Aus dem Epilepsiezentrum Erlangen

Leiter: Prof. Dr. med. Hermann Stefan

der

Neurologischen Klinik mit Poliklinik

der Friedrich-Alexander-Universität

Erlangen-Nürnberg

Direktor: Prof. Dr. med. Stefan Schwab

Durchgeführt im

Helen Wills Neuroscience Institute

der

University of California, Berkeley, USA

Direktor: Robert T. Knight, M.D.

Betreuer: Aurélie Bidet-Caulet, Ph.D.

Auditory Selective Attention: an introduction and

evidence for distinct facilitation and inhibition mechanisms

Inaugural-Dissertation

zur Erlangung der Doktorwürde

der Medizinischen Fakultät

der

Friedrich-Alexander-Universität

Erlangen-Nürnberg

vorgelegt von

Constanze Elisabeth Anna Mikyska

aus

München

 

Gedruckt mit Erlaubnis der

Medizinischen Fakultät der

Friedrich-Alexander-Universität

Erlangen-Nürnberg

Dekan: Prof. Dr. med. Dr. h.c. J. Schüttler

Referent: Prof. Dr. med. H. Stefan

Korreferent: Prof. Dr. med. Dipl.-Psych. Ch. Lang

Tag der mündlichen Prüfung: 29. Februar 2012

 

To my family

                   

 

Table of contents

1 Summary 1 1.1 Summary 1

1.2 Zusammenfassung 3

2 Introduction 5 2.1 Auditory system: anatomy and function 5

2.1.1 Ear 5

2.1.2 Sub-cortical auditory relays 10

2.1.3 Auditory cortex 13

2.2 Investigation of auditory perception and processing 14

2.2.1 Psychophysics – psychoacoustics 15

2.2.2 Brain activity – electroencephalography (EEG) 17

2.2.2.1 Introduction and history 17

2.2.2.2 Physiological fundamentals 17

2.2.2.3 Recording 22

2.2.2.4 Classification of frequency 25

2.2.2.5 Artifacts 27

2.2.2.6 Data analysis: preprocessing and

event-related potentials (ERP) 30

2.2.2.7 Main auditory electrophysiological components 31

2.3 Auditory attention 35

2.3.1 Psychological theories 35

2.3.1.1 Introduction to selective attention 35

2.3.1.2 Bottleneck theories: early- versus late-selection 37

2.3.1.3 Other capacity-limitation theories 40

2.3.2 Electrophysiological findings and theories 42

2.4 Aims of this dissertation 49

3 Material and methods 54 3.1 Subjects 54

3.2 Stimuli and task 54

 

3.3 Procedure 57

3.4 EEG recording 57

3.5 EEG data analysis 57

3.6 Statistical analysis 58

3.6.1 Selection of applied methods 58

3.6.1.1 Analysis of variance (ANOVA) 58

3.6.1.2 Statistic permutation test 59

3.6.2 Behavioral data 60

3.6.3 ERP standards 61

3.6.4 ERP deviants 62

4 Results 64 4.1 Behavioral data 64

4.2 ERP results of standards 65

4.2.1 Main attention effect (attended versus ignored) 65

4.2.2 Influence of the memory task difficulty

on attention effects 66

4.2.3 Timing of attention facilitation and inhibition 70

4.2.4 Topographies of attention facilitation and inhibition 72

4.3 ERP results of deviants 73

4.3.1 Attention enhancement of deviant processing 73

4.3.2 Memory effect on the P3-Component 75

5 Discussion 77

6 References 82

7 List of abbreviations 90

8 Publication 92

9 Acknowledgements 93 10 Curriculum vitae 94

 

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1 Summary 1.1 Summary

Objective

Auditory selective attention is a complex brain function that is still not completely

understood. The classic example is the so-called “cocktail party effect” (Cherry,

1953), which describes the impressive ability to focus one’s attention on a single

voice from a multitude of voices. This means that particular stimuli in the

environment are enhanced in contrast to other ones of lower priority that are ignored.

To be able to understand how attention can influence the perception and processing

of sound, background knowledge is essential.

One aim of this dissertation is to provide an overview of already existing literature.

Therefore, the auditory system and different methods to measure and evaluate

auditory processes are introduced at first, followed by a review about competing

theories, trying to explain how auditory attention operates.

The second aim of the dissertation is to specify the mechanisms and to elucidate how

they operate. It is generally accepted that distinct signals (top-down signals) are

important for cognitive control, enabling selective attention and leading to an

enhanced processing of task relevant information. But it is unknown whether

facilitation and inhibition of stimulus processing are based upon one   (unitary gain

control mechanism of facilitation) or two mechanisms (net activity of distinct top-

down facilitation and inhibition mechanisms). Results from a visual fMRI study (de

Fockert, 2001) suggest that facilitation and inhibition rely on distinct mechanisms

that would be differentially affected by the availability of cognitive resources (i.e. for

performing a task).

To reveal that facilitation and inhibition represent distinct mechanisms in auditory

selective attention, we conducted a study, where subjects performed an auditory

attention task, while the amount of available cognitive resources was modulated (by

varying the difficulty of a memory task).

Methods

Electrophysiological experiments were conducted in young healthy adults. 16

subjects performed an attention task and a memory task of varying difficulty (no,

easy and difficult memory) at the same time (dual task protocol) while EEG was

recorded. Facilitation and inhibition were measured by comparing

 

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electrophysiological responses to attended and ignored sounds with responses to the

same sounds when attention was considered to be equally distributed towards all

sounds.

Results

Two ERP-components were observed: a negative one in response to attended sound

and a positive one to ignored sounds. The two frontally distributed components had

distinct timing and scalp topographies and were differentially affected by the

difficulty of the memory load.

Conclusion

This dissertation provides an insight into the literature of auditory selective attention

and also enriches the existing knowledge with results of a new study about the

operating mechanisms of auditory selective attention. The study provides evidence

that top-down attention control can operate via distinct facilitation and inhibition

mechanisms.

 

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1.2 Zusammenfassung

Hintergrund und Ziele

Selektive auditorische Aufmerksamkeit ist ein komplexer Mechanismus, der noch

nicht vollständig verstanden ist. Das klassische Beispiel ist der sogenannte „Cocktail

Party Effekt“ (Cherry, 1953). Dieser beschreibt die beeindruckende Fähigkeit, die

Aufmerksamkeit auf einen einzelnen Sprecher zu konzentrieren und andere

Unterhaltungen auszublenden. Das bedeutet, dass bestimmte Reize in unserer

Umwelt verstärkt wahrgenommen werden, wohingegen Reize von niedrigerer

Priorität ignoriert werden. Um zu verstehen, wie Aufmerksamkeit die Wahrnehmung

und Verarbeitung von Reizen beeinflusst, gibt der erste Teil dieser Dissertation einen

Überblick der Grundlagenliteratur. Dabei werden zuerst das auditorische System

vorgestellt und verschiedene Methoden zur Messung und Beurteilung auditorischer

Verarbeitungsprozesse eingeführt. Dem folgt ein kurzer Überblick über

konkurrierende Theorien, die zu erklären versuchen, wie selektive auditorische

Aufmerksamkeit funktioniert.

Der zweite Teil dieser Arbeit befasst sich genauer mit der Frage nach den

Mechanismen und wie diese arbeiten. Es ist allgemein anerkannt, dass bestimmte

Signale (top-down Signale) wichtig für die kognitive Kontrolle sind. Sie aktivieren

selektive auditorische Aufmerksamkeit und führen so zu einer verstärkten

Verarbeitung eines relevanten Reizes. Aber es ist noch ungeklärt ob die Förderung

und Hemmung der Reizverarbeitung durch einen (einheitlicher, linearer

Verstärkungsmechanismus von Förderung) oder zwei Mechanismen

(Netzwerkaktivität von unabhängiger Förderung und Hemmung) geregelt wird.

Ergebnisse einer visuellen fMRT Studie zeigen, dass das Ausmaß der Hemmung

ablenkender Reize von der Verfügbarkeit kognitiver Ressourcen (z.B. für das Lösen

von Problemen) abhängig ist (de Fockert, 2001). Die Ergebnisse deuten darauf hin,

dass Förderung und Hemmung im visuellen System auf verschiedenen Mechanismen

basieren, die von der Verfügbarkeit kognitiver Ressourcen unterschiedlich

beeinflusst werden.

Um zu zeigen, dass Förderung und Hemmung unabhängig voneinander agieren,

führten wir eine Studie durch, in der Probanden einen auditorischen

Aufmerksamkeitstest lösten, während die Verfügbarkeit von kognitiven Ressourcen

variiert wurde (verschiedene Schwierigkeitsstufen in einem Gedächtnis Test).

 

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Methoden

Elektrophysiologische Versuche wurden mit 16 jungen, gesunden Erwachsenen

durchgeführt. Die Probanden lösten gleichzeitig (dual task protocol) einen

Aufmerksamkeits- und einen Gedächtnis Test mit variierenden Schwierigkeitsstufen

(no, easy und difficult memory) während elektrophysiologische Signale (EEG)

aufgezeichnet wurden. Förderung und Hemmung wurden gemessen, indem die

Antworten zu den beachteten und den ignorierten Reizen jeweils mit den Antworten

auf die gleichen Reize einer Kontrollbedingung verglichen wurden. In dieser

Kontrollbedingung wurde angenommen, dass die Aufmerksamkeit ausgewogen auf

alle Reize gerichtet war.

Ergebnisse und Beobachtungen

Zwei ERP-Komponenten wurden beobachtet: eine negative, in Antwort zu den

beachteten Reizen und eine positive, den ignorierten Reizen folgend.

Die zwei Komponenten zeigten verschiedene frontale Skalp-Topographien und

variierten auch in der zeitlichen Domäne. Außerdem wurden sie unterschiedlich von

der Schwierigkeit des Gedächtnis Tests beeinflusst.

Praktische Schlussfolgerungen

Diese Dissertation bietet einen Einblick in die Literatur über selektive auditorische

Aufmerksamkeit und bereichert das bestehende Wissen mit Ergebnissen einer neuen

Studie über die Wirkmechanismen. Die Studie erbringt den Nachweis, dass top-down

Kontrolle die Aktivität voneinander unabhängiger Förderungs- und

Hemmungsmechanismen widerspiegelt.

 

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2 Introduction The auditory system processes acoustic waves, leading to auditory percepts. An

important issue is to understand how attention can influence the perception of sound,

i.e. the processing of sounds. In other words, by which mechanisms and at which

step of sound processing, auditory attention operates. To address this question,

several basic principles will be introduced first: (1) the anatomy of the auditory

system and the sequence of sound processing from the outer ear to the auditory

cortices, (2) different methods to measure and evaluate auditory processes

(especially the electroencephalography), (3) auditory attention and the attempt of

psychological and physiological theories to elucidate its influence on sound

processing. Finally, (4) the aims of the present study are introduced.

2.1 Auditory system: anatomy and function

The auditory system is a remarkable sensory organ. Both ears are involved in sound

detection from all directions regardless of the organism’s current orientation.

Processing takes place, while information about the stimuli is transmitted along

complex sub-cortical relays to the auditory cortex. The final processing and

interpretation occurs in the auditory cortex and in surrounding higher order areas.

2.1.1 Ear

Outer ear

Sound waves first reach the outer ear, which is composed of the pinna (auricle), the

ear canal (external acoustic meatus) and the eardrum (tympanic membrane). The

pinna, the visible part of the outer ear, collects and focuses sound waves, and directs

them through the ear canal (approximately 30 to 35 mm long and 7 mm in diameter)

to the eardrum, which transmits sound vibrations to the middle ear (see Figure 2.1).

 

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Figure 2.1 – The anatomy of the ear (adapted from Netter, 2006).

Middle ear

The middle ear is an air filled cavity consisting of different muscles and the three

ossicles (malleus, incus and stapes). There are also two openings, linking the middle

ear to the inner ear over membranes: the oval (vestibular) window adjoining the

perilymph in the scala vestibuli and the round (cochlear) window connecting to the

perilymph in the scala tympani (see Figure 2.1).

The malleus is attached to the inner surface of the tympanic membrane and transmits

the arriving vibration to the incus and the stapes, which is attached to the membrane

of the oval window. This small bone is stabilized by the stapedius muscle, which

controls the amplitude of sound waves by pulling the stapes away from the oval

window and therefore protects the inner ear from high noise levels (Trepel, 2008).

The tensor tympani muscle functions in a similar manner by pulling the malleus, thus

tensing the tympanic membrane.

From a physical point of view, two mechanisms permit an increased

efficiency of sound transmission (Schmidt, 1993): (1) the reduced surface of the

membrane of the oval window compared to the surface of the tympanic membrane

causes an enhancement of pressure and (2) the lever system of the ossicles leads to

 

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an adaptation between the low impedance of the air in the middle ear and the high

impedance of the fluid in the inner ear.

The middle ear is only functioning as long as the tympanic cavity is

ventilated and its pressure is matched to the atmosphere. This is assured by the

Eustachian tube, which links the middle ear to the nasopharynx. An upper airway

infection can cause swelling and occlusion of the tube, which can result in an ear

infection as well as in a rupture of the tympanic membrane, caused by a pathological

pressure difference (Schmidt, 1993).

Inner ear

The inner ear contains the vestibular system, dedicated to balance and spatial

orientation, and the cochlea, which is essential for hearing. The cochlea is part of the

osseous labyrinth and turns like a snail two and a half times around a core of bone

(modiolus), in which the cochlear nerve runs. This labyrinth is filled with perilymph,

a derivative of the cerebrospinal fluid, similar to extracellular fluid, and also contains

a membranous labyrinth: the cochlear duct (scala media), filled with endolymph (a

fluid with a high content of potassium, similar to intracellular fluid). The cochlear

duct is formed by the Reissner's membrane above and the basilar membrane below

and also holds the organ of Corti (organum spirale). This is the sensory organ of

hearing and is comprised of receptor cells (hair cells), different types of supporting

cells (cells of Deiters, Hensen, Claudius and Boettcher) and the basilar membrane

(see Figure 2.2 A and B). The hair cells are arranged in one row of inner and three

rows of outer hair cells and make contact with neurons on their basis (see Figure 2.2

B). Additionally, they have stereo cilia (hair bundles) on their free surface, which are

attached to each other by filamentous structures, called tip-links (Roberts, 1988). The

stereo cilia from the outer hair cells are conjoined to the tectorial membrane – a

colloidal membrane that covers the organ of Corti. Furthermore the cochlear duct

separates two structures: the scala vestibuli (above) and the scala tympani (below),

that merge at the apex of the cochlea (helicotrema) and so the perilyphm can flow

from one scala to another. The organ of Corti sits on top of the basilar membrane

along the entire length of the scala media.

 

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Figure 2.2 – Models of the cochlea and the organ of Corti.

(A) Cross section through a turn of the cochlea showing the scala vestibuli, the scala

tympani and the cochlear duct with the organ of Corti. (B) The anatomical structures

of the organ of Corti (adapted from Hawkins, 1997).

 

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If a sound impacts the tympanic membrane and is transmitted from malleus to

incus and stapes, the arriving vibrations cause the membrane of the oval window to

produce pressure waves within the incompressible perilymph of the scala vestibuli.

These pressure waves lead to vibrations of the Reissner's membrane and moreover to

deflections of the basilar membrane, which is also called travelling wave (Von

Bekesy, 1960). Thus, the stereo cilia of the inner hair cells are displaced, causing an

action potential in the cochlear nerve. Because the basilar membrane is more rigid at

the basal aspect of the cochlea compared to the apical part, higher frequencies lead to

a bigger deflection of the basilar membrane at basal parts of the cochlea, whereas

lower frequencies are mapped at the apical parts. These spatial arrangements of

sound information is called tonotopy (Von Bekesy, 1960) and is more or less

maintained throughout the auditory pathway, so that the frequency content of a

sound is constantly decipherable.

Especially the hair cells are essential for the generation of action potentials

because of their characteristic features: the outer hair cells operate as an (cochlear)

amplifier, ensuring the sensitivity and tuning of the cochlea. By active contractions

that displace the basilar and tectorial membrane they can enhance the endolymphatic

flow and therefore also increase the travelling wave. They are innervated by efferent

nerve fibers from the superior olivary complex (see Figure 2.3). Interestingly, a

diminutive part of the kinetic energy of the outer hair cells travels back through the

middle ear to the ear canal, where it can be recorded as sound (Kemp, 1978). This is

called otoacoustic emission (OAE) and this method is used for examining the

function of the outer hair cells and for screening newborn babies for hearing defects.

The proper stimuli for the inner hair cells are hydrodynamic forces of endolymph,

moving the freestanding hair bundles (hydrodynamic coupling) (Hudspeth, 1983).

The inner hair cell is a seconday receptor cell and cannot generate an action

potential. The mechanical stimulus rather triggers a receptor potential (mechano-

electrical transduction) that is transferred from electrical to chemical signal and

transmitted to an afferent neuron.

More detailed: if the stereocilia are moved in one direction, tensing the tip

links, mechanically gated ion channels on the top of the hair cells open and

positively charged ions (especially potassium) enter the cell and cause a

depolarization, which leads to a receptor potential that can occur up to 5000 times

per second. This receptor potential opens voltage gated calcium channels and ions

 

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enter the cell and trigger the release of neurotransmitters (glutamate) at the basal end

of the inner hair cell. Another depolarization is inhibited, if the hair bundles are

deflected to the other direction, relaxing the tip links, (Schmidt, 2005). The released

glutamate diffuses through the synaptic cleft and binds to the postsynaptic receptor

(AMPA receptor), which triggers a postsynaptic potential that causes an action

potentials in the afferent neuron. This process is called transformation. The number

of axons firing and the frequency of the action potentials encode for the volume of a

sound (amplitude), i.e. high volume will result in higher frequencies of action

potentials.

2.1.2 Sub-cortical auditory relays

The signal is processed along six or more neurons consecutively forming synapses

(see Figure 2.3). Coming from the inner hair cells, the signal runs along the afferent

nerve fiber to the spiral ganglion (first order neuron), located within the central

aspects of the cochlea. Together with the vestibular nerve coming from receptor cells

of the vestibular system, the cochlear nerve forms the vestibulocochlear nerve (the

VIIIth cranial nerve), which runs through the internal acoustic meatus in the petrosus

part of the temporal bone and enters the cranium through the porus acusticus internus

(Trepel, 2008). The nerves run to the cerebellopontine angle and from there to the

brainstem, where the vestibulocochlear nerve splits again and each part runs to their

cranial nerve nuclei.

The cochlear nuclei consist of the nucleus cochlearis anterior (ventralis) and

posterior (dorsalis), both located at the inferior cerebellar peduncle, a part of the

medulla oblongata. In these nuclei not only the first relay takes place (to second

order neurons) but also the first processing of the sensory input occurs: an automatic

decoding of the basic signal (duration, intensity and frequency).

From the nucleus cochlearis anterior a minor aspect of the nerve fibers runs

on the ipsilateral side, whereas the major part decussates in the corpus trapezoideum

(located in the pons) to the contralateral side (Trepel, 2008). Moreover the corpus

trapezoideum contains the nuclei corporis trapezoidei and the nuclei olivares

superiors (superior olivary complex), where the neurons also form synapses (third

order neuron).

The fibers from the nucleus cochlearis posterior form a minor part of the

auditory system and decussate separately from the corpus trapezoideum to the

 

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contralateral side, without forming any synapse with other neurons. Additionally

some of the neurons starting from the nucleus cochlearis posterior hold partly

excitatory and also partly inhibitory neurons, which can inhibit processing in

subsequent levels of the pathway (Schmidt, 2005).

On the contralateral side all auditory nerve fibers form the lemniscus lateralis,

where the neurons come together in synapses (nuclei lemnisci lateralis; fourth order

neurons) and either decussate back to the originally ipsilateral side or run to the

colliculus inferior (fifth order neuron) – part of the corpora quadrigemnia, located in

the mesencephalon (Trepel, 2008). At this location and also in the superior olivary

complex the direction of the sound is analyzed by specialized neurons, comparing

the timing of action potentials, coming from both cochleae.

From here, some nerve fibers decussate again to the contralateral side through

the brachium colliculi inferioris, but most of them continue to the corpus

geniculatum mediale of the thalamus, located in the diencephalon. The geniculate

neurons (sixth order neurons) project their axons through the capsula interna to the

primary auditory cortex (radiatio acustica).

This complex and intensely interconnected pathway is crucial to connect both

cochleae to the left and right auditory cortices, which is important for bilateral

processing and comparing sounds from the right and left side (Schmidt, 2005).

 

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Figure 2.3 – Simplified scheme of the central auditory pathway.

CGM = Corpus geniculatum mediale, HG = Heschl’s gyrus (there can be two

Heschl’s gyri, HG1 and HG2), PP = planum polare, PT = planum temporale, STG =

superior temporal gyrus, MTG = medial temporal gyrus (adapted from Bidet-Caulet,

2006).

 

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2.1.3 Auditory cortex

The auditory cortex consists of the primary auditory cortex and higher level

surrounding areas. The primary auditory cortex (A1, Broadmann area 41) is located

on the supratemporal plane of the temporal lobe (Pandya, 1995) and it is only visible

after removing the frontal and parietal operculum. The medial part of the gyri

temporales transversi, also called Heschl’s gyrus, named after Richard Heschl, an

Austrian anatomist (1824 – 1881), forms the major part of the primary auditory

cortex. As an expression of cerebral anatomically asymmetry, some individuals can

have two Heschl’s gyri – mostly on the right side, whereas also morphological

variations, with the left side being larger, are postulated (Geschwind, 1968).

Most neurons in A1 are organized according to the frequency of sounds they

respond the best to (Howard, 1996). Afferent fibers carrying information about low

frequencies end more anterolateral in Heschl’s gyrus, whereas high frequencies are

mapped more posteromedial (Trepel, 2008). This frequency map corresponds to the

tonotopic organization of the auditory pathway. Other than that, there is also an

organization for binaural properties. The neurons are arranged in different stripes.

For instance: one stripe is excited by both ears (EE cells) whereas the neurons in

another stripe are firing by receiving information from one ear and are inhibited by

input from the other ear (EI cells). This organization is comparable to the ocular

dominance columns in the primary visual cortex (V1) (Purves, 1997).

The higher order auditory areas (Brodmann area 42 and 22) laterally adjoin

the primary auditory cortex and are located posteriorly in the planum temporale,

aneriorly in the planum polare and laterally in the superior temporal gyrus (STG).

These areas receive the majority of the afferent information from A1. They are less

precise in their tonotopic organization and mainly operate by interpreting the

detected sounds as words, melody, rhythm or noise (Trepel, 2008). In the dominant

hemisphere within an area of the secondary auditory cortex, named Wernickes area,

the information is processed and integrated into speech comprehension. This area is

located in the posterior section of the superior temporal gyrus (Brodmann area 22).

The dominant hemisphere is the one controlling and processing speech and

understanding, which is mostly located at the opposite side of the dominant hand –

i.e. the left hemisphere for about 95 % of right-handed population, but also for 70 %

of the left-handed people (Rickheit, 2003). A lesion in this area leads to the so-called

sensory aphasia, receptive aphasia or Wernicke’s aphasia, which main symptom is

 

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the disability to understand speech. The patient is still able to speak fluently, but it

makes little or no sense, because the word is not linked to its proper meaning.

The secondary auditory areas also receive afferent input from the angular gyrus that

gets it information from the secondary visual cortex. This circuit is important for the

combination of visual and auditory input to its meaning, crucial for reading and

writing.

Furthermore, there may be two major streams of information processing

comparable to the ‘what‘ and ‘where’ streams in the visual system (Kaas, 1999). To

simplify: the information about spatial location (‘where’) would run from A1 to

posterior higher order areas and continue to the parietal lobe and the posterior parts

of the dorsolateral prefrontal cortex. Object-related properties would be processed

within a ‘what’ pathway composed of the primary auditory cortex, anterior higher

order areas and ventral and medial prefrontal areas.

Given the knowledge about the complex auditory system, the obvious

questions follow: how does the brain interpret acoustic waves to produce a percept

and with what kind of methods and procedures is it possible to measure, evaluate and

interpret auditory perception and processing.

2.2 Investigation of auditory perception and processing

There are two main approaches attempting to investigate auditory perception and

processing. (1) Psychophysics analyzes the interaction between physical stimuli that

are quantitative measurable and the subjective perception, triggered by the stimuli. A

section of psychophysics is psychoacoustic describing the relationship between a

subjective auditory impression and the appropriate physical stimulus. (2) Brain

activity can be measured using four main techniques, each representing a different

approach.

Electrophysiological methods like the Electroencephalogram (EEG), on which the

following part is mainly concentrated, reveals electrical activity generated by the

brain. The recording electrodes can be placed in different locations: on the scalp

(EEG), directly on the cortex (Electrocorticogram, ECoG) or into structures deeper

in the brain (Stereotactic EEG, SEEG). ECoG and SEEG are both invasive

intracranial recording techniques.

 

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Also magnetic fields produced by electrical currents can be measured using the

Magnetoencephalogram (MEG).

Other techniques using Imaging technology provide a different view of brain

activity: Important to mention is the magnetic resonance imaging (MRI). It uses

powerful magnets to excite hydrogen nuclei. These atomic nuclei emit a signal while

returning to the initial point of excitement (relaxation). The signal can be measured

and computed into structural images of the brain. It is also possible to visualize the

brain function with the functional MRI (fMRI). Neuronal activity enhances

metabolic processes resulting in changes of blood flow. Hemoglobin features

different oxygenation levels that are measurable as different MRT signals showing

different activated structures in the brain. This is called the Blood Oxygen Level

Dependency effect (BOLD-effect).

Another imaging technique, the Positron emission tomography (PET), visualizes

metabolic processes by showing the distribution of a radioactive tracer in the brain.

The tracer is attached to a biological active molecule and injected into the blood

circulation; the most commonly used is fluorodeoxyglucose (FDG).

A compatible and complimentary use of some of these methods is possible.

2.2.1 Psychophysics – psychoacoustics

The appropriate stimulus for the ear is a sound wave, which is generally comprised

of several frequencies (expressed in Hertz) and pressure oscillations. The magnitude

of a pressure wave is the amplitude and is also called sound pressure (P, 1

Pa=1N/m). The human ear can detect sounds in a wide range of amplitude and

therefore sound pressure is often expressed as a level on a logarithmic decibel (dB)

scale, also called sound pressure level (SPL, L):

L = 20 log Px/P0 [dB] (Schmidt, 2005).

The term level means, that the sound pressure measured (Px), is in a logarithmic ratio

to another sound pressure (P0), which is the absolute threshold of hearing (2*10-5

Pa). That indicates, that few decibels imply a multiplication of the sound pressure.

The most important way to examine a persons hearing ability is an

audiometry test. Different tones are presented through headphones at different levels

and the tested person has to press a button as soon as the tone is heard to determine

 

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the individual threshold of audibility. The perception of loudness indicates how loud

a person perceives a sound and therefore it cannot objectively be measured. But it is

still related to the sound pressure level and the duration of a sound. If the sound

pressure increases, a sound is perceived louder and high frequencies are heard as a

high tone (and the other way around). Furthermore, at a constant sound pressure,

tones are perceived louder at frequencies between 2000 and 5000 Hz (Schmidt,

1993). Therefore the sound pressure must be adjusted to the frequencies in order to

perceive all tones at the same loudness (isophon). Thereby a chart is created (see

Figure 2.4) that shows equal loudness curves, which are also called Fletcher-Munson

curves (Fletcher, 1933). Values at 1000 Hz can also be named phon and per

definition, one phon equals one decibel at 1000 Hz. The human hearing is limited to

frequencies between 20 Hz and 16.000 Hz and loudness between 4 and 130 phon

(Schmidt, 2005). A normal spoken word would be found at around 50 to 70 decibel

and a painful tone at around 130 decibel (Schmidt, 2005).

 

Figure 2.4 – The Fletcher-Munson curves.

Equal loudness curves. The intensity (vertical axis) is adapted to the frequencies

(horizontal axis) to perceive all sounds at the same loudness. Values at 1000 Hz are

called phon. Phon levels of 0, 10, 20, 30,…120 are depicted (adapted from Fletcher,

1933).

 

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2.2.2 Brain activity – electroencephalogram (EEG)

2.2.2.1 Introduction and history

In the 1870’s electrical activity was recorded from a mammalian brain for the first

time. Richard Caton was an English psychologist, who reported in 1875 spontaneous

activity directly from the exposed cortex of rabbits and monkeys (Millett, 2001).

This laid the groundwork for Hans Berger (1873-1941), a German psychiatrist at the

University of Jena, who first recorded the activity from a human brain in 1929. He

applied electrodes on the head of patients, who had skull defects and recorded the

first elctroencephalogram (Millett, 2001). The skull defects are comparable to a

today’s decompressive craniectomy (performed in patients with traumatic brain

injuries to reduce elevated intracranial pressure by taking out a part of the skull for a

few months). This was the invention of a technique that revolutionized the current

clinical and psychological work and research.

Even though new great inventions like the Positron emission tomography (PET), the

Magnetic resonance imaging (MRI) or the MEG provide new opportunity to

investigate the Human brain, the EEG still remains the gold standard for diagnosing

numerous diseases and it is crucial for studying the dynamics of brain activity.

2.2.2.2 Physiological fundamentals

The human brain is mostly constituted of neurons – cells specialized in transmitting

and processing information via electrical and chemical signals – and glia cells (for

example: astrocytes, oligodendrocytes, radial glia, microglia) providing support and

electrical insulation for the neurons and also maintaining ion homeostasis of the

brain. Glia cells also show electrical activity, which is probably too small to

contribute to an EEG (Araque, 2004).

Neurons show a negative intracellular membrane potential (–70mV) compared to the

extracellular space. During a depolarization, positively charged ions (especially

sodium) enter the cell, generating an action potential. Subsequently, positively

charged ions (potassium) diffuse out of the cell (repolarisation) and the voltage

returns to its initial value. The duration of an action potential is between less than a

millisecond and up to few milliseconds and it can occur over 5000 times per second

(5000 Hz). The neurons communicate over synapses. One depolarized afferent

neuron releases neurotransmitter, which opens ion channels on the postsynaptic

membrane of the subsequent neuron. There are different neurotransmitters with

 

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distinct properties: glutamate (generally excitatory), GABA and glycin (generally

inhibitory) and acetylcholine (excitatory or inhibitory depending on the receptor).

The induced postsynaptic potentials (excitatory or inhibitory, EPSP or IPSP) are

relatively slow and followed by voltage fluctuations that can be measured from EEG

electrodes.

For instance, an excitatory neurotransmitter like glutamate causes a

depolarization of the dendrites, which leads to a sodium and calcium influx or to a

reduced potassium efflux. In this case, the surface of the dendrite shows a reduced

electric charge compared to a positive charge inside the cell (see Figure 2.5). This

potential difference generates a dipole. On the other hand, an inhibiting synapse

causes a hyperpolarisation of neurons and therefore a positivity of the extracellular

space. The ion flow induces a modified distribution of ions in the extracellular space

that is balanced from adjacent extracellular compartments.

Figure 2.5 – Model of a neuron generating a field potential.

The afferent fiber induces an EPSP at the dendrites of the neuron. The ion influx in

the cells results in a negative field potential and a dipole (dashed lines) (adapted from

Ebner, 2006).

 

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Electrical changes from one single neuron cannot be recorded from an EEG

electrode, because the amplitude is too small and there is a considerable distance

between neurons and electrodes. The recorded electrical activity is rather a

summation of voltage fluctuations caused by EPSPs and/or IPSPs of many neurons

within a population.

Given that a neuron is part of a population, extracellular potentials are

behaving according to the orientation and the polarity of the neurons. A summation

of the field potentials happens only if the neurons are organized in parallel or serial

networks and if they have similar morphological polarization. This situation is called

open field (see Figure 2.6 A). Special neurons in the human cortex (pyramidal cells)

– mainly organized vertically – primarily generate the electrical potentials one can

see in the EEG.

On the other hand, in a closed field (see Figure 2.6 B), the current flow is canceled

out within the population. This happens if the neurons are arranged in stellate

morphology with dendrites extending radially outward, or if the neurons are

randomly oriented, for example, interneurons show closed field potentials (Ebner,

2006).

Figure 2.6 – Orientation of neurons.

(A) A parallel orientation results in a measurable signal – it is called an open field.

(B) A stellate organisation results in a marginally measurable signal- it is called a

closed field (adapted from Ebner, 2006).

 

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The EEG signal recorded from the scalp is composed of frequencies between 0,5 –

80 Hz and amplitudes in a range from 1 – 100 µV (Schmidt, 2005). The spontaneous

EEG signal mostly displays noise, but nevertheless the state of arousal and the areas

of higher activity can be displayed and mapped on a scalp model. Therefore, it is

important to consider the orientation of the generator population. It can be located

vertically to the surface of the cortex (see Figure 2.7 B). In this case, the dipole

moment is presented as a radial dipole and the scalp topography shows about the

localization of the source (Ebner, 2006) (see Figure 2.7 D). A tangential dipole

would result if the neurons were arranged tangential to the cortical surface (see

Figure 2.7 A) and for this, the maximal negativity or positivity of the scalp

topography would not show the actual source (see Figure 2.7 C). The source is rather

located in between the two maxima with opposite signs on the scalp. Therefore, one

must not reason that the biggest signal in the EEG presents the location of the biggest

activity in the brain. This is called the inverse problem. Consequently, interpreting

EEG scalp results requires carefulness, especially with respect to locating the

generator.

Moreover, the further away a dipole is from the scalp, the broader the distribution

and the smaller the amplitude of the signal.

 

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Figure 2.7 – Model of the localization of the neurons generating a dipole.

(A) Neurons are orientated tangential to the folded cortical surface resulting in a

tangential dipole moment (C). Neurons are orientated perpendicular to the folded

cortical surface (B) resulting in a radial dipole moment (D) (adapted from Ebner,

2006).

 

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2.2.2.3 Recording

EEG corresponds to the difference of 2 electrodes potentials, one of interested

positioned on the scalp and one reference (see Figure 2.8).

Figure 2.8 – A subject set up with electrodes, ready to start the experiment

(picture taken in the testing booth of the Helen Wills Neuroscience Institute at the

University of California, Berkeley, USA).

Electrodes

Electrodes are small metal discs, which are mainly made of silver, but also platinum,

gold or tin. Mostly silver / silver chloride (Ag / AgCl) electrodes are used, because

this compound reduces the polarization effect. This is a counter voltage that arises

while the voltage on the scalp is constant or slowly changing (Ebner, 2006). These

kinds of electrodes not only record brain activity, but also interfering activity, for

instance, alternating current (AC) (see 2.2.2.5).

The electrodes are placed on the head (cap or glued) and the application of a

conductive paste, rich in electrolytes, lowers the impedance between electrode and

skin – preferably below 5 kOhm. To ensure standardized recording, the positions of

 

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the scalp are identified using the International 10 / 20 system (see Figure 2.9). The

number of recording electrodes can go up to 256. Each electrode is labeled with a

letter and a number: the letter refers to a brain area (‘F’ = frontal lobe, ‘T’ = temporal

lobe, ‘P’ = parietal lobe, ‘O’ = occipital lobe, and the ‘z’ refers to the central line),

even numbers refer to the right side of the head and odd numbers to the left side.

Figure 2.9 – The layout form the International 10 / 20 system with 64 recording

electrodes (adapted from BioSemi, the Netherlands).

EEG instruments

The small amplitude of the EEG signal (1-100 µV) requires amplification. Therefore

a differential amplifier is used. The difference between two signals is amplified by a

constant factor (usually 10.000) (Ebner, 2006).

The amplified signal is digitized or sampled, i.e. the signal is converted into a

series of numeric values (Analogue-to-Digital conversion - ADC). The samples,

representing the actual value of the EEG amplitude, are measured at constant time

 

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periods. Sampling rate (expressed in Hz) refers to the number of samples per second.

For clinical application a usual sampling rate is at about 250 Hz, whereas in research

studies the signal can also be sampled much higher, for instance at over 1000 Hz.

Furthermore, the sampled signal is filtered to reduce superimposed signal or

to distinguish EEG frequency bands of interest. The bandwidth of EEG signal is

from under 1 Hz up to over 50 Hz varying in relative amplitude. Different filters can

be used depending on the purpose of a study. A notch filter, or band stop filter can be

used to exclude contaminating frequencies (50 Hz or 60 Hz) caused by electrical

power. A low pass filter attenuates signal higher than a specified threshold (e.g. 35

Hz) such as high frequency artifacts, for instance muscular activity. In contrast, a

high pass filter passes high frequencies and reduces the amplitude of low frequencies

(e.g. below 1 Hz) to remove slow artifacts (Ebner, 2006). A band pass filter allows

setting a range of frequencies that remain unattenuated whereas the frequencies

outside that range are rejected.

Montage

The way a pair of electrodes is connected to the differential amplifier is called

montage. It is crucial for a study to carefully choose the reference because data

alteration or loss due to subtraction can occur. There are different montages:

referential montage, bipolar montage and (common) average reference.

The referential montage indicates that one electrode is used as a reference.

This signal is subtracted from the signal of all other electrodes. Therefore, the

reference electrode should not record brain activity or artifacts, because otherwise

subtraction could cause information loss or modification. For instance, electrodes

placed on both earlobes, the nose or the mastoids would be a reference with a minor

activity of their own.

In the bipolar montage, electrodes are subsequently linked together and

potential differences between two adjacent electrodes are measured. In general, both

montages are equally effective, but they are used for different purposes according to

the location and dimension of the potential field.

A special montage is the average reference (common average reference). The

signal from all electrodes is summed up, averaged and subtracted from every

electrode. But, since the potentials are statistically irregular distributed, big

 

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deflections in the EEG of one region due to physiological or pathological activity can

falsify the EEG (Ebner, 2006). This montage is often used in ECoG recordings.

2.2.2.4 Classification of frequency

The recorded spontaneous electrical activity appears to be chaotic, but after applying

different filters, there is a rhythmic activity that can be classified into different bands

by their frequencies (typically, a negative deflection is depicted up in a graph). The

EEG of an awake, healthy adult is composed of several frequency bands: delta (< 4

Hz), theta (4 – 7 Hz), alpha (8 – 13 Hz), beta (14 – 30 Hz), and gamma (> 30Hz) (see

Figure 2.10). The amplitude is negatively correlated with the frequency, which

means that the amplitude decreases with increasing frequency (Pfurtscheller, 1999).

Furthermore, the amplitude is proportional to the number of synchronously active

neuronal populations (Elul, 1971), i.e. slow fluctuations reflect a bigger active cell

assembly than fast oscillations (Singer, 1993).

The alpha rhythm occurs with amplitude of about 50 µV, in an awake,

relaxed person with closed eyes during a low input of environmental stimulation.

This activity is bilaterally distributed mainly on occipital electrodes but also on

temporal and central electrodes. Every human being appears to have his own

individual alpha frequency that can vary according to the state of arousal. Exhaustion

can decrease the alpha to 8 Hz or below. If a tested person suddenly opens their eyes

or focuses on a mental activity (for instance, mathematical task), the alpha rhythm

disappears and is replaced by beta activity. This is called alpha block or

desynchronization.

The beta rhythm is the fastest rhythm for the main purposes of EEG

recordings but of course the brain shows activation in higher frequencies (gamma

rhythm), too. Beta is low in amplitude and shows a maximal distribution over fronto-

central sites of the scalp. It occurs during waking state and with open eyes, in

particular when the tested person focuses their attention or receives a high input of

environmental stimulation.

The theta rhythm also shows low amplitude (< 30 µV) and is mostly

distributed over parieto-occipital areas of the scalp. It is often seen in minor

occurrence in young adults. It mainly occurs in pathological states, for instance if the

patient has a lesion (e.g. tumor), an encephalopathy, or is under an antipsychotic

therapy (Ebner, 2006).

 

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Big populations of synchronously oscillating neurons generate the delta

activity. It is mostly present during sleep, but also considered normal in young,

awake adults over occipital sites of the scalp. There is also a temporal theta and delta

activity in older adults (over 60 years), which is considered normal as long as there

are only single waves or short sequences of theta and certain criteria are met: the

proportion of delta should be less than 1 % and theta less than 10 % compared to the

background activity (Ebner, 2006).

Figure 2.10 – EEG frequency bands.

The profiles are obtained during various state of consciousness. The particular band

is written in brackets (adapted from Kolb, 1996).

 

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2.2.2.5 Artifacts

Artifacts are deflections in the EEG that do not represent activity from the brain. A

distinction is drawn between biological and non-biological (technical) artifacts

(Cacioppo, 2005). If the artifacts show a typical shape and localization, they are easy

to identify, but artifacts can often modify the EEG in a minor way that is difficult to

notice. Therefore, observation and video monitoring are indispensable and make it

easier to identify the artifacts.

Sources for biological artifacts are: eyes, heart, arteries (pulse), tongue, skin

(sweat) and muscle activity. Especially eye movements and blinks are a problem in

experimental paradigms. The bulbus oculi (globe of the eye) forms an electrical

dipole that causes measurable potentials while the eyes move (see Figure 2.11).

Figure 2.11 – Schema of EEG artifacts due to eye movements.

The cornea is charged positive, whereas the retina is charged negative. According to

this, looking up, leads to positive potentials on frontal electrodes and looking down

to negative ones. If the person takes a look to the left, positive potentials are recorded

at left fronto-temporal electrodes (F7-T3) and negative potentials at opposite

electrodes of the right side (F8-T4). Corresponding potentials are elicited, when

looking to the right (adapted from Ebner, 2006).

 

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To avoid blinks and saccades, the tested person is instructed to blink as less

as possible and for instance, to fixate the gaze on a centrally presented cross on the

testing screen. Additionally, an electrooculogram (EOG) is recorded from electrodes

placed on both external canthi and below an eye. Thus, vertical and horizontal eye

movements are recorded (see Figure 2.13 A) and can be removed later in the

analysis.

Other artifacts, coming from muscle activity, i.e. chewing, frowning or tense

face muscles can highly contaminate the EEG (see Figure 2.13 B). Especially

difficult to deal with are complex biological artifacts, i.e. prolonged movement of the

subject. The best way to reduce these artifacts is to avoid them in a prophylactic

manner by carefully instructing the person to stay as relaxed as possible during

testing.

A very common technical artifact is the contamination of the signal with AC,

coming from other devices near the tested person (for instance, a cell phone) (see

Figure 2.12). Most electrical power is generated either at 50 Hz (for example in

Germany) or at 60 Hz (in the United States). If the noise source cannot be located,

special filters, i.e., a notch filter, can be used during data collection or offline to

remove the superimposed activity (Cacioppo, 2005).

Figure 2.12 – Technical artifact: contamination of the EEG signal with 60 Hz

signal (recorded in the Helen Wills Neuroscience Institute at the University of

California, Berkeley, USA).    

 

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Figure 2.13 – Biological artifacts. (A) Blink artifacts propagated over the frontal

scalp electrodes. The three last channels represent the EOG channels: rEOG =

electrode placed next to the lateral canthus of the right eye, lEOG = electrode placed

next to the lateral canthus of the left eye, vEOG = electrode under the left eye.

Together rEOG and lEOG record horizontal eye movements, whereas vEOG records

vertical eye movements. (B) Muscle artifact probably from chewing (recorded in the

Helen Wills Neuroscience Institute at the University of California, Berkeley, USA).    

 

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2.2.2.6 Data analysis: preprocessing and event-related potentials (ERP)

Preprocessing

The collected data does not provide clear information about the source of a lesion or

differences according to cognitive tasks. Therefore, it needs to be analyzed. After the

data is imported and visualized an inspection of the raw data is useful to identify

electrodes with no or poor signal or to classify artifacts and their amplitudes. So that,

thresholds are set to automatically exclude artifacts with amplitude above the cut off.

If the data suffers from major contamination with eye blinks that cannot be excluded

because of too much data loss, it can be corrected using independent component

analysis (ICA). This method separates a multivariate signal into independent

components using linear decomposition (Hoffmann, 2008). The component that

shows a scalp distribution corresponding to an eye blink can be identified and

removed. The last step is filtering the data to reject contaminated signal or to cut

down data to the frequency bands of interest (see 2.2.2.3).

Event-related potentials

Before, during and after a sensory, motor or cognitive event, specific electrical

events arise in the cerebral cortex. These effects can be measured as evoked-

potentials (EP) or event-related potentials (ERPs), which are very small signals

embedded in the ongoing EEG signal. ERPs refer to time locked perceptual,

cognitive or response potential, whereas evoked potential (EPs) refer to early sensory

responses such as the brainstem auditory evoked potentials (BAEP; see below). All

ERPs feature specific polarity, latency, localization and amplitude that characterize

the different components. ERPs reflect brain responses time-locked to an event or

stimulus in an experimental paradigm. ERPs are obtained by averaging the EEG

traces from a series of trials, aligned according to the event that is, for instance, the

onset of a stimulus or a response. Given that the background EEG is assumed to be

random, averaging random activity sums zero and the EP or ERP emerges from the

EEG. The signal to noise ratio (SNR) indicates to what extent the signal is

compromised by noise. The SNR is defined as the ratio of signal to noise power. A

ratio higher than 1:1 indicates more signal than noise. To increase the SNR, the

number of trial needs to increase as well because the SNR is proportional to the

square root of the number of sums (Schmidt, 2005). For instance, 81 trials improve

the SNR to 9:1 (given an initial SNR of 1:1). Moreover, the number of trials needed

 

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for ERPs or EPs is also dependent on the amplitude of the ERP or EP of interest. For

instance, the P300 (see 2.2.2.7) can already be visible after averaging 10 trials,

whereas the BAEP requires at least more than 100 trials. Due to standardized

electrode positions, the amplitude value of the averaged signal at a favored time can

be plotted on a topographic scalp map. However, if the source of brain activity is

causing a tangential dipole, the ERPs can be mapped paradoxically. Moreover, the

activity can also reflect processes, executed in parallel. Therefore, before interpreting

activity as function or processes and allocating it to distinct brain areas, the

orientation and possible source of dipoles and underlying cognitive processes that

might be present during the experimental paradigm need to be considered.

2.2.2.7 Main auditory electrophysiological components

ERPs have found to be a powerful tool for clinicians and researchers. For instance,

tumors in the auditory system can compress auditory processing areas and therefore

compromise hearing. Even, if MRT is the goldstandard, the localization of such a

tumor could also be identified using auditory evoked potentials (AEPs) that are

generated in certain areas of the ascending auditory pathway. There are a series of

responses that index the neural activity in the brainstem, midbrain, thalamus and

cortex (Gazzaniga, 2002). Electrodes placed on the vertex and mastoid can measure

these ERPs.

The earliest AEPs are very small electrical voltage potentials that arise within the

first 10 ms. They are called the early latency ERPs or brainstem auditory evoked

potentials (BAEP) and are used to test the auditory pathway up to the inferior

colliculus (see Figure 2.14 A). The middle latency ERPs emerge after the brainstem

response at about 10 to 40 ms (Picton, 1980). The thalamus (medial geniculate

ganglion) and the auditory cortex are the related structures to the middle and long

latency ERPs (> 40 ms) (see Figure 2.14. B and C).

These ERP waveforms are referred to as exogenous components, because

they are driven from the physical features of the stimuli (Schmidt, 2005). That

means, the amplitudes of exogenous components are altered according to the

intensity of the stimulus. Endogenous components, on the other hand, show

variations according to cognitive processes.

 

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Figure 2.14 – The auditory event-related potentials.

(A) The brainstem auditory evoked potentials. The anatomical locations are related

to the different waves: wave I = cochlear nerve, wave II = cochlear nuclei, wave III =

superior olivary complex, wave IV = lateral lemniscus and wave V = inferior

colliculus wave V and VI are not surely assigned to specific anatomical structures

(B) The middle-latency, and (C) the long-latency deflections of the auditory ERPs

with the main components: P50, N1 and P2. Negativity is depicted up (adapted from

Picton, 1980).

P50- N1- and P2-components

These three auditory components are exogenous evoked potentials, which are mainly

generated in the auditory cortices and result from sensory analysis of stimuli – even

in the absence of auditory attention. These potentials are assigned to the long

(>40ms) latency deflections of the auditory ERPs (Picton, 1980) and they are named

for their characterizing polarity and latency.

The P20-50- or P50-effect, is a positive potential occurring at around 20-50

ms after stimulus onset. It is thought to reflect neural activity in primary and

associative auditory cortices (Liegeois-Chauvel, 1994) and it shows a central

distribution on scalp electrodes (see Figure 2.15).

 

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The N100 or N1 is one of the major components of the auditory evoked

potentials. It is a large negative component, which peaks around 80-110 ms after

stimulus onset and shows a fronto-central scalp distribution (see Figure 2.15). The

N1 was first recorded by Pauline A. Davis at Harvard University (Davis, 1939). The

origin of the wave was unknown for a long time and finally conjoint with the

auditory cortex in the 1970s (Näätänen, 1987; Vaughan, 1970). From intracranial

depth recordings in humans, it has been shown that the N1 is generated in several

primary and associative auditory areas (Liegeois-Chauvel, 1994; Yvert, 2005).

Moreover, a dynamic dipole model analysis showed that neural generators are not

only active in the auditory areas, but possibly also in the motor and supplementary

motor areas and/or the cingulate gyrus (Giard, 1994b). This response is generated

after an abrupt acoustic event, and thus is involved in detection and perception of

acoustic transitions. Furthermore, the N1 amplitude depends upon several physical

features of the stimulus e.g. rise time of sound onset, inter stimulus interval (ISI),

loudness and frequency of the stimulus and preceding sounds (Näätänen, 1999).

After reaching the peak, the N1 returns abruptly to positivity and flows into

the P200 or P2 component, which appears as a positive deflection in the ERP

waveforms with a latency of about 200 ms and it shows a positive scalp distribution

at central electrodes (see Figure 2.15). Together, N1 and P2 are often referred to as

the N100-P200 or N1-P2 complex.

In addition, there can also be a N2, which is a negative component peaking around

200-350 ms after stimulus onset (Folstein, 2008) and is present during processes

enabling cognitive control. Moreover this component is also sensitive to novel

stimuli in terms of mismatch negativity (see 2.3.2) (Schmitt, 2000).

 

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Figure 2.15 – The long-latency deflections of the auditory ERPs at Fz electrode.

P50- N1- and P2-components with the corresponding scalp distributions (top views,

green represents positive amplitude and red negative amplitude) (curves and

topographies created from data collected for the current study).

P300 Component

The P300 or P3 is a positive ERP component, which reaches its maximum around

300 ms after stimulus onset. The strongest signal can be measured at parietal

electrodes. The P3 was first reported by Sutton (Sutton, 1965) in response to

unpredictable stimuli presented in an oddball paradigm. In this kind of paradigm a

rare target stimulus is presented amongst more frequent standard background stimuli

and the P3 arises when the target stimulus is detected. A larger P3 is elicited by those

events representing a low-probability category of stimuli (McCarthy, 1981).

The P3 wave is composed of two subcomponents known as P3a and P3b. These

subcomponents reflect distinct information-processing events. The P3a is usually

observed in response to non-expected meaningful stimulus, such as novels.

Therefore, it has been proposed that the P3a originates from stimulus-driven frontal

attention mechanisms. The P3b is elicited in response to detected targets and is

 

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considered as target-related. The P3b arises from temporal–parietal activity

associated with top-down attention and appears to be related to subsequent memory

processing (McCarthy, 1981).

Both P3a and P3b depend on a number of variables, in particular the subject's

mental state, the task that has to be accomplished, the significance of the stimulus,

and the degree of attention. Therefore, these responses are often used as indicators of

higher-order cognitive functions such as decision-making or selective attention.

In consequence, various studies have suggested that several cortical generators of P3

may co-exist: the medial temporal cortex, the temporo-parietal junction, and the

lateral prefrontal cortex (Soltani, 2000).

2.3 Auditory attention

2.3.1 Psychological theories

2.3.1.1 Introduction to selective attention

Attention is an abstract concept, which is not easy to define; but already in

the 19th century William James, a psychologist at Harvard University, proposed a

definition of 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 simultaneously possible objects or

trains of thought. Localization, concentration, of consciousness are of its essence. It

implies withdrawal from some things in order to deal effectively with others, and is a

condition which has a real opposite in the confused, dazed, scatterbrained state which

in French is called distraction, and Zerstreutheit in German.” (James, 1890)

W. James emphasized the main characteristics of attention: it is a cognitive brain

mechanism that enables processing relevant inputs, thoughts, or action while

ignoring irrelevant or distracting stimulation (Gazzaniga, 2002).

And by his statement “it is the taking possession by the mind” he also

stressed one of the two categories of attention: voluntary or endogenous attention,

which involves one choosing to focus their attention on an event of interest. This

process is driven by so-called top-down signals, which means that cognitive

influence and decisions can alter the perception of stimuli. The other category is

reflexive, automatic or exogenous attention, which occurs when an external object or

a sensory event captures our attention, also called bottom-up. The attention process is

 

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based on the analysis of the stimulus characteristic (e.g. color, brightness). For

example, one red balloon in a bunch of blue balloons will grab the attention and

attract it involuntarily.

The ideas of attention W. James proposed over 100 years ago are still today’s

purpose of research. The main goal in studying attention is to investigate how

attention enables and influences detection, perception and encoding of stimulus

events (Gazzaniga, 2002). Most studies were conducted in the visual and auditory

modalities. During the last decades the number of studies in visual attention

increased and displaced the emphasis of auditory attention, predominant in the 1950s

and 1960s (Broadbent, 1958; Cherry, 1953). Auditory attention seems to be a greater

challenge because of crucial physiological differences in structure and function.

Visual attention is linked to the position of the head and eyes since the stimuli are

already mostly fully processed in the fovea (Pashler, 1998). The human cochlea on

the other side is not an equivalent to the fovea. The characteristic of auditory

selective attention is that it is mostly independent of the position of the head and the

ears, which makes it a system that is ready to receive and process stimuli from all

directions regardless of the organism’s current orientation (Pashler, 1998). On the

other side this openness to all inputs from the environment means that efficient

selection mechanisms need to distinguish relevant from irrelevant sounds.

Colin Cherry, a British psychologist, described the classic auditory example

of this phenomenon – the so-called cocktail party effect (Cherry, 1953): a person can

focus on one particular speaker while tuning out several other simultaneous

conversations. This can only be achieved by auditory selective attention: the

perception of a certain stimuli in the environment is enhanced relative to other

stimuli of lower immediate priority. In Cherry’s study, competing speech input was

provided through earphones into the two ears of a subject. The subjects were asked

to attend and verbally shadow (immediately repeat each word) a relevant input in one

ear while ignoring irrelevant information presented to the other ear; this approach is

called dichotic listening. He noticed that the subjects were only able to report the

input from the attended ear and could not report one detail from the ignored channel.

He also observed a significant decrease in performance when the subjects attempted

to attend to both input channels simultaneously in comparison to selectively

attending to one channel.

 

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Cherry proposed that attention focused on one ear results in better encoding

of inputs in this channel, whereas the input of unattended channels might be

attenuated or rejected. These findings led to general models of attention, which fall

into two categories: bottleneck theories (see 2.3.1.2) and other capacity model

theories (see 2.3.1.3). The bottleneck is the most influential one. It is worth noting

that all theories are based on the idea that humans have limited information

processing capacity: i.e. it is impossible to process and react to all exogenous and

endogenous inputs that continuously excite our senses.

2.3.1.2 Bottleneck theories: early- versus late-selection

A few influential psychologists proposed different models of attention to explain

results like those from Cherry’s experiment (Broadbent, 1958; Deutsch, 1963;

Treisman, 1960). The theories all share the underlying idea, that every processing of

the brain, even sensory inputs, have a limited capacity channel (bottleneck) and

thereby, only a certain amount of information could pass (Gazzaniga, 2002).

Therefore, all sensory inputs need to be screened, sorted and filtered, to let only the

relevant stimuli pass for further processing i.e. irrelevant inputs are rejected and

relevant ones are admitted for higher order processing. The main differences between

the following models of attention are the proposed location of the processing

bottleneck that is either early or late, and the extent to what ignored inputs are

actually processed before they are rejected or admitted for further processing. Thus,

the competing theories arise: early-selection represented by Broadbent’s and

Triesman’s models and late-selection represented by Deutsch’s model.

In Boradbent’s theory (Broadbent, 1958), the incoming stimuli are

temporarily held in a sensory register, which allows attending to unanalyzed

information later on. Then, the stimuli are analyzed in parallel by a selective filter on

the basis of their physical characteristics such as spatial location (attended versus

ignored ear, the later are rejected), spectral content, and temporal features. This

filtering process happens unconsciously. The selected stimuli pass along a limited

capacity channel and, subsequently, semantic analysis takes place, which is essential

for influencing a response or entering long-term memory. The not-selected stimuli

are not further analyzed and do not reach consciousness, which is an all-or-nothing

view of perception.

 

38  

Some features of Broadbent’s filter theory explained Cherry’s data well, but

Neville Moray showed in 1959 that high priority information in an unattended input

channel was also processed to the extent that it could break through the attentional

barrier. In his experiment, Moray found that a persons’ own name in an ignored input

channel could often direct attention to this channel (Moray, 1959). These findings led

to the assumption that all information was actually analyzed equivalently regardless

whether it was attended or ignored during testing.

Therefore, Treisman proposed a direct modification of Broadbent’s model on

which he agreed a year later. The theories are quite similar, but the main difference is

the filter. Treisman's filter passes the attended input as well through the limited

capacity channel but also allows unattended messages to go through, but in an

attenuated form, i.e. their signal strength is lowered. Accordingly, certain unattended

messages can be processed semantically and also reach consciousness, if they meet

certain criteria. Most important criteria are differing thresholds that can be variable

and also function as a filtering mechanism. For example, biologically important

signals have permanently lowered thresholds, thus, even very attenuated signals can

be facilitated and semantically analyzed. This could explain why one's own name in

an unattended message can attract attention to it. This model is, therefore, an early

selection theory, and an attenuation model of attention.

Taken together, the bottleneck in the early selection theory is located around

the level of perceptual analysis (see Figure 2.16), thus, attended input is perceptually

processed and continues to higher order processing (e.g. encoding as semantic or

categorical information), whereas unattended input is either rejected categorically

(Broadbent’s theory) or attenuated so that important messages in an unattended

channel are enabled for further processing (Treisman’s theory). So, it may be

possible, that inputs are selected or rejected even before the perceptual analysis of

the stimuli’s characteristics is fully completed (Gazzaniga, 2002).

The early selection models can be contrasted with the late selection one,

which proposes that attended and unattended stimuli are processed equivalently by

the perceptual system and both inputs reach further processing of semantic encoding.

After that, selection for further processing or for conscious awareness can take place.

Thus, selection takes place at higher stages of information processing about whether

the stimuli should gain complete access to awareness, be encoded in memory, or

initiate a response. J. A. Deutsch and D. Deutsch first proposed the most influential

 

39  

late selection theory (Deutsch, 1963). In this model all incoming stimuli are stored in

a sensory register and fully processed even at a semantic level without any

attenuation. This perceptual analysis happens automatically and independently

whether attention was paid or not and it is accomplished before any selection due to

attention takes place. The information is then grouped by mechanisms, activated by

particular features of the incoming stimuli, i.e. importance of the stimulus. The

highest level represents a criterion by which all the other levels are compared. This

level represents a reference point enabling the appropriate output, such as a motor

response, and inhibits the output associated with other levels. Furthermore, the

general state of arousal alters the access to an output system, i.e. for a low level of

arousal (e.g. sleep), only very high-priority information will be able to alter storage

or motor response.

Figure 2.16 – Diagram of early and late selection.

This schema shows the location of the bottleneck regarding early- and late selection

theories, i.e. the extent to what a stimulus is processed before it is rejected or

admitted for further processing. The limitation of early selection is located during or

even previously to perceptual analysis. Late selection, on the other hand, occurs after

complete semantic encoding of all stimuli (adapted from Gazzaniga, 2002).

 

40  

In summary, the early selection theories allow few automatic processing and

no semantic processing of the unattended input before the selection takes place.

Therefore, the bottleneck of these theories is located in the perceptual system. On the

contrary, in the late selection theories, the bottleneck appears to be in the response

system. Indeed, all inputs are processed rather automatically by the perceptual

system and reach the stage of semantic encoding and are therefore able to influence

the executive functions, such as decision, memory or simply making a response.

However, the theories might not be so different. One argument is that it might

be a terminological issue, because in all theories selecting mechanism operate by

similar conditions: levels of importance (Deutsch and Deutsch) or different threshold

levels (Treisman). More importantly, only the highest level of importance (Deutsch

and Deutsch) and the information with a triggered threshold (Treisman) can pass on

to further processing such as making a response. Besides, both theories propose

pattern recognition units and mechanisms selecting highly salient stimuli dependent

on bottom-up (physical features of the stimulus) and top-down (contextual features).

This shows that the theories feature major differences but also similarities.

2.3.1.3 Other capacity-limitation theories

As already mentioned, all attention theories are based on the idea that humans have

limited information processing capacity. During the fruitless discussion between

early and late selection theories other attention models based on assumptions from

this debate were developed.

Nilli Lavie, a researcher at University of London developed two theories on

attention and distraction: the perceptual load theory and the cognitive load theory.

The perceptual load theory is based on the hypothesis, that perception has limited

capacity (early selection) but processes all stimuli automatically (late selection) until

it runs out of capacity (Lavie, 2005). The idea is that the perceptual load of relevant

information determines selective processing of irrelevant information (Lavie, 1995).

That means that a high perceptual load would engage all capacities available and

would leave no spare capacity for processing irrelevant stimuli. On the other hand,

under the condition of a low perceptual load (when the relevant stimuli do not

demand all of the available capacity) irrelevant stimuli will unintentionally capture

spare capacity and be processed at the perceptual level, which would lead to an

increased distraction (Lavie, 1995). Therefore, rejection of irrelevant stimuli results

 

41  

only from an overload of the perceptual system by relevant information, i.e. in case

the capacity limit is exceeded.

Nilli Lavie observed that the effect of load on distractor processing is mainly

depending on the type of mental process that is loaded, because load on executive

functions such as working memory had the opposite result. So, she proposed the

cognitive load theory. This theory is about the interaction between attention and

working memory, which is the ability to hold and manipulate information in mind for

a short time, or rather, actively maintain stimulus-processing priorities through out

the task (Lavie, 2005). Lavie and colleagues suggested that load on working memory

results in a reduced availability of working memory for a selective attention task (by

loading working memory in a concurrent, yet unrelated task). This in turn should

result in reduced efficiency of focusing attention on the relevant stimuli, with greater

interference by distractors. More precisely, a high cognitive load would increase the

interference by an irrelevant low-priority distractor, and a low cognitive load would

decrease distractor interference (Lavie, 2004). Therefore, load on an executive

function such as working memory has the opposite effect than perceptual load. This

idea was supported by results from de Fockert (de Fockert, 2001) showing a causal

role for working memory in the control of selective attention (see 2.4).

The effects of different types of load on distractor processing provide a better

understanding of how distractor processing is affected by capacity limits in different

mental processes. These load models also provides a more complete view at the

early- and late-selection debate: early selection depends on high perceptual load,

whereas late selection depends on cognitive control functions, available for the

selective attention task (Lavie, 2005). Thus, these capacity-limitation models also

reconcile the competing bottleneck models by combining the assumption that

perception is a limited process (early) with the view that perception is an automatic

process (late) to the extent that there is spare capacity available. This suggests that

there is no single bottleneck in the information processing system, but there are a

series of filters so that incoming stimuli can be selected at early or late stages

depending on the situation. Therefore, these are also flexible theories of selective

attention.

 

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2.3.2 Electrophysiological findings and theories

Many electrophysiological studies have been conducted to investigate the electrical

changes in the brain due to selective auditory attention. Although this is difficult

enough, it is even more difficult to combine the findings with corresponding

attention theories. Especially important are studies using EEG because ERPs can

provide information about perceptual and cognitive processes and their alteration due

to the state of attention in real time. An introduction and review about the most

important studies follows.

Support for early selection theories of attention

Steven Hillyard (Hillyard, 1973) developed a selective listening task, which became

the classic auditory selective attention paradigm (see Figure 2.17), to investigate

brain mechanisms of auditory attention with scalp EEG. Streams of sounds differing

in pitch were delivered through headphones to the ears of the subjects. In one

condition they were asked to attend to the sounds in one ear and make a response to a

target sound in this ear (see Figure 2.17: the grey notes represent the targets) while

ignoring all sounds in the other ear (for instance: attend left ear and ignore right ear).

Then, in a second condition, they were asked to pay attention to the stimuli in the

other ear (in this example: attend right and ignore left). This way, Hillyard separately

got ERPs to the same stimuli when they were attended and when they were ignored.

Figure 2.17 – Scheme of the selective listening task.

The sounds occur successively to the left and the right ear. The subjects are asked to

detect stimuli of interest in one ear (for instance, grey notes in the left ear) and ignore

all stimuli presented to the other ear (adapted from Bidet-Caulet, 2008).

 

43  

Hillyard controlled the global state of arousal during testing by engaging the subject

in this difficult task subjects. Thus, only the direction of attention varied (i.e., which

ear the subjects directed their attention to). The researcher discovered that auditory

ERPs - more precisely the N1 component - was substantially larger in amplitude for

the attended stimuli compared to ignored ones (Hillyard, 1973). Since the N1 is

known to be generated in the auditory cortex (Vaughan, 1970), Hillyard and his

colleagues interpreted their findings as an increased activity of the N1 generators, i.e.

an enhanced activation of neurons involved in automatic sensory analysis of sounds

in the auditory cortex. Consequently, they proposed that selective attention acts as a

filtering or gain mechanism that can inhibit or gate unattended stimuli at an early

stage of sensory analysis (about 100 ms). This represents a physiological version of

the psychological attenuation model of early selection (Broadbent, 1958; Treisman,

1960).

A few years later Näätänen (Näätänen, 1978) suggested that the increased

negativity observed by Hillyard could be dissociated from the N100 component.

Näätänen used a longer and constant inter stimulus interval (ISI, 800 ms) than

Hillyard (250-1250 ms) and he did not observe an enlargement of the N1, but when

he subtracted the ERPs to ignored tones from those to the same tones when they

were attended, he found a negative difference wave (Nd), or also called processing

negativity (PN). This deflection began to emerge at around 150 ms after stimulus

onset and persisted for at least 500 ms. Näätänen proposed that this wave is an

endogenous component, representing attention-specific activity, which is different

from the activity resulting from automatic sensory analysis (Näätänen, 1978). He

concluded that the N1-effect Hillyard reported was the exogenous N1 overlapped by

the endogenous Nd and thus, not an increased activity of the N1 generators.

Näätänen also observed that the Nd is composed of two subcomponents: an early

one, which could be generated in the auditory association cortices and is independent

of the ISI and a later one of larger amplitude and longer duration at frontal sites,

which is elicited with long ISI (800-2000 ms) (Näätänen, 1981).

Based on these findings, he developed the attentional trace model of selective

attention (Näätänen, 1982). This attentional trace would be an actively maintained

cortical representation of the physical features (e.g. pitch, location) of stimuli. These

features separate relevant irrelevant stimuli from ones. The model proposes that there

is an early selection in terms of a comparison between the sensory input and the

 

44  

attentional trace in the auditory cortex. The earlier Nd component, which would

explain the attention effect at the N1 latency, would be generated by the comparison

of the stimuli features with the trace. If the sounds do not match with the trace they

would be rejected from further analysis. Accordingly, the late Nd could reflect a

frontal component controlling and maintaining the attentional trace.

Hillyard’s and Näätänen’s models led to a controversy and numerous studies

about the relationship of the processing negativity and the N1. The main difference

between the two models is that Hillyard’s filtering mechanism would represent

modulation of the exogenous components of the ERPs in addition to an endogenous

attention effect presented as a frontally distributed negativity. In Näätänen’s

attentional trace model on the other hand all ERP effects would be of endogenous

origin and modulation.

Furthermore, another important study provided evidence for the early-

selection theory. Based on the idea that the auditory cortex can be activated as early

as 20 to 25 ms after the onset of a sound, Marty Woldorff tried to find attentional

changes on the earliest components of the auditory ERP, brainstem auditory evoked

potentials (BAEP) and the middle-latency deflections (latency range 10-40ms)

(Woldorff, 1987). He also used the classic dichotic listening paradigm but modified

it to facilitate early selection attention effects: the ISI was rather short and the

subjects had to perform a difficult detection task. The targets were of a low

probability and of lower intensity than the other stimuli, which increased the

attentional load and force the participants to closely pay attention to the sounds. He

did not find any evidence for an attentional modulation on the BAEP. However, he

found attentional changes of the ERPs even prior to the N1 and the P2, that is the

affection of the mid-latency ERPs around 20-50 ms (see Figure 2.18). More

specifically, Woldorff concluded that the P50 was modulated as function of selective

attention, since it showed enlargement of the amplitude to attended sounds in

comparison to unattended sounds. In addition, he replicated the results in another

study and finally showed that neural processing of attended versus unattended

sounds can differ significantly even at 20 ms post stimulus (Woldorff, 1991). Thus,

Woldorff provided support for the early-selection theory that stimuli can be selected

or gated before perceptual processing is fully completed.

 

45  

Figure 2.18 – ERPs to attended and ignored stimuli in a dichotic listening task

(grand average across all subjects).

Except for the N1 and P2 effect, the essential finding is the attentional modulation of

the positive deflection at around 20-50 ms post stimulus (P50 effect). Thus,

providing support for early selection theories of attention (adapted from Woldorff,

1991).

Furthermore, Aurélie Bidet-Caulet showed attention effects in the auditory

cortex at 30 ms recorded from depth electrodes implanted in the temporal cortex of

patients with pharmacologically resistant partial epilepsy (Bidet-Caulet, 2007).

Attention effects have also been shown to alter the sensory analysis of

acoustic inputs peripheral to the central auditory system. Findings from Lukas first

suggested that attention alters the sensory analysis of acoustic inputs not only in the

central auditory system but also in the brainstem. He observed that efferent axons

within the olivocochlear bundle in the brainstem even might function by attenuating

irrelevant acoustic stimuli during a visual attention task at an early stage of

processing (Lukas, 1980; Lukas, 1981). A few years later, Marie-Helene Giard

provided evidence that evoked otoacoustic emissions (EOAEs) to tones in one ear

had larger amplitude when they were attended compared to when they were ignored

 

46  

(Giard, 1994a). These results indicate that selective attention related modulations

could occur already at the cochlear receptor, which indicates the existence of top-

down control mechanisms at a very early level. Marie-Helen Giard concluded that

selective attention could already operate as a peripheral band-pass filter at the

cochlear receptor level prior to transduction process (transduction of sound into a

neural signal).

Altogether these studies support the early selection theories of attention but

they do not answer the question by which mechanisms the attentional selection

happens.

Mechanisms of auditory attention

The studies described above addressed auditory selective attention by comparing

brain responses to attended and ignored sounds. However, another important issue is

whether selective attention is operating via one or more mechanisms and how they

interact. More precisely, do attentional mechanisms operate by facilitating the

processing of relevant sounds and/or by inhibiting the processing of irrelevant

sounds? To answer these questions, it is essential to use a baseline condition in

which all sounds are fairly similarly processed, to be able to see attentional

modulations corresponding to facilitation or inhibition effects.

Merlin Donald used the classic auditory selective attention task and also

added a baseline condition (Donald, 1987). This baseline (neutral condition) was

defined by the situation, in which the task was so difficult that ERPs to attended and

ignored standard stimuli did not differ. He obtained difference waveforms by

subtracting the ERPs of the baseline condition from those to attended stimuli or to

ignored stimuli, to distinguish the effect of attention on the processing of relevant

and irrelevant stimuli (see Figure 2.19: Difference waves attended-neutral and

ignored-neutral). The obtained difference wave for attended sounds was a negative

deflection with a very early onset (at about 25-30 ms), to which Donald referred as

the facilitation effect. The obtained difference wave for the ignored sounds was a

positive waveform starting around 125 ms that Donald considered as the rejection

effect. Donald proposed that, since the facilitation and rejection effects of attention

show different timing, they should be independent from each other.

 

47  

Figure 2.19 – ERPs to tones in an attended, ignored and neutral condition.

ERPs to tones in the neutral condition are depicted as the dashed line, whereas

attended (left) and ignored (right) are shown as a solid line. The difference wave

between ERPs in the attended and neutral condition reflects the facilitation effect,

whereas the difference between ERPs in the unattended and neutral condition

represents the rejection effect. Both effect show different timing and polarity

(adapted from Donald, 1987).

Aurélie Bidet-Caulet (Bidet-Caulet, 2007) also found facilitation and

inhibition (Donald referred to as rejection) effects of auditory attention from direct

recordings of the human auditory cortex. The characteristic of her study was that the

subjects did not perform the classic dichotic listening task because it is not

representing a physiological situation one is confronted with in everyday life.

Instead, participants were presented with two binaural (in both ears) streams

simultaneously, to examine the influence of active selection on the processing of

overlapping binaural streams. Two concurrent streams at different pitches and

amplitude modulation frequencies (21 and 29 Hz) were presented binaurally and

changed in spatial direction at the end (left or right ear). Subjects were asked to focus

their attention on one of the two streams and to indicate the final direction (left or

right) of this attended stream. Bidet-Caulet also used a baseline condition in which

the subjects had to detect a rare noise burst, superimposed to the streams, to force the

subjects to orient their attention away from the streams (control condition). This

 

48  

seems to be a better baseline condition than Donald’s, because he changed the

difficulty of the target detection task to influence the ERPs but he did not try to

control for the direction the subjects were paying attention to. The researchers

recorded data from epileptic patients implanted with multicontact depth electrodes in

the temporal cortex and analyzed the brain responses to the same streams in the three

different attentional conditions (attended, control and ignored). Each stream elicited

an evoked activity and a steady state response (SSR), which features the property to

occur at the same frequency than the amplitude modulation of the sound (21 or 29

Hz). The results showed that, in a situation of sound rivalry, selective auditory

attention could affect the SSR in the primary auditory cortex and modulate the

evoked responses in secondary auditory areas. These findings show that selective

attention can modulate the sensory processing of sound within distinct auditory

areas. Therefore, they are consistent with Hillyard’s gain theory (Hillyard, 1973), but

this does not rule out the existance of an attentional trace in the selection process, as

proposed by Näätänen (Näätänen, 1978). Moreover, the study provides insights in

the neural mechanisms of selective attention, because it shows that there is not only

an enhancement of neural representation of the attended stream, but also a reduction

of the neural representation of the ignored stream.

Altogether, these electrophysiological studies brought together several pieces

about auditory selective attention. It is now well accepted that auditory selective

attention can modulate the sensory analysis of relevant and irrelevant stimuli not

only in the auditory cortex (Bidet-Caulet, 2007) but also on the level of the brainstem

(Lukas, 1980; Lukas, 1981) and even at the level of the cochlear receptor (Giard,

1994a). Therefore, attention can operate as the gain mechanism Hillyard proposed,

which would operate as a filtering mechanism selecting relevant stimuli at early

stages of perceptual analysis. However, there is also evidence for the attentional trace

Naatanen porposed, observed as a sustained negative deflection (Nd). This

attentional trace would be of endogenous origin and would be an actively maintained

cortical representation of the relevant acoustic features to which incoming stimuli are

compared. Finally, auditory selective attention seems to operate via facilitation and

inhibition mechanisms, reflecting enhanced or reduced processing of relevant or

irrelevant sounds, respectively (Bidet-Caulet, 2007; Donald, 1987; Michie, 1990;

Michie, 1993).

 

49  

However, little is known about how these mechanisms interact, in particular

if facilitation and inhibition are functionally distinct.

2.4 Aims of this dissertation

Attention is the ability to focus on external activities or internal processes while

preventing being distracted by irrelevant information. The ability to ignore irrelevant

distracting stimuli and subsequently adapt behavioral response is very important in

every day life, as the effect of distraction can have a broad range of consequences,

for instance from decreased life quality to dangerous incidents (e.g. during driving).

The mechanisms of selective attention have been extensively investigated. It

is generally accepted that selective attention is capable of modulating responses to

relevant (Hillyard, 1973) and irrelevant stimuli (Donald, 1987) at different levels of

the auditory processing including the auditory cortex (Bidet-Caulet, 2007; Jancke,

1999), the brainstem (Lukas, 1980; Lukas, 1981) and the cochlea (Giard, 1994a).

Furthermore, it has been shown that particular signals, so-called top-down signals

(see 2.3.1.1) are important for cognitive control to enable selective attention. These

top-down signals derive from knowledge about the current task and are able to

modulate the neural activity in sensory cortices. Imaging and electrophysiological

studies have found neural correlates for top-down and bottom-up signals in the

frontal and sensory cortex (Buschman, 2007; Kastner, 2000), which seems to provide

evidence for an involvement of the frontal lobe in attention and selection processes.

However, it is still unknown by which mechanism the brain activity is

regulated. Regarding the enhancement and reduction of responses to relevant and

irrelevant stimuli, there are two competing theories. One proposes that attention is a

unitary gain control mechanism that regulates activity either up or down along one

continuum (see Figure 2.20 A). The other one represents attention as a net activity of

top down distinct facilitation and inhibition mechanisms (see Figure 2.20 B).

 

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Figure 2.20 – Competing theories about the regulation of brain activity in

response to stimuli.

(A) One mechanism representing a unitary gain mechanism that regulates the activity

either up or down along one continuum. (B) Two distinct mechanisms: attentional

facilitation and inhibition operating as a net activity (adapted from Bidet-Caulet,

2008).

An argument for two distinct attentional mechanisms also come from an EEG

(Gazzaley, 2008) and a fMRI (Gazzaley, 2005) study. Gazzaley found, that in the

visual modality, older adults exhibit a selective deficit in suppressing task-irrelevant

information during working memory encoding. He further showed that suppression

mechanisms are rather delayed in time than lost with age.

Attention and working memory are strongly related to each other. Lavie’s

cognitive load theory suggests that in order to direct attention and to specify which

stimuli are currently relevant, the active maintenance of stimulus properties in the

working memory is required (Lavie 2005). Therefore, a high load on working

memory should lead to less differentiation between high and low priority stimuli

(target versus distractor) and thus, increase distractor processing and thereby increase

distraction.

An fMRI study in young adults observed that the extent to which distractors

are inhibited can be determined by the availability of cognitive resources, assessing a

direct causal role for working memory in the control of selective attention (de

Fockert et al., 2001). Cognitive resources were manipulated in a dual task protocol

where subjects performed, at the same time, two unrelated tasks: an attention and a

memory tasks. In the visual attention task, subjects had to classify famous written

 

51  

names as pop stars or politicians while ignoring distractor faces, which could be

congruent or incongruent with the target name or anonymous (see Figure 2.21 A).

In the working memory task, subjects were asked to remember a 5-digit order on

each trial at the beginning of the attention task. In order to manipulate the memory

load, subjects were asked to remember either a fixed order of digits (0 1 2 3 4) or a

random order of digits (0 3 1 4 2). After the attention task, a memory probe was

presented to the subjects, who were asked to report the digit that followed this probe

in the memory set (see Figure 2.21 A).

Functional magnetic resonance imaging (fMRI) was used to measure brain activity

while participants performed the two tasks. Distractor related activity was obtained

by comparing the activity during attention condition with a neutral condition in

which the distractor faces were absent (see Figure 2.21 B: face present versus face

absent).

De Fockert observed that a high working memory load increased the

distractor interference effect on behavioural performance of subjects. A high load

also resulted in an increase of activity elicited by the distarctor faces in visual areas,

especially in the extrastriate visual cortex and the fusiforme gyrus (known to be

selective for face processing) (see Figure 2.21 B). These findings indicate that

distractor faces were more extensively processed under high than under low working

memory load. De Fockert concluded that working memory serves to control visual

selective attention and suggested that there might be two distinct attentional

mechanisms regulating the responses to stimuli. However, this study did not assess,

to what extent the availability of cognitive resources affects the processing of

relevant information.

 

52  

Figure 2.21 – Interaction between working memory and selective attention.

(A) Example for the dual task protocol (in order, from top to bottom): memory set,

pop star with congruent face, politician with incongruent face, memory probe. (B)

Distractor related activity in high and low memory load. Left side: views of the

ventral surface of the template brain, on which superimposed loci indicate greater

activity in the presence than in the absence of distractor faces under condition of low

(top) and high working memory load (bottom). Right side: mean signal change of the

distractor related activity (percent signal change for face presence minus face

absence) for the maxima of the interaction in the right fusiform gyrus (adapted from

de Fockert, 2001).

These results in the visual modality suggest that, facilitation and inhibition

rely on distinct mechanisms that would be differentially affected by the amount of

available cognitive resources, and thus the difficulty of a memory task in dual task

protocol. More precisely, facilitation would not to be affected by the memory task

difficulty, whereas inhibition is most likely to decrease with increasing memory task

difficulty.

 

53  

The aim of the current article was to dissociate the two competing attentional

theories (see Figure 2.20) and to test whether facilitation and inhibition can operate

independently. To do so, a dual task protocol was also used. Subjects had to perform

an auditory selective attention task and a memory task. Electrophysiological

responses to the same sounds in three conditions were compared (attended, ignored

and a control condition) to measure facilitation and inhibition. The amount of

available cognitive resources was manipulated by varying the difficulty of a

concurrent sound memorization task. Based on the idea that facilitation and

inhibition operate independently, the hypothesis was, that they should not be

correlated, but rather feature different electrophysiological properties and should be

differentially affected by the memory task difficulty.

The following is largely content of the already published article: Bidet-Caulet, A.,

Mikyska, C., and Knight, R. T., (2010), Load effects in auditory selective attention:

evidence for distinct facilitation and inhibition mechanisms, Neuroimage, 50, (p.

277-84)

 

54  

3 Material and methods 3.1 Subjects

Sixteen subjects (5 female, 1 left-handed, aged 18-30 years) participated in this

experiment. All subjects were free from neurological or psychiatric disorder, and had

normal hearing. They all gave written informed consent in accordance to the study

protocol approved by the University of California, Berkeley Committees on Human

Research.

3.2 Stimuli and task

Subjects had to perform an attention and a memory tasks at the same time (dual task

protocol).

In the attention task, subjects were randomly presented with 3 different kinds

of stimuli (see Figure 3.1). One was the standard (50-ms duration) and the other one

the deviant stimulus (100-ms duration), varying in duration. Both stimuli were band-

pass noises (5-semitone wide, 5 ms rise/fall times) and were successively delivered

to each ear. The third stimulus was a binaural pure tone occurring in both ears at the

same time (carrier frequency 988 Hz, 50-ms duration). In one ear, the standard and

deviant sounds were low-pitch noises (554-740 Hz). In the other ear, the standard

and deviant sounds were high-pitch noises (1319-1760 Hz). The loudness of these

noises was matched by previous subjective matching in 11 subjects. The sound

pitches presented in each ear were balanced across blocks. In each block (about 25

s), 49 sounds were played: 20 standards and 3 deviants in each ear (41% and 6%

probability in each ear, respectively), and 3 pure tones (6% probability). The inter-

stimulus-interval (ISI) between 2 successive sound onsets varied between 300 and

500 ms. Subjects had to perform 3 different detection tasks. They either had to pay

attention to the left (right) ear and press the right button of a joystick when they

heard a duration deviant in the left (right) ear; or they had to press the right button

when they heard a binaural sound (control condition). Thus, in the two first

conditions, half of the standards were considered as attended (in the attended ear)

and half were considered as ignored (in the unattended ear). In the control condition,

all standards (in right and left ear) were considered as “control” standards.

 

55  

The memory task consisted in the memorization of a sequence of four 5-

harmonic sounds (100-ms duration, 5 ms rise/fall times). Subjects were presented

with this sequence, then performed the attention task, and finally were presented with

a second sequence they had to compare to the first one. Thus, they had to keep the

short sequence in memory while performing the attention task. To construct the

sequences, 4 different sounds were used with the following fundamental frequencies:

1724, 4023, 5747, or 8046 Hz. When the memory task was easy the first sequence

was the 4-time repetition of one of these sounds, and the second was either the same

(left button press) or a sequence of the 4 different sounds (right button press). When

the memory task was difficult the first sequence was a sequence of the 4 different

sounds, and the second was either the same (left button press) or a sequence of the 4

different sounds in a different order (right button press). Three memory conditions

were considered: no, easy or difficult memory task (see Figure 3.1).

 

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Figure 3.1 – Scheme of the dual task protocol: memory and attention task.

Subjects were presented with a sequence of 4 notes. Afterwards, they had to perform

3 different attentional tasks (detection of: (1) duration deviants in the left ear and (2)

in the right ear, (3) pure tones in both ears) while they were keeping in memory the

auditory sequence. 1 attention block consisted of 20 standards and 3 duration

deviants in each ear, respectively, and 3 pure tones in both ears. After the attention

block was completed, subjects had to do an easy or difficult (easy or difficult

memory task) test of the acoustic memorization (adapted from Bidet-Caulet, 2008).

 

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3.3 Procedure

Participants were seated in a sound-attenuated EEG recording room. The sounds

were delivered through earphones at an intensity level judged comfortable by the

subjects, using ‘Presentation’ software (Neurobehavioral Systems, Albany, NY,

USA). The experiment started with a familiarization with the sounds and tasks and

the participants were trained on the attention and memory tasks separately. EEG was

then recorded while subjects performed 12 blocks of the attention task (4 in each

attention condition) for each memory condition, resulting in a total of 160 attended

standards, 160 ignored standards and 160 standards in the control condition. The

blocks were run by memory condition (e.g. 12 attention blocks were run under the

condition of easy memory and so forth). The order of memory conditions was

balanced across subjects. The order of the 12 attention blocks was the same for each

memory condition, and was balanced across participants using a Latin-square design.

During all the experiment, subjects were instructed to perform as well and as fast as

possible and to favor accuracy in the memory task if it was difficult to perform both

tasks correctly. They were also asked to keep their eyes fixated on a centrally

presented cross and to minimize any eye movements and blinks while performing the

tasks.

3.4 EEG recording

EEG data were recorded from 64 electrodes using the ‘ActiveTwo’ system (BioSemi,

the Netherlands; see Figure 2.8 and 2.9). Vertical and horizontal eye movements

were recorded from electrodes placed at both external canthi and below the left eye.

Data were amplified (-3dB at ~819 Hz low-pass, DC coupled), digitized (1024 Hz),

and stored for offline analysis. Data were referenced offline to the average potential

of two earlobe electrodes (referential montage, see 2.2.2.3).

3.5 EEG data analysis

Trials contaminated with eye movements, eye blinks or excessive muscular activity

were excluded from further analysis (examples see Figure 2.12 and 2.13 A and B).

Trials corresponding to standards after a target, before or after a button press were

also excluded. In seven subjects, the flat or excessively noisy signals at one or two

 

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electrodes were replaced by their values interpolated from the remaining adjacent

electrodes. Averaging, locked to standard or deviant onset, respectively, was done

separately for each attention condition (attended, ignored and control) in each

memory condition (no, easy, difficult memory task). For the standard analysis, at

least 108 trials were averaged for each participant, for each condition. For deviant

analysis, trials contaminated corresponding to missed targets were excluded from

further analysis. At least 21 to 24 trials were averaged for each participant and for

each condition.

With this procedure, the average acoustic content of the sounds was the same

for all obtained event-related potentials (ERPs), only the attention orientation and the

memory task difficulty varied. ERPs were corrected with a -100 to 0 ms baseline

before standard or deviant onset, and were digitally filtered (low-pass 35 Hz). Since

the shortest ISI was 300 ms, only the -100 to 300 ms time-window was retained for

further analysis of standards. ERP scalp topographies were computed using spherical

spline interpolation (Perrin, 1989; Perrin, 1987).

3.6 Statistical analysis

3.6.1 Selection of applied methods

Given the small sample number (16 subjects) we preferred non-parametric tests. But

for the calculation of the interference of the two factors (attention and memory) there

are no such parametric tests. Therefore, we used analysis of variance (ANOVA) with

appropriate corrections for non-sphericity.

3.6.1.1 Analysis of variance (ANOVA)

Analysis of variance is a set of statistical methods. It is a method that assigns sample

variance to different sources and therefore determines, whether the variation arises

within or among different population groups. It applies to classical linear models and

it has also been extended to generalized linear models and multilevel models

(Gelman, 2005).

We performed repeated measure ANOVA, which means that the different

measures are conducted within the same individuals. Besides, in this experiment one-

way and two-way ANOVAs were used. In a two-way ANOVA groups have two

defining characteristics instead of one. Both ANOVAS are special cases of the linear

 

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model. The method assumes that the samples are normally distributed within

different population groups, each featuring the same variance. Thus, the ANOVA is

analyzing whether or not the means of several groups are all equal in order to

determine whether the groups are actually different or not. This is similar to a t-test.

But since there are more than two groups, multiple t-test would be necessary and this

in turn would increase the chance of committing a type I error. Therefore, ANOVAs

are useful for comparing more than two means.

The result of an ANOVA only states whether the tested groups are different

or not, but it does not reveal which means differ. For this reason it is necessary to

perform so called post-hoc tests like the permutation test (see 3.6.1.2). Another

important issue is the problem of multiple comparisons that arises from testing

multiple hypotheses at the same time. It means, the more tests performed the higher

the probability of obtaining at least one false positive result. For this reason it is

important to correct for the numbers of comparisons by performing statistical tests

like the Bonferroni correction.

3.6.1.2 Statistic permutation test

To limit assumption on the data distribution, we used as most as possible a test based

on randomizations (Edgington, 1995). Each randomization consisted in (1) the

random permutation of the 16 pairs (corresponding to the 16 subjects) of values, (2)

the sum of squared sums of values in the 2 obtained samples, and (3) the

computation of the difference between theses two statistic values. We did 10.000 of

such randomizations to obtain an estimate of the distribution of this difference under

the null hypothesis. We then compared the actual difference between the ERPs in the

2 conditions of interest to this distribution.

When this test was used over several time-windows and electrodes, we

corrected for multiple tests. In the temporal dimension, we used a randomization

procedure (Blair, 1993) to estimate the minimum number of consecutive 10-ms time-

windows that must be significant for the effect to be globally significant on the entire

time-window of interest (0-300 ms). For the spatial dimension, we considered the

data to be independent and therefore set the statistical threshold to P < 0.0005 (see

Figure 3.2).

 

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Figure 3.2 – Significant differences between ERPs to attended and ignored

sounds.

Permutation tests were performed over all 64 electrodes and 10-ms time-windows

between 0 and 300 ms, contrasting ERPs to attended and ignored standards,

independent of memory conditions. P values are plotted in a time (horizontal axis) by

electrodes (vertical axis) space. Only significant P values after correction for

multiple tests in space and time are plotted (P < 0.0005).

3.6.2 Behavioral data

In the attention task, a button press within the interval of 200-1000 ms after target

onset was considered a correct response, and a press at any other time was counted as

a false alarm. Reaction times, percentage of correct responses and number of false

alarms were averaged across attention conditions for each memory condition,

separately. The effect of the memory task difficulty on these measures was assessed

using a repeated-measure one-way analysis of variance (ANOVA) with memory

difficulty (3 levels: no, easy, difficult) as within-subject factor. When necessary,

ANOVA results were corrected with the Greenhouse-Geisser procedure (epsilon and

 

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corrected P are reported). Significant effects were explored using 2-tailed paired t-

tests. The Bonferroni correction to was used to correct the P-value for multiple

comparisons.

3.6.3 ERP standards

To compare ERPs to attended and ignored standards, we conducted a permutation

test on the ERP mean amplitude in successive 10-ms time-windows at each electrode

between all attended and ignored standards (collapsing memory conditions) with

correction for multiple tests (see 3.6.1.2 and Figure 3.2).

Furthermore, we performed a two-way repeated-measure ANOVA with

memory difficulty (3 levels: no, easy, difficult) and attention condition (3 levels:

attended, ignored, control) as within-subjects factors, on 1 fronto-central group of

electrodes (Fz, F1, F2, FCz, FC1, FC2), in 3 successive 50-ms time-windows (150-

200, 200-250 and 250-300 ms). The selection of electrodes and time-windows of

interest was based on results in previous EEG studies on auditory selective attention

and on the permutation test results in the present study. Significant effects were

explored using post-hoc permutation tests.

We assessed topography differences on the difference between ERPs to

attended and control standards, and the difference between ERPs to control and

ignored standards (collapsing memory conditions). To avoid any bias from amplitude

effect, these difference values were first normalized for each subject using a division

by the norm of the vector in electrode space (McCarthy, 1985). We then used two

different methods to assess topographical differences. First, we performed a two-way

repeated-measure ANOVA with attention effect (2 levels: “attended – control” and

“control – ignored”) and electrode group (2 levels: anterior frontal and posterior

frontal) as within-subjects factors, on the 250–300 ms time-window. The anterior

frontal group included Fz, F1 and F2 electrodes, and the posterior frontal, FCz, FC1

and FC2 electrodes.

The second method consisted in computing the center of mass of

components. In physics, the center of mass of a system of particles is the point at

which the system's whole mass can be considered to be concentrated, and is a

function only of the positions and masses of the particles that compose the system.

Applied to ERPs, ERP amplitudes at each electrode are considered as the masses,

and the electrode coordinates as the positions of the particles (Manjarrez, 2007). We

 

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computed the center of mass for “attended-control” and “ignored-control” effects

from the mean ERP value in the 250-300 ms time-window from 21 frontal electrodes

(Fpz, AFz, Fz, FCz, Cz, F1, FC1, C1, Fp1, AF3, F3, FC3, C3, F2, FC2, C2, Fp2,

AF4, F4, FC4, C4) in each subject. We used a repeated-measure ANOVA with

attention (2 levels: “attended-control” and “ignored-control”) and coordinates (3

levels: X, Y and Z) as within-subjects factors to compare the coordinates of the

centers of mass. Significant effects were explored using post-hoc permutation tests.

All data analyses were performed with ELAN-Pack software developed at

INSERM U821 (Lyon, France).

3.6.4 ERP deviants

To define latencies and electrodes of interest for further analysis, we computed the

grand-average ERP across all conditions (see Figure 3.3). Three main responses were

considered: N1 (time-window: 100-150 ms; electrode group: Fz, F1, F2, FCz, FC1,

FC2), N2b (time-window: 205-255 ms; left electrode group: F5, F7, FC5, FT7, C5,

T7; right electrode group: AF4, AF8, F4, F6, F8, FC6), and P3 (time-window: 350-

450 ms; electrode group: Pz, POz, P1, P2). The time-windows are 50-ms or 100-ms

around the maximum of N1 and N2b, or P3, respectively. Electrode groups were

chosen as electrodes with maximum amplitude on the topographies in these time-

windows.

For each response, we performed a two-way repeated-measure ANOVA with

memory difficulty (3 levels: no, easy, difficult) and attention condition (3 levels:

attended, ignored, control) as within-subjects factors, on the corresponding electrode

groups and time-windows. Significant effects were explored using post-hoc

permutation tests.

 

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Figure 3.3 – Main ERPs components to deviants.

(A) Grand average ERPs across all conditions on 22 out of the 64 recorded

electrodes. Three main waves can be observed: the N1 between 100 and 150 ms

(orange shaded area), the N2b between 205 and 255 ms (red shaded area) and the P3

maximal around 350-450 ms (purple shaded area). (B) Left scalp topography of the

N2b wave (205-255 ms). (C) Top topography of the N1 wave (100-150 ms). (D)

Back topography of the P3 wave (350-450 ms). (E) Right topography of the N2b

wave (205-255 ms). The black ovals surround group of electrodes used for further

analysis of ERPs to deviants.

 

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4 Results We used a dual task protocol to orthogonally manipulate attention and cognitive

resources. For the attention task, we adapted the classic auditory attention protocol

by adding a third condition (control condition) in which attention was considered as

equally distributed to all sounds. We measured with electroencephalography (EEG)

the effects of three distinct levels of attention by comparing the event-related

potentials (ERPs) to the same sounds when they were attended (in the attended ear),

ignored (in the opposite, non-attended ear) or during the control condition. The

availability of cognitive resources was modulated by varying the difficulty of a

concurrent sound memorization task (3 difficulty levels: no, easy or difficult memory

task). Our hypothesis was that if attention-mediated facilitation and inhibition are

distinct mechanisms, they would be differentially affected by the difficulty of the

memory task.

4.1 Behavioral data

Participants performed better in the easy (99.0% of correct responses) than in the

difficult (82.1%) memory task (t16 = 4.65, P = 0.0003). These results indicate that

manipulation of the memory load was effective.

We observed a significant effect of the memory task difficulty on the performance of

the attention task, both in terms of percentage of correct responses (F2,30 = 6.0, ε =

0.781, P = 0.012) and reaction times (F2,30 = 4.5, ε = 0.932, P = 0.023), but not in the

number of false alarms (F2,30 = 2.7, ε = 0.771, P = 0.098) in the attention task (see

Table 1). Post-hoc t-tests showed that the percentage of correct responses was lower

during the difficult than during the easy memory task (t16 = 3.00, P = 0.027) or when

there was no memory task (t16 = 2.88, P = 0.033). Subjects were also faster to detect

the targets when there was no memory task than when the memory task was difficult

(t16 = 3.06, P = 0.024). These results indicate that the higher the memory load, the

worse the attention performance.

 

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Table 1 – Effects of the memory task difficulty on the attention task

performances.

Mean percentage of correct responses, mean number of false alarms and mean

reaction time (and their standard error to the mean, SEM) in the attention task are

indicated as a function of the memory task difficulty.

4.2 ERP results of standards

4.2.1 Main attention effect (attended versus ignored)

Previous studies investigating auditory selective attention compared ERPs to

attended and ignored (unattended) standard sounds and found a negative frontally

distributed activity (called Nd) starting around 100-150 ms (reviewed in Giard,

2000). We confirmed these results by performing an analysis of our data using

permutation tests over all 64 electrodes and 10-ms time-windows between 0 and 300

ms (with correction for multiple comparisons), comparing ERPs to attended and

ignored standards, independently of the memory conditions. We found that ERPs to

attended and ignored standards begin to differ around 150 ms (see Figure 3.2 and 4.1

A) and that this difference is reflected in a negative frontally distributed component

maximal over fronto-central electrodes (see Figure 4.1 B).

Following these and previous authors, we focused our analysis of ERPs to

standard stimuli on a fronto-central group of electrodes (Fz, F1, F2, FCz, FC1 and

FC2) and on 3 successive 50-ms time-windows between 150 and 300 ms.

 

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Figure 4.1 – Main attention effect on ERPs to standards.

(A) Mean ERPs at Fz electrode. ERPs to attended and ignored standards are depicted

in green and red, respectively. The difference between ERPs to attended and ignored

standards is represented by a dashed black line; the shaded area corresponds to the

150-300 ms period, used for further analysis, when this difference is significant (see

Fig. 3.2). (B) Scalp topography (top view) of the mean difference between ERPs to

attended and ignored standards (200-300 ms). The black dot indicates the position of

the Fz electrode and the black oval surrounds the fronto-central group of electrodes

used for further analysis of ERPs to standards.

4.2.2 Influence of the memory task difficulty on attention effects

We examined the ERPs to attended, control and ignored standards across three

conditions of no, easy or difficult memory task (see Figure 4.2) and performed a two-

way ANOVA on the ERP mean amplitude, with memory difficulty (no, easy and

difficult) and attention condition (attended, control, ignored) as factors. On the three

50-ms time-windows, we found a significant main effect of attention, but not of the

memory task difficulty (see Table 2).

 

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Figure 4.2 – Mean ERPs by attention and memory conditions.

Mean ERPs at the fronto-central electrode group to attended (green), control (grey)

and ignored (red) standards in the no (A), easy (B) and difficult (C) memory tasks

are depicted. Shaded areas correspond to the 3 successive 50-ms windows, in the

150-300 ms period, used for statistical analysis.

We also found a significant interaction between attention condition and the

memory task difficulty between 200 and 250 ms, but not for the other time-windows.

To assess whether these results are independent of the control condition, we

performed the same statistical analysis excluding the control condition: a two-way

ANOVA on ERP mean amplitude, with memory difficulty (no, easy and difficult)

and attention condition (attended and ignored) as factors. We obtained similar results

with and without factoring in the control condition (see Table 2).

 

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Table 2 – Effect of the memory task difficulty and attention conditions on the

ERP amplitude.

Results of the two-way ANOVA on ERP mean amplitude, with memory difficulty

(no, easy and difficult) and attention condition as factors, for the three tested time-

windows. Statistical values (F, ε and P) of attention and memory difficulty main

effects and of attention by memory interaction effect are indicated with the control

condition included (attention condition factor with 3 levels: attended, control and

ignored) and with the control condition excluded (attention condition factor with 2

levels: attended and ignored). Significant effects are highlighted in grey.

To further investigate the effect of the memory task on attention modulations

between 200 and 250 ms, we assessed, for each memory difficulty, the amplitude of

the facilitatory (ERP difference between attended and control standards) and

inhibitory (ERP difference between ignored and control standards) attention effects

(see Figure 4.3 A). We found that amplitudes of ERPs to attended and control

standards were significantly different in all memory conditions (P < 0.004), whereas

the amplitudes of ERPs to ignored and control standards significantly differed in the

easy memory task only (P = 0.018). These results indicate that the memory task

difficulty differentially affects facilitation and inhibition mechanisms.

 

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Figure 4.3 – Effect of the memory task difficulty on attention effects. (A) Mean

ERP amplitudes (fronto-central group, 200-250 ms) of attention-mediated facilitation

(green) and inhibition (red) effects as a function of the memory task difficulty (no,

 

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easy, difficult). Facilitation and inhibition effects are represented as the mean

difference between ERPs to attended and control, and to ignored and control

standards, respectively. Error bars represent 1 SEM. Stars indicate significant

differences assessed by permutation post-hoc tests of the interaction (attention by

memory) effect (*: P < 0.05; **: P < 0.01; ***: P < 0.001). (B) Scalp topographies

(top view) of the attention effects (200-250 ms): facilitation (mean difference

between ERPs to attended and control standards) and inhibition (mean difference

between ERPs to ignored and control standards). The black oval surrounds the

fronto-central electrode group used for computation of mean amplitudes and

statistical analysis represented in (A).

4.2.3 Timing of attention facilitation and inhibition

We assessed the timing and amplitude of the facilitatory and inhibitory attention

effects, independently of the memory load. ERP amplitudes to attended and control

standards were different between 150 and 200 ms, (P = 0.001), between 200 and 250

ms (P = 0.0001), and between 250 and 300 ms (P = 0.0001). Amplitudes of ERPs to

ignored and control standards were not different between 150 and 200 ms (P = 0.85),

but were significantly different between 200 and 250 ms (P = 0.002) and between

250 and 300 ms (P = 0.0003).

These results, in combination with the ones observed in the attention by memory

interaction, suggest that facilitation and inhibition mechanisms have different timing:

facilitation starts as early as 150 ms after stimulus onset in all memory conditions,

whereas inhibition begins around 200 ms in the easy memory condition and not

before 250 ms in the other memory conditions (see Figure 4.4 A).

 

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Figure 4.4 – Timing and topography of attention-mediated facilitation and

inhibition.

(A) Mean ERPs at the fronto-central electrode group to attended, control and ignored

standards are depicted in green, grey and red, respectively. The differences between

ERPs to attended and control (facilitation), and to ignored and control (inhibition)

standards are represented below by green and red lines, respectively. Yellow shaded

areas correspond to the 50-ms windows, in the 150-300 ms period, used for statistical

analysis. Stars indicate significant differences assessed by permutation post-hoc tests

of the main attention effect (**: P < 0.01; ***: P < 0.001). (B) Scalp topographies

(top view) of facilitation (mean difference between ERPs to attended and control

standards) and inhibition (mean difference between ERPs to ignored and control

standards) between 250 and 300 ms. The black oval surrounds the fronto-central

electrode group used for ERP computation and statistical analysis represented in (A).

 

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4.2.4 Topographies of attention facilitation and inhibition

We also observed that the inhibitory component had a more posterior scalp

distribution than the facilitatory component (see Figure 4.3 B and 4.4 B). Since

facilitatory and inhibitory components were both found to be active between 250 and

300 ms, we used this time-window to test if the topographies of these two

components were different.

We first performed a two-way ANOVA on normalized ERP mean amplitude

(averaging across memory conditions), with electrode group (anterior or posterior

frontal) and attention effect (“attended – control” and “control – ignored”) as factors

(see Figure 4.5). We found a significant interaction between electrode groups and

attention effects (F1,15 = 15.3, P = 0.001), suggesting that facilitation and inhibition

mechanisms have distinct topographies.

Figure 4.5 – Comparison of facilitation and inhibition topographies.

Scalp topographies (left, top and right views) of the normalized mean attention

effects (250-300 ms). On the top: facilitation (mean difference between ERPs to

attended and control standards). On the bottom: inhibition (mean difference between

ERPs to control and ignored standards). The black ovals surround the anterior and

posterior frontal groups of electrodes used for statistical analysis of the topography

differences.

 

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Second, we computed the center of mass for “attended – control” and

“control – ignored” effects from the mean ERP value in the 250–300 ms time-

window from 21 frontal electrodes. We obtained the mean coordinates X = 0.55,

Y = 10.16, Z = 11.00 for the facilitatory component, and X = 0.24, Y = 2.70, Z = 3.51

for the inhibitory component. Using a two-way ANOVA with attention (2 levels:

“attended – control” and “control – ignored”) and coordinates (3 levels: X, Y and Z)

as factors, we found a significant effect of attention (P = 0.050), of coordinates

(P = 0.008), and attention by coordinates interaction (P = 0.044). The centers of

mass of the facilitatory and inhibitory components were found to be different in their

Y coordinates (P < 0.05), but not in their X or Z coordinates (P > 0.26), suggesting

that the topography of the facilitatory component is more anterior than the

topography of the inhibitory one.

It is noteworthy that these results are independent of the control condition since this

condition is subtracted to extract both the facilitatory (attended – control) and the

inhibitory (control – ignored) components, and the remaining difference can only be

attributed to a difference between the ERPs to attended and ignored sounds. In this

analysis, the control condition is only used to eliminate the overlapping P2 response.

4.3 ERP results of deviants

4.3.1 Attention enhancement of deviant processing

We investigated the effect of attention condition and memory difficulty on the mean

amplitude of three classically investigated responses to deviant sounds: N1, N2b, and

P3 (see Figure 3.3). We found a significant main effect of attention for the N1 (F2,30

= 7.3, ε = 0.912, P = 0.0037) and the N2b (left: F2,30 = 16.980, ε = 0.0.911, P =

0.00003; right F2,30 = 8.370, ε = 0.952, P = 0.0016), but no memory task difficulty (P

> 0.21), nor interaction (P > 0.39) effects (see Figure 4.6). N1 was of larger

amplitude to attended than control (P = 0.016) and ignored (P = 0.0048) deviants and

N2b was actually present only when deviants were attended, i.e. targets (P < 0.02).

Interestingly, the N1 amplitude to control and ignored deviants was not significantly

different (P > 0.19).

Furthermore, the P3 was also modulated by attention (F2,30 = 62.743, ε =

0.905, P < 0.00001). Interestingly, the P3 was only present in response to attended

deviants, i.e. targets (see Figure 4.7).

 

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Figure 4.6 – N1 and N2b ERPs to deviants.

(A) Mean ERPs at frontal (left), left temporal (center) and right temporal (right)

electrode groups. ERPs to attended, control and ignored deviants are depicted in

green, grey and red, respectively. The shaded areas correspond to the 100-150 ms

and 205-255 ms periods, used for N1 and N2b analysis, respectively.

(B) Scalp topographies (top views) of the N1 and N2b waves to attended, control and

ignored deviants (100-150 ms). Top views of N1 topographies between 100 and 150

ms (left). Left and right views of N2b topographies between 205 and 255 ms (center

and right panels, respectively). The black or white ovals surround the electrode group

used for N1 and N2b analysis and to compute the mean ERP in (A).

 

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Figure 4.7 – P3 to targets.

(A) Mean ERPs at parietal electrode group. ERPs to attended, control and ignored

deviants are depicted in green, grey and red, respectively. The shaded area

corresponds to the 350-450 ms period, used for P3 analysis. (B) Scalp topographies

(back views) of the mean ERPs to attended, control and ignored deviants (350-450

ms). The black oval surrounds the parietal group of electrodes used for P3 analysis

and to compute the mean ERP in A. The P3 is only present in response to targets

(attended deviants).

4.3.2 Memory effect on the P3-Component

In addition, we found a significant interaction between the attention conditions and

the memory task difficulty on the P3 (F4,60 = 2.822, ε = 0.863, P = 0.041). The

amplitude of the P3 to target sounds was larger when there was no memory task than

when the memory task was difficult (P = 0.0008; see Figure 4.8).

 

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Figure 4.8 – Effect of the memory task difficulty on the P3 to targets. (A) Mean

P3 amplitudes (parietal group, 350-450 ms) to attended, control and ignored deviants

(depicted in green, grey and red, respectively) as a function of the memory task

difficulty (no, easy, difficult). Error bars represent 1 SEM Stars indicate significant

differences assessed by permutation post-hoc tests of the interaction (attention by

memory) effect (***: P < 0.001). (B) Scalp topographies (back views) of the mean

ERPs to targets (attended deviants) as a function of the memory task difficulty,

between 350 and 450 ms. The black oval surrounds the parietal group of electrodes

used for P3 analysis. (C) Mean ERPs at the parietal electrode group. ERPs to targets

in the no, easy and difficult memory conditions are depicted with thick, thin and

dashed green lines, respectively. The shaded area corresponds to the 350-450 ms

period, used for P3 analysis.

 

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5 Discussion

Auditory selective attention is a complex mechanism constantly operating in our

daily life: the perception of a certain stimuli in the environment is enhanced relative

to other stimuli of lesser immediate priority (cocktail party-effect). Numerous

theories and studies were put forward to explain the phenomenon and to elucidate the

operating mechanisms and the associated anatomical structures. However, to date it

is still not known exactly how auditory selective attention is operating. Nevertheless,

the studies brought together several pieces of information about the mechanisms of

auditory selective attention.

It is now well accepted that auditory attention can modulate the sensory

analysis of sounds at multiple levels. First, selective attention can operate at early

stage of sensory processing, i.e. as early as 30 ms after sound onset, of the

automatic/exogenous ERPs generated in the auditory cortices. Second, attention can

also modulate stimuli processing at late selection stages via an attentional trace

observed as a sustained negative deflection of endogenous origin, called ‘Negative

difference’ (Nd) or Processing Negativity (PN). Additionally, auditory selective

attention seems to operate via facilitation and inhibition mechanisms, reflecting an

enhanced or reduced processing of relevant or irrelevant sounds, respectively.

In this study we tried to find out whether facilitation and inhibition are

distinct mechanisms and could operate independently at a late selection stage. To do

so, we modulated the amount of cognitive resources in an auditory selective attention

task, because if facilitation and inhibition are distinct mechanisms they should be

affected differently by the variation of the cognitive load (memory task). Therefore,

we used a dual task protocol: subjects had to perform an auditory attention task and a

memory task at the same time. We compared the electrophysiological responses to

the same sounds when they were attended, ignored or under a control condition,

where attention was considered equally distributed towards all sounds.

After analyzing the data, we found two frontally distributed components: a

negative one in response to attended standard sounds (facilitatory component), and a

positive one to ignored standard sounds (inhibitory component). These frontal

electrophysiological responses have distinct timing and topographies, and are

differentially modulated by the difficulty of the memory task. These results provide

 

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evidence that auditory attention is enabled by distinct facilitation and inhibition

mechanisms.

We first observed a negative frontally distributed ERP component onsetting

at about 150 ms that differentiated attended and ignored standard sounds. This

response probably corresponds to components of the Nd or PN described in several

previous studies (reviewed by Giard, 2000). This component is felt to index late

selective attention mechanisms, involved in controlling and maintaining the

representation of stimuli according to their behavioral relevance (Giard, 2000;

Näätänen, 1992; Näätänen, 1982) and can be elicited without being preceded by N1

enhancement (Näätänen, 1978), as was observed in the present experiment. Indeed,

we observed no difference in ERPs to attended and ignored sounds during the first

150 ms, probably until sufficient information is processed in order to decide whether

the sound belongs to the task-relevant or to the task-irrelevant ear, in agreement with

the findings and theory of Näätänen and colleauges (Näätänen, 1992; Näätänen,

1982).

To dissociate facilitatory and inhibitory components, we used a control

condition in which the participants had to detect binaural pure tones. We

acknowledge that the perfect control condition is elusive but we considered the

current choice better than a passive task (what is the subject actually doing?) or a

visual task (inter-modal attention is involved). Furthermore, this control task has

already been shown to be valuable in understanding the mechanisms of auditory

selective attention in intracranial recordings (Bidet-Caulet, 2007). We assume that, in

this control condition, participants' auditory attention was equally distributed towards

all monaural standard sounds. Indeed, to detect these binaural pure tones, they had to

pay attention to the auditory modality, but they did not need to actively ignore the

other sounds since the pure tones were quite salient and the task was easy. The

control condition did not require selective attention, but necessitated broad auditory

attention towards all sounds to be correctly performed. One can argue that the control

task we used actually required the inhibition of the standard monaural noises. In this

case, we might be underestimating the inhibitory component. More importantly, to

further address the issue of the control condition, we reanalyzed the data

independently of the control condition. This analysis did not affect the effect of the

memory difficulty manipulation: processing of attended and ignored sounds is

 

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differentially affected by the memory difficulty. Moreover, the topographical

differences are independent of the control condition since the control condition is

subtracted to extract both the facilitatory and inhibitory components.

Using this control condition, we found that the Nd response can be

dissociated into two distinct components: (1) a negative ERP component in response

to attended standards, with onset at about 150 ms, with an anterior frontal scalp

distribution; and (2) a positive ERP component in response to ignored standards,

with onset between 200 and 250 ms, with a fronto-central scalp distribution. These

findings are consistent with results from several previous scalp EEG studies

dissociating the Nd component into two facilitatory and inhibitory sub-components,

using control conditions in the auditory modality (Donald, 1987; Melara, 2002;

Schroger, 1997) or in the visual modality (Alho, 1987; Alho, 1994; Berman, 1989;

Degerman, 2008; Michie, 1990; Michie, 1993). These researches found a positive

response or “rejection positivity” to unattended sounds compared to the control

condition, starting later in latency than the negative response to attended sounds. It

has been suggested in some of this previous work that the topographies of facilitatory

and inhibitory components are different (Degerman, 2008; Donald, 1987; Melara,

2002). In the current paper, these two components are directly compared and

dissociated. The distinct scalp topographies provide support that different brain

sources support the facilitatory and inhibitory components. However, we cannot

precisely infer the brain origin of these components from the present data. These

components most likely reflect neural activity from the auditory cortices in the

superior temporal lobes and/or from frontal areas, as it has been suggested for the Nd

components (Alcaini, 1994; Degerman, 2008; Giard, 1988; Woldorff, 1993).

To test if these facilitatory and inhibitory components correspond to two

functionally distinct mechanisms or are generated by a single control mechanism, we

manipulated the availability of cognitive resources. The hypothesis was that the

control of facilitation and inhibition mechanisms requires cognitive resources, and

that if these two mechanisms are independently controlled they should not covary

according to the amount of available cognitive resources. It has been shown,

previously, that increasing the load on executive functions, such as increasing

memory, decreases the availability of cognitive resources to perform other cognitive

task, such as an attention task (Lavie, 2005). We manipulated the availability of

cognitive resources by varying the difficulty (or load) of a concurrent sound

 

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memorization task. We found that facilitation and inhibition mechanisms in auditory

selective attention are differentially modulated by the memory difficulty, providing

evidence for distinct functional roles, as reported in the visual modality (Gazzaley,

2008; Gazzaley, 2005). More precisely, we found that the availability of cognitive

resources differentially influenced the timing of attention-mediated facilitation and

inhibition mechanisms: facilitation starts at the same latency (150 ms) in all memory

loads, whereas inhibition is activated at 200 ms for low memory load (easy memory

task), and after 250 ms for no and high (difficult memory task) memory loads.

In a previous visual study employing fMRI, brain activation by distracting

sounds was found to be larger under high rather than low memory load, suggesting a

reduction of inhibition mechanisms under high memory load (de Fockert, 2001).

Accordingly, our findings show that the inhibition mechanism is delayed from low to

high memory load conditions. Thus, the less cognitive resources are available, the

later the inhibition mechanisms are activated and the more distractors are processed.

We did not observe inhibition before 250 ms in the no memory condition likely

because of the ease of the attention task. These results extend the cognitive load

theory (Lavie, 2005) to the auditory modality, but importantly, we have also shown

using the time resolution of electrophysiology, that the availability of cognitive

resources influences late selection processes (after the first steps of the sensory

analysis) which control access to memory and response. When cognitive resources

are available, distractor inhibition can be activated early (as early as 200 ms). Late

attention-mediated inhibition mechanisms also seem to be influenced by the task

difficulty: they are delayed when the task is easy even if the cognitive resources are

available.

Analysis of ERPs to deviants reveals strong effects of attention on deviant

processing, consistent with previous findings (Hansen, 1984; Näätänen, 1993). We

observed an early attention-dependent enhancement of the sensory N1 response

between 100 and 150 ms. Moreover, N2b and P3 components, known to be related to

target processing (Muller-Gass, 2002; Näätänen, 1983), were only elicited in

response to attended deviants. ERPs to ignored and control deviants were not

different, suggesting that only facilitation mechanisms are modulating deviant

processing.

The present study provides new insights on the brain mechanisms of selective

attention: late selection of the relevant stream of stimuli relies on the engagement of

 

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distinct attention-mediated facilitation and inhibition mechanisms. Sustained

facilitatory and inhibitory frontally distributed components represent distinct

cognitive processing of the attended and ignored streams of sounds, enhancing the

rapid and accurate detection of targets without interference by distracting stimuli.

These findings provide evidence that, at a late selection stage, attention operates by

employing distinct facilitation and inhibition mechanisms.

 

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7 List of abbreviations A1 primary auditory cortex

AC alternating current

ADC analogue to digital conversion

AEP auditory evoked potential

Ag/Cl silver chloride

ANOVA analysis of variances

BAEP brainstem auditory evoked potential

BOLD blood oxygen level dependency

CGM corpus geniculatum mediale

dB decibel

ECoG electrocorticogram

EEG electroencephalogram

(r/l/v) EOG (right/left/vertical) electrooculogram

EP evoked potential

EPSP excitatory postsynaptic potential

ERP event related potential

FDG fluorodeoxyglucose

fMRI functional magnetic resonance imagining

GABA gamma aminobutyric acid

HG Heschl’s gyrus

Hz Hertz

ICA independent component analysis

ISI inter stimulus interval

IPSP inhibitory postsynaptic potential

kOhm kilo Ohm

mm millimeter

mV millivolt

mmol/l millimol per liter

N1/N100 auditory ERP at around 100 ms post stimulus (negative deflection)

Nd negative difference wave

(E)OAE (evoked) otoacoustic emission

MEG magnetencephalogram

 

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MRI magnetic resonance imaging

MTG medial temporal gyrus

P20-50/P50 auditory ERP at around 20-50 ms post stimulus (positive deflection)

P2/P200 auditory ERP at around 200 ms post stimulus (positive deflection)

P3/P300 auditory ERP at around 300 ms post stimulus (positive deflection)

Pa Pascal (1 Newton/meter)

P pressure

PET positron emission tomography

PN processing negativity

PP planum polare

PT planum temporale

SEEG stereotactic electroencephalogram

SEM standard error to the mean

SNR signal to noise ratio

SPL sound pressure level

SSR steady state response

STG superior temporal gyrus

V1 primary visual cortex

µV microvolt

 

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8 Publication Results from this study are already published:

Bidet-Caulet, A., Mikyska, C., and Knight, R. T., (2010), Load effects in auditory

selective attention: evidence for distinct facilitation and inhibition mechanisms,

Neuroimage, 50, (p. 277-84)

 

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9 Acknowledgements I would like to express my gratitude to everyone, who contributed to this thesis.

Especially I would like to thank Aurélie for introducing me to the world of science

and showing me everything she knew. It was a pleasure working with her and

learning from her. But more important, during the time in Berkeley, she became a

close friend. Thank you for the support (e.g. lab meeting, SfN, CNS), guidance,

advice, corrections, good music in the pod, French food and wine….

Also I would like to thank Robert T. Knight, M.D. for giving me the opportunity to

spend a year at Berkeley and do research at the Helen Wills Neuroscience Institute.

I am also grateful to Prof. Dr. H. Stefan, for supporting the cooperation with UC

Berkeley, for corrections and advices and for encouraging me to finish writing.

A very special appreciation goes to Prof. Dr. H-J Heinze, for the initial idea and the

support and initiative to bring it to life.

My deepest thank you is for my family, without you I would not be where I am now.

Thank you for all your love and support.

Nic, you always believe in me. Thank you for your love and understanding.

 

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10 Curriculum vitae

Personal

Name Mikyska

First name Constanze Elisabeth Anna

Date and place of birth December 06th, 1984 in Munich, Germany

Parents Dr. med. Veit Mikyska

Dr. med. Maria-Magdalena Mikyska, nee Mittermair

Siblings Christoph Maximilian Vitus Mikyska

Education

School

2004 University-entrance diploma (Abitur), Heimschule

Kloster Wald, Wald (secondary school and boarding

school)

Professional education

2000 – 2004 Apprenticeship as a tailor, Heimschule Kloster Wald,

Wald

University

2004 – 2011 Medical student at the Friedrich-Alexander University

of Erlangen-Nuremberg

09/2006 Preliminary medical examination

12/2011 Final medical licensing examination

Research experience

05/2008 – 09/2009 Visiting researcher, Helen Wills Neuroscience

Institute, University of California, Berkeley, USA

Employment

12/2009 – 10/2010 Student assistant, Epilepsiezentrum (ZEE), Department

of Neurology, University hospital Erlangen-

Nuremberg