attention enables us to focus on certain relevant features of the environment or

inner-self and to attenuate or exclude other features. Since only relevant features have to be taken

into account selection plays a crucial role in attention processes. How selection plays a role in

attention has been the subject of discussion for several decades. Until now the mechanism is not

fully understood.

Unfortunately, attention research is characterized by a great amount of different points of view,

research paradigms and theories. This, combined with the inconsistent terminology –the

terminology used to describe the subject of attention varies from one researcher to another- turns

the field of research into a maze of seemingly disorganized facts. In order to outline the work

domain of the present investigation, it is useful to categorize similar points of view and theories.

In the next section a concise taxonomy of distinguishable categories of attention will be

described.

A taxonomy of attention

As early as 1890 James noted that “Attention may be either … passive, reflex, nonvoluntary,

effortless: or … active and voluntary” (James, 1890, p. 416). This distinction still holds and, in

essence, boils down to the issue of exogenous and endogenous processes, respectively. Passive

attention and its derivatives are thought to be elicited by the changing environment whereas

active attention is often referred to as being voluntary, thus stemming from within the individual

(e.g. Posner, 1980). Both passive and active attention can be subdivided into two subcategories,

as illustrated by figure 2.


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Orienting response

Intensive

Automaticattention reaction

Selective

Passive

Divided attention

Intensive

Anticipatoryattention

Focused attention

Selective

Active

Attention

Figure 2. A taxonomy of attention. See text for details. Adapted from Kok & Boelhouwer

(1997, p. 2)

Passive attention as well as active attention can be either intensive or selective in nature. The term

“intensive” in the context of attention refers to the amount of energy allocated to the stimulus or

task as a whole (e.g. Kahneman, 1973), whereas “selective” refers to man’s ability to focus

attention on a specific part of the environment. Four types of attention can thus be distinguished

(see figure 2): passive intensive attention, passive selective attention, active intensive attention

and active selective attention.

James (1890, p. 416-418) noted that attention can be drawn automatically to stimuli by virtue of

either their “immediate” characteristics, such as suddenness or high intensity, or their “derived”

characteristics, which are acquired through experience. Thus, for a non-immediate stimulus to

attract attention, the stimulus should somehow be of importance to an individual. One way of

attributing importance to a stimulus is through training (Schneider, 1985). Thus, these stimuli are

not intrinsic attention-attractors but have acquired this ability by persistent training. The reactions

to derived stimuli are termed automatic attention reactions. Since these attention reactions are

automatic and selective, they are passive (i.e. nonvoluntary) selective attention processes.

The other subcategory, in which attention is attracted by the physical properties of the stimuli, is

termed passive intensive attention. Aspects of passive intensive attention can be observed during

the phenomenon termed “the Orienting Reflex” (Sokolov, 1963). Sokolov (1963) describes an


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increase in arousal after the presentation of an unexpected auditory stimulus or an auditory

stimulus of a high intensity. Divided attention can be classified as active intensive attention,

whereas active selective attention involves focused attention.

A description of the way active and passive attention enable an individual to locate an object in

space provides a nice example of how both subcategories differ. To go short, there are two main

ways of cuing an object’s location: an exogenous cue initiates orienting from the onset of a

stimulus away from the current alignment of attention and an endogenous cue induces higher-

order processes of the subject to initiate orienting. While the top-down control of orienting

induced by an endogenous cue typically involves voluntary processing, the bottom-up control of

orienting initiated by an exogenous cue is typically similar to a reflex. In general, the time taken

to orient following an endogenous cue is more than the time taken to orient following an

exogenous cue. This exogenous cue can cause attention to switch because of either the cue’s

physical or derived characteristics.

In the experimental setting, eliciting active or passive processes requires different designs

(Verbaten, 1997). If one wants to elicit active attention, the experiment is such that the subject has

to respond in a way that can be evaluated in a qualifiable manner, for instance correct or

incorrect. The subject is well informed on the upcoming task and is able to change the outcome of

the task by means of his own behavioral output. Hence, the subject has a precise mental

representation of the stimulus and his own actions in memory. In passive attention tasks, the

subject is unaware of the task. As a matter of fact, the design does not even contain a task. In

contrast with “active attention experiments”, “passive attention experiments” are not evaluated in

a qualifiable manner, for a desired behavioral performance is simply lacking. The key research

goal here is determining how new, unfamiliar and unexpected stimuli are processed by the

nervous system.

On the basis of the provided taxonomy, one could infer that the current investigation into the

realm of anticipatory attention is related to active selective attention. Hence, the subject

voluntarily focuses on a specific part of his environment in order to improve perception and

thereby improve task performance.


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Note that the athlete, who was mentioned in the introduction, had to focus attention on the sound

of the starting gun in order to make a good start. This anticipatory process implicitly encompasses

a selective aspect for it is the starting gun that has to be marked and not the encouragements of

the crowd nor the cameras in the athlete’s proximity.

The phenomenon of anticipatory attention

Although this taxonomy may fit the majority of types and subtypes of attention, this does not

imply that the taxonomy is suitable for every type of attention. These attention processes may be

the result of an accumulation of several of the four subcategories.

Anticipatory attention differs from other types of attention in at least one way. In general,

anticipatory attention is measured on a different timescale. Selective attention, for example, can

operate at snapshot durations while preparatory attention may operate on a scale of seconds

(Laberge, 1995).

Moreover, anticipatory attention differs from the defined subcategories in the sense that it

requires an expectation: it occurs before some expected perception or action, whereas, for

instance, selective attention can occur after as well as before the onset of an event, whether this

event was expected or not (Laberge, 1995).

The benefits of anticipatory processes in the case of the athlete, i.e. an increase in both speed and

accuracy in processing perceptual stimuli and actions, are presumably achieved by pre-activating

certain brain structures (Brunia, 1999). Brunia (1999) points out that anticipation is a selective

process and that this selective process can operate in two ways: by inhibiting irrelevant structures

or by increasing the excitation of the relevant brain structures (see also Laberge, 1995).

In order to gain insight into the process of anticipatory attention, it is necessary to isolate this

process (see the section on experimental design). For instance, it is not clear whether the faster

response time after hearing the starting gun is due to anticipation of the stimulus, anticipation of

the response, or both. Laberge et al. (1967) conducted a series of experiments of which the results

suggest that response time can be reduced by anticipating the stimulus without varying

anticipation of the response. Furthermore, the results of a subsequent experiment (Laberge et


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al.,1969) indicate that response time can be reduced by anticipating the response without varying

anticipation of the stimulus. Thus, it seems that anticipation for a stimulus or a response can be

varied independently and that they can occur simultaneously.

A neurophysiological model for anticipatory processes

Since adapting to the environment seems crucial for surviving, man must keep notion of the

changing environment surrounding him by relying on his sense organs. The sensory sytems of the

three main (i.e. visual, auditory and somatosensory) modalities have a common denominator with

respect to the processing of sensory information. Before the afferent input stemming from the

sense organs reaches its corresponding primary projection area in the posterior cortex cerebri, the

stream passes the thalamus. Here, each of the sensory systems has a private first order thalamic

‘relay’ nucleus (e.g. Guillery et al., 1998).

Visual information is transmitted via the lateral geniculate body to the primary visual cortex (V1),

whereas auditory information is transmitted via the pars ventralis (e.g. Jones, 1985) of the medial

geniculate body to the temporal cortex (A1). For the somatosensensory system it is the

ventrobasal nucleus (e.g. Jones, 1985) that transmits information to the cortex (S1). Besides first

order nuclei, higher order (association) nuclei are also present in the thalamus. The pulvinar can

be seen as a higher order nucleus for the visual system (Laberge & Buchsbaum, 1990) although it

is far from exclusively involved in the processing of visual information: somatosensory and

auditory information is also transmitted to this nucleus (e.g. Brunia, 1999). Parts of the dorsal

division of the medial geniculate nucleus (MGm) can be regarded as a higher-order relay to

secondary auditory areas (e.g. Conley & Diamond, 1990). For the somatosensory system, the

medial division of the posterior group (POm) can be seen as a higher-order (S2) relay (e.g.

Crabtree, 1996) connecting to the second somatosensory area.

The Reticular Nucleus All the thalamocortical relay (TCR) nuclei send afferents to the cortex. One distinct nucleus

however, does not. This specific nucleus, which is termed the reticular nucleus (RN), caps the

entire lateral aspect of the thalamus and lies like a shield between thalamus and cortex. All fibers


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passing either way between thalamus and cortex must go through this nucleus (Guillery et al.,

1998). Therefore, this nucleus is believed to play a pivotal role in information processing.

Although Laberge (1995) pointed to the fact that there are still uncertainties regarding the exact

wiring of the RN, it has been demonstrated that the RN exerts a local inhibitory influence upon

the underlying TCR nuclei. (e.g. Schlag & Waszak, 1970; Steriade, 1990). This inhibitory

influence constitutes the core of Skinner and Yingling’s gating model of attention (Skinner &

Yingling, 1977; Yingling & Skinner, 1977). As they put it: “the possibility that the RN may

function as a topographically specific inhibitory feedback circuit makes it a prime candidate for

selective regulation of thalamocortical activities” (Skinner & Yingling, 1977). Their model is an

attempt to describe inter-modal selective attention and the way it is brought about in the central

nervous system. Skinner and Yingling’s model was based on a large number of experiments, in

which rhythmic brain activity, slow potential shifts and evoked potentials were studied under

different conditions in the cat. According to their theory, inhibition of what is irrelevant underlies

selective attention. In other words, the signal-to-noise ratio is ameliorated by suppressing the

noise. This inhibition is believed to be brought about by the RN. Skinner and Yingling (1977)

suggested that neurons in the RN are under a dual control from both the frontal cortex and the

reticular formation (RF). These two major sources of input exert different influences on the RN.

The input from the (pre)frontal cortex appears to be selectively aimed at specific sectors only,

whereas the input from the RF provides a more diffuse innervation.

The RN was long regarded as a diffusely organized nucleus, having global rather than localized

actions on thalamocortical pathways (e.g. Scheibel & Scheibel, 1966). However, Skinner and

Yingling (1977) demonstrated that activating a part of the RN overlying a certain TCR nucleus

results in an inhibition of that TCR nucleus. In 1985 Jones pointed out that the RN is divided into

several distinct sectors, each related to a particular group of thalamocortical pathways. Now,

evidence points to the notion that there is a topographically ordered representation of relevant

cortical areas and thalamic nuclei in each of the sectors (e.g. Crabtree et al.,1989, 1992) and that

the RN is indeed organized in a modality-specific manner (Guillery et al., 1998; Mitrofanis &

Guillery, 1993).

The input from the (pre)frontal cortex and the input from the RF differ in yet another aspect,

besides the aforementioned difference in innervation pattern (i.e. selective versus diffuse

innervation). Whereas the influence of the (pre)frontal cortex on the RN is excitatory, the


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influence of the RF is inhibitory in nature. Excitation of RN –thereby facilitating its inhibitory

function – by the (pre)frontal cortex causes a relative closing or blocking of a thalamocortical

channel. According to Skinner and Yingling (1977), this is what happens during periods of

selective attention. Irrelevant channels are inhibited whereas the relevant channel is not. This

process enables information in the relevant channel to pass to the cortex while information in the

irrelevant channel does not. The inhibitory influence of the RF on the RN causes a global

disinhibition of the underlying thalamic relay nuclei that results in a relative deblocking of all

channels. Skinner and Yingling (1977) hypothesized that the two main sources of input, i.e.

(pre)frontal cortex and RF, underlie two distinct functions: selective attention and arousal,

respectively. They assigned the term selective attention to the selective innervation from the

(pre)frontal cortex and the term arousal to the diffuse innervation from the RF. Selection between

one of the main sensory modalities can be realized by a selective lack of activation from the

prefrontal cortex of a sector of the RN that overlies the TCR nucleus corresponding to the

attended channel (Skinner & Yingling, 1977).

Note that, at the level of the TCR nuclei, there is a balance between the ascending activation from

the RF and the descending inhibition from the (pre)frontal cortex.

Skinner and Yingling’s model is often referred to as a gating model for the selective blocking of a

channel can be regarded as a closing of the respective gate.

Brunia (1993) suggested that the anatomic interrelations of the RN and the thalamic relay nuclei

may have more consequences than Skinner and Yingling noted (1977). Brunia pointed to the fact

that the RN does not only cover the thalamic nuclei involved in sensory information processing

but that the RN covers the thalamic motor nuclei as well. The mechanisms involved in attention

in the motor domain are therefore thought to be similar to those involved in perceptual selective

attention. As Brunia (1997) puts it: “Since the thalamic motor nuclei are equally under control of

the RN, it seems plausible that the RN, comparable to what we have seen in perception, is also in

a pivotal position to influence motoric processes.” Furthermore, Brunia (1993) notes that this

model, holds for anticipatory attention and motorpreparation as well as for selective attention

and motor execution. Brunia (1999) points out that the aim of anticipatory processes is to pre-set

relevant brain structures in order to ameliorate the processing of information. As was stated in the

introduction, anticipatory attention and motor preparation are similar processes. According to

Brunia (e.g. 1993), this resemblance can be traced down to the roots: the RN.


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The model of Skinner and Yingling (1977) described only two major input sources of the RN (i.e.

the frontal cortex and reticular formation), there is, however, a third source of input that deserves

attention. Many of the fibers that go through the RN, passing either way between thalamus and

cortex, give off excitatory collaterals to the cells of the RN (e.g. Jones, 1985), thereby forming a

thalamocortical loop.

To exemplify the thalamocortical interrelations figure 3 shows the major connections between

thalamic relay cells, cells of the RN and the cerebral cortex in the visual system. Note that the

thalamic nuclei of the visual system and their associated cortical and reticular connections can be

categorized as being either first order or higher order nuclei.

Figure 3. The thalamocortical network in the visual system. Adapted from Guillery et al.,

1998.


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As was described in this section, the selective mechanism of attention seems to be related to the

thalamocortical network. The following section will describe the thalamocortical network in more

detail. In addition, an outline of the neural basis of activity in the thalamocortical network will be

given. For a more thorough understanding of the neural basis the reader is referred to Lopes da

Silva (1991) and Steriade et al. (1990).

Rhythmic activity and the thalamocortical network

The prime candidate for the generation of rhythmic activity in the alpha band seems to be the

thalamocortical network (Steriade et al., 1990). In the ensuing sections the components of this

network and their characteristics will be described.

Intrinsic electrophysiological oscillatory properties of neurons in the

thalamocortical network Steriade (1990) points out that there is ample evidence, that under imposed experimental

conditions, isolated neurons can display oscillations, usually within a frequency band of 1-20 Hz.

In the intact brain, these single cells are subject to influences from other sources that unite single

elements into ensembles. Three types of cells will be discussed.

Thalamocortical relay neurons Jahnsen and Llinás (1984 a,b) demonstrated that the TCR neurons display oscillatory behavior at

either 6 or 10 Hz, depending on their level of polarization. The type of repetitive activity

described above (i.e. relatively low frequency repetitive activity) occurs when the membrane

potential is negative to –70 mV and has been termed burst-like activity (Deschenes et al., 1984;

Jahnsen & Llinás 1984 a,b). However, in addition to this oscillatory activity the cells are capable

of tonic repetitive activity and may serve as relay elements, when the cell membrane depolarizes

to a level of –60 mV or more. This activity may correspond to the transmission of afferent

activity to the cortex (Deschenes et al., 1984; Jahnsen & Llinás, 1984 a,b; Steriade & Llinás,

1988).


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Thus, TCR neurons operate in two distinct modes:

1. burst mode, which is characterized by oscillations with a relatively low frequency

(around 10 Hz);

2. tonic mode, which is characterized by repetitive activity with a relatively high frequency.

Corticothalamic neurons

Cortical neurons are also capable of repetitive firing, as are TCR neurons. They are able to

respond very specifically to given thalamic input at given frequencies (Steriade et al., 1990).

Reticular thalamic neurons

Steriade et al. (1986) pointed out that the reticular thalamic neurons oscillate easily, in fact, more

readily than the other thalamic neurons due to their conductance properties (Llinás & Geijo-

Barrientos, 1988).

Oscillatory Properties of the thalamocortical network

For several types of brain oscillations, the pacemaker is thought to be located within the RN

(Steriade et al., 1990). One line of evidence comes from a study performed by Steriade et al.

(1985) who demonstrated that after disconnection of cortically projecting thalamic nuclei from

their RN inputs, oscillatory activity is abolished in TCR neurons.

The thalamocortical network includes two main feedback loops:

• The hyperpolarization of the TCR cells caused by IPSPs from the RN neurons (Steriade

& Deschenes, 1984; Steriade et al., 1985) leads to the generation of burst mode action

potentials in the TCR neurons. The dendrites of the RN neurons have synaptic contacts

with the axons of the TCR cells. This produces rebound excitation that returns to the RN

neurons which results in an increased inhibitory influence on the TCR neurons (Steriade


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& Llinás, 1988). This leads to a hyperpolarization of the TCR cells thereby establishing

the oscillatory state (Lopes da Silva, 1991).

• In addition, corticothalamic neurons give collaterals to the reticular thalamic nucleus on

their way back to the thalamus thereby forming another feedback loop (e.g. Guillery et

al., 1998). Several studies (e.g. Steriade et al., 1993; Contreras & Steriade, 1996) suggest

that the corticothalamic projections may contribute to the regulation of the synchrony in

large portions of the thalamus. A slow cortical oscillation of 1 Hz (Steriade et al., 1993)

has been brought into relation with this regulatory function.

Synchronization and desynchronization

Steriade et al. (1990) pointed out that the various wave patterns of the EEG can be referred to as

synchronized or desynchronized patterns. While the first term implies the occurrence of high-

amplitude oscillations with relatively slow frequencies, the second term indicates a replacement

of synchronized rhythms by lower-amplitude and faster waves. Lopes da Silva et al. (1973)

demonstrated that there are coherences between alpha rhythms simultaneously recorded in

thalamus and cortex. Thus, scalp recorded activity may reflect the state of certain thalamic relay

nuclei. Synchronized EEG activity would thus correspond to the firing of thalamic relay nuclei in

burst mode whereas desynchronized activity would reflect the firing of thalamic relay nuclei in

tonic mode. As Steriade et al. (1990) and Lopes da Silva (1991) pointed out, the transfer to the

cortex is disengaged in burst mode. Thus, the cortex is deprived of relevant input, and will not be

engaged in active processing. Pfurtscheller (1992) suggested that the corresponding synchronized

oscillations that can be recorded at the scalp reflect a state of cortical inactivity, which he terms

cortical idling.

However, this distinction between burst mode (inactivity) and tonic mode (activity) may be

somewhat oversimplified, as Guillery et al. (1998) point out. Guillery et al. point to observations

that show that, even in burstmode, TCR cells can respond to sensory stimuli (Guido et al., 1995)

and that afferent activity is transmitted to the cortex. Strikingly, the signal-to-noise-ratio of the

afferent activity appears to be higher in the burst mode than in the tonic mode (Guillery et al.,

1998). Guillery et al. (1998) concluded that the thalamic cells, when in burst mode, are capable of

responding to novel activity patterns and then change to tonic mode so that new stimuli can be


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accurately transferred to the cortex. “In burst mode the system is primed to react to changes in

input activity rather than to transfer this activity reliably to the cortex for analysis. For the latter,

the system needs to switch to the tonic mode” (Guillery, 1998).

Detecting oscillatory activity at the scalp

Scalp potentials are determined by the electrical equivalent dipoles of cortical activity. The

amplitude of the electrical equivalent dipoles depends on the total area of activated cortex and the

degree of synchrony between cortical neurons (Lopes da Silva & Pfurtscheller, 1999; Misulis,

1997). Furthermore, the detection of cortical activity depends on the topology of the cortical area

displaying synchrony and on the relation between this area and the electrodes at the scalp (Lopes

da Silva & Pfurtscheller, 1999). Computer simulations (Nunez, 1995) led to the general

assumption that, in order to present a frequency spectrum with a clear peak, a high degree of

synchronicity over a relatively large cortical area (about 100 mm2) is required (Lopes da Silva &

Pfurtscheller, 1999). Nunez (1995) estimated that the contribution to the local EEG of a number

of synchronously active generators (M) relative to the number of asynchronous active generators

(N) can be expressed in the equation: M/square root N. This formula indicates that an equal

number of synchronous and asynchronous generators results in a preponderant contribution of the

synchronous generators to the local EEG. The relative contribution of synchronously active

generators depends on the total number of active generators and thus on the extent of the cortical

area displaying oscillatory activity: a large cortical area requires a lesser degree of synchrony than

a relatively small one to maintain the relative preponderant contribution of synchronous

generators to local EEG. Hence, the larger the cortical area displaying oscillatory activity, the

greater the chance that it will be detected at the scalp, even though the degree of synchrony over

that particular area may be fairly low. Small cortical areas on the other hand require a high degree

of synchrony over the specific cortical area to be recorded at the scalp.

On the existence of different rhythms

Traditionally, cortical rhythms have been classified, solely on the basis of frequency bands. The

four frequency bands: 0.5-4 Hz, 4-7 Hz, 8-13 Hz and 13-25 Hz have been termed the delta, theta,

alpha and beta rhythm (Bickford, 1987; Misulis, 1997). Nowadays, it is not uncommon to view


20

rhythms with a frequency >30 Hz (e.g. Pfurtscheller, 1993) as a distinct rhythm (i.e. gamma

rhythm). However, describing cortical rhythms in terms of frequency bands alone, without taking

the alleged functionality into consideration may lead to false conclusions regarding the number of

distinct cortical rhythms. Nunez (1995) argued that next to a general cortical rhythm of

approximately 10 Hz, there are several local cortical rhythms that can be functionally

distinguished from each other. This distinction can be made clear by taking the differences in

scalp distribution and reactivity into account.

The notion of distinguishable rhythms in the same frequency band is not restricted to alpha band,

as studies several studies (e.g. Papakostoupolos et al., 1980; Pfurtscheller et al., 1997) indicate.

Papakostoupolos et al. (1980) observed central beta activity during self-paced movements with an

inconsistent blocking pattern. They demonstrated that whereas certain beta rhythms were blocked

during movement others were not affected and some even became more prominent.

Bastiaansen et al. (1999) enunciated that each of the three main sensory modalities and the motor

system all have their own corresponding rhythm. The rhythms of the visual, somatosensory/motor

and auditory system will be discussed.

The alpha rhythm

As Nunez (1995) pointed out, rhythms with a frequency of approximately 10 Hz can be measured

over large portions of the neocortex. These rhythms react differently to experimental

manipulations, indicating that these rhythms are distinct. However, since activity in the 10 Hz

frequency range is termed alpha (-like) activity, one could argue that these distinct rhythms are all

alpha, in essence. In order to avoid confusion, the term alpha rhythm, in this thesis, is restricted to

10 Hz rhythmic activity that reacts to manipulations in the visual modality and are characterized

by an occipital maximum.

The alpha rhythm has been recorded as early as 1929 by Berger. The alpha rhythm does not seem

to be a unitary phenomenon. Walter (1969) describes alpha rhythms in adjacent cortical areas,

that are believed to be involved in visual processing, which show clear differences in the

reactivity to experimental manipulations (Walter, 1969). This notion implies that there are distinct

rhythms in the visual system.


21

The central mu rhythms

The rolandic cortex of a relaxed human subject exhibits rhythmic oscillations that have become

known as (central) mu rhythms. These rhythms, previously known as “rolandic rhythm”,

“sensorimotor rhythm”, “precentral alpha rhythm” or “wicket rhythm” (Pfurtscheller, 1986), can

be detected both invasively (e.g. Toro et al., 1994) and noninvasively with

electroencephalography (e.g. Pfurtscheller et al., 1999) and magnetoencepahalography (e.g.

Salmelin et al., 1995).

The (central) mu rhythm is considered by many authors to be a normal resting rhythm of the

sensorimotor cortex area (e.g. Kuhlman, 1978; Salmelin & Hari, 1994, 1995; Tiihonen et al.,

1989). However, Niedermeyer (1993) points out that when the EEG is visually scored, the central

mu rhythm is observed in no more than 15% of the clinical EEG records. Pfurtscheller and

Neuper (1994) point to the notion that the prevalence of the (central) mu rhythm has been

reported to fall into the range of 50 to 100% when appropriate EEG derivations and computer

methods are used. Furthermore, Salmelin and Hari (1994, 1995) argue that the (central) mu

rhythm can be detected in practically all subjects in MEG records.

The (central) mu rhythm is neither affected by opening/closing of the eyes nor by auditory stimuli

as is the case with occipital alpha rhythm (Walter, 1969) and tau/third rhythm (Niedermeyer,

1990, 1991), respectively. Furthermore, unlike tau and occipital alpha rhythms, the (central) mu

rhythm is suppressed by tactile stimulation, motor preparation, execution of movements and even

imagination of movement (e.g. Chatrian et al., 1974). These findings suggest that the (central) mu

rhythm is related to the sensorimotor system.

Gastaut (1952), who described the (central) mu rhythm in detail referred to the mu rhythm as the

“rhythme en arceau” because of its arch-like shape. This shape suggests that it is built up of

distinct components. Storm van der Leeuwen et al. (1978) and Pfurtscheller (1981) argue that a

harmonically related frequency results from the arched waveform if spectral analysis is used.

This harmonic relation was confirmed by various studies (e.g. Niedermeyer & Lopes da Silva,

1982). Two spectral peaks are found when the classical Fourier method is used for estimation of


22

the power spectrum of the arch-like mu rhythm: one in the alpha band representing the basic

frequency of the mu rhythm and another in the beta band at the first harmonic frequency. (e.g.

Pfurtscheller, 1997). The dominant component rhythms are considered to peak at approximately

10 and 20 Hz (e.g. Tiihonen et al., 1989; Hari & Salenius, 1999). Salmelin and Hari (1994) argue

that these frequencies may display independent activity besides the coherent oscillations present

in the central mu rhythm. For this reason, “the configuration of the mu rhythm signal can deviate

strongly from the classical wicket shape; this might be one reason for the poor detection of mu in

the scalp EEG records” (Salmelin & Hari, 1994).

The notion that both components may, to some extent, occur independently persuaded many

authors to make a distinction between 10 Hz mu and 20 Hz beta activity despite the original

definition of Gastaut (1952) who considered the whole arch-liked rhythm (10 Hz plus 20 Hz) to

be mu.

Recent experiments seem to justify the distinction between 10 Hz and 20 Hz components of the

mu rhythm for they suggest that both components differ in their generation sites, timing and

reactivity (Salmelin & Hari, 1994, Salmelin et al., 1995; Tiihonen et al., 1989) and thus can be

functionally segregated (Salmelin et al., 1995).

A number of electrocorticographic studies have provided evidence for the notion that the central

mu rhythm is generated by neuronal structures in the pre and postcentral gyri (e.g. Gastaut, 1952;

Jasper & Andrews, 1938; Jasper & Penfield, 1949; Kuhlman, 1978; Kruger & Henry, 1957;

Papakostopoulos et al., 1980). The precise locations of the generator sites of the distinct

component rhythms, however, was not decisively revealed by this array of studies.

In his pioneering work Berger (1929) demonstrated that electrocortical 20 Hz activity could be

recorded in the precentral cortex. The first observation of the attenuation of precentral beta

rhythms stems from Jasper and Andrew (1938). They reported on a depression of beta rhythms

(with an average frequency of about 25 Hz) by tactile stimuli from the contralateral part of the

body. The observation that precentral beta over the motor hand area was blocked by fist clenching

led Jasper and Penfield (1949) to interpret the precentral beta rhythm as the idling activity (see

the section on synchronization and desynchronization) of the resting motor cortex.


23

On the basis of their intra-operative recordings, Jasper and Penfield (1949) considered the central

fissure to be an important borderline, separating 20 Hz rhythms (precentral) and 10 Hz rhythms

(postcentral). Several studies, however, do not seem to favor a strict application of this borderline

model. Papakostopoulos et al. (1980), for example, demonstrated that the central sulcus does not

constitute a definite borderline between 10 and 20 Hz rhythms, for he recorded beta activity (16-

32 Hz) in both pre- and postcentral areas in man. Furthermore, Rougeul et al. (1979) found 20 Hz

rhythms, which were blocked by the smallest body movement, over the SI hand area and posterior

parietal cortex in monkeys.

Yet, recent studies indicate that the two rhythms which constitute the classical mu rhythm may

indeed differ in their generation sites (Salmelin & Hari, 1994; Salmelin et al., 1995; Tiihonen et

al., 1989) and that these differences center round the borderline as suggested by Jasper and

Penfield in 1949. Furthermore, the two main component rhythms display differences in timing

and reactivity. Taken together, these differences may point to differences in functionality as well.

Source analysis of the 10 Hz and 20 Hz components of the classical mu rhythm (Salmelin and

Hari, 1994; Salmelin et al., 1995) demonstrated that the generators of the 10 Hz rhythm were

confined to the postcentral gyrus (and extended to the parietal lobe; Salenius et al., 1997) while

generators of the 20 Hz components were predominantly located in the precentral gyrus. Note that

they found some 20 Hz generators in the post-central gyrus as well. Several EEG studies

displayed similar topographic differences consisting of a clear attenuation of 20 Hz activity

localized slightly anterior to the desynchronization of 10 Hz activity (e.g. Pfurtscheller & Neuper,

1994).

Salmelin et al. (1995) found that the sites of maximal suppression and subsequent rebound of the

20 Hz rhythm followed the somatotopic (or motorotopic, as they termed it) representation of

fingers, toes and tongue over the motor cortex. Furthermore, they found that the reactivity of the

10 Hz rhythm did not vary with the type of movement. However, Toro et al. (1994) reported that

subdural recordings revealed that the 10 Hz reactivity also follows a somatotopic organization.

This somatotopic organization was not present in simultaneously recorded scalp EEG.

Their neuromagnetic recordings brought Salmelin and Hari (1995) to hypothesize that the 10 Hz

signal is a true somatosensory rhythm whereas the 20 Hz activity is essentially somatomotor in

nature.


24

The distinction between mu and beta rhythms is a rather crude one, however, and several studies

indicate that there may be a wide variety of distinct cortical rhythms, originating in the

somatomotor cortex, each of them displaying different intrinsic characteristics. (e.g. Hari &

Salenius, 1999; Papakostoupolos et al. 1980; Pfurtscheller, 1999; Pfurtscheller and Neuper,

1997).

The tau rhythm

The tau rhythm presumably does not form part of the present investigation. Therefore, the

discussion of this rhythm will focus on familiarizing the reader with this rhythm rather than

providing a thorough description.

In 1990 Niedermeyer recorded rhythmical activity in the alpha frequency band over the temporal

lobe by means of epidural and intracortical recordings. Niedermeyer (1990, 1991), demonstrated

that “the third rhythm” could be functionally distinguished from the (occipital) alpha and

(sensorimotor) mu and beta rhythms. Tiihonen et al. (1991) observed a magnetoencephalographic

rhythm with comparable characteristics. Hari (1993) reported on similar findings and termed this

rhythm the “tau rhythm”. Source analysis and the notion of a clear attenuation of the tau rhythm

following auditory stimuli strongly suggest that the tau rhythm is an intrinsic rhythm of the

auditory cortex (Hari et al. 1997).

Physiological measures in attention research As the historical overview pointed out, attentional phenomena have received a great deal of

interest during the second part of the 20th century. Nowadays researchers in the field of attention

have a wide variety of measurement techniques at their disposal. The most commonly used brain

imaging techniques include: EEG (scalp recordings, intraoperative/subdural recordings of

neuronal populations, intraoperative single cell recordings), MEG, MRI, fMRI, event-related

fMri, CAT, PET, and rCBF. It is remarkable that EEG, which is the oldest measurement

technique of human brain activity – Hans Berger discovered the human EEG in 1929- still in use

today, is probably the most frequently applied technique in attention research. Three main


25

characteristics of EEG seem to account for this situation to evolve. Firstly, the costs of EEG

equipment are modest compared to, for instance, MEG and fMRI. Secondly, EEG measurements

are characterized by a high temporal resolution, which is not matched by certain imaging

techniques as rCBF, PET and MRI, which have a higher spatial resolution. It must be noted that

the temporal resolution of certain imaging techniques (e.g. Event Related fMRI) is increasing due

to further modifications, but is still on the scale of seconds. Thirdly, EEG (and MEG) data,

provided that their acquisition meets certain requirements, can be analyzed in distinct ways. One

possible analysis is the transition into power values by means of Event Related

Desynchronization (Pfurtscheller & Aranibar, 1977). EEG (and MEG) thus enables the

investigator to derive distinct and complementary measures from one and the same dataset.

EEG

Potentials

The proposed neurophysiological mechanism underlying Event Related Potentials (ERPs) is the

summation of depolarizing Excitatory Post-Synaptic Potentials (EPSPs) and hyperpolarizing

Inhibitory Post-Synaptic Potentials (IPSPs) on the membrane of efferent neurons in the cortex

area underlying the electrodes (Birbaumer et al., 1990; Misulis, 1997).

EPSPs lead to a depolarization, which brings the membrane potential nearer to the firing

threshold, thereby increasing the chance that the cell will actually fire. IPSPs, on the other hand

decrease the chance that an action potential will develop (e.g. Böcker, 1994).

The spatial and temporal integration of all EPSPs and IPSPs determines the membrane potential

at the triggerzone near the axon hillock. If this potential exceeds about –50mV an efferent action

potential develops, i.e. the cell fires.

ERD

Sensory stimulation can elicit two types of changes – both poststimulus and prestimulus- in the

electrical activity of the cortex (see figure 3): evoked activity changes and induced activity

changes (Pfurtscheller & Lopes Da Silva, 1999). The first change is both time-locked and phase-


26

locked to the occurrence of an event, whereas the second change is time-locked but not phase-

locked. This difference has important implications for the analysis of electrical activity: the two

changes require distinct methods of analysis. Whereas simple linear methods (e.g. averaging)

suffice to extract the evoked activity changes from the raw data set, these methods fail in

extracting induced activity. The method of averaging, common in ERP studies, cannot be applied

for it causes cancellation of the non-phase-locked manifestations present in induced activity

changes.

Figure 4. Schema of generation of two types of changes in the electrical activity of the

cortex: induced activity and evoked activity. TCR: thalamocortical relay cells;

RE: reticular nucleus. From Lopes da Silva & Pfurtscheller (1999).

In order to be able to reveal induced activity changes, a different method of analysis should be

applied. ERD is such a method.

Note that averaging is an intrinsic characteristic of scalp recorded EEG. Firstly, the summation of

EPSPs and IPSPs at the neuronal level can be regarded as a form of averaging, since several

inputs result in one net output. Secondly, electrodes are not able to detect all of the potential


27

changes at the cortical surface. Potentials are volume-conducted through the meninges, skull, and

scalp before they are picked up by the surface electrodes. These tissues act as a spatial low pass

filter that causes the potential at the scalp to appear blurred and attenuated in comparison to

cortical activity (Van Burik et al., 1999) i.e. one does not only record activity from cortical areas

directly underlying the electrode, but also activity from adjacent cortical areas. Therefore, if an

electrode is placed on the scalp, the electrical activity from a restricted cortical area is averaged

(Pfurtscheller & Aranibar, 1980). Cooper et al. (1965) pointed out that potentials recorded with

subdural electrodes show a wide variability in form and phase over small cortical areas. Since one

scalp electrode may cover several of these small cortical areas, the variability in form and phase is

partially cancelled out by spatial averaging.

The computation of ERD

The major advantage of Event-Related Desynchronization over linear averaging techniques is its

ability to quantify induced activity in a reliable manner, i.e. without phase cancellation. Several

ways of calculating ERD and topographical mapping have been suggested since Pfurtscheller and

Aranibar reported on this technique in 1977. Each of these classical ERD derivatives seems to

bear intrinsic advantages and disadvantages. The method of choice therefore depends on the

experimental parameters (such as electrode configuration) and the (expected) characteristics of

the bioelectrical data (see Appendix B).

The classical ERD

Pfurtscheller (1999) states that several requirements should be met in order to perform ERD. One

must have at least 30 event-related EEG trials, synchronously time-locked to an internal or

external event, at one’s disposal. Furthermore, both these trials and the intervals between

consecutive events should span at least some seconds. The quantification of classical Event-

Related Desynchronization encompasses 6 steps (e.g. Bastiaansen, 2000):


28

1. select the frequency bands of interest;

2. apply a (digital) band pass filter with the desired characteristics to the data, eliminating

frequencies that lie out of the desired frequency band1;

3. the amplitudes of the filtered EEG epochs are squared in order to avoid phase

cancellation, thereby obtaining power values of that particular frequency band;

4. the power values are integrated over a number of consecutive samples in order to obtain a

more reliable estimate of the power rendering power values over a certain time window

(e.g. 250 ms);

5. the power values in each time window (e.g. 250 ms) are averaged over all corresponding

epochs (e.g. 250 – 0 ms premovement);

6. the quantification of ERD is expressed as the change of power at each time window

relative to the (average) power in a reference interval. A power increase is denoted by an

Event-Related Synchronization (ERS) whereas a power decrease is denoted by and an

Event-Related Desynchronization (ERD), respectively2.

A possible drawback of the classical ERD is that it is not capable of differentiating between

induced activity and evoked activity. Furthermore, the classical ERD is characterized by a rather

poor temporal resolution. Appendix B describes a method that can account for evoked activity

and a method that has a higher temporal resolution than the classical ERD.

Preparatory processes: slow potentials and ERD

Motor preparation

Slow Potentials

In 1965 Kornhuber and Deecke reported on a negative slow potential in the EEG that preceded

self paced movements. They termed this negativity Bereitschaftspotential (BP). The BP, or

Readiness Potential (RP) in English, is characterized by a maximum amplitude over the vertex

and a preponderance of negativity over the hemisphere contralateral to the movement side, for

hand movements (e.g. Böcker, 1994). Preceding foot movements, the RP is larger over the

1 See appendix B 2 ERD%= ((A-R)/R)*100 with “A” denoting the power in the timewindow of interest and “R” denoting the average power in the reference interval. Note that this equation assigns a negative value to an ERD and a positive value to an ERS.


29

ipsilateral motor cortex. (Brunia, 1980; Brunia & Vingerhoets, 1981). Its scalp distribution is also

influenced by other movement parameters, such as the complexity of movement (e.g. Lang et al.,

1989) and the number of fingers involved in he movement (Kitamura et al., 1993). The

characteristics of the RP indicate that the locus of its maximum amplitude roughly corresponds to

the cortical representation of the moving body part.

The RP may develop as early as 1500 ms prior to movement onset. Note that a RP is not a

prerequisite for movement execution: in non-forewarned Reaction Time experiments, no RP is

recorded (Deecke & Kornhuber, 1977; Kutas & Donchin, 1980).

ERD

In contrast to the BP, the ERD may already start around 2 s prior to the onset of a voluntary,

selfpaced finger movement (Pfurtscheller & Berghold, 1989; Stancák & Pfurtscheller, 1996) over

the contralateral hemisphere. Shortly before movement onset the ERD appears on the ipsilateral

side as well (e.g. Pfurtscheller, 1999). During execution of movement the ERD becomes almost

symmetric on both hemispheres. After movement execution, the ERD slowly makes way for a

rebound of synchronized activity. Although there is some variability in the reported onset of

synchronization, the beta rhythm synchronization is generally reported to develop well within the

first second after movement offset whereas the mu rhythm synchronization builds up 1 to 2

seconds after movement offset (e.g. Leocani et al., 1997; Pfurtscheller et al., 1996). The

topography of the ERD depends on, at least, the handedness of the subject, response side,

analyzed frequency band, moved limb and several other kinematic parameters (e.g. Pfurtscheller,

1999; Stancák & Pfurtscheller, 1996; Toro et al. 1994).

Several studies suggest that the post-movement rebound of central rhythms represents a localized

hypersynchronization of those motor cortical areas, which have been active during motor

preparation (Pfurtscheller et al., 1996; Salmelin and Hari, 1994; Salmelin et al., 1995; Stancák &

Pfurtscheller, 1995; Toro et al., 1994).


30

Anticipatory attention

Slow potentials

In order to study processes related to stimulus anticipation, the presentation of a stimulus is

required. Furthermore, the design should prevent non-stimulus related activity from interfering

with stimulus related activity. In a series of experiments Damen and Brunia (1987a, 1987b, 1994;

Brunia & Damen, 1988) solved this problem by separating motor related and stimulus related

processes in time. Preceding stimulus presentation, which took place 2 seconds after movement

onset, they recorded a negativity that differed from that of the RP. They termed this negativity

“Stimulus Preceding Negativity”. The SPN has been recorded prior to three types of stimuli:

1. Knowledge of Results stimuli (e.g. Damen & Brunia, 1987a);

2. Instruction stimuli conveying information about a future task (e.g. Gaillard & Van

Beijsterveld, 1991);

3. Probe stimuli, with which the outcome of a previous task has to be matched (e.g. Chwilla

& Brunia, 1991).

The amplitude and the distribution of the SPN appear to vary with the type of stimulus that is

anticipated. Prior to KR stimuli the SPN shows a widespread distribution. Over the parietal cortex

a steep increase in negativity is found (see Brunia, 1999) whereas over the frontal areas (fronto-

temporal; e.g. Bastiaansen et al., 1999) the SPN is manifest as a sustained negativity. The SPN

shows a right-hemispheric preponderance. Preceding instruction stimuli, the SPN shows a parietal

maximum but shows a bilateral symmetrical distribution. Moreover, the amplitude is smaller than

the pre KR SPN. The SPN prior to Probe stimuli shows a parietal maximum but exhibits a left-

hemispheric dominance. These findings indicate that the SPN might not merely reflect a

perceptual process.

Thus, like the RP, the distribution and the amplitude of the SPN seems to depend on the

characteristics of the anticipated event. However, not all parameters influence the manifestation

of the SPN. Böcker et al. (1994) found that KR stimuli of different modalities (i.e. auditory and

visual) were preceded by a SPN with a similar scalp topography. Bastiaansen (2000) found the

same results with respect to the SPN in the visual and somatosensory modalities. Although recent

findings from our lab seem to show differences in scalp distribution of the SPN the SPN does not


31

give strong support for the extended thalamocortical model. As pointed out in the section on

EEG, this could be a result of a shortcoming of the method.

ERD

Although Bastiaansen was the first to carry out experiments that were aimed at elucidating

preparatory processes by making use of ERD, four earlier studies touched upon the topic of

anticipatory attention and ERD. In 1991, Pfurtscheller and Klimesch, described an occipitally

localized ERD in the lower alpha band prior to the presentation of visual stimuli. Furthermore,

Pfurtscheller (1992) found an occipital ERD starting at 1 s before the presentation of visual

stimuli and simultaneously recorded an ERS at central electrodes. Klimesch et al. (1992, 1998)

suggested that the alpha band can be divided into two functionally distinct bands. The upper alpha

rhythm (approximately 10 – 12 Hz) is selectively associated with the processing of sensory-

semantic information. The 8 – 10 Hz band would reflect expectancy, since the rhythmical activity

in this band clearly attenuates about 1 s before stimulus presentation. However, experiments by

Bastiaansen (1999, 2000) did not support these findings.

Bastiaansen (1999) performed an ERD computation on Böcker’s (1994) dataset and did find

differences in scalp distribution between the visual and auditory modality. This study clearly

shows that slow potentials and ERD are measures that can be used in a complementary way

because they convey different types of information. Bastiaansen (1999) reported on a significant

ERD in the 10-12 and 12-16 Hz frequency bands at occipital sites and not at temporal sites prior

to the presentation of a visual stimulus. However, such a difference was not found prior to the

presentation of an auditory stimulus: there was no significant ERD neither at temporal nor at

occipital sites in the 10-12 Hz frequency band. With MEG Bastiaansen (2001) replicated the

results of the first study with regard to the occipital ERD preceding visual KR stimuli. Moreover,

he demonstrated that two out of five subjects displayed a clear ERD at temporal sites preceding

the auditory KR stimulus in the 8-10 Hz frequency band. Although two out of five subjects may

not seem convincing, Bastiaansen demonstrated that the level of tau power in the baseline interval

was significantly higher for the two subjects. This may be the result of a well-developed tau

rhythm, or may be due to the orientation of the tau generators relative to the MEG sensors: a

tangential orientation increases the likelihood of recording the tau rhythm.


32

Aims of the present study

The main aim of this thesis is to test the thalamocortical gating model (1999). In this model anticipatory attention in the three main modalities is mediated by one underlying

mechanism: the thalamocortical network. Several components of this network display oscillatory

properties. The manifestation of this oscillatory activity can be recorded by means of scalp

recorded EEG. However, in scalp recorded EEG this oscillatory activity may not be detected

without proper computational techniques, such as ERD. There is reason to believe that

anticipatory attention in both the visual and auditory modality is reflected by an ERD over the

respective sensory projection areas. The finding that anticipatory attention in the somatosensory

modality is reflected by an ERD over the somatosensory cortex would provide indirect evidence

for the thalamocortical gating model (Brunia, 1999).

The following hypotheses will be tested and discussed in the ensuing sections of this report:

1. Preceding somatosensory stimuli a postcentral ERD is present

2. The pattern of stimulus-related power changes, differs between conditions

3. Preceding visual stimuli an occipital ERD is present

4. The pattern of movement-related power changes does not differ between conditions


33

Methods

Subjects Nine healthy right-handed subjects, 5 male and 4 female, participated in the experiment. The

subjects, with ages ranging from 18 to 22 years (M=20) were all undergraduate students. They

received either study credits or were paid fl. 7.50 (about 3.3 US $) an hour for their voluntary

cooperation. All subjects met the following requirements:

• no psychiatric history

• no history of psychotropic drug treatment

• no history of clinical brain examinations

• normal eyesight (after correction)

• normal hearing

Experimental design & procedure

Experimental design

The experiment consisted of three conditions:

• Voluntary Movement (VM);

• Time estimation task with a visual feedback stimulus (VIS);

• Time estimation task with a somatosensory feedback stimulus (SS).

The Time estimation with a Knowledge of Results (KR) - paradigm, presumably allows for a

separation of response-related and stimulus-related processes in the time domain. Thus, motor

preparation and anticipatory attention are dissociated on a time basis.


34

Figure 5. The time estimation paradigm with KR stimulus. See text for details.

The experiment was made up of 6 blocks in total: each condition encompassed two blocks, one

for each response side (i.e. left hand or right hand). During a block subjects produced unilateral

responses of one hand only, which were measured by a force transducer.

In the Voluntary Movement condition subjects were instructed to produce self-paced rapid

unilateral flexions of index-finger and thumb about once every 10 seconds with a minimum inter-

response time of 7.5 s.

The voluntary movement condition always preceded the two time estimation conditions because

unwanted differential carry-over effects were expected to occur. The carry-over effects from the

voluntary movement task to the two time estimation tasks were expected to be less disturbing

than vice versa. The carry-over effects were expected to occur due to the time constraints

involved in the time estimation conditions.

Each trial in the time estimation condition started with an auditory warning signal (910 Hz, 53

dB, 200 ms). The subjects had to estimate a four second interval following the auditory warning

signal by pressing the force transducer. They had to produce the same kind of rapid unilateral

response as in the voluntary movement condition. Two seconds after the response, they were

informed about the correctness of the estimated time interval by either a visual or somatosensory

KR stimulus, depending on the condition at issue. Note that the subjects were not informed about

WS response

KR


35

the actual length (i.e. 4 seconds) of the target-interval. Subjects had to determine the target

interval by relying solely on the provided KR stimulus to guide their future estimations.

The order of the four KR blocks was randomized over subjects as was the order of the two

Voluntary Movement blocks preceding the time estimation blocks.

The intertrial interval between KR and the next WS varied randomly in steps of 1 second between

7 and 11 seconds.

A block consisted of 80 behaviorally valid trials. In order for a time estimation trial to be

behaviorally valid, the estimated time interval should fall into the range of 3500 ms to 4500 ms.

The inter-trial interval in the Voluntary Movement condition should measure at least 7.5 seconds

in order to be behaviorally valid. The completion of the experiment thus rendered 6 * 80

behaviorally valid trials.

Procedure

The experiment took place in an electrically shielded, sound attenuating and dimly lit cabin. The

cabin consisted of two separate compartments.

Subjects were seated on a comfortable, slightly reclining chair (height bottom of chair

approximately 50 cm) while their feet rested on a pedestal mounted on the chair and placed in a

position most comfortable to the subject.

Preceding the experiment, subjects were instructed to sit still and to minimize the number of

blinks and eye-movements during the intervals of interest to the experimenter.

In order to establish a criterion for a valid response, the maximal voluntary force that the

individual could exert on the force transducer was measured. Subjects were asked to hold the

transducer between thumb and index finger (pincer-grasp) and to flex both extremities as hard as

possible without overstraining. The criterion of a valid response was set a 20% of the maximal

voluntary force.


36

The width of the time-window considered correct was individually adjusted and obtained in a

training session preceding the two time estimations. In total 4 training blocks were carried out.

The width of the time window was set in such a manner that approximately 60% correct trials

would be obtained. The time window used in the subsequent blocks was calculated by averaging

the time windows in the training blocks.

Subjects were instructed to refrain from counting or any other rhythmic behavior during the time-

estimation interval. Furthermore, they were stripped of all devices that could enhance the

performance on the time estimation task.

Every block was preceded by an amplifier calibration trial followed by an EOG calibration trial.

During this EOG calibration trial subjects had to track dots appearing at different locations on the

screen by means of eye-movements only. After every experimental block a second amplifier

calibration trial took place.

Apparatus and KR Stimuli

Apparatus

Experimental control and stimulus timing was accomplished using a PC and logical circuitry.

Two 8 mm non-polarizing Beckman electrodes (interelectrode distance from center to center

measured 3 cm) were placed on the subjects’ right calf muscle (m. gastrocnemius medialis). The

electrodes were positioned in a rostro-caudal manner, thereby forming an imaginary vertical axis

on the calf.

The electrical stimulations were administered using a Grass S88 stimulator, a Grass SIU 5

Stimulus Isolation Unit and a Grass Constant Current Unit, mounted in series.

Response manipulanda (static isometric force transducer, 5.5 cm) were placed at the end of each

arm support. They were positioned in a fashion most comfortable to the subject. The subject held

the response manipulandum between thumb and index finger (pincer-grasp). A computer screen

and a loudspeaker were placed in front of the subject at eye-level. The distance between subject

and computer screen measured approximately 1.3 meters.


37

Stimuli

In both the visual condition and the somatosensory condition the KR stimulus informed the

subject about the quality of their time estimation by indicating whether their estimated time

interval was too short, correct or too long. The visual KR stimulus consisted of a white vertical

bar (length 4 cm, width 1 cm) against a black background, centrally presented on the computer

screen placed in front of the subject. The KR stimulus was coded by the number of repetitive

presentations of the visual stimulus on the screen. This number could be 1, 2 or 4, encoding an

estimated interval which was too short, correct or too long, respectively. The visual stimulus

appeared on the screen for 30 ms. The inter-stimulus interval in the case of multiple presentations

measured 120 ms. Therefore, conveying a KR stimulus indicating that the subjects’ estimated

interval was too long took 480 ms in total.

The somatosensory KR stimulus was coded by the number of electrical stimulations. During the

stimulation intervals a pulse (duration 10 ms) was generated by the stimulator and administered to

the subject either 1, 2 or 4 times with an inter-pulse interval of 140 ms. Therefore, conveying a

KR stimulus indicating that the subjects’ estimated interval was too long took 460 ms in total.

Electrophysiological recordings

EEG recordings

For the EEG-recordings 27 non-polarizing Beckman 8 mm Ag-AgCl electrodes were affixed to

the scalp. Most of them were placed according to the international 10-20 system. Standard

positions were Fp1, Fp2, F7, F3, Fz, F4, F8, T3, T4, T5, T6, P3, Pz, P4, O1, Oz, O2. Non-

standard positions were C3’, C1’, Cz’, C2’, C4’, C3’’, C1’’, Cz’’, C2’’, C4’’. C3’, Cz’ and C4’

were mounted on the scalp 1 cm anterior to C3, Cz, C4 respectively. C1’ and C2’ were placed at

one half of the distance between C3’ and Cz’ and between Cz’ and C4’ respectively. C3’’, Cz’’

and C4’’ were affixed to the scalp 2 cm posterior to C3, Cz, C4 respectively. C1’’ and C2’’ were

placed at one half of the distance between C3’’ and Cz’’ and between Cz’’ and C4’’ respectively.

The distance between the primes and their corresponding doubles thus measured 3 cm from

center to center. The electrode montage was designed to cover the scalp above the entire brain

with an increased spatial resolution over the primary motor and somatosensory cortices.


38

Electrode impedance was kept below 5 kOhm. Software-linked mastoids served as a reference.

The EEG signals were amplified by home made amplifiers with a 30 s time-constant. The low

pass filter was set at 70 Hz (-42Db/octave).

Epochs of 3000 ms premovement to 3500 ms postmovement were digitized online with a

sampling frequency of 256 Hz using a 12 bit AD converter.

EOG recordings

The EOG signals were amplified by home made amplifiers with a 30 s time-constant. The low

pass filter was set at 70 Hz (-42Db/octave). The EOG was recorded using 6 non-polarizing

Beckman 2.1 mm electrodes. The horizontal EOG from the outer canthi and the vertical EOGs of

both eyes were recorded for off-line EOG correction (Van den Berg-Lenssen et al., 1989).

Data reduction and statistical analysis

Artifacts

An automatic artifact detection was performed, discarding trials of which the epochs of interest

did not meet the following criteria (note that these criteria are the most liberal ones used):

• the epoch did not contain spikes that exceeded 100 µV

After applying a 2 Hz low pass filter to the data

• individual sample values did not differ from each other by more than 90 µV

• the mean amplitude in 4 subsequently sampled intervals of equal length may not differ

from baseline by more than 35 µV.

The minimum number of trials was set at 30.


39

Trials that did meet the afore described criteria were EOG-corrected using an autoregression

model based on EOG calibration trials recorded before each experimental block of trials (see Van

den Berg-Lenssen et al., 1989)

ERD computation

In order to obtain reference-free data and to eliminate ERD/ERS effects at the reference

electrodes, Perrin (1987, 1989) recommends to transform the recorded potentials into Scalp

Current Density (SCD) fields by estimating a spherical spline function. Compared to classically

used methods (e.g. the four neighbors method used in Hjorth’s (1975) source derivation), this

interpolation technique can provide better estimates at the borders of the electrode montage.

Next a FFT was performed on the entire sampling epoch, after which the data were smoothed

twice using a moving Hamming window with a 3-sample length. Subsequently, the weights for

frequencies outside the desired frequency band were set to zero. This procedure (i.e. band pass

filtering) yielded the frequency band of interest. Finally, the data were transformed back to the

time domain. The entire procedure was carried out twice resulting in two separate frequency

bands: 8-12 Hz (mu) and 17-23 Hz (beta) frequency bands.

Power values were computed by squaring the amplitudes. Intervals of 32 consecutive samples

were averaged, giving rise to 26 time intervals of 250 ms each. Although these settings are

detrimental to the temporal resolution, these parameters are necessary to avoid unreliable

estimates of the power. The 250 ms interval prevents this from happening since the sinusoidal

period of the slowest rhythms of interest measures 125 ms (8 Hz, f = 1/t). Hence, the sample

interval covers 2 periods of the slowest frequency component.

Since the data were recorded on a trial-by-trial basis, the first and the last 250 ms intervals of the

6500 ms sampling epoch were invalid because of an inherent discontinuity in the data. For each

subject, data were averaged over trials, and ERD was computed as the percentage power change

for a particular time interval in one of two selected frequency bands, relative to the reference

interval. This interval ranged from 2750 to 2000 ms pre-movement.

The resulting percentages were averaged over all subjects for display purposes.


40

Statistical analysis

Behavioral data

Before trials were rejected to rid artifacts, each behavioral data block contained 80 behaviorally

valid trials (for criteria see section on artifacts) and could contain a number of behaviorally

invalid trials as well. Behavioral measures (as described below) were not corrected for trial

rejections, unless this is explicitly stated. The time estimation values were measured from WS

onset to response onset for each trial. In order to ascertain the effects of the KR stimulus two

behavioral measures were derived from the time estimation values:

• the percentage of estimated interval too short/OK/too long;

• the effectiveness of the KR stimulus.

The percentage too short/OK/too long is a measure for the quality of the time estimation. These

data were analyzed by an ANOVA with Response category (Too short/OK/Too long), KR

modality (Visual, Somatosensory) and Response side (Left hand, Right hand) as repeated

measures.

In order to ascertain the effectiveness of the KR stimulus, the percentage of correctly adjusted

trials following trials with too short or too long time values was computed for each KR condition

separately. Correctly adjusted trials were defined as trials that show a change in time estimation

values in the desired direction, indicated by the KR. In practice, this means that for a correct

adjustment of a “too short time estimation value” to take place, the subsequent trial should show

an increase in time estimation value relative to the former trial. In case of a “too long time

estimation value” the subsequent trial should display a decrease in time estimation value. These

data were analyzed by and ANOVA with KR modality (Visual, Somatosensory) and Response

side (Left hand, Right hand) as repeated measures.

Physiological data

The analysis of the physiological data centered around two presumably distinct processes: motor

preparation (motor related activity) and stimulus anticipation (stimulus related activity). Due to


41

the experimental design, both processes are thought to take place at different points in time. The

analysis of the power changes (relative to the reference interval) related to either motor

preparation or stimulus anticipation are therefore dealt with separately.

Since the largest anticipatory effects are expected to become maximally manifest just before the

upcoming event -whether it is movement onset or stimulus presentation – only the last 250 ms

intervals preceding that event were statistically analyzed.

Motor related ERD

Since postmovement effects are thought to interfere with the pre stimulus activity (e.g.

Bastiaansen, 1999) masking the latter, the elimination of these effects would presumably result in

an uncontaminated display of anticipatory attention preceding the KR stimulus. Bastiaansen

(1999), reported on such a removal of movement-related activity by subtracting the ERD data of

one experimental condition, which was thought to encompass mere movement-related activity

(VM condition), from the experimental condition believed to encompass both stimulus-related

and movement-related activity (KR condition). This subtraction can only be justified if the

movement-related activity does not differ significantly between the three different conditions, as

Bastiaansen (1999) pointed out. These movement-related effects were most prominent at central

electrodes, showing an ERD pre-movement and a strong ERS postmovement, which is in line

with previous findings (e.g. Pfurtscheller et al., 1999).

Several studies (e.g. Pfurtscheller, 1981; Pfurtscheller et al., 1999) suggested that the level of

power change over a certain cortical area is the result of a summation of power changes

stemming from neurons located in the cortical area covered by the registration device. Thus, one

should realize that the power change over a certain area is not as much a unitary level of power

change present in the entire area covered by the registration device but rather an average power

change over groups of neurons in a cortical area that is covered by the registration device.

Opposite power changes in adjacent cortical areas may therefore overrule one another in favor of

the larger of the two opposing power changes, or, in the rare case of similar magnitudes of the

opposing power changes, cancel each other out. Movement-related effects may interfere with the

manifestation of stimulus-related activity, given that the two processes overlap each other in time.

Hence, the motor cortex and somatosensory cortex are in close vicinity to each other and both


42

motor related activity and anticipatory attention are presumed to be prominent at central

electrodes. For this reason, the elimination of movement-related activity would seem highly

profitable, if not necessary.

The main point of interest regarding the movement related ERD is thus to test whether motor

related activity was similar in all three conditions (VM, Vis, SS). If this is indeed the case, the

VM condition can be subtracted from the KR conditions, thereby removing its masking effect on

the manifestation of anticipatory attention.

A second point of interest was determining the effect of response side on the distribution of

powerchanges over the scalp.

Furthermore, since the 10 Hz rhythm and the 20 Hz rhythm are believed to reflect different

processes and are reported to display distinct topographic characteristics (Salmelin & Hari, 1995),

both bands are incorporated in the analysis.

An ANOVA was performed with Experimental condition (VM, SS, Vis), Response Side (Left

hand, Right hand) Band (10 Hz, 20 Hz), Electrode Position (Precentral, Postcentral) and

Hemisphere (Left hemisphere, Right hemisphere) as repeated measure factors. For this analysis,

electrodes C3’ and C4’ were used to assess pre-movement power changes at precentral sites

whereas electrodes C3’’ and C4’’ were used to assess premovement power changes at postcentral

sites. The movement related ERD as far as upper-limb movements are concerned (Pfurtscheller et

al., 1999) is reported to be most prominent at these positions.

Stimulus related ERD

Single sample t-tests were carried out to test whether the power change at Oz and Czd in the last

interval preceding stimulus presentation -or in case of the VM condition the corresponding

interval- differed from 0, indicating an ERD or ERS.

Furthermore, an ANOVA with Experimental Condition (VM, SS, Vis), Response Side (Left

hand, Right hand) Band (10 Hz, 20 Hz) and Electrode Position (Postcentral, Occipital) was

performed at the last interval preceding the presentation. This was done in order to answer the


43

main research question of the present study: does the pattern of power changes indicating

anticipatory attention differ between modalities? For this analysis, electrode Czd was used to

assess the postcentral powerchanges and Oz to assess the occipital powerchanges. In theory, these

positions should represent the sites of maximal ERD preceding stimulus presentation in the

somatosensory and visual modality, respectively.

For all ANOVA’s that were performed, degrees of freedom were corrected using the Greenhouse-

Geisser Epsilon (GGE, Vasey and Thayer, 1987) when necessary. Significant interactions were

clarified by breaking them down into simple effects.

In addition to the ANOVA a Signtest was carried out. Instead of addressing the question whether

the mean power change over all trials in the last interval prior to stimulus onset significantly

deviated from 0, the Signtest focused on the number of trials (within each subject) belonging to

one specific condition (for instance: right hand, SS KR, 8-12 Hz band, Czd) in which power

changes could be denoted as either being above baseline level (ERS) or below baseline level

(ERD) for every subject. The Signtest is was used at the single subject level. A possible

advantage of this approach is that deviational large power changes do not influence the outcome

of the test since only the number of trials in which a dichotomous power change occurs, is taken

into account and not the mean. The main goal of the current Signtest is to assess whether the

number trials exhibiting an ERD (or ERS) per subject is significant when compared to chance

probability (p = 0.5). The number of power changes at both electrode positions prior to the

stimulus presentation was incorporated in the signtest so that double dissociations, if any, could

be displayed.

In order to check whether the behavioral data coincided with the physiological data, bivariate

correlations between several behavioral measures and the levels of power change were computed.

Two distinct approaches with respect to trial selection have led to two sets of behavioral data. The

first method - denoted by (1)- simply incorporates all trials irrespective of the constraints imposed

on the behavioral (i.e. within the range of 3500-4500 ms) and physiological (artifacts) trial

selection. Thus, if a subject needs 100 trials to complete a block (consisting of 80 behaviorally

valid trials) of which several trials will be eliminated due to artifacts, the total number of trials in

the behavioral data set remains 100. The second method (2), on the other hand, does take the

behavioral and physiological constraints into account and thus results in a restricted and less

numerous behavioral dataset. This set thus, consists only of trials that are incorporated in the


44

physiological dataset. Note that the measure “correctly adjusted (2)” cannot be computed due to

the incontinuities in the resulting dataset. The following behavioral measures of interest were

encompassed in this correlational analysis:

• the percentage of correctly adjusted trials (see the section on behavioral data)

• the percentage of incorrect trials (1)

• the percentage of incorrect trials (2)

• the percentage of TE trials (1)

• the percentage of TL trials (1)

• the mean response time (1) (estimated interval)

• the mean response time (2) (estimated interval)

• the standard deviation (1)

• the standard deviation (2)

If not reported otherwise an alpha level of .05 was used for all statistical tests.


45

Results

Behavioral data

The quality of the time estimation did not differ significantly between KR modalities and

Response categories as indicated by the ANOVA on the percentage Too short/OK/Too long.

The ANOVA revealed one significant effect: subjects produced more correct (OK) time

estimation intervals (54%) than too short or too late time estimation intervals (22% and 24%

respectively; main effect of Response category: F2, 16 = 41.14, p<0.0001, GGE = 0.9711). No

statistically significant differences between response side and KR modality were found.

The effectiveness of the KR stimulus, did not differ significantly between KR modalities as

indicated by the ANOVA on the percentage correctly adjusted trials. The ANOVA revealed one

significant effect: the mean percentage of correctly adjusted trials was higher for left hand than

for right hand responses (89.1% and 86.2% respectively, main effect of Response side: F(1, 8) =

15.19, p = 0.0046).

Both ANOVAs indicate that the modality in which the KR stimulus was presented did not affect

the subjects’ performance on the time estimation task. Hence, these behavioral data imply that

both stimulus categories are equally capable of guiding future responses in a time estimation

paradigm under the described experimental conditions.

Physiological data

Premovement data

Figure 6 displays the power changes in the 10 Hz frequency band at the last interval preceding

movement. These plots show an ERD prior to movement execution at central electrode positions.

The pattern of power changes at the last interval preceding movement differs between conditions

as indicated by the ANOVA on the last interval preceding movement onset. The results of the

ANOVA on the last interval preceding movement are summarized in table 1. Note that the power


46

changes do not only seem to differ at the last interval preceding movement but also during and

following movement, as can be seen in appendix C.

Figure 6. Power changes in the 10 Hz frequency band at the last interval preceding

movement.

The power changes at pre- and postcentral electrode positions differ between response sides and

that this difference depends on Experimental Condition as well. In both the VM and SS condition,

left hand responses are accompanied by a preponderant ERD at precentral sites whereas left hand

responses in the Visual condition are accompanied by a preponderant ERD at postcentral

electrodes. Right hand responses show no differences between pre- and post-central sites for the

VM and Vis condition, whereas a right hand response in the SS condition is accompanied by a

preponderant ERD at postcentral electrodes. Thus, condition (VM, Vis, SS) affects the interaction

between responseside and the power changes at pre- and postcentral positions. This is indicated

by the Electrode Position * Response Side * Experimental Condition interaction of the ANOVA.

Note that none of the simple effects (Electrode Position * Response Side in Experimental

Condition) are significant.


47

Condition does not only affect the power changes at pre- and postcentral sites, it also affects the

lateralization of the power changes, as indicated by the Experimental Condition * Electrode

Position * Hemisphere interaction. In both the Visual and the VM condition the ERD at

precentral sites is preponderant in the left hemisphere, whereas the ERD at precentral sites in the

SS condition is preponderant in the right hemisphere. In both the SS and VM condition the ERD

at postcentral sites is preponderant in the left hemisphere whereas the ERD at postcentral sites is

preponderant in the right hemisphere. In the VM movement condition the described interaction

between electrode position and hemisphere are statistically significant (Simple effect of Electrode

Position * Hemisphere in VM: F(1, 8) = 5.57; p = 0.046). Simple effects of Electrode Position *

Hemisphere in SS and Electrode Position * Hemisphere in Vis were not significant.

These findings indicate that subtracting the ERD of the Voluntary Movement condition from the

ERD in the time estimation conditions is unjustifiable.

In addition, the location of the ERD in the 10 Hz band seems to differ from the location of the

ERD in the 20 Hz band, as indicated by the nearly significant Band * Electrode Position

interaction. None of the simple effects of Band in Electrode Position were significant. The

location of power changes does not only seem to differ between frequency bands but this

difference is also affected by the factor Hemisphere, as indicated by the Band * Electrode

Position * Hemisphere interaction. However, note that this interaction is only marginally

significant (F(1, 8) = 4.85, p = 0.0587). In the right hemisphere, the location of preponderant

ERD is precentral for the 10 Hz band and postcentral for the 20 Hz band (simple effect of Band *

Electrode Position in Hemisphere: F(1, 8) = 6.58; p = 0.0334).

Effect Df Є F p

Band * Electrode position 1, 8 5.31 0.0502

Condition * Hemisphere 2, 16 0.82263 4.40 0.0403

Band * Electrode position * Hemisphere 1, 8 4.85 0.0587

Condition * Electrode positon * Hemisphere 2, 16 0.87996 8.46 0.0048

Condition * Response side * Electrode position 2, 16 0.89660 3.98 0.0458

Table 1 Statistically significant effects of the ANOVA on the last 250 ms interval

preceding movement. (Note that table 1 includes marginally significant effects as

well).


48

Prestimulus data

Figure 7 displays the power changes in the 10 Hz frequency band at the last interval preceding

stimulus presentation.

Figure 7. Power changes in the 10 Hz band at the last interval preceding stimulus

presentation for the three experimental conditions.

Visual inspection of figure 7 reveals that an ERD (statistically significant, see table 2) can be

observed at electrode position Oz preceding visual KR stimuli. Furthermore, prior to

somatosensory KR stimuli, a (statistically nonsignificant) ERD can be found at electrode position

Czd. However, the Voluntary Movement condition shows a (statistically nonsignificant) ERD at

Czd following left hand movements. Following right hand movements, an (statistically

nonsignificant) ERS can be found in the Voluntary Movement condition. Note the absence of an

ERD at electrode position Oz in the somatosensory condition and the absence of an ERD at

electrode position Czd in the visual condition (right hand). Table 2 presents the results of the t-

tests at the interval corresponding to 250-0 ms preceding stimulus presentation for both response

sides.


49

Power change in 8-12 Hz band

Response side

Left Hand Right Hand

Condition VM SS VIS VM SS VIS

Czd

-1.6 -3.5 -5.0 5.5 -9.2 14.8

Oz

5.9 9.8 -37.0

p<0.01

32.7 0.3 -23.3

Table 2. Results of the t-test at the interval corresponding to 250-0 ms preceding stimulus

presentation for both response sides. Reported values are percentages of power

change. An ERD is denoted by a negative value.

Figure 7 shows the topographic maps of the three last intervals preceding stimulus presentation

when averaged over response side in order to gain insight in the temporal evolution of the power

changes. The statistically significant ERD (see table 3) at Oz prior to the presentation of visual

stimuli remains present.


50

Figure 8. Power changes in the 10 Hz band at the last three intervals preceding stimulus

presentation when averaged over response side.

Power change in 8-12 Hz band

Condition VM SS VIS

Czd

1.9 -6.4 4.9

Oz

19.3 5.0 -30.2

p<0.05

Table 3. Results of the t-test at the interval corresponding to 250-0 ms preceding stimulus

presentation when averaged over response side. Reported values are percentages

of power change. An ERD is denoted by a negative value.


51

The time courses of the power changes at both electrode positions (Czd, Oz) for the three distinct

conditions are shown in figures 9-14.

8-12 Hz power changes in VM condition

-60

-50

-40

-30

-20

-10

0

10

20

30

40

-2750

ms

Respo

nse

+ 2000

ms:

KR

+ 3000

ms

perc

enta

ge p

ower

cha

nge

VM CzdVM Oz

8-12 Hz power changes in VIS condition

-60

-50

-40

-30

-20

-10

0

10

20

-2750 m

s

Respons

e

+ 2000

ms:

KR

+ 3000

ms

perc

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ower

cha

nge

VIS CzdVIS Oz


52

8-12 Hz power changes in SS condition

-60

-50

-40

-30

-20

-10

0

10

20

-2750

ms

Respo

nse

+ 2000

ms:

KR

+ 3000

ms

perc

enta

ge p

ower

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nge

SS CzdSS Oz

8-12 Hz power changes at Czd

-60

-50

-40

-30

-20

-10

0

10

20

30

-2750

ms

Respo

nse

+ 2000

ms:

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+ 3000

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perc

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ge p

ower

cha

nge

Czd SSCzd VISCzd VM


53

8-12 Hz power changes at Oz

-50

-40

-30

-20

-10

0

10

20

30

40

-2750

ms

Respo

nse

+ 2000

ms:

KR

+ 3000

ms

perc

enta

ge p

ower

cha

nge

Oz SSOz VISOz VM

Figures 9-14. Time courses for the 10 Hz band in the three experimental conditions. See the

respective legends on the right of the figure.

Taken together, the single sample t-tests, the topographic maps and the time courses seem to

indicate that the distribution of power changes at both Czd and Oz differs between conditions.

However, the ANOVA at the interval corresponding to 250-0 ms preceding stimulus presentation

does not reveal any differences of the kind (see table 4).

The ANOVA revealed only one marginally significant interaction. This Band * Electrode position

interaction indicates that the ERD in the two frequency bands (10 Hz, 20 Hz) is maximal at

different electrode positions. The ERD at Czd is larger in the 10 Hz band than in the 20 Hz band

as indicated by the simple effect of Band at Czd: F(1, 8) = 17.20; p = 0.0001. The simple effect

of Band at Oz was not significant.


54

Effect Df Є F p

Band * Electrode position 1, 8 4.84 0.0590

Table 4. Results of the ANOVA at the last interval preceding stimulus presentation.

Overall, the signtests at the interval corresponding to the interval 250-0 ms preceding stimulus

presentation did not show any consistent results, thereby confirming the ANOVA.

Correlational analysis.

Correlation between behavioral and physiological data.

Note that the physiological measures in this analysis are restricted to the last interval (250-0 ms)

prior to stimulus presentation.

The correlational analysis revealed a significant correlation (r = 0.730, p = 0.025, N = 9) between

the percentage of incorrect trials (2) and the power change at electrode position Oz in the visual

KR condition. No such correlation appeared to be present in somatosensory KR condition.

Furthermore, correlations between the percentage of incorrect trials (2) and the power change at

electrode position Czd were found neither in the somatosensory KR condition, nor in the visual

KR condition. The variable “percentage of incorrect trials (1)” did not show any significant

correlations with the power changes at either electrode position.


55

Discussion

In order to study the power changes related to anticipatory attention, subjects carried out a time

estimation task and received feedback about their performance. This feedback stimulus was

presented 2 seconds after their response and could be either presented on a monitor (visual) or on

the calf muscle (somatosensory). A third experimental condition (i.e. voluntary movement)

consisted of self-paced responses about once every 10 seconds without having to perform the

time estimation task and thus without feedback stimuli.

The statistical analyses reveal that the movement-related activity is not the same in the three

conditions. Therefore it is not possible to remove movement-related activity from the two

experimental KR conditions by subtracting the voluntary movement from these datasets. Visual

inspection of the plots reveals a medial postcentral ERD preceding somatosensory stimuli

although the statistical analyses do not confirm this notion. Preceding visual stimuli the plots

show an ERD at occipital leads. This is confirmed by the statistical analyses. The plots of the

voluntary movement condition show a small postcentral ERD at the interval corresponding to the

last interval preceding stimulus presentation. This effect is non-significant.

The differences between the three conditions at the last interval preceding stimulus presentation

are not statistically significant.

Differences between conditions in movement-related activity

It is not clear what caused the differences in movement-related activity between the three

experimental conditions (contrary to hypothesis 4). Although Bastiaansen (1999) carried out a

very similar experiment, he did not find any differences in movement-related activity. Several

factors could account for the differences between the two studies. The most prominent one is the

electrode configuration over the scalp. The current study encompassed more central electrodes in

the statistical analyses and may therefore be more sensitive to differences.

In order to address the question why does the movement-related activity differ between

conditions? it is useful to take the experimental design into account. One may hypothesize that

the actual movement differed somewhat between the time-estimation and voluntary movement


56

conditions. Since force (e.g. Stancák, 1997; Pfurtscheller, 1999) and other the kinematic

parameters (e.g. Toro, 1994) are believed to affect the pattern of ERD change, different kinematic

parameters may result in a different pattern of power changes. But why would the time estimation

conditions differ from the voluntary movement conditions? Note that the voluntary movement

condition always preceded the other conditions. Since subjects initially were not familiar with the

requirements of the movement (e.g. they did not know how hard they should press), it can be

hypothesized that the first series of responses were different from the latter.

Furthermore, since the VM condition always preceded the other two conditions, carry-over

effects may occur. Several studies (e.g. Karni et al., 1995; Pascual-Lenone et al., 1995; Zhuang et

al., 1997; Gerloff & Hallett, 1999) point to the fact that motor cortical activation changes as a

function of learning. Zhuang (1997) points out that there is prominent alpha band ERD over the

contralateral central region, which tends to increase during the development of learning. After the

learning process is completed, the ERD over the sensorimotor cortex diminishes again. Although

the task of pressing a manipulandum may not seem to require learning, subjects were explicitly

instructed to produce self-paced brisk unilateral flexions with an inter response rate of at least 7.5

seconds and they were given auditory feedback if they deviated too far from these constraints.

Moreover, it is not clear how and if time estimation tasks affect the distribution of activity over

the scalp (e.g. Mohl & Pfurtscheller, 1991; Treisman, 1994). Since the time estimation is

conveyed through motoric activity (i.e. the response), it is well possible that non-motor related

processes interfered with motor-related brain activity. Note that the movement-related – that is,

supposedly movement-related - differences in power change between the three conditions are not

restricted to desynchronized activity. Appendix C shows that the postmovement ERS in both

frequency bands clearly differs between condition, especially with regard to the magnitude of the

power changes. As mentioned in the introduction, this synchronization may indicate that there

were differences in neural activation at an earlier point in time.

Furthermore, since the three experimental conditions are distinct in nature, different expectations

and motivational aspects may influence the state of the subject and therefore the distribution of

the scalp recorded power changes.

The ERD preceding and during movement does not only seem to deviate from the findings of

Bastiaansen (1999). Although the central leads do represent the sites of maximal


57

desynchronization, the degree of lateralization is not in line with previous studies (e.g.

Pfurtscheller and Berghold, 1989; Pfurtscheller, 1999). However, Stancák and Pfurtscheller

(1996) found differences in laterality between left and right hand responses in right-handed

subjects. They reported a larger contralateral preponderance of mu rhythm for right hand

movements. As can be seen in appendix C, left hand responses in the current study seems to be

characterized by a predominantly ipsilateral ERD in both frequency bands whereas right hand

movements seem to be characterized by a more a contralateral preponderant ERD. Thus, the

reported results do seem to bear some resemblance to the results presented by Stancák and

Pfurtscheller (1996).

Power changes at Oz in the visual modality

Although the manifestation of prestimulus ERD is undoubtedly hindered by the postmovement

ERS, an occipital ERD was found preceding visual stimuli. This ERD was neither present in the

voluntary movement condition nor in the SS KR condition. The results of the present study are in

line with previous findings (Bastiaansen,1999a, b, 2001) and seem to indicate that stimulus

anticipation in the visual modality is reflected by an occipital ERD. Note that this is in accordance

with hypothesis 3.

Power changes at Czd in voluntary movement

Figure 7 shows a non-significant ERD at Czd for left hand responses at the interval corresponding

to the last interval preceding stimulus presentation. This effect is usually not found (e.g.

Bastiaansen, 1999, 2000; Pfurtscheller et al., 1999) and hampers the interpretation of

desynchronized activity at Czd in the somatosensory modality. Figure 7 also shows that right

hand responses are characterized by a comparable pattern of power changes over the scalp.

However, figure 8 makes clear that the activity in the previous intervals was more outspoken.

Therefore, it may be a remnant of movement-related activity.


58

Power changes at Czd in the somatosensory modality

According to the statistical analyses, no significant ERD is present over electrode position Czd

preceding the presentation of somatosensory stimuli. This is not in line with the key hypothesis of

this study (hypothesis 1). However, the topographic maps of the power changes do appear to

show such an ERD, although it is quite small. Two factors may underlie the lack of statistical

significance. Firstly, the power of the statistical analyses is rather low owing to the small number

of subjects. Thus, the probability of detecting true differences is low. Secondly, there is quite a

large amount of inter-subject variability in power changes at Czd preceding SS KR stimuli (SD of

power change at Czd = 30.78). Before turning to the topic of inter-subject variability, several

possible causes of the rather small ERD at Czd will be discussed.

It is not surprising that prestimulus ERD at Czd suffers more from postmovement ERS than the

prestimulus activity at Oz, for postmovement synchronization is expected to occur predominantly

at central electrodes (e.g. Pfurtscheller, 1992, 1999). Therefore it is likely that the expected

prestimulus ERD is masked by the postmovement synchronization (see methods section:

physiological data, motor related activity).

Furthermore, as Pfurtscheller et al. (1996) point out, the foot (and leg) area mu rhythm is difficult

to detect in EEG recordings because of the anatomical location of the foot (and leg) area within

the mesial wall in the interhemispheric fissure. It might be hypothesized that the mu activity that

is related to the leg does not contribute much to the mu power in the baseline interval due to these

detection characteristics of the leg related mu rhythm. Therefore, the shifting from synchronized

activity to desynchronized activity may result in a marginal desynchronization - irrespective of

possible interference with postmovement synchronization - localized in the proximity of the

cortical leg area. Since no studies have been reported to encompass both ERD and somatosensory

stimulation of the leg, this hypothesis remains speculative.

Inter-subject variability

All experimental conditions are characterized by a large inter-subject variability. The standard

deviation (SD) of the voluntary movement condition at electrode position Cz and Oz was 41.71


59

and 42.69, respectively. The SD of the visual condition at electrode position Oz was 35.79

whereas the SD of the somatosensory condition at electrode position Czd was 30.78.

The latency of the postmovement synchronization is a factor that may have caused inter-subject

and even intra-subject variability. Since the mu rhythm builds up in the 2 seconds following

movement (e.g. Pfurtscheller et al., 1999), the point in time at which the stimulus is presented is

decisive for the level of interference. Stancák and Pfurtscheller (1995) point out that the onset of

postmovement synchronization depends on the type of movement – brisk or slow- , and thus on

movement offset. In the current experiment, some subjects may have consistently pressed the

transducer longer than others, thereby postponing the onset of mu synchronization. Thus, stimuli

may have been presented at different points in time relative to the ERS peak. While some subjects

receive their stimuli at the time of the ERS peak, others, who took more time for their response,

received their stimuli well before the ERS peak.

Another factor that could play a role is the use of prefixed frequency bands (see Appendix B). As

Pfurtscheller (1999) puts it: “Due to large inter-individual differences of alpha frequency, large

portions of alpha power may fall outside a fixed frequency window and animate to misleading

interpretations”.

A large number of ERD studies that address the mu rhythm are based on movement-related-

power changes in the mu frequency band, as opposed to power changes that are elicited by

stimulation. However, the findings of the movement-related mu may not be generalized without

restraint for the simple reason that the two processes are different in essence. Unlike the source of

excitation for power changes related to somatosensory stimulation, the source of excitation for

movement-related power changes appears to be primarily proprioceptive (Sterman, 1999).

Sterman (1999) points out that the proprioceptive pathways are mainly separate from cutaneous

afferents. For this reason, propriceptive and non-proprioceptive activation might result in a

different pattern of power changes. The afore-mentioned inter-subject variability in the

somatosensory modality may be related to the issue of proprioceptive and cutaneous activation.

Several subjects reported feeling contractions of the gastrocnemius during stimulus presentation.

This combined cutaneous and proprioceptive stimulation that some subjects experienced may

have resulted in a different pattern of power changes over subjects. However, since no studies

have addressed this specific topic, this hypothesis remains speculative.


60

Correlation between behavioral and physiological data

As mentioned in the results section, there is a significant correlation between the percentage of

incorrect trials (2) and the power change at electrode position Oz in the visual KR condition.

Thus, the higher the percentage of incorrect time estimations, the greater the magnitude of the

ERD at electrode position Oz. This finding might be interpreted as follows. The more mistakes a

subject makes, the harder the time estimation task appears to be for that subject. In order to

perform well on the task he must use the KR stimuli to guide him through the subsequent time

estimation trial. Therefore, the subject anticipates the KR stimulus with care, which results in a

more pronounced ERD at Oz.


61

Conclusions

The results of this experiment indicate that anticipated visual stimuli are preceded by an occipital

ERD (hypothesis 3). Presumably, this ERD reflects visual anticipatory attention. Anticipated

somatosensory stimuli are not preceded by an occipital ERD. However, although the mean power

change seems to indicate an ERD, the results fail to show a significant postcentral ERD preceding

somatosensory stimuli. Thus, hypothesis 1 is rejected. Furthermore, this experiment could not

demonstrate that the distribution of power changes preceding stimuli differs between the visual,

the somatosensory and the control condition (VM). Consequently, hypothesis 2 is rejected as

well. In addition, the significant differences in power changes preceding movement between the

three conditions lead to the rejection of hypothesis 4.

The present data do not support the claim of the extended thalamocortical model (Brunia, 1999)

that the manifestation of anticipatory attention depends on the stimulus modality. Nevertheless,

these findings do not falsify the model either. As the discussion points out, a large number of

factors may have affected the manifestation of anticipatory attention in the somatosensory

modality.


62

Recommendations

§ Due to the described interference, a time estimation paradigm with the current timing

parameters does not seem to be a suitable paradigm for studying anticipatory attention in

the somatosensory modality.

§ However, if a time estimation paradigm with KR is used:

§ Record EMG of the moving limb to determine movement onset and movement

offset. The interval between the response and the KR stimulus should be locked

to movement offset to circumvent the effect of movement-duration on the onset

of postmovement synchronization. This procedure allows for a more reliable

analysis of the latency and the generation sites of postmovement synchronization.

In addition, this procedure can shed light on other movement parameters that may

affect the pattern of power changes.

§ It is useful to determine the site of maximal reactivity to sensory stimulation prior

to the statistical analysis. Although the cortical representation of the leg is well

defined, the power changes in the 10 Hz band during sensory stimulation of the

leg are not. A statistical analysis merely based on assumptions about this site may

render errors. In order to determine the site of maximal reactivity, the inter trial

variance method can be used on the interval of stimulus presentation.

§ The physiological data should be subdivided into trials that are either TE, OK or

TL. This classification allows for a more reliable analysis of the correlation

between behavioral measures and the physiological data than the correlational

analyses described in this thesis.

§ Although the trials that fell out of the 3500 ms to 4500 ms range were discarded

from further analysis it is interesting to compare the power changes in these trials

to OK trials.


63

§ The behavioral data from the training blocks should be recorded for each subject.

The resulting window may have consequences for the correlations between the

behavioral and physiological data.

§ Electrical stimulation may yield non-physiological artifacts in the data records.

Furthermore, this type of stimulation may trigger unwanted physiological reactions such

as reflexes. For these reasons, it is not feasible to analyze the EEG data at the interval of

stimulus presentation reliably. The use of a vibrating device attached to the skin is

recommended.

§ A larger number of subjects would increase the power of the statistical tests.

§ Whenever possible, the use of fixed frequency bands should be avoided. It may blur

effects that would have been clear with the use of individually adjusted frequency bands.

§ The usefulness of the EOG correction in ERD and the effect it has on the pattern of

power changes should be ascertained.


64

References

Allport, D.A. (1980). Patterns and actions: cognitive mechanisms are contentspecific. In G.

Claxton (Ed.), Cognitive psychology: new directions (pp. 26-64). London: Routledge

and Kegan Paul.

Allport, D.A., Antonis, B., & Reynolds, P. (1972). On the division of attention: a disproof of

the single-channel hypothesis. Quarterly Journal of Experimental Psychology, 24,

225-235.

Bastiaansen, M.C.M. (2000). Anticipatory attention: an Event-related desynchronization

approach. Unpublished doctoral dissertation, Katholieke Universiteit Brabant, Tilburg.

Bastiaansen, M.C.M., Böcker, K.B.E., Cluitmans, P.J.M., & Brunia C.H.M. (1999a). Event-

related desynchronization related to the anticipation of a stimulus providing

knowledge of results. Clin. Neurophysiol., 110, 250-260.

Bastiaansen, M.C.M., Brunia, C.H.M., & Böcker, K.B.E. (1999b). ERD as an index of

anticipatory behavior. In G. Pfurtscheller & F.H. Lopes da Silva (Eds.), Event-related

desynchronization. Handbook of Electroencephalography and Clinical

Neurophysiology, revised series, Vol 6 (pp. 203-217). Amsterdam: Elsevier.

Bastiaansen, M.C.M, Böcker, K.B.E., Brunia, C.H.M., de Munck, J.C., & Spekreijse, H. (2001).

Event-Related Desynchronization during anticipatory attention for an upcoming stimulus:

a comparative EEG/MEG study. Clinical Neurophysiology, 112,

393-403.

Berger, H. (1929). Uber das Elektroencephalogramm des Menschen. Arch. Psychiat. Nerv.

Krankh., 87, 527-570.

Berger, H. (1930). Uber das Elektroenkephalogramm des Mennschen II. Psychol. Neurol., 40,

160-179.


65

Bhansali, P.V., & Potter, R. (1986). Digital demodulation. IEEE Trans. Instrum. Meas., Im-

35, 324-327.

Bickford, R.G. (1987). Electroencephalography. In: G. Adelman (Ed.), Encyclopedia of

Neuroscience,Vol. 1 (pp. 371-373). Boston: Birkhauser.

Birbaumer, N., Elbert, T., Canavan, A., & Rockstroh, B. (1990). Slow potentials of the cerebral

cortex and behavior. Physiol. Rev., 70, 1-41.

Böcker, K. (1994). Spatio-temporal dipole models of slow cortical potentials. Unpublished

doctoral dissertation, Katholieke Universiteit Brabant, Tilburg.

Böcker, K.B.E., Brunia, C.H.M. and Van den Berg-Lenssen, M.M.C. (1994). A spatiotemporal

dipole model of the stimulus-preceding negativity (SPN) prior to feedback stimuli. Brain

Topogr., 7, 71-88.

Broadbent, D.E. (1958). Perception and communication. London: Pergamon Press.

Broadbent, D.E. (1971). Decision and stress. London: Academic Press.

Broadbent, D.E. (1982). Task combination and the selective intake of information. Acta

Psychologica, 50, 253-290.

Brunia, C.H.M. (1980). What is wrong with legs in motor preparation? In: H.H. Kornhuber &

L. Deecke (Eds.), Progress in brain research: vol. 54, motivation, motor and sensory

processes of the brain (pp. 232-236). Amsterdam: Elsevier.

Brunia, C.H.M (1993). Waiting in readiness: gating in attention and motor preparation.

Psychophysiol., 30, 327-339.

Brunia, C.H.M. (1997). Op het kruispunt van anticipatoire attentie en motorische preparatie.

In: A. Kok & A.J.W. Boelhouwer (Eds.), Aandacht: een psychofysiologische

benadering (pp. 186-231). Assen: Van Gorkum.


66

Brunia, C.H.M. (1999). Neural aspects of anticipatory behavior. Acta Psychologica, 101, 213-

242.

Brunia, C.H.M., & Damen, E.J.P. (1988). Distribution of slow brain potentials related to motor

preparation and stimulus anticipation in a time estimation task. Electroenceph. Clin.

Neurophysiol., 69, 234-243.

Brunia, C.H.M., & Vingerhoets, A.J.J.M. (1981). Opposite hemisphere differences in

movement related potentials preceding foot and finger flexions. Biol. Psychol., 13,

261-269.

Chatrian, G.E., Canfield, R.C., Lettich, E., & Black, R.G. (1974). Cerebral responses to

electrical stimulation of tooth pulp in man. J. Dent. Res., 53 (5), 1299.

Cherry, E.C. (1953). Some experiments on the recognition of speech, with one and with two

ears. Journal of the acoustical society of America, 25, 975-979.

Chwilla, D.J., & Brunia, C.H.M. (1991). Event-related potential correlates of non-motor

anticipation. Biol. Psychol., 32, 125-141.

Clochon, P., Fontbonne, J.M., Lebrun, N. & Etevenon, P. (1996). A new method for

quantifying EEG event-related desynchronization: amplitude envelope analysis.

Electroenceph. Clin. Neurophysiol., 98, 126-129.

Cooper, R., Winter, A.L., Crow, H.J., & Walter, W.G. (1965). Comparison of subcortical,

cortical and scalp activity using chronically indwelling electrodes in man.


Contreras, D., & Steriade, M. (1996). Spindle oscillations in cats: the role of corticothalamic

feedback in thalamically generated rhythm. J. Physiol. (Lond.), 490, 159-180.

Crabtree, J.W. (1989). Prenatal development of retinocollicular projections in the rabbit: an

HRP study. J. Comp. Neurol., 286 (4), 504-513.


67

Crabtree, J.W. (1992). The somatotopic organization within the cat’s thalamic reticular

nucleus. The European Journal of Neuroscience, 4 (12), 1352-1361.

Crabtree, J.W. (1996). Organization in the somatosensory sector of the cat’s thalamic reticular

nucleus. J. Comp. Neurol. 366 (2), 207-222.

Damen, E.J.P., & Brunia, C.H.M. (1987a). Changes in heart rate and slow brain potentials

related to motor preparation and stimulus anticipation in a time estimation task.


Damen, E.J.P., & Brunia, C.H.M. (1987b). Precentral potential shifts related to motor

preparation and stimulus anticipation: a replication. In R. Johnson, Jr., J.W.

Rohrbaugh and R. Parasuraman (Eds.), Current trends in event-related potential

research. Electroenceph. Clin. Neurophysiol., Suppl. 40 (pp.13-16). Amsterdam:

Elsevier.

Damen, E.J.P., & Brunia C.H.M. (1994). Is a stimulus converying task-relevant information a

sufficient condition to elicit a stimulus-preceding negativity? Psychophysiol., 31, 129-

139.

Deschenes, M., Paradis, M., Roy, J.P., & Steriade, M. (1984). Electrophysiology of neurons

of lateral thalamic nuclei in cat: resting properties and bust discharges. J. Neurophysiol.,

51, 1196-1219.

Deecke, L., & Kornhuber, H.H. (1977). Cerebral potentials and the initiation of voluntary

movement. In J.E. Desmedt (Ed.), Attention, voluntary contraction and event-related

cerebral potentials (pp. 132-150). Basel: Karger.

Deutsch, J.A., & Deutsch, D. (1963). Attention: some theoretical considerations. Psychological

Review, 70, 80-90.

Doppelmayr, M., Klimesch, W., Pachinger, T., Ripper, B. (1998). Individual differences in

brain dynamics: important implications for the calculation of event-related band power.

Biol. Cybern., 79, 49-57.


68

Downing, C.J., & Pinker, S. (1985). The spatial structure of visual attention. In M.I. Posner &

O.S.M. Marin (Eds.), Attention and performance XI. Hillsdale, NJ: Lawrence

Erlbaum Associates Inc.

Gaillard, A.W.K., & Van Beijsterveld, C.E.M. (1991). Slow brain potentials elicited by a cue

signal. J. Psychophysiol., 5, 337-347.

Gastaut, H. (1952). Etude électrocorticographique de la réactivité des rhythmes rolandiques.

Rev. Neurol., 87, 176-182.

Gerloff, C., Hallett, M. (1999). ERD and coherence of sequential movements and motor

learning. In G. Pfurtscheller & F.H. Lopes da Silva (Eds.), Event-related



Guido, W., & Weynand, T. (1995). Burst responses in thalamic relay cells of the awake

behaving cat. J. Neurophysiol., 74, 1782-1786.

Guillery, R.W., Feig, S.L., Loszadi, D.A. (1998). Paying attention to the thalamic reticular

nucleus. Trends Neurosci., 21, 28-32.

Hari, R. (1993). Magnetoencephalography as a tool for clinical neurophysiology. In: E.

Niedermyer & F. Lopes da Silva (Eds.), Electroencephalography. Basic principles,

clinical applications and related fields (3rd ed., pp.1035-1061). Baltimore: Williams

and Wilkins.

Hari, R., & Salenius, S. (1999). Rhythmical corticomotor communication. Neuroreport, 10,

R1-R10.

Hari, R., Salmelin, R., Makela, J.P., Salenius, S., Helle, M. (1997). Magnetoencephalographic

cortical rhythms. Int. J. Psychophysiol., 26, 51-62.


69

Helmholtz, H. von, (1896). Handbuch der physiologischen Optik (2nd ed.). Hamburg: Voss.

Hinton, G., & Anderson, J.R. (1981). Parallel models of associative memory. Hillsdale, NJ:

Lawrence Erlbaum Associates Inc.

Hjorth, B. (1975). An on-line transformation of EEG scalp potentials into orthogonal source

derivations. Electroenceph. Clin. Neurophysiol., 39, 526-530.

Hoffman, J.E., Nelson, B., & Houck, M.R. (1983). The role of attentional resources in

automatic detection. Cognitive Psychology, 51, 379-410.

Jahnsen, H., & Llinás, R.R. (1984a). Electrophysiological properties of guinea-pig thalamic

neurons: an in vitro study. J. Physiol.(Lond.), 349, 205-226.

Jahnsen, H., & Llinás, R.R. (1984b). Ionic basis for the electroresponsiveness and oscillatory

properties of guinea-pig thalamic neurons in vitro. J. Physiol. (Lond.), 349, 227-247.

James, W. (1890). The principles of psychology. New York: Holt.

Jasper, H.H., & Andrews, H.L (1938). Electro-encephalography III. Normal differentiation of

occipital and pre-central regions in man. Arch. Neurol. Psychiatry, 39, 96-115.

Jasper, H.H., & Penfield, W. (1949). Electrocorticograms in man: effect of voluntary

movement upon the electrical activity in the pre-central gyrus. Arch. Psych., 183,

163-173.

Johnston, W.A., & Heinz, S.P. (1979). Depth of nontarget processing in an attention task.

Journal of Experimental Psychology: Human Perception and Performance, 5, 168-

175.

Jones, E.G. (1985). The thalamus. New York: Plenum Press.

Kahneman, D. (1973). Attention and effort. New York: Prentice-Hall.


70

Kalcher, J. & Pfurtscheller, G. (1995). Discrimination between phase-locked and non-phase-

locked event-related EEG activity. Electroenceph. Clin. Neurophysiol., 94, 381-384.

Kantowitz, B.H., & Knight, J.L. (1976). Testing, tapping, timesharing: II. Auditory secondary

task. Acta Psychologica, 40, (5), 343-362.

Karni, A., Meyer, G., Jezzard, P., Adams, M.M., Turner, R., & Ungerleider, L.G. (1995).

Functional MRI evidence for adult motor cortex plasticity during motor skill learning.

Nature, 377, 155-158.

Kitamura, J., Shibasaki, H., & Kondo, T. (1993). A cortical slow potential is larger before an

isolated movement of a single finger than simultaneous movement of two fingers.


Klimesch W. (1999). Event-related band power changes and memory performance. In: G.

Pfurtscheller & F.H. Lopes da Silva (Eds.), Event-related desynchronization.

Handbook of Electroencephalography and Clinical Neurophysiology, revised series,

vol 6 (pp. 161-178). Amsterdam: Elsevier.

Klimesch, W., Doppelmayr, M., Russegger, H., Pachinger, Th., & Schweiger, J. (1998). Induced

alpha band power changes in the human EEG and attention. Neurosci. Lett., 244, 73-76.

Klimesch, W., Pfurtscheller, G., & Schimke, H. (1992).Pre-and poststimulus processes in

category judgment tasks as measured by event-related desynchronization (ERD). J.


Kok, A., Boelhouwer, A.J.W. (1997). Aandacht: een psychofysiologische benadering. Assen:

Van Gorcum.

Knowles, W.B. (1963). Operator loading tasks. Human Factors, 5, 151-161.

Kornhuber, K.K., & Deecke, L. (1965). Hirnpotentialanderungen bei Willkurbewegungen und

passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente

Potentiale. Pflugers Archiv, 284, 1-17.


71

Kuhlman, W. (1978). Functional topography of the human mu rhythm. Electroenceph. Clin.


Külpe, O. (1902). Attention, Monist, 13.

Kutas, M., & Donchin, E. (1980). Preparation to respond as manifested by movement related

brain potentials. Brain Res., 202, 95-115.

Laberge, D. (1983). Spatial extent of attention to letters and words. Journal of Experimental

Psychology: Human Perception and Performance, 9, 371-379.

Laberge, D. (1995) Attentional processing: the brain’s art of mindfulness. Cambridge: Harvard

University Press.

Laberge, D., & Buchsbaum, J.L. (1990). Positron emission tomography measurements of

pulvinar activity during an attention task. Journal of Neuroscience, 10, 613-619.

Laberge, D., Legrand, R., Hobbie, R.K. (1969). Functional identification of perceptual and

response biases in choice reaction time. J. Exp. Psychol., 79 (2), 295-299.

Laberge, D., Tweedy, J.R., Ricker, J. (1967). Selective attention: incentive variables and choice

time. Psychonomic-Science, 8 (8), 341-342.

Lang, W., Zich, O., Koska, C., Lindinger, G., & Deecke, L. (1989). Negative cortical DC shifts

preceding and accompanying simple and complex sequential movements. Exp. Brain

Res., 74, 99-104.


72

Leocani, L., Toro, C., Manganotti, P., Zhuang, P., & Hallet, M. (1997). Event-related

coherence and event-related desynchronization/synchronization in the 10 Hz and 20

Hz EEG during self-paced movements. Electroenceph. Clin. Neurophysiol, 104, 199-

206.

Lindsley, D.F., Barton, R.J., & Atkins, R.J. (1970). Effects of subthalamic lesions on

peripheral and central arousal thresholds in cats. Exp. Neurol., 26 (1), 109-119.

Llinás, R.R., & Geijo-Barrientos, E. (1988). In vitro studies of mammalian thalamic and

reticularis thalami neurons. In M. Bentivoglio & Spreafico (Eds.), Cellular thalamic

mechanisms (pp. 23-33). Amsterdam: Elsevier.

Lopes da Silva, F.H. (1991). Neural mechanisms underlying brain waves: from neural

membranes to networks. Electroenceph. Clin. Neurophysiol., 79, 81-93.

Lopes da Silva, F.H. & Pfurtscheller, G. (1999). Basic concepts of EEG synchronization and

desynchronization. In G. Pfurtscheller & F.H. Lopes da Silva (Eds.), Event-related



Lopes da Silva, F.H., Van Lierop, T.H.M.T., Schrijer, C.F.M., & Storm van Leeuwen, W.

(1973). Organization of thalamic and cortical alpha rhythm: spectra and coherences.


McClelland, J.L., Rumelhart, D.E., & Hinton, G.E. (1986). The appeal of parallel distributed

processing. In D.E. Rumelhart & J.L. McClelland (Eds.), Parallel distributed

processing: explorations in the microstructure of cognition (Vol. 1, pp. 2-44).

Cambridge, MA: MIT Press.

Misulis, K.E. (1997). Essentials of clinical neurophysiology. Newton: Butterworth-

Heinemann.

Mitrofanis, J., Guillery, R. (1993). New views of the thalamic reticular nucleus in the adult

and developing brain. TINS, 16, 414-417.


73

Mohl, W., & Pfurtscheller, G. (1991). The role of the right parietal region in a movement time

estimation task. Neuroreport, 2, 309-312.

Moray, N. (1959). Attention in dichotic listening: affective cues and the influence of

instructions. Quarterly Journal of Experimental Psychology, 11, 56-60.

Näätänen, R. (1986). The neural-specificity theory of visual selective attention evaluated: a

commentary on Harter and Aine. Biol. Psychol., 23, 281-295.

Näätänen, R. (1992). Attention and brain function. Hillsdale: Erlbaum.

Navon, D., & Gopher, D. (1979). On the economy of the human-processing system.

Psychological Review, 86, 214-255.

Niedermeyer, E. (1990). Alpha-like rhythmical activity of the temporal lobe. Clin.

Electroenceph., 21, 210-224.

Niedermeyer, E. (1991). The “third rhythm”: further observations. Clin. Electroenceph., 22,

83-96.

Niedermeyer, E. (1993). The normal EEG of the waking adult. In: E. Niedermeyer & F. Lopes

da Silva (Eds.), Electroencephalography: basic principles, clinical applications and

related fields (pp. 131-152). Baltimore: Williams & Wilkins.

Niedermeyer, E., & Lopes da Silva, F. (1982). Electroencephalography, basic principles, clinical

applications and related fields. Baltimore, munich: Urban & Schwartzenberg.

Norman, D.A., & Bobrow, D.G. (1975). On data-limited and resource-limited processes.

Cognitive Psychology, 7, 44-64.

North, R.A. (1977). Task components and demands as factors in dual-task performance (Report

No. ARL-77-2/AFOSE-77-2). Urbana, University of Illinois at Urbana-Champaign,

Aviation Research Laboratory.


74

Nunez, P.L. (1995). Neocortical dynamics and human EEG rhythms. New York: Oxford

University Press.

Papakostopoulos, D., Crow, H.J., & Newton, P. Spatiotemporal characteristics of intrinsic

evoked and event related potentials in the human cortex. In G. Pfurtscheller & F. Lopes

da Silva (Eds.), Rhythmic EEG activities and cortical functioning (pp. 179-199).

Amsterdam: Elsevier.

Pascual-Leone, A., Dang, A., Cohen, L.G., Brasil-Neto, J.P., Cammarota, A., & Hallet, M.

(1995). Modulation of muscle responses evoked by transcranial magnetic stimulation

during the acquisition of new fine motorskills. J. Neurophysiol., 74, 1037-1045.

Pashler, H.E. (1998). The psychology of attention. Cambridge: The MIT Press.

Pavlov, I.P. (1927). Conditioned reflexes. Oxford: Oxford University Press.

Perrin, F., Bertrand, O., & Pernier, J. (1987). Scalp current density mapping: value and

estimation from potential data. IEEE transactions on biomedical engineering, 34, 283-

288.

Perrin, F., Pernier, J., Bertrand, O, & Echallier, J.F. (1989). Spherical splines for scalp

potential and current density mapping. Electroenceph. Clin. Neurophysiol., 72, 184-

187.

Pfurtscheller, G. (1981). Central beta rhythm during sensorimotor activities in man.


Pfurtscheller, G. (1986). Rolandic mu rhythms and assessment of cerebral functions. Am. J.

EEG Technnol., 26, 19-32.

Pfurtscheller, G. (1992). Event-related synchronization (ERS): an electrophysiological

correlate of cortical areas at rest. Electroenceph. Clin. Neurophysiol., 83, 62-69.


75

Pfurtscheller, G. (1999). Quantification of ERD and ERS in the time domain. In G.



Vol 6 (pp. 89-105). Amsterdam: Elsevier.

Pfurtscheller, G., & Aranibar, A. (1977). Event-related cortical desynchronization detected by

power measurements of scalp EEG. Electroenceph. Clin. Neurophysiol., 42, 817-826.

Pfurtscheller, G., & Aranibar, A. (1980). Voluntary movement ERD: normative studies. In: G.

Pfurtscheller, P. Buser, F.H. Lopes da Silva & H. Petsche (Eds.), Rhythmic EEG

activities and cortical functioning (pp. 151-177). Amsterdam: elsevier.

Pfurtscheller, G., & Berghold, A. (1989). Patterns of cortical activation during planning of

voluntay movement. Electroenceph. Clin. Neurophysiol., 72, 250-258.

Pfurtscheller, G., & Klimesch, W. (1991). Event-related desynchronization during motor

behavior and visual information processing. In: C.H.M. Brunia, G. Mulder & M.N.

Verbaten (Eds.), Event-related brain research. Electroencephal. Clin. Neurophysiol.,

suppl. 42 (pp. 58-65). Amsterdam: Elsevier.

Pfurtscheller, G., & Lopes da Silva, F.H. (1999). Event-related EEG-MEG synchronization

and desynchronization: basic principles. Clin. Neurophysiol., 110, 1842-1857.

Pfurtscheller, G., & Lopes da Silva, F.H. (Eds.) (1999), Event-related desycnronization.


Vol 6. Amsterdam: Elsevier.

Pfurtscheller, G., & Neuper, C. (1994). Event-related synchronization of mu rhythm in the

EEG over the cortical hand area in man. Neurosci. Lett., 174, 93-96.

Pfurtscheller, G., & Neuper, C. (1997). Motor imagery activates primary sensorimotor area in

humans. Neurosci. Lett., 239, 65-68.


76

Pfurtscheller, G., Neuper, Ch., & Kalcher, J. (1993). 40 Hz oscillations during motor behavior

in man. Neursci. Lett., 164, 179-182.

Pfurtscheller, G., Pichler-Zalaudek, K., Neuper, Ch. (1999). ERD and ERS in voluntary

movement of different limbs. In G. Pfurtscheller & F.H. Lopes da Silva (Eds.), Event-

related desynchronization. Handbook of Electroencephalography and Clinical


Pfurtscheller, G., Stancák, A., Jr., & Edlinger, G. (1997). On the existence of different types

of central beta rhythms below 30 Hz. Electroencephal. Clin. Neurophysiol., 102, 316-

325.

Pfurtscheller, G., Stancák, A., Jr., & Neuper, C. (1996). Post-movement beta synchronization.

A correlate of an idling motor area? Electroenceph. Clin. Neurophysiol., 98, 281-293.

Posner, M.I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology,

32, 3-25.

Poulton, E.C. (1953). Two-channel listening. J. Exp. Psychol., 46, 91-96.

Poulton, E.C. (1956). Listening to overlapping calls. J. Exp. Psychol., 52, 334-339.

Rohrbaugh, J.W. (1984). The orienting reflex. In R. Parasuraman & D.R. Davies (Eds.),

Variaties of attention (pp. 323-373). New York: Academic Press.

Rougeul, A., Bouter, J.J., Dedet, L., & Debray, O. (1979). Fast somatoparietal rhythms during

combined focal attention and immobility in baboon and squirrel monkey.


Salenius, S., Schnitzler, A., Salmelin, R., Jousmaki, V., Hari, R. (1997). Modulation of human

cortical rolandic rhythms during natural sensorimotor tasks. Neuroimage, 5, 221-228.

Salmelin, R., & Hari, R. (1994). Spatiotemporal characteristics of rhythmic neuromagnetic

activity related to thumb movement. Neuroscience, 60, 537-550.


77

Salmelin, R., Hamalainen, M., Kajola, M., & Hari, R. (1995). Functional segregation of

movement-related rhythmic activity in the human brain. Neuroimage, 2, 237-243.

Sanders, A.F. (1979). Some remarks on mental load. In N. Moray (Ed.), Mental workload: its

theory and measurement (pp. 41-71). New York: Plenum Press.

Scheibel, M.E., & Scheibel, A.B. (1966). The organization of the nucleus reticularis thalami.

A Golgi study. Brain Res., 1, 43-62.

Schlag, J., & Waszak, M. (1970). Characteristics of unit responses in nucleus reticularis thalami.

Brain Res., 21, 286-288.

Schneider, W. (1985). Toward a model of attention and the development of automatic processing.

In M.I. Posner & O.S.M. Marin (Eds.), Attention and performance, XI (pp. 475-492).

Hillsdale: Erlbaum.

Shannon, C.E., & Weaver, W. (1949). The mathematical theory of communication. Urbana,

IL: University of Illinois Press.

Skinner, J.E., & Yingling, C.D. (1977). Central gating mechanisms that regulate event-related

potentials and behavior. In J.E. Desmedt (ed.), Attention, voluntary contraction and

slow potential shifts (pp. 30-69). Basel: Karger.

Sokolov, E.N. (1963). Perception and the conditioned reflex. New York: Pergamon Press.

Stancák, A., Jr., & Pfurtscheller, G. (1995). Desynchronization and recovery of beta rhythms

during brisk and slow self-paced finger movements in man. Neurosci. Lett., 196, 21-

24.

Stancák, A., Jr., & Pfurtscheller, G. (1996). The effects of handedness and type of movement

on the contralateral preponderance of mu-rhythm desynchronisation. Electroenceph.

Clin. Neurophysiol., 7, 1161-1164.


78

Stancák, A., Jr., & Riml., A., & Pfurtscheller, G. (1997). The effects of external load on

movement-related changes of the sensorimotor EEG rhythms. Electroenceph. Clin.


Steriade, M., & Deschenes, M. (1984). The thalamus as a neuronal oscillator. Brain RES. Rev., 8,

1-63.

Steriade, M., Deschenes, M., Domich, L., & Mulle, C. (1985). Abolition of spindle

oscillations in thalamic neurons disconnected from nucleus reticularis thalami. J.


Steriade, M., Domich, L., & Oakson, G. (1986). Reticularis thalami neurons revisited: activity

changes during shifts in states of vigilance. J. Neurosci., 6, 68-81.

Steriade, M., Gloor, P., Llinás, R.R., Lopes da Silva, F.H., & Mesulam, M.M. (1990). Basic

mechanisms of cerebral rhythmic activities. Electroenceph. Clin. Neurophysiol., 76,

481-508.

Steriade, M., & Llinás, R.R. (1988), The functional states of the thalamus and the associated

neuronal interplay. Physiol. Rev., 68, 649-742.

Steriade, M., Nunez, A., & Amzica, F. (1993). A novel slow (<1 Hz) oscillation of neocortical

neurons in vivo: depolarizing and hyperpolarizing effects. J. Neurosci., 13, 3252- 3265.

Sterman, M.B. (1999). Functional patterns and their physiological origins in the waking EEG:

a theoretical integration with implications for event-related EEG responses. In G.




Storm van Leeuwen, W., Wieneke, G., Spoelstra, P., & Versteeg, H. (1978). Lack of bilateral

coherence of mu rhythm. Electroenceph. Clin. Neurophysiol., 44, 140-146.

Styles, E.A. (1994). The psychology of attention. Hove: Psychology Press.


79

Tiihonen, J., Hari, R., Kajola, M., Karhu, J., Ahlfors, S., & Tissari, S. (1991).

Magnetoencephalographic 10 Hz rhythm from the human auditory cortex. Neurosci.

Lett., 129, 303-305.

Tiihonen, J., Kajola, M., & Hari, R. (1989). Magnetic mu rhythm in man. Neurosci., 32, 793-

800.

Titchener, E.B. (1908). Lectures on the elementary psychology of feeling and attention. New

York: Macmillan.

Toro, C., Deutschl, G., Thatcher, R., Sato, S., Kufta, C., Hallet, M. (1994). Event-related

desynchronization and movement-related cortical potentials on the EcoG and EEG.

Electroenceph. Clin. Neurophysiol, 93, 380-389.

Treisman, A. (1960). Contextual cues in selective listening. Quarterly Journal of Experimental

Psychology, 12, 242-248.

Treisman, A. (1986). Features and objects in visual processing. Scientific American,

November, 106-115.

Treisman, A., & Davies, A. (1973). Dividing attention to ear and eye. In S. Kornblum (Ed.),

Attention and performance IV (pp. 101-117). NY: Academic Press.

Treisman, A. & Gelade, G. (1980). A feature integration theory of attention. Cognitive

Psychology, 12, 97-136.

Treisman, M., Cook, N., Naish, P.L.N., & MacCrone, J.K. (1994). The internal clock:

electroencephalographic evidence for oscillatory processes underlying time

perception. Quart. J. Exp. Psychol., 47A, 241-289.

Van Burik, M., Edlinger, G., Pfurtscheller, G. (1999). Spatial mapping of ERD/ERS. In G.



80



Van Den Berg-Lenssen, M.M.C., Brunia, C.H.M., & Blom, J.A. (1989). Correction of ocular

artifacts in EEGs using an autoregressive model to describe the EEG: a pilot study.


Van Essen, G.W., & Maunsell, J.H.R. (1983). Hierarchical organization and functional streams in

the visual cortex. Trends in Neuroscience, 6, 370-375.

Vasey, W.M., & Thayer, J.F. (1987). The continuing problem of false positives in repeated

measurements ANOVA in psychophysiology: a multivariate solution. Psychophysiol.,

24, 474-486.

Verbaten, M.N. (1997). Onvrijwillige aandacht en orientatie. In: A. Kok & A.J.W.

Boelhouwer (Eds.), Aandacht: een psychofysiologische benadering (pp. 93-118). Assen:

Van Gorcum.

Welford, A.T. (1952). The “psychological refractory period” and the timing of high speed

performance: a review and a theory. Britisch Journal of Psychology, 43, 2-19.

Wickens, C.D. (1976). The effects of divided attention on human information processing in

tracking. Journal of Experimental Psychology: Human Perception and Performance,

1, 1-13.

Wickens, C.D. (1980). The structure of attentional resources. In R. Nickerson, & R. Pew (Eds.),

Attention and performance, VIII (pp. 239-257). Hillsdale: Erlbaum.

Wickens, C.D. (1984). Processing resources in attention. In: R. Parasuraman, & R.T. Davies

(Eds.), Varieties of attention (pp. 63-102). New York: Academic Press.

Wundt, W. (1904). Principles of physiological psychology (E.B. Titchenere, trans.). New York:

Macmillan.


81

Yerkes, R.M. & Dodson, J.D. (1908). The relation of strength of stimuli to rapidity of habit-

information. Journal of Comparative Neurology and Psychology, 18, 459-482.

Yingling, C.D., & Skinner, J.E. (1977). Gating of thalamic input to the cerebral cortex by

nucleus reticularis thalami. In J.E. Desmedt (Ed.), Attention, voluntary contraction

and slow potential shifts (pp. 70-96). Basel: Karger.

Zhuang, P., Toro, C., Grafman, J., Manganotti, P., Leocani, L., Hallet, M.(1997). Event- related

desynchronization (ERD) in the alpha frequency during development of implicit and

explicit learning. Electroenceph. Clin. Neurophysiol., 102, 374-381.


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Acknowledgements

I am indebted to the following people for their contributions to this paper:

Marcel Bastiaansen

Geert van Boxtel

Ton van Boxtel

Kees Brunia

Franc Donkers

Charles Rambelje


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Appendix A

Contemporary views on attention are shaped by early theories of attention. Therefore, a historic

overview of the main influential theories will be provided. Along the way probable mechanisms

and once probable mechanisms of attentional processes will be discussed.

A historic overview of attention research

The concept of attention has evolved over more than a century and has branched out into several

subsets of attention research. Since the issue of anticipatory attention constitutes the core of the

present thesis, the emphasis will be put on the category of Active attention (see the section on a

taxonomy of attention). Before turning to the field of Active attention, however, a concise

description of Passive attention will be provided.

Passive attention and the orienting response

Passive attention refers to the phenomenon of attention switch caused by an initially unattended

stimulus (Näätänen, 1992, p.60). This type of attention is therefore involuntary rather than

voluntarily controlled. Automatic attention reactions can be seen as a form of passive selective

attention (see the section on a taxonomy of attention). The orienting (-exploratory) reflex as

described by Sokolov (1963) has received a great deal of interest and falls into the range of

passive intensive attention. The Orienting response, or the ‘investigation reaction’ as Pavlov

(1927) termed it, is elicited by a stimulus characterized by a high level of intensity. There are

several ways to elicit orienting responses in humans but traditionally the subject is presented with

an auditory stimulus at an unforeseen point in time. If the subject is paying attention to some

other matter in his environment and the stimulus is presented, his attention suddenly switches to

that unexpected stimulus because of its immediate (James, 1890, 416-418) properties. In general,

abrupt onsets of stimulus energy and a changes in (repetitive) stimulus presentation as well as

peripheral movements in vision tend to be attention catching (Broadbent, 1982).

The orienting response can be divided into two components: attentional (informatical) and

activational (energetical) responses. Most of the physiological effects occurring in the orienting


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response can be interpreted as resulting from a sudden increase in reticular or thalamic

nonspecific activation (e.g. Lindsley, 1970). This suggests that the principal center of the

orienting-response release might be located in the nonspecific activating systems of the brain such

as the reticular formation (Sokolov, 1963; see also Rohrbaugh, 1984). These arousal-related

reactions include sensory effects as well, associated with lowering of sensory thresholds. A

number of responses included in the pattern of bodily effects composing the orienting response,

can, however, be regarded as primarily attentional in nature (Näätänen, 1986). Such effects

include heart-rate deceleration and certain muscular responses, at least those that orient the

sensory receptors in the optimal way, a delay in breathing, perhaps even the arrest of the ongoing

behavior. The immediate interruption of current processing is important to the survival of an

individual who must respond appropriately when a new and possibly threatening object or event

signals its presence.

A concise history of active attention research

Until the end of the nineteenth century, psychology was considered a discipline embedded in the

academic discipline of philosophy. Although Kant treated attention in a footnote in the Critique of

Pure Reason of 1787, philosophy had avoided the term because it was viewed as an aspect of

consciousness. James (1890) was the first well-known philosopher to treat it separately. In the

opening pages on his chapter on attention, James pointed out that the traditional empiricist

position of philosophy regarded the person as “absolutely passive clay, upon which ‘experience’

rains down” (p. 403). In contrast, he claimed, “each of us literally chooses, by his was of

attending to things, what sort of universe he shall appear himself to inhabit” (p.424). James is

often credited for helping distinguish the disciplines of philosophy and psychology because he

looked into the physiological characteristics of the individual. These characteristics did not fit in

with the ideas of pure theorizing, common in philosophy at the time.

Between 1880 and 1920 a great deal was written about the psychology of attention by William

James, Oswald Kulpe and Edward Titchener. Their method of attack involved a mixture of

introspection and, to a lesser degree, formal experimentation. Physiologists such as Wundt

contributed to the field by carrying out relevant experiments, thereby providing other researchers

with valuable data. It is known, for example, that James credited Wundt for the reaction-time

experiments that supported the claim that attention shortens perception time.


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The dominance of behavioral psychology had a suppressing influence on research into the

internal mechanisms of selective attention in the first half of the 20th century and by 1920, ten

years after James died, behaviorism imposed an almost total blackout on academic discussion of

unobservable entities such as attention. For thirty years the ideal explanation in psychology was

to account for a response solely as a function of stimulus input. In the early 1950s however, both

empirical and theoretical studies indicated that the stimulus input was subject to some kind of

selective manipulation, and that this manipulation came from processes within the organism. By

the end of the 1950s the behaviorist approach that had effectively shut off serious consideration of

attention for thirty years was giving selective manifestation a central role in the stimulus-response

paradigm, the backbone of behaviorist learning theory.

Accepting attention as an endogenic phenomenon did not result in a consensus about the

mechanisms of attention, however. Although the notion that selection plays an important role in

attention was undisputed, controversy existed on how selection mechanisms operate and above

all: at what point in time these selections take place. This controversy was fanned by the highly

influential book Perception and Communication (Broadbent, 1958). Broadbent proposed a theory

of attention based on notions of information theory in the communication of messages. Based on

his own research and other contemporary evidence (e.g. Cherry (1953) and Poulton (1953, 1956))

Broadbent proposed a new conception of the mind, in which psychological processes could be

described by the flow of information within the nervous system. The achievement of producing a

diagram of the flow of information through the nervous system should be reckoned as one of his

major contributions. Broadbent drew three main conclusions. First, he concluded that it was

valuable to analyze human functions in terms of information-flow. Second he concluded that the

whole nervous system could be regarded as a single channel which was limited in the rate at

which information could be transmitted, just as is the case with a telecommunications channel.

Third, Broadbent concluded that the limited capacity section of the nervous system would need to

be preceded by a selective filter or switch, which protected the system from overload. The model

became known as Broadbent’s Filter Theory and is characterized by an early “bottleneck” in the

processing of signals. The concept of a bottleneck implies one place where processing can

proceed only at a limited rate, or a limit in capacity to process information. The processing

system is capable of processing one single channel at a time; the other channels are simply

discarded. What type of information is then thought to be filtered out? In short, Broadbent

suggested that stimuli that do not need response are, if possible, discarded before they have been


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fully processed, and that, as physical features of the input are effective cues for separating

messages, there is a filter which operates at the level of physical features. This filter allows the

information characterized by that feature through the filter for further processing. Broadbent’s

theory is an early selection model for the selection from parallel input is made at early levels of

processing. It is critical to note that the word early does not refer to time, but rather to the

sequence of processing stages: selection is said to precede stimulus identification. Since the

machinery that identifies stimuli is capable only of handling one stimulus at a time, the model is

characterized by seriality in processing.

Although the bottleneck metaphor has often been attributed to Broadbent, it was Welford who

touched on the subject in 1952. In the experiments he carried out, two signals were presented in

rapid succession to the subjects and they had to make a speeded response to both signals. He

noticed that reaction time to the second stimulus depended on the difference in stimulus onset,

which he referred to as Stimulus Onset Asynchrony (SOA). There appeared to be a delay in

reaction time when the second stimulus was presented after a short SOA, and this delay, which

Welford termed the Psychological Refractory Period (PRP), seemed to be inversely proportional

to the stimulus onset asynchrony. Welford explained the PRP in terms of seriality: the first

stimulus had to be completed before processing of the fist stimulus must be completed. Therefore,

Welford implied the existence of a bottleneck in human processing capability.

In the late 1950s and the 1960s the search was on for experimental results that challenged

Broadbent’s original theory. An immediate challenge to the 1958 version of filter theory came

from a series of studies by Moray (1959). In shadowing tasks Moray found that listeners often

recognized their own name when it was presented on the presumed unattended ear. This was quite

contradictory to the notion of a selective filter that allowed input to the serial, limited capacity

channel only on the basis of physical attributes. This “breakthrough of the unattended”

(Broadbent, 1982) resulted in Treisman’s (1960) modification of the first version of Broadbent’s

filter theory. The strong assumption of Broadbent’s (1958) eliminative filter began to be called

into question by these experiments that showed that under certain conditions some of the

information on the rejected channel is sampled and processed. It was now clear that selection

from the parallel stages of processing could be much later, or further along the processing

continuum, than Broadbent had initially thought. Treisman (1960) suggested that information

flowing in unattended channels is not switched off but simply weakened or attenuated. This

theory, known as the filter-attenuation theory, assumes that the information available in the


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rejected channel, though attenuated, is sometimes sufficient to activate highly primed entries in

the “mental dictionary” (Treisman, 1960).

The break-through of the unattended observed in several experiments resulted in the emergence

of the late-selection theories of selective attention. The theory of Deutsch and Deutsch (1963),

which could account for semantic effects of the “unattended” message, has been interpreted as the

first late-selection theory. According to this theory, perceptual processing to the semantic level is

entirely automatic and independent of selective attention; the role of attention is restricted to the

control of access of stimuli to consciousness, memory and response. In other words, one cannot

voluntarily choose to identify or recognize something. They suggested that incoming signals were

weighted for importance and in some way compared to determine the currently most important

signal: “Only the most important signals will be acted on and remembered” (Deutsch & Deutsch,

1963, p.84). This theory assumes that breakthrough of the unattended could occur quite

frequently. However breakthrough of the unattended happened only occasionally. If all incoming

information was processed fully it seemed unlikely that there could be so little breakthrough.

Treisman’s filter-attenuation theory (1960) seemed to be strengthened by these facts and in 1964

she published her second filter-attenuation model. Again she proposed that the filter was not an

all-or-nothing affair as Broadbent had said. In this theory, however, Treisman elaborated upon the

dictionary units, which constitute an elemental part in the theory.

In 1971 Broadbent seemed to regard Treisman’s (1960) filter-attenuation version of his filter

theory as a good compromise. In his book on attention, “Decision and stress”, Broadbent (1971)

modified his original theory somewhat, expanding the role of the filter in a way Treisman had

proposed in order to meet the challenge of the data since 1958.

Nonetheless, the filter-attenuation theory could not answer all the arising questions. "The

implications for this theory for divided attention for instance were never clear" (Pashler, 1998,

p.24). What, for example, would happen if one attends to more than one stimulus at the same

time? Would all the attended stimuli be attenuated to some extent in the same way ignored stimuli

were attenuated?

The filter-attenuation theory is a modification of the early selection theory and provides an

alternative to the fairly extreme claims made by the early and late selection theories. Although

this theory was one of the first well recognized alternatives to arise, it was not the only one. Since


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the early 1970s a number of alternatives have been suggested, and most investigators of the time

seemed to suggest that some form of compromise theory will be necessary.

Although the controlled parallel theory (e.g. Johnston and Heinz, 1978) is still considered a

structural theory (i.e. concerned with the location of the bottleneck where parallel processing

stops and serial processing begins), the theory attributes characteristics distinct from classic

structural theories to this bottleneck. When two stimuli are attended, both are identified in

parallel, as in late selection theory. However, when one stimulus is attended and another is

rejected, the rejected stimulus is not analyzed beyond the physical level, as is the case in early

selection theory. Johnston and Heinz (1978) thus allow the bottleneck to move in a flexible way,

so that selection will take place as early as possible in processing. Where selection takes place

would depend current task demands and prevailing circumstances.

The bottleneck metaphor seemed to be wearing thin, for even the structural views of selective

attention discarded the idea of an entirely fixed bottleneck. Furthermore, if the bottleneck can be

moved around as Johnston and Heinz (1978) suggested, perhaps it ceases to be a bottleneck after

all. A decade before Johnston and Heinz published their theory, the call for a metaphor different

from the single channel processor had already emerged. According to original filter theory, there

was just one processing channel and therefore task combination could be achieved only by rapid

switching of the filter and multiplexing, or time-sharing tasks. If it could be demonstrated that

two complex tasks which should require continuous attentional processing could be combined

without loss of speed or accuracy, then the argument that there was only a single processing

channel would have to be abandoned.

Allport, Antonis, and Reynolds demonstrated this in 1972. Nonetheless Broadbent (1982) pointed

out that it is possible to detect decrements in performance data when two tasks are combined.

Furthermore, a major problem with this kind of studies is that it is extremely difficult to

determine whether or not each individual task requires absolutely continuous attentional

processing. According to Broadbent many tasks, including the ones used by Allport et al. (1972)

involve stimuli that have a certain amount of redundancy in them. Therefore, subjects do not have

to attend to both sets of stimuli simultaneously all the time.

Although Broadbent’s criticism may be in place, capacity sharing theories have received a great

deal of interest (e.g. Kahneman, 1973). Capacity sharing theories discard the idea of just one


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processing channel or a fixed bottleneck. Capacity sharing refers to the possibility that

recognizing stimuli takes mental capacity or resources, and that the total amount of this capacity

is limited. Attention theories borrowed the notion of capacity from physics, and from Shannon’s

information theory in particular (Shannon and Weaver, 1949). This limited amount of processing

capacity, or resource, is the second major metaphor that can be found in this thesis. The first of

course is the bottleneck metaphor.

During the 1970s, the resource metaphor, facilitated by the theoretical treatments of Kahneman

(1973), Norman and Bobrow (1975), and Navon and Gopher (1979) underwent an evolution from

a “loose concept to a quantitative theory with testable predictions …” (Wickens, 1984, p.66). The

concept of resource limitation began to gain popularity and was considered the major alternative

to structural theories.

Three different main types of resource theories may be distinguished: the undifferentiated-

resource theory (one pool; differentiated or undifferentiated resources), Kahneman’s (1973)

theory proposing undifferentiated highest level resources (in one pool) in conjunction with a set

of differentiated satellite structures that are usable for one task at a time, and last, the multiple

resource theories (several pools; differentiated resources).

Undifferentiated resource theories assume that there is one reservoir of undifferentiated resources

only. Although there are many differences between undifferentiated resource theories and

structural theories, they are related: if either the attentional resource, or capacity that limits the

system is of a “general purpose” type, then all tasks which require attention will draw upon the

same resource or compete at the same bottleneck.

Preceding the findings of Allport et al. (1972) by almost a decade, Knowles (1963) proposed that

the “human operator” could be thought of as having a “pool” of processing resources, and that

this pool was indeed of limited capacity. If one task demands more of the resources, then there

will be less of the pool available to the “secondary” task. The theory proposed by Knowles (1963)

can be classified as an undifferentiated resource theory: one pool of undifferentiated resources.

Norman and Bobrow (1975) put forward a theory which Styles regards as perhaps the first, best

developed theory of resources in attention (Styles, 1994). Norman and Bobrow (1975) introduced

the hypothetical construct of the performance-resource function (PRF). This function describes


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the relationship of performance level to the quantity of resources invested in a task. Norman and

Bobrow considered performance to be either data- or resource limited. If performance on a single

task does not improve, no matter what amount of "resource" is invested, performance is regarded

as data limited. A performance might be data limited, for instance, because the quality of the

(perceptual or memory) data, is poor. In contrast, in the case that performance would improve if

the amount of resources allocated were increased, the performance is said to be resource limited.

Norman and Bobrow used Performance Operating Characteristics (POC), obtained by plotting the

performance of one task against the performance on the second task, to depict in what way

resources could be allocated to two tasks. It is worthy to note that if two tasks interfere, which is

visible in the POC, they are said to be competing for the same resource; if they don’t interfere,

they are using separate resources or are data limited.

Norman and Bobrow believed that there were a variety of resources in the resource pool, such as

“processing effort, the various forms of memory capacity and communication channels” (Norman

& Bobrow, 1975, p.45).

The single resource model, which is the original resource model, has encountered a number of

difficulties (Wickens, 1984). First of all, this model fails to account for the effects of an increase

in the difficulty of the primary task on the performance of the secondary task. According to the

model, an increase in the demand of the primary task will result in a deterioration of the

performance on the secondary task because the first task presumably consumes more resources.

Yet, this deterioration does not always take place (e.g. North, 1977). The second problem

encountered is the issue of perfect time sharing. As is already referred to above, Allport et al.

(1972), had reason to believe that performing two different tasks in parallel does not necessarily

mean that the performance level of both tasks differs from the individual performance level of the

tasks, i.e. if they were performed one at a time. A third problem that arose involved structural

alternation effects. A change in the processing structure (such as the stimulus modality) involved

in the performance of a certain task causes a change in interference with a concurrent task. This

effect even appeared when the difficulty level of the changed task did not change (e.g. Treisman

& Davies, 1973). A fourth problem refers to cases in which a third task is coupled with one of the

two original tasks. Wickens (1976) found that when a third task is paired to the more difficult of

the original two tasks, this more difficult task interferes less with the third task than does the

easier one of the two original tasks. The specific structures of the two tasks combined seem to be

of importance as well and not just the amount of capacity required.


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It seemed as if the undifferentiated single resource theory was in need of some modification in

order to come into accordance with the available experimental data. Kahneman (1973) proposed a

theory that likens attention to a limited resource which can be flexibly allocated as the human

operator changes its allocation policy from moment to moment as is the case with the

undifferentiated single resource model. However, in addition to the general reservoir of freely

allocatable resources, he proposed more or less dedicated (one performance at a time) and thus

differentiated satellite structures, for example encoding and response modalities. Kahneman

suggested that the resource notion was limited to the highest level of the processing system. With

his theory, Kahneman (1973) provided a model of mind, including dispositions, momentary

intentions and an evaluative decision model that determines the current demand on capacity. The

more difficult a particular task is to an individual, the more effort, or resources, one has to put

into the task. The amount of effort available is also presumed to be related to overall arousal

level: as arousal increases or decreases, so does attentional capacity. The degree of motivation

can influence the amount of attentional capacity available.

But Kahneman’s theory, how well stated it may be, is surrounded by problems that cannot be

solved easily. For instance, Kahneman suggests that an increase in arousal is monotonically

coupled with an improvement in performance. According to Yerkes-Dodson’s law (Yerkes &

Dodson, 1908) however, as arousal increases, so does performance, up to an optimum level,

beyond which further increases in arousal, rather than improving performance, produce

decrements. Another problem involves the independent measurement of difficulty of a certain

task (Allport, 1980b). Kahneman noted that task difficulty could be determined by examining the

amount of interference with a concurrent task. This means that task difficulty of the primary task

can only be established by performing two tasks at a time. However, in what way the secondary

task exerts influence on the interference is not known because again, an independent measure of

the difficulty level of, this time the secondary, task is lacking. The difficulty of this particular task

can only be determined by examining the amount of interference with some other task. In other

words, interference does not say much about one task because the independent characteristics

(especially difficulty) of the other task are unknown. Therefore, Kahneman's theory (1973) is

hard to verify.

A few of the problems discussed above can be overcome by hypothesizing that there is not a

general reservoir or pool of undifferentiated resources but instead a variety of different resources


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available. Multiple resources theories postulate the existence of multiple resources rather than one

general pool (e.g. Kantowitz & Knight, 1976; Navon & Gopher, 1979; Sanders, 1979; Wickens,

1980). Instead of one commodity in the human processing system there are more that may be

assigned resource-like properties such as shareability, flexibility and allocatability.

In 1980 Wickens proposed that resources may be defined by a three-dimensional matrix. Wickens

suggests that that perceptual (encoding) and central processing rely on common resources. These

resources are functionally separate from the resource underlying response processes. Spatial and

verbal processes are each drawing on functionally different resources as is the case with manual

and verbal responses. The resources, defined by Wickens, are nonoverlapping and one of several

implications of this postulation should be noted: two tasks which draw upon completely

nonoverlapping resources will always be perfectly timeshared or, stated differently, will not

interfere with each other and thus will not lead to a deterioration in performance. This postulation

has been the focus of almost all criticism Wickens’ multiple resource theory has received.

Wickens’ theory cannot account for the finding that two tasks, demanding separate

(nonoverlapping) resources, can still interfere with one another, though the degree of interference

is less compared to the degree of interference between two tasks drawing upon the same resource

pool (Treisman & Davies, 1973).

The notion of interference mentioned in the last paragraph seems to support Kahneman’s theory

of differentiated as well as undifferentiated resources, and even seems to support the structural

views on attention.

Since the 1980s a new model of mind seems to prevail (see e.g. Hinton and Anderson, 1981;

McClelland and Rumelhart, 1986), which can be attributed to the explosion in the use and

development of computers. These new computers, in contrast to the older serial digital computers,

were capable of processing information in parallel over multiple processing units. This model,

called the artificial neural network approach, also known as parallel distributed processing (PDP)

or “new connectionism”, has had a profound influence on current metaphors of mind.

Results from neurophysiological studies indicate that the brain is a parallel, highly interconnected

and interactive computing device, with different specialized subsystems designed to respond

selectively to particular perceptual events and compute specialized information processing

options (e.g. van Essen & Maunsell, 1983). With regard to the neurophysiological make-up of the


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brain, especially considering the multiple processing units and interconnections, the brain indeed

seems to resemble a parallel processing computer.

The notion of parallel distributed processing is, after being applied to the field of learning and

memory, now being applied to the modeling of attentional phenomena as well. The postulation of

the existence of multiple processing units has one important implication for theorizing about

attentional processes; that is, every processing unit can function in a different manner. This is a

major advantage over previously discussed models and theories that try to encompass the

attentional process in one single mechanism. In theory, this new connectionism implies that one

separate processing unit could be of the early bottleneck kind, whereas another unit falls into the

category of late selection processing models. Many previously insurmountable problems,

concerning the position of the bottleneck and numbers of resources for the system as a whole, can

be evaded by focusing on the separate units of the system.

One must keep in mind that tasks, used in attention research can differ in the way they are

handled by the human processing system, even if they are considered similar at first glance. When

the results of a certain task indicate that the task is handled in a manner different from the manner

described in a certain theory, this frequently resulted in a rejection of the theory in question.

Because a theory was long considered a theory of attention as a whole, all data available should

fit into the model. However, with the shift from a general theory of attention to a theory of the

separate processing units, it has become possible to determine what processing units are involved

in the handling of different tasks. Thus, paradoxically, striving for a general, all-in-one-theory of

attention thus seems to cloud the comprehension of the underlying mechanisms of attention.

The data, that used to lead to the rejection of a certain theory should be reevaluated, because it is

possible that two seemingly similar tasks rely on different processing units with each their own

processing mechanism. The performed tasks should therefore be thoroughly examined to find out

what the characteristics and demands of the tasks really are.

In the overview, presented above, three major metaphors of attention have been discussed. These

analogies, i.e. the bottleneck theories, resource theories and computer models, are all metaphors

of mind and are concerned with the working mechanisms of attention. These are not the only

metaphors one encounters while in the field of attention. One metaphor in particular, regarding

the mechanism of attention and especially the expression of (selective) attention, deserves to be


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shed light upon for it can provide essential insight in the subjective manifestation of attention.

Attention is often compared with a mental spotlight that can be directed at the relevant features,

illuminating them while other features remain in the dark. (Treisman, 1986). The spotlight can be

viewed as the opposite of the filter; instead of inhibiting information flow in the surround of the

attended area, as a filter does, a spotlight facilitates the information flow within the boundaries of

the attended area. The beam of this spotlight is not restricted to the foveal field (in the case of

visual attention; note that this metaphor can be used for attention in other modalities as well.) but

can be directed outside the foveal visual field as well, and this spotlight can assume various sizes

and shapes. Although attention can be moved away from the center of the fovea, it becomes less

efficient the farther away it is moved (e.g. Downing & Pinker, 1985). James (1890) and

Helmholtz (1896) were both already familiar with the dissociability of the attentional spotlight of

the fixation point. The adjustable width of the attentional spotlight was demonstrated by Laberge

(1983; see also Hoffman, Nelson, & Houck, 1983). The limited processing outside the attentional

spotlight led Johnston and Dark (1986) to conclude that “stimuli outside the spatial focus of

attention undergo little or no semantic processing” (p.54) and that “stimulus processing outside

the attentional spotlight is restricted mainly to simple physical features” (p.56). These findings

have been replicated by a number of studies (e.g. Treisman & Gelade, 1980; for a review, see

Treisman, 1988).

A note of caution is at place, regarding the use of metaphors to clarify the mechanisms of

attentional processes. As Laberge (1995) puts it: ”One problem with using devices like these to

refer to attention is that properties of the device may be inappropriately included in the model

along with features that do usefully describe how attention operates” (p.38).


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Appendix B

On the determination of frequency bands

The upper and lower limit of the band pass filter should be chosen in such a way that no large

portions of alpha power fall outside the frequency window. For this reason the use of fixed

frequency bands may lead to misleading interpretations (e.g. Pfurtscheller, 1999). Three methods

are commonly used to determine the upper and lower limit of the band pass filter.

• Determination of frequency bands relative to the peak frequency. The individual alpha peak

frequency is determined through spectral analysis and this frequency serves as an anchorpoint

to determine other frequency bands (e.g. Doppelmayr et al., 1998; Klimesch et al., 1999).

• Detection of the most reactive frequency band based on the comparison of two power spectra

(e.g. Pfurtscheller, 1999). This method is based on the assumption that the reactivity of

different frequency bands may vary over experimental manipulations. Two power spectra,

one spectrum during an inactive reference period and one in an active period, are compared.

Determining and selecting the band with the maximal reactivity relative to the reference

period presumably covers the assumed functional process best.

• Detection of the most reactive frequency band using a neural network based classifier (e.g.

Pfurtscheller, 1999).

Drawbacks of the classical ERD method: alternatives

A possible drawback of the classical ERD is that it is not capable of differentiating between

induced activity and evoked activity see on EEG). This means that an ERP may mask EEG

desynchronization or synchronization (Kalcher & Pfurtscheller, 1995). The shape of the ERP,

might be such that a portion of the power of this ERP in a selected frequency band may be

mistaken for the power of induced activity. Pfurtscheller (1999) points out that the majority of

ERP power is concentrated in the lower alpha band. For this reason analyzing and interpreting

power in this band should take place with caution. The intertrial variance method may account for

the effect of evoked activity on the power changes. Kalcher and Pfurtscheller (1995) proposed a

method that should be capable of removing the effect of evoked activity on the overall power

change. Although certain elements of the computation of this method, termed the intertrial


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variance method (this method is also referred to as the induced band power (IBP) method), are

quite similar to those of the classical ERD computation, one major difference must be noted. This

difference relates to the squaring of the filtered EEG epochs. In the computation of the classical

ERD the filtered EEG epochs are simply squared, whereas in the computation of the intertrial

variance method , a subtraction is performed on the filtered EEG epochs. From each sample in a

trial, the mean of that particular sample over trials is subtracted. This makes sense because the

ERP effect is time-locked, i.e. remains stable over trials when averaging is applied. The averaged

sample therefore represents the ERP effect on the power change. The remaining activity in the

epochs is therefore believed to be (predominantly) induced. After this subtraction, the filtered

EEG epochs are squared.

Another drawback of the classical ERD method is that although the classical ERD method may

yield an improved spatial resolution compared to ERP data, as far as temporal resolution is

concerned, the two described methods of ERD computation cannot compete with ERP data.

Inherent to the computation of the former, the resulting sample rate depends on the interval over

which consecutive samples are averaged. This often results in time windows exceeding 200 ms,

depending on the frequency range of interest. ERP quantification is not hampered by such

stringent methodological restrictions and therefore temporal resolution mainly depends on the

experimental sample rate. Since many phasic EEG phenomena are thought to be manifest only

over short periods of time (Bastiaansen, 2000), classical ERD does not seem to be equipped for

the analysis of these phenomena. Bhansali and Potter (1986) reported on a method that uses the

Hilbert transformation to estimate the envelope of band pass filtered signals (see Pfurtscheller,

1999 for an overview). The precise calculation of amplitude modulation of the EEG (AM-EEG) is

beyond the scope of the present study. Reports on the AM-EEG (Clochon, 1996) demonstrate that

the time resolution is much better than that of ERD methods while the integrated AM-EEG data,

which are ideally expected to resemble the ERD data, appeared to be very similar to the ERD

results. The amplitude modulation of the EEG, combines a good spatial resolution and a fair

temporal resolution, that is comparable to ERP data. Thus the AM-EEG method may capture fast

non-phase-locked changes in the signal envelope, which might be overlooked with the other

described ERD methods (Pfurtscheller, 1999).


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Appendix C

Figures

In order to gain insight into the evolution of the power changes over the skull in the distinct

conditions the figures of the power changes in these conditions for each band will be presented.

The time window of the plots ranges from 1750 ms premovement to 2000 ms postmovement and

is divided into 15 250 ms interval (see methods). Note that the interval of 1750-2000 ms

postmovement in the VM condition equals the last interval preceding stimulus presentation in the

KR conditions.

The figures of the power changes for the left and right hand conditions are accompanied by a

short explanatory text of the visible power changes. Since the figures of the power changes that

are averaged over response side are only meaningful at the last few intervals preceding stimulus

presentation, no accompanying explanatory text will be provided. Note that the reported events

are not necessarily statistically significant. The explanatory text is merely a guide to reading the

presented figures. Where appropriate, the preponderant locations of powerchange (i.e. precentral

and postcentral) are presented between square brackets.

Voluntary movement, right hand condition, 10 Hz

At 1250-1000 ms premovement an ERD is present that centers round C1’ and Czd. At 1000-750

ms the ERD expands and covers ipsilateral positions as well. At the last interval preceding

movement, the ERD focuses round contralateral electrodes [precentral] and round ipsilateral

central electrodes. At about 500-750 postmovement the ERD decreases and makes way for an

ERS, which emerges at contralateral [C1’ and C3”] electrode positions (1750-2000

postmovement). Note that the ERD at Czd continuously decreases from 500-750 ms

postmovement on, but that at 1500-1750 ERD, this ERD remains present.

Voluntary movement, left hand condition, 10 Hz

At 1500-1250 ms premovement a clear ERD emerges which centers round electrode positions

C2’ and C2’’. At 100-750 ms a second focus is present at electrodes C1’ and C3’. At the last

interval preceding movement the ERD has increased at both positions. At 500-750

postmovement, a decrease sets in and an ERS becomes clear at 1250-1500 ms postmovement at

C3” and P3. At 2000-2250 ms postmovement the reported ERS seems to expand and increase.


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Note that although the ERD at Czd does decrease after 500-750 ms postmovement, this ERD is

still present at the last interval prestimlus.

Somatosensory KR, right hand condition, 10 Hz

At 1500-1250 ms premovement an ERD is present that centers round C3”. In the subsequent

interval an ipsilateral component emerges [postcentral]. At the last interval preceding movement

the ERD preponderantly centers round ipsilateral electrode positions. The ERD decreases from

500-750 ms postmovement on to eventually make place for a preponderantly contralateral ERD at

1500-1750 postmovement [C3”]. However, although the ERD decreases at Czd and C2”, the

ERD does not vanish. Note that the ERD during stimulus presentation centers round Czd.

Somatosensory KR, left hand condition, 10 Hz

At 1500-1250 ms premovement a contralateral [C2”] ERD is already present together with an

ipsilateral component [C3’], yet the ipsilateral ERD appears to be quite frail until 750-500 ms

premovement. At the last interval preceding movement the bilateral ERD predominantly focuses

around C2’ and C2” and C1’. From 500-750 ms postmovement on, the ERD continuously

decreases to make way for a contralateral ERD [C4’ and C4”]. However, although the ERD

decreases at Czd, the ERD does not vanish. Note that the (marginal) ERD during stimulus

presentation centers round Czd.

Visual KR, right hand condition, 10 Hz

At 1250-1000 ms premovement an ERD emerges that centers around C1’. At 750-500 ms a clear

ipsilateral ERD develops [C2”]. The last interval preceding movement is characterized by a

bilateral ERD with a rather focused but strong ipsilateral component [C2”] and a more extensive

contralateral ERD that, nonetheless, seems less outspoken. From 500-750 ms postmovement on,

the ERD continually decreases and makes place for a bilateral ERS at 1500-1750 ms [C1’ and

C4’]. At 1000-1250 ms postmovement an ERD emerges at P4 and the focus of this ERD shifts to

occipital electrodes [O2] in the subsequent time intervals. Note that Czd displays a mild ERS at

the last interval preceding stimulus presentation.

Visual KR, left hand condition, 10 Hz

At 1250-1000 ms premovement a bilateral ERD with a rather frail ipsilateral component is

present. At the last interval preceding movement a bilateral ERD can be identified which centers

round Czd and overlies electrodes C1” and C2” as well. From 750-1000 ms following movement


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a clear decrease in ERD can be distinguished, that eventually makes way for a contralateral ERS

at 1500-1750 ms postmovement. At 1000-1250 ms an ERD emerges at predominantly

contralateral parietal and occipital electrodes. In the subsequent time-windows, this ERD

increases and a clear bilateral component emerges (1500-1750 postmovement). At the last

interval preceding movement electrode position Czd is covered by a marginal ERD. Note that the

ERD during stimulus presentation centers round the same electrode positions as the ERD in the

prestimulus intervals.

Voluntary movement, right hand condition, 20 Hz

At 1750-1500 premovement a rather frail bilateral ERD is present at several central electrode

positions. In the subsequent time intervals both the contralateral as the ipsilateral component

increase and center round C1’, C1” and C4”, respectively. At the last interval preceding

movement the contralateral ERD covers both pre- and postcentral electrode positions whereas the

focus of the ipsilateral ERD is restricted to postcentral electrodes. The ERD decreases from 250-

500 ms postmovement and makes way for a clear contralateral ERS [C1’] and a small ipsilateral

ERS [C2’] in subsequent time intervals. At 1750-2000 postmovement, both Czd and Oz display a

(marginal) ERS.

Voluntary movement, left hand condition, 20 Hz

At 1250-1000 ms premovement a bilateral ERD is present which centers round postcentral

electrode positions. At 500-250 ms premovement the ipsilateral component becomes

preponderant [C1’ and C1”]. This profile remains unchanged during movement. At 250-500 ms

postmovement the ERD decreases and at 750-1000 ms postmovement, an ERD with a

contralateral focus [C2’] can be distinguished. Note the presence of a contralateral

parietal/occipital ERD at the same time interval. The subsequent intervals are characterized by a

massive bilateral ERD at all central electrode positions. Note that Oz displays a marginal ERS at

the last interval preceding stimulus presentation.

Somatosensory KR, right hand condition, 20 Hz

At 1500-1250 ms premovement a contralateral ERD can be distinguished which centers around

C1”. A clear ipsilateral component emerges at 750-500 ms premovement [C4”]. At the last

interval preceding movement, the bilateral ERD becomes ipsilaterally predominant. At 250-500

ms following movement the ERD decreases and at 500-750 ms postmovement an ERS emerges

[C1’]. In the subsequent time intervals, the ERS expands (predominantly contralaterally) and


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covers all contralateral central electrode positions. At 1500-1750 ms postmovement a frail ERD

can be distinguished [between C2” and Pz]. Note the massive bilateral ERS following movement.

Somatosensory KR, left hand condition, 20 Hz

At 1250-1000 ms premovement an ERD is present which centers round C2”. A clear ipsilateral

component can be distinguished at 750-500 ms premovement [C3”]. At the last interval preceding

movement this profile is still intact, though the ERD seems contralaterally predominant. At 250-

500 the ERD decreases and in the subsequent interval an ERS emerges [Cz’, C1’]. At 750-1000

ms following movement, an ipsilateral component emerges [C3’]. In the following time intervals

the ERS expands. Note that the ipsilateral component centers around precentral electrode

positions whereas the contralateral component covers all central electrodes and note the massive

bilateral ERS following movement.

Visual KR, right hand condition, 20 Hz

At 1500-1250 ms premovement an ERD can be distinguished that centers round C3” and C1’. At

750-500 ms premovement an ipsilateral component is present, though being very frail. In the

subsequent time intervals the ERD remains to be contralaterally predominant. At 500-750 ms

postmovement, the ERD decreases drastically and a contralateral ERS emerges [C1’] and

continues to increase over the following intervals. At 1000-1250 poststimulus an ERD emerges

[Oz2] that increases and expands over several other occipital and parietal electrodes. Note that the

ERS is predominantly contralateral.

Visual KR, left hand condition, 20 Hz

At 1750-1500 ms premovement an ERD can be distinguished that centers round Czd. In the

subsequent time intervals this ERD remains to be centered round Czd. At 750-500 ms the ERD

seems to expand somewhat in the lateral directions. At the last interval preceding movement, the

ERD is slightly ipsilaterally preponderant (postcentral). The ERD decreases from 250-500 ms

postmovement on and at 500-750 ms postmovement an ERS emerges [C1’]. Note that this ERS is

an ipsilateral postmovement power change. In the subsequent time intervals, a strong contralateral

ERS emerges at both pre- and postcentral electrode positions. Not that the ipsilateral ERS is

restricted to precentral electrodes. Note further that the parietal/occipital ERD at 500-750 ms

postmovement remains present in all subsequent time intervals, and shifts slightly towards

occipital electrode positions.

Health & Medicine

Does Event Related Desynchronization reveal anticipatory attention in the somatosensory modality?