The Neural Correlates of Endogenously Cued Covert

THE NEURAL CORRELATES OF ENDOGENOUSLY CUED COVERT

VISUOSPATIAL ATTENTIONAL SHIFTING IN THE CUE-TARGET INTERVAL:

AN ELECTROENCEPHALOGRAPHIC STUDY

Edward Justin Modestino

A Dissertation Submitted to the Faculty of

The Charles E. Schmidt College of Science

in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

Florida Atlantic University

Boca Raton, FL

December 2009

VITA

Edward Justin Modestino, son of Louis Anthony Modestino and Elaine Frances

Palaima, was born November 05, 1970, in Norwood, Massachusetts. He graduated from Stoughton High School in Stoughton, MA in June 1989. He married Danielle Jean

(Guido) Kradin, daughter of Joseph Francis Guido and Carol Anne Kradin, on June 19,

1996. Edward graduated with a Bachelor’s degree in Psychobiology from Harvard in

Cambridge, MA in June 1997. His undergraduate thesis, under advisor Michael

Hasselmo, D.Phil., was theoretical; it merged neuroscience with clinical neurology on the topic of attention deficit disorder in childhood predisposing to the subsequent development of narcolepsy in adulthood. Next, he entered graduate school at the PENN in Philadelphia, PA, to further explore the theory with Douglas Frye, Ph.D. He graduated with a Master’s degree in Psychobiology in May 1999, and a post-Master’s degree (Master of Philosophy) in Cognitive Neuroscience in May 2001. The focus of his penultimate degree was an fMRI pilot study designed to test his theory using neuroimaging. In August 2001, he entered the Ph.D. program in Complex Systems and

Brain Sciences at FAU. Soon thereafter, he began working with Steven Bressler, Ph.D., studying covert visual attentional shifting using EEG. After completing his Ph.D., he will commence a post-doctoral research fellowship at the University of Virginia conducting EEG/neuroimaging research of cognition with Edward Kelly, Ph.D.

ACKNOWLEDGEMENTS

I wish to express sincere gratefulness to the following people. I am thankful for my wife and mother-in-law for their support and encouragement throughout my extensive education. I am indebted to the following people for tutelage in advanced mathematics and programming in C++ and MATLAB: Drs. Taylor, Vallabha, Almonte,

Zanto and Nichols. Additionally, Dr. Winchester went out of her way to help with statistics and tutelage in SPSS. I also am indebted to former DIS student/intern Elise

Naimo for assisting in the collection of the data and recruiting subjects in trying and near impossible circumstances. Bill McLean, the Center Engineer with a unique sense of humor, also made it his second job to assist me in testing, fixing, repairing, ordering, and building custom equipment. I am most grateful to Dr. Steven Bressler for being a meticulous scientist and mentoring me to become the same. I am grateful to my dissertation committee members: Dr. Howard Hock for his expertise in psychophysics in relation to my research; Dr. Viktor Jirsa for encouraging me to complete my studies here since the very beginning and his insight into non-linear dynamics and EEG; and

Dr. Edward Large for his expertise in EEG and vast knowledge of attentional processes.

Finally, I am grateful to my Neuroscience II professor, Dean of the College of Science,

Dr. Gary Perry. He has believed in me from the very beginning. His assistance in dealing with red tape, administrative and otherwise, has been invaluable.

ABSTRACT

Author: Edward Justin Modestino

Title: The Neural Correlates of Endogenously Cued Covert Visuospatial Attentional Shifting in the Cue-Target Interval: An Electroencephalographic Study

Institution: Florida Atlantic University

Dissertation Advisor: Dr. Steven L. Bressler

Degree: Doctor of Philosophy

Year: 2009

This study investigated electroencephalographic differences related to cue

(central left- or right-directed arrows) in a covert endogenous visual spatial attention task patterned after that of Hopf and Mangun (2000). This was done with the intent of defining the timing of components in relation to cognitive processes within the cue- target interval. Multiple techniques were employed to do this. Event-related potentials

(ERPs) were examined using Independent Component Analysis. This revealed a significant N1, between 100:200 ms post-cue, greater contralateral to the cue.

Difference wave ERPs, left minus right cue-locked data, divulged significant early directing attention negativity (EDAN) at 200:400 ms post-cue in the right posterior which reversed polarity in the left posterior. Temporal spectral evolution (TSE) analysis of the alpha band revealed three stages, (1) high bilateral alpha precue to 120 ms post-cue, (2) an event related desynchronization (ERD) from approximately 120 ms:

500 ms post-cue, and (3) an event related synchronization (ERS) rebound, 500: 900 ms post-cue, where alpha amplitude, a measure of activity, was highest contralateral to the ignored hemifield and lower contralateral to the attended hemifield. Using a combination of all of these components and scientific literature in this field, it is possible to plot out the time course of the cognitive events and their neural correlates.

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DEDICATION

This manuscript is dedicated to my wife, Danielle Jean Kradin. In this world of chaos, filled with negative and selfish people, she is the one person upon whom I can rely. She has made my life worth living. Additionally, this is dedicated to my maternal grandmother, Helen Pacewicz Palaima, who died on my 8th birthday. Because of her, I took guitar lessons and studied the French language to an advanced level. Although I did not know her very long, she instilled in me the passion and desire to learn and obtain an education. Without her influence during those early years, my life may have gone in a different direction. Finally, this manuscript is dedicated to one of the best feline friends a man could ever have, Nigel Kradin-Modestino. He died unexpectedly of a horrific stroke on one of the very last days I was working on my dissertation. THE NEURAL CORRELATES OF ENOGENOUSLY CUED COVERT

VISUOSPATIAL ATTENTIONAL SHIFTING IN THE CUE-TARGET

INTERVAL: AN ELECTROENCEPHALOGRAPHIC STUDY

LIST OF TABLES ...... xi

LIST OF FIGURES ...... xiii

1.0 Introduction ...... 1

1.1 Motivation ...... 1

1.2 Background ...... 2

1.2a Cognitive Studies of Covert Visual Attention ...... 2

1.2b Neural Network of Covert Visual Attention ...... 7

1.2c Visual ERPS and Covert Attentional Shifting with the Posner

Paradigm ...... 16

1.3 Hypotheses ...... 26

1.3a Posner Task ...... 26

1.3b Cue Contrast ...... 27

1.4 Overview ...... 27

1.4a Content of Dissertation ...... 27

1.4b Outline of Chapters ...... 27

2.0 Methods ...... 29

2.1 Participant Screening and Recruitment ...... 29

2.2 Experimental Design and Task ...... 30

viii

2.3 Behavioral Analysis ...... 44

2.3a Behavioral Recording ...... 44

2.3b Behavioral Data Processing ...... 44

2.3c Behavioral Statistical Analysis ...... 44

2.4 EOG Analysis ...... 46

2.4a EOG Processing ...... 46

2.5 EEG Analysis ...... 47

2.5a EEG Recording ...... 47

2.5b EEG Data Processing ...... 50

2.5c EEG Statistical Analysis ...... 56

3.0 Results ...... 57

3.1 Behavioral Data ...... 57

3.2 Grand Averaged ERPs ...... 64

3.3 Cue Contrast: Difference ERPS ...... 73

3.4 Cue Contrast: ANOVAS ...... 88

3.5 Cue Contrast Components: ANOVAS ...... 96

3.6 Cue Contrast: α-Activity in the Time Domain ...... 114

4.0 Discussion ...... 151

4.1 Behavioral Results ...... 151

4.2 ERPS ...... 152

4.3 Early Components ...... 153

4.4 Late Components ...... 161

5.0 Conclusions ...... 163

5.1 Main Conclusions ...... 163

5.2 Conclusions in Relation to the Hypotheses ...... 168

References ...... 169

LIST OF TABLES

Table 3.1a Mean Reaction Times ...... 58

Table 3.1b Behavioral Data ...... 59

Table 3.1c ANOVA of Behavioral Data ...... 62

Table 3.1d T-tests on Behavioral Data ...... 63

Table 3.3a ANOVA on EDAN-Like Difference Waves Means, Window 200:400 ...... 79

Table 3.3b Paired T-tests on EDAN-Like Difference Wave Means, Window 200:400 .. 80

Table 3.3c ANOVA on EDAN Difference Wave Means, Window 260: 300 ...... 85

Table 3.3d Paired T-test of EDAN Difference Waves Means, Window 260:300 ...... 86

Table 3.4a ANOVA on 50 ms Means Windowed Data ...... 89

Table 3.4b ANOVA on 100 ms Means Windowed Data ...... 91

Table 3.4c ANOVA on 50 ms RMS Windowed Data ...... 93

Table 3.4d ANOVA on 100 ms RMS Windowed Data ...... 95

Table 3.5a ANOVA on N1 ...... 100

Table 3.5b Post-Hoc Paired T-Tests on N1 ...... 101

Table 3.5c ANOVA on RMS of P1-N1-P2 Complex ...... 104

Table 3.5d ANOVA on P1 RMS ...... 106

Table 3.5e ANOVA on P2 Means ...... 108

Table 3.6a ANOVA on Alpha TSE Means of PO3 and PO4 ...... 120

Table 3.6b Paired T-test on Alpha TSE Means of PO3 and PO4 ...... 121

Table 3.6c ANOVA on Alpha TSE Means of PO355 and PO455 ...... 123

Table 3.6d Paired T-test on Alpha TSE Means of PO355 and PO455 ...... 124

Table 3.6e ANOVA on Alpha TSE Means of Two Channel ROIs 500:900 ...... 126

Table 3.6f Paired T-test on Alpha TSE Means of Two Channel ROIs 500:900 ...... 127

Table 3.6g ANOVA on Alpha TSE Means of ROIs 500:900 ...... 129

Table 3.6h Paired T-tests on Alpha TSE Means of ROIs 500:900 ...... 130

Table 3.6i ANOVA on Alpha TSE Means of ROIs 50 ms Window 500:550 ...... 133

Table 3.6j ANOVA on Alpha TSE Means of ROIs 50 ms Window 550:600 ...... 134

Table 3.6k ANOVA on Alpha TSE Means of ROIs 50 ms Window 600:650 ...... 135

Table 3.6l ANOVA on Alpha TSE Means of ROIs 50 ms Window 650:700 ...... 136

Table 3.6m ANOVA on Alpha TSE Means of ROIs 50 ms Window 700:750 ...... 137

Table 3.6n ANOVA on Alpha TSE Means of ROIs 50 ms Window 750:800 ...... 138

Table 3.6o NOVA on Alpha TSE Means of ROIs 50 ms Window 850:900 ...... 139

Table 3.6p Paired T-tests on Alpha TSE Means of ROIs 50 ms Windows ...... 140

Table 3.6q ANOVA on Alpha TSE Means of ROIs 100 ms Window 500:600 ...... 142

Table 3.6r ANOVA on Alpha TSE Means of ROIs 100 ms Window 600:700 ...... 143

Table 3.6s ANOVA on Alpha TSE Means of ROIs 100 ms Window 700:800 ...... 144

Table 3.6t ANOVA on Alpha TSE Means of ROIs 100 ms Window 800:900 ...... 145

Table 3.6u Paired T-tests on Alpha TSE Means of ROIs 100 ms Windows ...... 146

Table 3.6v ANOVA on Group TSE Means, Window 500:900 ms ...... 148

Table 3.6w Paired T-test of Group TSE Means, Window 500:900 ms ...... 149

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LIST OF FIGURES

Figure 2.2a Eye Movement Control Task Schematic ...... 32

Figure 2.2b Cue Types ...... 35

Figure 2.2c Targets ...... 36

Figure 2.2d Valid Non-Matching Non-Targets ...... 37

Figure 2.2e Invalid Matching Non-Targets ...... 38

Figure 2.2f Invalid Non-Matching Non-Targets ...... 39

Figure 2.2g Match and Non-Match Pairs...... 40

Figure 2.2h Single-Trial Schematic ...... 43

Figure 2.5a 84 Channel EEG Montage ...... 49

Figure 3.2a Left Cue-Locked Grand Averaged Data ...... 65

Figure 3.2b Left Cue-Locked Grand Averaged Data ...... 66

Figure 3.2c Right Cue-Locked Grand Averaged Data ...... 67

Figure 3.2d Right Cue-Locked grand Averaged Data ...... 68

Figure 3.2e Left and Right Cue-Locked Grand Averaged Data ...... 69

Figure 3.2f Left and Right Cue-Locked Grand Averaged Data ...... 70

Figure 3.2g Left Cue-Locked Grand Averaged Data Peak ...... 71

Figure 3.2h Right Cue-Locked Grand Averaged Data Peak ...... 72

Figure 3.3a Difference Waves ...... 74

Figure 3.3b Difference Waves ...... 75

xiii

Figure 3.3c Difference Waves of N1 ...... 76

Figure 3.3d Difference Waves of N1 in Topographic Brain Map ...... 77

Figure 3.3e Difference Waves of EDAN-Like Component ...... 78

Figure 3.3f Significance Histogram of EDAN-Like Means ...... 81

Figure 3.3g Difference Waves of EDAN in Topographic Brain Map ...... 83

Figure 3.3h Difference Waves of EDAN ...... 84

Figure 3.3i Significance Histogram of EDAN Means ...... 87

Figure 3.5 Left and Right Cue-Locked Data ICA Component Topos ...... 97

Figure 3.5a Left and Right Cue-Locked Data ICA Components ...... 98

Figure 3.5b Significance Histogram of N1 Means ...... 102

Figure 3.5c Component 2 Topographic Maps at 250 ms ...... 111

Figure 3.5d ICA Components of Component 2 ...... 112

Figure 3.5e ICA Components of Component 2 ...... 113

Figure 3.6a Left and Right Cue-Locked Grand Averaged TSE Data ...... 115

Figure 3.6b Left and Right Cue-Locked Grand Averaged TSE Data ...... 116

Figure 3.6c Left and Right TSE Data Select Channels ...... 117

Figure 3.6d Left and Right TSE Data Select Channels ...... 118

Figure 3.6e Significance Histogram of Alpha ...... 150

Figure 5.1 The Approximate Time Course of Components found in the CTI ...... 167

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1.0 Introduction

1.1 Motivation

The aim of this study was to examine the neural correlates of the cognitive processes within the cue-target interval (CTI) in an endogenously cued covert visual attentional shifting task, a version of the Posner Paradigm. This was done using electroencephalography and concurrent behavioral measures of reaction time and other types of responses (false alarms, omissions, etc.) to gauge that attention had indeed shifted. The CTI within this task involves many consecutive or even simultaneous cognitive processes. Various studies have focused on merely a few of these processes at the exclusion of others. The following is a composite based on several disjointed publications. This CTI includes: (1) selective foveal attention (exclusion of extrafoveal loci), (2) interpretation of meaning from the cue, (3) preparation for action/shifting attention based on meaning from cue, (4) disengagement of focus at the fovea, (5) shifting attention covertly to the cued location, (6) reengaging selective attention at the cued location (with exclusion of other loci), and (7) maintaining attention at this location in preparation for the anticipated target (Posner, 1980; Posner and Petersen,

1990; Hopf and Mangun, 2000; Worden, Foxe, Wang, and Simpson, 2000; van Velzen and Eimer 2003; Brignani, Guzzon, Marzi, and Miniussi, 2009). The proposed neural correlates of these processes are explained in detail in the following background section. To the best of our knowledge, no published study has examined the CTI of this

type of task using event-related potentials (ERPs), difference ERPs, and spectral analysis in combination. Studies using of these techniques have been conducted separately using different data. Thus, this is the first study to examine and define the

CTI comprehensively, with all processes leading up to/and including the attentional shift, using these various methods.

1.2 Background

1.2a Cognitive studies of covert visual attention. Covert visual attention was first described in the works of Hermann Ludwig Ferdinand von Helmholtz in 1867,

“…attention is entirely independent of the position and accommodation of the eyes...”

(von Helmholtz, 1962, p. 455), and William James in 1890, “…we may attend to an object on the periphery of the visual field and yet not accommodate the eye for it”

(James, 1890, p. 437). More recently, Posner (1980) described covert visual attention as selectively shifting one’s attention in space without moving one’s eyes. During normal behavior, this may serve as the early part of selective spatial attention, which is subsequently followed by overt visual attention or moving one’s eyes to an attended location or object, i.e., saccades. One may have several shifts of covert attention prior to each saccade in preparation of choosing the most appropriate location for this saccade. Thus, covert attentional shifts in essence are seeking the next locale for saccades (Posner and Petersen, 1990; McPeek and Nakayama, 1995). Evidence of this has been suggested by studies recording saccades post-target, using targets that were presented too briefly for one’s eyes to saccade. Accurate performance was associated with saccades to the target loci post-target presentation. This suggests that attention was covertly oriented to the target prior to the saccade. Thus, the saccades were drawn

to the loci that covertly were scouted out as the place to orient (Bryden, 1961; Crovitz and Davies, 1962). This is reasonable (Treisman and Gelade, 1980; Duncan, Ward, and

Shapiro, 1984; Koch and Ullman, 1985; Briand and Klein, 1987; Niebur and Koch,

1994) as covert shifts of attention can occur in as fast as 30-50 ms between objects or locations, whereas saccades tend to occur no earlier than 200-250 ms. Furthermore, a recent article suggests that covert orienting can have an effect on cognition and insight as well (Thomas and Lleras, 2009).

Posner’s seminal article titled “Orienting of Attention” (1980) may be considered the canon for those conducting research on covert visual attention. In this article, he described that attentional shifts are independent of saccades. He suggested that a covert visual focus of attention precedes overt attention to this area of focus.

Covert attention, akin to selective visual attention only covertly shifted, enhances a specific region of space at the exclusion of the rest of the visual field. This dissociation seen between attention and foveal focus in lab experiments is not something one uses in most ordinary circumstances. However, with some practice, participants in such studies can easily learn this.

Posner (1980) defined several terms to be used in this work. Orienting was defined as aligning one’s attention to a sensory stimulus. Detecting was defined as when a person can confirm the occurrences of specific stimuli. Additionally, the difference between exogenous reflexive orienting to the periphery versus endogenous central cueing that is intention-based was appropriately stressed. There is a clear difference in the time course between exogenous and endogenous cueing related shifts, with the exogenous-reflexive being more rapid. Most noteworthy, both of these tasks,

one with the central cue and peripheral target (endogenous) and the other with a peripheral cue and target (exogenous), subsequently have been referred to as the Posner

Paradigm (for instance, refer to Perchet and García-Larrea, 2000).

According to Posner (1980), covert attentional shifting, although not obvious, can be shown based on performance. Additionally, reaction times can be used as a measure of attention to validly cued versus invalidly cued loci. Cue validity decreases reaction times, whereas cue invalidity increases reaction times. More specifically, cue validity versus cue invalidity for both foveal and peripheral targets were both similar.

Furthermore, the economics of shifting attention appear to be independent not only from saccades but in the distance from foveal fixation. Saccades compatible with attention versus the converse, a dissociation of the two, are indeed more rapid, although a task with trials doing both compatible and incompatible shifts divulged that detection was no different. Participants could saccade in the same direction of attention, or the opposite, and this did not affect their ability to detect targets.

Attentional selection in relation to covert attentional shifts has been the topic of much research. Much of the focus has been based around the feature integration theory

(FIT) proposed by Treisman and Gelade (1980). Treisman’s FIT imparts that attributes such as orientation, color, texture, location, shape, and other visual features are integrated together by focused attention upon each object. In support of Treisman,

Mack, Tang, Tuma, and Kahn (1992) impart that perception of attributes (features) as mentioned in Treisman’s FIT are not possible without attention. However, the exact nature of covert attentional selection guiding overt orienting has been debated widely.

Kanwisher and Driver (1992) related that covert attention could be directed either to

locations, segmented targets, or both. Baylis and Driver (1992) suggested a way that integrates both spatial and object theories. They suggested that covert attention is mediated by a preattentive process of grouping that is not based on mere location.

Similarly, Yantis (1994) provided evidence that spatial and locational selection can be integrated so that one can follow multiple objects if they are clumped in a moving gestalt.

Posner (1980) suggested a purely location-based strategy in selection. He provided a rudimentary model of an attentional spotlight, with emphasis on selection being within the region of focus. However, much research, even Posner’s subsequent research, has contradicted this. According to Posner’s subsequent work (Posner and

Petersen, 1990), the spotlight metaphor of attentional focus is limited. It does aid in explaining the dynamics of (1) disengaging attention, (2) shifting attention in space, and

(3) reengaging attention at the new location. However, the lens metaphor, in which a lens can be focused in on a limited area and then panned out to include a much larger area of focus, appears to include dynamics not present in the spotlight metaphor of simple shifting of a fixed window. This lens metaphor seems to be what LaBerge

(1983) and Tipper and Driver (1988) have shown. Furthermore, much research has shown that non-contiguous regions maybe selected concurrently, unlike a spotlight

(McLeod, Driver, and Crisp, 1988; Driver and Baylis, 1989; Baylis and Driver, 1992;

Pylyshyn, Burkell, Fisher, Sears, Schmidt, and Trick, 1994).

In essence, all of these theories, ranging from object based feature selection to a locational spotlight, appear to be in conflict. They vastly vary about selection in relation

to covert visual attention and subsequent overt attention (Humphreys, Romani, Olsen,

Riddoch, and Duncan, 1994; Vecera and Farrah, 1994).

In addition to attentional selection, other mechanisms related to covert attention shifting have been studied. The majority of this research is based on the Inhibition of

Return (IOR), as first described by Posner and Cohen (1984). Attention is oriented to a peripherally cued location with an exogenous task. Once attention shifts from this cued location, this very location is inhibited from attentional return for a duration. This may maximize search capabilities elsewhere and prevent perseveration. Posner and Cohen explain that the facilitation of orientation and reaction times as shown in an endogenous task appears to be based on a retinotopic map for these cued spatial loci. In contrast, the inhibition of orientation shown in an exogenous task is based on more of an environmental map.

Tipper, Driver, and Weaver (1991) claimed that this inhibition of a specific location would not be optimal in the dynamic and naturalistic visual field. Their research showed that there was in fact an object based IOR independent of location with moving objects. Therefore, location and object based IOR both are possible. This is in line with the Kahneman, Treisman, and Gibbs (1992) theory of evolving object-files that are continually updated for each object. To further tease this apart, Gibson and

Egeth (1994) were able to show that specific parts of an object, in this case a brick, may cause this IOR. Thus, IOR is not limited to whole objects, but even to parts or segments of such objects. For example, in their experiments, they showed that cueing a specific surface of a computer graphical image of a brick would subsequently cause an IOR on

that very surface. This occurred despite the fact that the brick was subsequently revolved in a different orientation than when cued.

In summary, covert visual attention may not be obvious (Posner, 1980). It occurs as a filter to prepare for the next appropriate location to saccade (Posner and

Petersen, 1990). Accurate performance is used as evidence that attention has shifted without the occurrence of a saccade in endogenous and exogenous tasks (Posner and

Petersen, 1990). Covert attention may aid in integrating features of objects and location maps as theorized in Treisman’s FIT (Treisman and Gelade, 1980). Based on this FIT and other research, purely location based theories of covert attention appear to be incomplete (Baylis and Driver, 1992). Inhibition of return appears to be an adaptive mechanism based on enhancing performance of covert visual attention by preventing perseveration (Posner and Cohen, 1984).

1.2b. Neural network of covert visual attention. Posner and Petersen (1990) described a neural network underlying covert visual attentional shifting. This model was based on PET studies, EEG studies, single unit recordings, lesions patients, etc.

The network consists of three linked regions: (1) posterior parietal lobe which disengages attention from one location (contralateral to the attended field), (2) midbrain/superior colliculus which controls shifting attention to a new location (i.e., a target), and (3) the pulvinar nucleus of the thalamus which reengages attention at the new location while filtering out distractors. These regions are innervated primarily by norepinephrine to maintain vigilance/sustained attentional processes with a primary focus on the right hemisphere of this posterior parietal network. This is controlled primarily through the right prefrontal cortex, which counterbalances with the activity of

the anterior cingulate. Incidentally, the anterior cingulate is vital in detection of targets.

The left and right parietal regions are needed for spatial areas but also have further functions with the right being involved more with global/lower spatial frequency and the left being involved more with local/higher spatial frequency processing.

Posner and Petersen (1990) further elaborated that his posterior parietal attention system is part of the dorsal “where” system of spatial attention. This is integrated with the ventral system “what” at the level of the pulvinar nucleus of the thalamus to include feature detection of the attended objects. In essence, attentional selection of features in the anterior-“what”-ventral network is overseen by this posterior attentional network when not otherwise occupied.

The model described by Posner and Petersen (1990) of covert visual attentional shifting is modular in that it is completely dissociated from overt visual attention. This lies at one extreme end of the spectrum. Corbetta (1998) suggested a more mixed view, in that covert attention and overt attention may be independent. They may each share common brain areas of function and have some unique ones unto themselves. Finally, at the other end of the spectrum, Rizzolatti, Riggio, Dascola, and Umilta (1987) describe a premotor theory of attention. In this model, both covert and overt shifting of attention involves the very same neural network. In fact, the only difference between the two is a saccade. Furthermore, both covert and covert processes recruit premotor areas used for planning and executing eye movements. Several researchers have compared and contrasted overt and covert shifting of attention using various neuroimaging and neuroscience techniques. These include positron emission tomography (PET), functional magnetic resonance imaging (fMRI), single-unit

recordings and microstimulation in primates, and transcranial magnetic stimulation

(TMS) in humans. The following is a review of these studies comparing and contrasting overt and covert attention using these various techniques.

Before researchers initiated combined studies of overt and covert attention, several studies examined them separately. Overt attention, visual attentional shifting with saccades, was a topic of two seminal neuroimaging studies. In the first, Sweeney,

Mintun, Kwee, Wiseman, Brown, Rosenberg, and Carl (1996) studied overt attentional shifting using PET. Voluntary shifting of overt attention with saccades was associated with bilateral activation of the frontal eye fields (FEF), striate and posterior temporal cortices, the cerebellum and the right posterior thalamus. In the second study, Paus

(1996) related that saccades and oculomotor processes were associated with augmented cerebral blood flow (CBF) as measured by PET in the FEF/Brodmann’s area (BA) 8.

Covert attentional shifting was studied in several foundational neuroimaging works as well. Corbetta, Miezin, Shulman, and Petersen (1993) used an endogenous task to study covert visual attention with PET. Covert attentional shifting was associated with increased activity in the superior parietal and superior frontal cortices.

Nobre, Sebestyen, Gitelman, Mesulam, Frackowiak, and Frith (1997) utilized an exogenous covert attentional shifting task with PET. Activation in the right anterior cingulate (BA 24), right posterior parietal and premotor cortices (BA 6) was correlated with covert attention. Coull and Nobre (1998) used a combined modality neuroimaging study, PET and fMRI, to investigate and contrast covert attention as elicited by an endogenous central cueing task with perception of time. Both neuroimaging modalities revealed that spatial attentional shifting increased activation in the right parietal cortex.

Finally, Gitelman, Nobre, Parrish, LaBar, Kim, Meyer, and Mesulam (1999) combined fMRI with an endogenous, central arrow cue, Posner task. They revealed a network of

FEF, posterior parietal and cingulate cortices linked with covert attention. Li, Lu, Tjan,

Dosher, and Chu (2008) used event-related (ER) fMRI with a complex parametric design using a contrast response function to examine modulation of striate and extrastriate in response to an exogenous grating task discrimination. Retinotopic outlines of striate and extrastriate regions were identified by flickering checkerboard pattern annulus wedges and rings. The parametric design revealed a modulation of contrast gain on striate (V1) and extrastriate (V2, V2, V3A and V4) by attention. The authors claim much of these regions are obscured by the use of standard boxcar designs, which is why they used a parametric ER fMRI design. Finally, Mander Reid, Davuluri,

Small, Parrish, Mesulam, Zee, and Gitelman (2008) examined neural differences due to sleep deprivation on an endogenous covert attention/Posner task using ER fMRI. In normal well-rested individuals, there is activation in the posterior cingulate cortex

(PCC). This area is coupled with an “anticipatory bias” toward target loci. It appears that sleep deprived individuals recruit the intra parietal sulcus (IPS) to do this instead.

This area is usually paired with suppressing invalidly cued locations. The authors theorized that this change may augment receptivity to targets in invalidly cued loci in the sleep deprived.

From all this research, which includes various anterior (i.e., anterior cingulate,

FEF, premotor/BA6) and posterior (i.e., posterior parietal) regions, it is apparent that many brain areas are shared by both covert and overt (involving saccades) visual attentional processes. This overlap appears to include a fronto-parietal neural network.

This has lead to the assumption that they are indeed the same process (Kanwisher and

Wojciulik, 2000), both intertwined with the premotor theory of attention (Rizzolatti et al., 1987). However, research studies combining tasks of covert and overt attentional processes might yield more impressive results, as this would allow for statistical contrasting between both tasks within the same study.

With the question in mind of a shared neural network, it was a natural progression to combine both covert and overt attentional tasks within individual studies.

Corbetta, Akbudak, Conturo, Snyder, Ollinger, Drury, Linenweber, Petersen, Raichle,

Van Essen, and Shulman (1998) used block design fMRI with a covert visual search task. Participants were instructed to shift attention covertly, endogenously, before a target probe appeared. This was paired with a task that required repeated saccades.

Both tasks elicited a network with parietal, frontal and temporal activation, specifically, bilateral frontal along precentral and superior frontal sulci, medial frontal and posterior intraparietal sulcus. The only difference appeared to be that covert attention activated this network with less intensity than overt attention. The authors attributed this network as consistent with the premotor theory of attention. Next, Nobre, Gitelman, Dias, and

Mesulam (2000) employed block design fMRI with tasks of an endogenous covert attentional shifting and saccades to peripheral target dots. They concluded that both covert and overt attentional shifting shared a fronto-parietal network. That same year,

Perry and Zeki (2000) created a unique task including both a peripheral and central cue simultaneously. As part of the cue is in the periphery, fixation from the center is drawn to it. Thus, this is an exogenous task. Additionally, there was an overt shifting task requiring a saccade. Both tasks divulged a fronto-parietal network that included:

supplementary motor area (SMA), anterior insulae, FEF, superior parietal, striate and prestriate, and the right supramarginal gyrus (SMG) of the parietal lobe. The most notable difference between both tasks was that covert attention recruited greater activation within this very same network. The following year, Beauchamp, Petit,

Ellmore, Ingeholm, and Haxby (2001) attempted the same with fMRI in a block design.

However, their task was exogenous. Overt and covert attention activated the same network of precentral and intraparietal sulci, and lateral occipital regions. However, the overt task did this with greater intensity.

Most recently, de Haan, Morgan, and Rorden (2008) set out to resolve this issue of covert and overt attention. This was based on the fact that the literature is filled with various studies using endogenous and exogenous covert tasks as contrasts to saccadic tasks, lack of comparability between the two tasks (overt and covert) within each study due to task difficulty or distance shifted, and conflicting results of greater activation for covert versus overt or the converse. De Haan’s group claimed that the studies that showed greater activation for covert than overt utilized an endogenous task, such as

Corbetta et al. (1998), Perry and Zeki, (2000) and Nobre et al. (2000). This was contrasted with Beauchamp et al. (2001) which found greater activity for overt than covert using an exogenous task. However, upon very close reading of Perry and Zeki

(2000), their covert task clearly is exogenous. Thus, to contrast Perry and Zeki (2000) with Beauchamp et al. (2001), saying the difference that one was endogenous and the other exogenous respectively makes little sense. The differences in these two studies, both of which in actuality are using exogenous tasks, may be related to the specifics of their tasks.

De Haan and colleagues (2008) studied this question using a block fMRI design with two tasks: an endogenous covert task and an eye movement task that differed very little. The major difference was the type of shift. Both tasks revealed a similar frontal- parietal network that included bilateral activation, yet slightly greater in the right, in the

FEF, superior parietal regions and the interparietal sucli, and the occipital lingual gyri.

Overt attention appeared more ubiquitous than covert. Furthermore, it showed greater activation in the calcarine sulcus of the occipital pole and the occipital lingual gyri.

Furthermore, overt was associated uniquely with activation in the left frontal operculum. This group concluded the study with claims that overt and covert attentions are both the same, sharing the very same fronto-parietal neural network. Furthermore, they made the questionable claim that exogenous and endogenous forms of attentional shifting are one in the same. Most notably, research at a more micro level, the neuron, seems to conflict with this overlap between covert and overt attentions. Oddly, de

Haan’s group does mention that the spatial resolution of fMRI may not be great enough to see differences between small groups of neurons, which may dissociate these two attentions. The remainder of this review will focus on that very research.

Covert attention is observable in primates in the lab. However, it has been suggested that covert attention is natural to primates, as direct fixation may be considered an act of aggression. Thus, covert visual attention among primates is a method to protect oneself from danger while still avoiding direct gaze (Moore,

Armstrong, and Fallah, 2003). Similar to human studies, various primate single recording studies have shown a neural overlap between covert and overt attention

(Moore et al., 2003; Schall, 2004; Awh, Armstrong, and Moore, 2006; Moore, 2006).

Cavanaugh and Wurtz (2004) showed that microstimulation of the superior colliculus

(SC) in monkeys countered change blindness (a saccade that interrupts one’s ability to notice changes in the visual scene). Subsequent saccades to targets were increased, suggesting covert attention had shifted to target locations prior. Thus, the SC that is involved with saccades appears to have a direct effect on covert attention. This suggests the SC plays a role in both covert attention and overt attention (saccades). In fact, others have suggested this shared role of the SC in overt and covert attentional shifting, lending credence to the premotor theory (Kustov and Robinson, 1996;

Ignashchecnkova, Dicke, Haarmeier, and Their, 2004).

Dissociation between covert and overt attentional processes has been shown in primate studies as well. This provides us with much deeper insight than the studies with humans. Thompson, Biscoe, and Sato (2005) illustrated that covert attention in monkeys augmented activity of visual and visuomotor neurons in the FEF.

Interestingly, unlike overt attention, covert attention inhibited motor neurons (distinct from visuomotor neurons). Similarly, Ignashchecnkova et al. (2004) demonstrated with Rhesus monkeys that in the intermediate layer of the SC, visuomotor and visual neurons were active both to covert and overt attentional tasks. However, premotor neurons in the SC only were active to overt attention/saccades. These studies show a divergence in the SC and FEF between covert and overt attention. Nevertheless, this divergence may be due solely to eye movements, which might be the only difference between overt and covert attentional shifting (Rizzolatti et al., 1987).

An alternative approach might be advantageous in teasing this issue apart, as there is a conflicting body of literature of human neuroimaging and single unit

recordings in primates about the divergence and converge of overt and covert attentions.

This was what Ro and colleagues attempted to do (Ro, Farne, and Chang, 2003). TMS was used in humans to cause a temporary functional ablation in the FEF. Following the premotor theory, if covert and overt attentions are linked, ablation of the FEF will not only prevent an overt saccade, but also covert shifts in attention. Interestingly, this was the case. On the contrary, since the FEF are further subdivided by function in overt and covert attention as shown in the aforementioned primate study (Thompson et al., 2005),

TMS could be suppressing all of these areas simultaneously.

Posner and Petersen (1990) suggested that covert and overt attentions involve unique neural networks. Also, a more intermediate theory has been suggested in that they may share part of the same network, yet still have unique regions unto themselves

(Corbetta, 1998). At the other end of this spectrum, Rizzolatti and colleagues suggested they share the very same neural network, identical in behavior without saccade, invoking the premotor theory of attention (Rizzolatti et al., 1987). PET and fMRI studies have confirmed this overlap in the fronto-parietal network between both covert and overt visual attentions (Sweeney et al., 1996; Gitelman et al., 1999; Corbetta et al.,

1998). Single unit recordings and microstimulation in primates have shown this overlap as well (Cavanaugh and Wurtz, 2004). Yet, differences in intensity in neuroimaging studies and neuronal subpopulations have been shown (Beauchamp et al, 2001;

Thompson et al., 2005). At present, based on the intricacies presented in the scientific literature, the issue remains unresolved and still in question.

1.2c. Visual ERPS and covert attentional shifting with the Posner paradigm.

The majority of electrophysiology studies of covert visual attention in humans have involved the Posner task or a variant of it. As described earlier, there are two versions of this task, (1) endogenous and (2) exogenous. The endogenous task involves a central fixation and a central cue. The cue can be an arrow, non-directional cue (such as a texture gradient difference) that will be assigned a direction, or a neutral cue that means nothing in relation to the target. With the directional and non-directional cues, one interprets the cue and then volitionally shifts one’s attention covertly to the direction the cue imparted. Next, the target comes. This target may be at the cued location, cue validity; it may be at the opposite location, cue invalidity; or there may be a further discrimination to complete here within the target (for example, refer to Hopf and

Mangun, 2000). As expected, validly cued targets are responded to more quickly

(Posner, 1980; Harter, Miller, Price, LaLonde, Keyes, 1989; Yamaguchi, Tsuchiya, and

Kobayashi, 1994, Yamaguchi, Tsuchiya, and Kobayashi, 1995; Hopf and Mangun,

2000; Eimer 2000).

Additionally, there is the exogenous task. The task involves focusing on a central fixation and then covertly shifting one’s attention in response to a peripheral cue. Next, a target may appear there, cue validity, which requires a response.

Alternatively, a target may appear in the opposite hemifield, cue invalidity, requiring one to shift there and then respond. Obviously, one is delayed with responding to this target in the uncued location. This type of covert attention appears to be more reflexive and orienting as the cue may cause an automatic orienting toward it (Posner, 1980;

Yamaguchi et al., 1994; Yamaguchi et al., 1995; Eimer 2000).

The underlying electrophysiological correlates of these two tasks, endogenous and exogenous, are best divided into the (1) cue-target interval (Eimer, 2000), also referred to as the cue-to-S2-delay (Dale et al., 2008) which is the time between the cue and target; and (2) the target interval or the S2 (Dale et al., 2008), referring to the time after or around the target. Importantly, there appear to be different processes occurring during each interval, in fact even within the time course of each interval. Therefore, based on the keen temporal resolution of electrophysiological techniques, this has been employed for such discernment. The following is a chronological overview of these two time intervals (CTI and target) and associated visual ERP components (excluding cross modality or other sensory modality studies) for both of these Posner tasks

(endogenous and exogenous) as presented in the scientific literature.

The CTI of endogenous tasks has a wealth of various ERP components. The earliest ERP related component in the literature is an enhanced P1 and N1 contralateral to cue direction in occipital-temporal regions (Talsma, Slagter, Nieuwenhuis, Hage, and

Kok, 2005). Brignani et al. (2009) saw a similar enhancement in P1 and N1 amplitude.

They attributed these as early visual components, with emphasis on the N1 from 100-

170 ms, to the features of the cue being brought into attention. Similarly, van Velzen and Eimer (2003) saw this N1 contralateral to cued direction in occipital electrodes.

Interestingly, this N1 that van Velzen and Eimer found was the onset of an “early directing attention negativity-like (EDAN-like) potential lasting from 150 to 300 ms post-cue” (Brignani et al., 2009). Others have seen this EDAN-like component in the

ERPs. However, the true EDAN usually is garnered by examining difference waves of the CTI, i.e., left cue locked-data minus right cue-locked data, or the converse, and

showing a reverse polarity between hemispheres. This first was described by Harter et al. (1989) using left minus right DERPs (difference event-related potentials) from

200:400 ms post-cue. Located in the occipital and parietal lobes, this was more negative to the direction of the cue, based on the direction of the difference wave. They attributed this to visuo-spatial orienting and not to features of the cues. Further research from Harter (Harter and Anllo-Vento, 1991) imparted this same EDAN, but this time in the period of 200:500 ms post-cue. Most importantly, this paper explained the necessity of using difference waves to find components like the EDAN. Apparently, an arrow cue induces a large contingent negative variation (CNV) that can be removed via this type of cognitive subtraction. Components like the EDAN may be blurred or masked due to the CNV, but may readily be revealed in the difference waves. Yamaguchi et al.

(1994) saw this EDAN emerging at 240 ms posterior regions, contralateral to the cued direction. Subsequently, this continued expanded to more anterior regions. A year later, this same group found an EDAN, greater negativity contralateral to the cue, starting at approximately 240 ms in the posterior-temporal areas, and a bit later at 260 ms post-cue in the posterior parietal regions (Yamaguchi et al. 1995). They termed this attention shift-related negativity (ARN), more specifically central (as in endogenous central cue) attention shift-related negativity (cARN, differentiated from peripheral cue shift-related negativity/pARN). The end of this EDAN was approximately 380 ms post- cue. As their acronym reveals, Yamaguchi et al. (1995) attributed this to be due to attentional shifting. Eimer (2000) saw an EDAN emerging at approximately 250 ms post-cue. Similarly, Hopf and Mangun (2000) used difference waves (left cue-locked data minus right cue-locked data) to identify the EDAN at 200-400 ms post-cue in

posterior parietal lobes. They attributed this to the emergence of spatial orientation in preparation of shifting one’s attention based on meaning derived from the cue. Talsma et al. (2005) revealed an EDAN (right minus left) in their study within the window of

250:400 ms post-cue in the temporal-parietal-occipital junction (TPO). Most recently,

Dale et al. (2008) located an EDAN from 200:400 ms post-cue. They referred to the

EDAN as N2pc-like, citing van Velzen and Eimer (2003), who claimed the same for their EDAN that started with an N1. The N2pc is a component found in search array tasks when a target is found embedded within distractors. The effect is enhanced negativity in the contralateral posterior parietal area. Based on the studies of Eimer and van Velzen (2003), Dale et al. (2008), and Brignani et al. (2009), it appears that the post-cue N1 enhancement contralateral to the cued direction may be the emergence of the EDAN. Furthermore, the N2pc may be a homologue of this in visual search array tasks. The N2pc (N2 posterior contralateral) is attributed to the interface with parietal visuo-spatial representation, analogous to the feature and location map integration mentioned earlier in Tresiman’s FIT model (Treisman and Gelade, 1980; Brignani et al., 2009). Finally, as a homologue of the N2pc, this early negative component (N1-

EDAN) in the endogenous version of the Posner task appears to be the process of deriving meaning from the cue and not due to attentional shifting which must occur after this. This was determined by using complex sets of stimuli that showed a double dissociation between compatible and incompatible stimuli, suggesting the same cue direction from opposite sides of the fixation. When data was pooled, both compatible and incompatible stimuli locked EEG data combined to average, and the EDAN disappeared. However, individually, each showed a lateralized component of reverse

polarity of one another while both cueing to the same hemifield. This suggests that this difference in the EDAN was not due to attentional shifting causing this lateralization; the difference was based on the cue itself (van Velzen and Eimer, 2003; Brignani et al.,

2009).

The next prominent electrophysiological component in the literature for the CTI is the anterior directing attention negativity (ADAN). Yamaguchi et al. (1995) refer to this as their cARN (synonymous with the EDAN) shifting anterior with an onset of 380 and ending at 420 ms and 440 ms in central and frontal regions respectively, enhanced contralateral to the cue. Hopf and Mangun (2000) saw this ADAN in the frontal regions within the window of 300:500 ms post-cue. They ascribed this to executive functioning required to shift and maintain attention. Similarly, Talsma et al. (2005) found an ADAN in the same window (300:500 ms) by examining difference waves (right minus left).

Dale et al. (2008) imparted an EDAN in the window of 400:600 ms post-cue, preferential to the left hemisphere. Based on their task and experiment, they theorized that this process was related to the execution of the task, independent of spatial attributes.

Further into the chronology of the CTI, the literature reveals what are termed late components. Harter et al. (1989) found a component in the 400:700 ms window post-cue with increased amplitude contralateral to the cued direction in the occipital- parietal region. They termed this as late directing attention positivities (LDAP), as there was an enhancement of positive amplitudes contralateral to the cued-direction.

Harter’s group believed this was akin to the sensory readiness potential (SRP), or the visuo-spatial homologue to the motor readiness potential. Harter’s next work on this

(Harter and Anllo-Vento, 1991) found this LDAP initiating at 400:500 ms post-cue and terminating 100 ms post-target. Harter and colleague suggested this was the process of regulating attention to the spatial demands of the task just prior to the target. Hopf and

Mangun (2000) uncovered an LDAP in the window of 400:850 ms post-cue in occipito- temporal sites. Based on their task, they considered this invoked the ventral “what” pathway, perhaps for the discrimination needed with their complex target. Next, van

Velzen and Eimer (2003) found the LDAP in the last 200 ms of the CTI, enhanced contralateral to the cued. In contrast to this, Dale et al. (2008) found the reverse polarity at 800:1,000 ms post-cue contralateral to the cued direction. They termed this late directing attention negativity (LDAN). They associated this with maintaining attention, independent of spatial attributes. Furthermore, they claimed that tasks with variable SOAs (stimulus onset asynchronies) in the CTI, or a variable range of CTI intervals, and/or requiring intricate target discrimination, would cause this LDAN

(CNV-like) in lieu of an LDAP. Similarly, Yamaguchi et al. (1995) found what they termed a lateral negative deflection at the 600:800 ms post-cue window. They related this as response preparation to the target.

Another late component seen in the second half of the CTI window is alpha amplitude as examined in the time domain. Worden et al. (2000) used the temporal spectral evolution (TSE), a technique attributed to Salmelin and Hari (1994), in which one band pass filters the data in the alpha range, 8:14 Hz., and then full-wave rectifying the data (take the absolute values) takes place. Keeping all values positive prevents cancellation in the averaging process that occurs when using combined positive and negative values. Using this TSE, Worden’s group found a clear divergence within left

and right occipital regions for between left and right cued conditions. This was shown with enhanced amplitude (alpha activity) contralateral to the unattended hemifield within a window of 500 ms post-cue and lasting until 100 ms post-target (S2). The group attributed increased alpha amplitude to suppressing the ignored hemifield, which may have potential distractors. Sauseng, Klimesch, Stadler, Schabus, Doppelmayr,

Hanslmayr, Gruber, and Birbaumer (2005) used a modified version of the TSE consisting of complex Morlet wavelets. They found that 200 ms prior to target, there was a divergence in alpha amplitude in the contralateral to the cued direction. As their targets were jittered from 600:800 ms, this suggests their earliest divergence is at 400 ms post-cue. They concluded this was synonymous with the attentional shift. Finally,

Thut, Nietzel, Brandt, and Pascual-Leone (2006) used the TSE. Thut’s group found the same enhanced alpha contralateral to the unattended hemifield. However, the timing of their divergence is not mentioned in the text. Furthermore, it is not readily discernable from their plots. This divergence was significant when the subject attended to the right hemifield, but not the converse. However, qualitatively, there was still a divergence with greater amplitude to the contralateral hemifield when attending to the left hemifield. This was confined to narrow left and right regions of interest (ROIs) of three electrodes each in the parietal-occipital region. Thut’s group claimed that this asymmetrical significance was due to left visual field preference as supported by reaction time faster in that field. Additionally, they developed a lateralization index that offered no clear explanation of this difference. However, as there was a significant difference when attending right, and an obviously identifiable qualitative difference when attending left, this still does suggest similar findings to the two previous studies. It

is important to note that Worden et al. (2000) and Thut et al. (2006) both used the

Butterworth IIR band pass filter to perform the TSE. This high pass may induce temporal smearing as much as 100 ms or less (Worden et al., 2000). Thus, the time windows of this alpha divergence may be slightly off in their reports. Sauseng et al.

(2005) appeared to have side stepped this by using complex Morlet wavelets.

Next, we move on to the target interval of endogenous tasks. The majority of the research in this interval for endogenous tasks has focused on cue validity and the

P1-N1. Harter et al. (1989) demonstrated this cue validity effect of enhanced the P1 and N1 in the occipital area contralateral to the correctly cued target. Interestingly, the amplitudes of the P1 and N1 were larger in the right hemisphere apart from attended hemifield. Harter and Anllo-Vento (1991) correlated an enhanced contralateral LDAP in occipital regions with larger P1 and N1 post-cue. Additionally, cue validity enhanced the P1 and N1, in contrast to invalidly cued targets. In contrast with Harter and Anllo-Vento (1991), Dale et al. (2008) showed that frontal LDAN/CNV correlated well with enhanced subsequent post-target P1 and N1 in the occipital lobe. Hopf and

Mangun (2000) claimed that such enhancements occurred in P1 and N1 contralaterally in the occipito-parietal lobes due to attention based on cue validity. Talsma et al.

(2005) found an enhanced N1 with cue validity as well in occipito-parietal areas.

Similarly, Doallo, Lorenzo-Lopez, Vizoso, Rodriguez Holguin, Amenedo, Bara, and

Cadaveira (2005) saw a cue validity effect as well in the N1 in the TPO (temporal- parietal-occipital) junction that was dependent on SOA, maximal with 500 ms intervals.

Brignani et al. (2009) found an enhanced posterior P1 (105-145 ms post-cue) associated with cue validity.

In addition to the P1-N1 cue validity effect in the target interval in endogenous tasks, various studies have shown effects on later components. Talsma et al. (2005) found an enhanced P2, with a peak of 240:260, for non-informative cues, presumably in response to invalidly cued targets. Harter et al. (1989) presented results of cue validity enhancing P3 in centro-parietal regions. Correspondingly, Hopf and Mangun (2000) saw an enhancement of P3 (400:600 ms post-target) to attended, thus validly cued, stimuli. Furthermore, they saw enhanced negative difference (Nd) waves between

200:400 ms post S2 in occipital regions contralateral to attended S2 stimuli. This was carried out by taking validly cued stimuli (but non-targets as they did not match) and deducting invalidly cued non-matches. This represented attention to a cued location with stimuli minus attention to a cued location where no stimuli appears, but the same stimuli appeared in the no cued location. Thus, the only difference between the two is attention to one side. As both of these conditions did not require a response, this allowed for the data to be free of motor responses yet still show the difference between attended stimuli and the same stimuli unattended. Finally, they saw an ARN in the window of 200:800 ms post-S2 in contralateral occipito-temporal areas. This was influenced by task difficulty causing a reduction.

There have been a few studies that have used the exogenous Posner task in addition to the endogenous one. These studies set about to compare and contrast the two. In the CTI of exogenous tasks, Yamaguchi et al. (1994) examined the N1

(140:200 ms post-cue), which they found more negative contralateral to the cued- direction in posterior regions. This was 100 ms earlier than the first cue related effect as seen in their endogenous task. Further work by this same group (Yamaguchi et al.,

1995) showed the same results. Similarly, Eimer (2000) saw the enhanced negativity at

150 ms, an N1 in the exogenous task, but no cue related negativity until 250 ms post- cue in the endogenous task. In contrast, some of the endogenous studies have shown this enhanced N1, much earlier negativity than expected as based on the aforementioned studies (van Velzen and Eimer, 2003; Talsma et al., 2005; Brignani et al., 2009).

Additionally, both Yamaguchi studies (Yamaguchi et al., 1994 and 1995) reported a sustained negative component (pARN) at approximately 460: 800 ms post-cue, which enveloped posterior regions moving toward temporal and central or late negative deflection (LND) 600:800 ms post-cue as broad as the pARN. Perhaps this is the exogenous homologue of the endogenous LDAN described within the same 1995 study or later by Dale et al. (2008).

Very little is information is available with regards to the post-target interval in exogenous studies. Doallo et al. (2005) examined the post-target interval in both exogenous and endogenous tasks. The post-target N1 was enhanced due to cue validity in the exogenous task with SOA of the CTI interval at 300 ms. The endogenous task had the cue validity effect on the N1 as well, although these effects lasted longer and were associated with a longer CTI SOA of 500 ms.

In summary, it appears that the CTI in the endogenous task often starts with differences in the N1, which is perhaps tied to the EDAN. This appears to represent feature and meaning of the cue being evaluated (van Velzen and Eimer, 2003).

Similarly, the N1 and sustained attention related negativities can be seen in the exogenous task in this same interval. However, an earlier N1 than in the endogenous task suggests that attention has shifted far earlier than in the endogenous task

(Yamaguchi et al., 1994 and 1995). The second half of the endogenous task appears to be concluded with ADAN and LDAP or even the reverse LDAN depending upon the task specifics. These processes appear to be related to the directing attention, shifting of attention, preparation and priming for the expected target (Yamaguchi et al., 1995; Hopf and Mangun, 2000; Dale et al., 2008). Furthermore, differences in alpha power in the time domain appear to be related to the attentional shift as well as suppressing the to-be- ignored hemifield (Worden et al., 2000; Sauseng et al., 2005; Thut et al., 2006). At this time period in the exogenous task, there is only continuing sustained negativity, which appears to be preparation for the target (Yamaguchi et al., 1994 and 1995).

The target interval for the endogenous task has been associated with many components (P1, N1, P2, P3), which are enhanced based on cue validity and when attended (Harter et al., 1989; Hopf and Mangun, 2000; Talsma et al., 2005). Similarly, the target interval in exogenous tasks has shown cue validity with the N1 (Doallo et al.,

2005).

1.3 Hypotheses

1.3a. Posner task. Hypothesis one: Behavioral performance will show that responses to matches (targets) in the validly cued hemifield are far more significant than chance; whereas false alarm response to matches in the non-cued side (non-targets) will be no greater than chance, as participants were not attending that hemifield.

Hypothesis two: These two types of responses, correct responses to targets

(validly cued matches) and false alarms to non-cued matches (non-targets), will show a clear dissociation as they are independent of one another statistically.

1.3b. Cue contrast. Hypothesis one: There will be an enhanced, greater negativity in amplitude, N1 contralateral to the cued direction in occipito-parietal regions representing deriving meaning from the cue.

Hypothesis two: There will be an early directing attention negativity (EDAN) shown in the difference waves (left minus right cue locked data) significantly more negative in the right posterior hemisphere, connected with the N1 and deriving meaning from the cue. This will be paired with a simultaneous positivity in the left hemisphere within the same time window.

Hypothesis three: There will be significantly greater alpha activity, as represented by amplitude in the time domain at 500 ms post-cue lasting until the target

(900 ms post-cue), contralateral to the ignored hemifield and ipsilateral to the cued- direction in the posterior regions to suppress the ignored hemifield and any potential distractors. This will be paired with suppression of this alpha amplitude contralateral to the cued location to allow for attention there.

1.4 Overview

1.4a. Content of dissertation. This dissertation is the culmination of a study about covert visual attention using a version of the Posner paradigm with simultaneous electrophysiological recording (EEG). The goal is to examine the neural correlates associated with the cue-target interval of this task.

1.4b. Outline of chapters. Chapter one is the introduction. It includes an explanation of the motivation of the study. This is followed by a review of the background literature explaining what we know about covert attention, the neural

network of covert attention and electrophysiological studies using the Posner task.

After the review, there is a list of hypotheses that will be tested.

Chapter two is the methods section, which explains the specifics of subject recruitment and screening as well as the task itself. Following that is an in depth explanation of recording, data processing, and statistical analysis of the behavioral data, electrooculogram data, and EEG data.

Chapter three is the results. This includes results analysis of the behavioral data and plots of ERPs. This is followed by plot of difference waves and results of the statistical analysis of the data.

Chapter four is the discussion. In this section, interpretations of the behavioral and electrophysiological results are given. Next, this is placed within the context of scientific literature. Finally, any discoveries about the electrophysiology of covert visual attention will be discussed.

Chapter five is the conclusion. The main conclusions from the study are given directly to the point. This section concludes with the outcome of the statistical analysis in relation to the original hypotheses.

2.0 Methods

2.1 Participant Screening and Recruitment

Participants were recruited from the undergraduate student population of Florida

Atlantic University. The institutional review board of the University approved the protocol. All participants gave informed consent prior to participation in the experiment. Potential participants were screened and recruited in an elaborate process.

First, approximately 400 undergraduates were screened using emailed questionnaires to find suitable candidates to test in the behavioral/electro-oculogram (EOG) screening.

The questionnaire included questions about handedness, visual acuity, psychiatric, neurologic and other medical conditions, as well as prescribed medications. For consistency, all participants in the full study needed to be right handed, and have 20/20 vision or better, or corrected to 20/20 or better (i.e., 20/15). Any history of known neurologic/psychiatric history, and/or concurrent medications for such, automatically precluded potential inclusion in the study. Second, the 70 participants who passed the questionnaire were scheduled for the behavioral/EOG screening. The purpose of this screening was to make sure that participants could maintain fixation and perform the task greater than chance. Third, out of the participants who did show up as scheduled for the screening, approximately 30 passed. Fourth, from these 30, 17 were able to be scheduled and subsequently run in the EEG experiment. Finally, of this 17, five were excluded for failure to maintain fixation and/or perform above chance for the

entire length of the experiment. Therefore, the final cohort consisted of 12 participants

(n=12): eight males and four females, all right-handed, with an age range of 18-23 and a mean age of 19.41 years.

2.2 Experimental Design and Task

Participants completed the study within a custom soundproof Faraday cage EEG booth. They were comfortably seated, centered with respect to an LCD monitor at a distance of 60 cm. This monitor was the only light source within the booth. A custom touch sensor response pad was centered under the participant’s index finger.

Participants were instructed to remain as still as possible in a comfortable position and to refrain from any muscle and eye movements or blinks during the main task runs. An infrared (IR) camera was used to monitor gross movements of the participants. Two- minute breaks were built in between each run, as it took this long to set up the next run.

Quarter time breaks were given for 15 minutes, with the exception of the half time break given for a mandatory minimum of 30 minutes, which often included lunch.

Bathroom breaks and other breaks were given as requested. Participants were encouraged to ask for breaks to maintain vigilance and performance. Furthermore, the experimenter gave breaks when gross movements were seen in the IR camera or excessive eye movements or lack of responses were witnessed during the recording of a run. During short breaks, participants were encouraged to rest their eyes.

The initial task was an eye movement control task, Figure 2.2a. This was developed to calibrate electro-oculogram (EOG) movements at the individual level for a

2º distance from the fixation at 20º and 160º angles. In contrast to the main task which required shifting attention without moving one’s eyes, covert attentional shifting, this

control task had intentional eye movements, overt attentional shifting. These eye movements were for far less (2º) than the distance on the screen than from fixation to the target on the main task, which was 10.7º. This allowed us to have a gauge of each participant’s eye movements, a combination of horizontal and vertical movements, at a scale smaller than would need to take place to overtly shift attention in the main task.

Thus, the control task was able to show, within an accuracy of 2º movements or more from the fixation, that each participant’s movement could be detected. The first part of this control task lasted approximately 60 seconds. The participant was instructed to begin the task by focusing on the central fixation plus sign, which remained constant.

Next, participants were instructed to shift their attention overtly up and to the right when the angled line appeared and to keep their focus on this line. The line was at a 20- degree angle for two degrees in the visual field on the screen from the fixation and lasted one second. When this line disappeared, participants were to shift their eyes back to the fixation. This was repeated 30 times for this task. Next, the same task was done again for a line 160 degree angle for two degrees in the visual field, to the left and up, from the central fixation.

Figure 2.2h is a schematic of a single trial of the main task adapted from Hopf and Mangun (2000). Participants were seated with eyes 60 cm from the computer screen monitor (Samsung SyncMaster 192N-Black LCD 19 inch monitor) with a refresh rate of 85 Hz. They were instructed to focus on a central fixation dot (0.19º). An outlined box (3.3º width and 5.5º height) was placed in the right and left hemifields to outline the region where targets would appear. These boxes were stationary throughout the whole experiment. These boxes were 10.7º to the left and right of the vertical meridian and 4.06º above the horizontal meridian.

Stimuli were white on a light gray background; thus they were of low contrast.

The mean background luminance was 52.585 cd/m2, whereas the mean luminance of the stimuli was 125.82 cd/m2. We used the Michelson Contrast Ratio in which “Contrast

(c) was expressed as Michelson contrast: c = (Lmax - Lmin)/ (Lmax + Lmin), where Lmax is the background luminance and Lmin is the character (foreground) luminance”

(Naseanen, Ojanpaa, and Kojo, 2001, p. 1819). Following this, the contrast ratio is c =

0.4104. All luminance measures were made using a Tektronix J17 Photometer.

The task was programmed using a C++ compiler (Borland Turbo C++, 1990 version) in DOS on an OptiPlex GL 5100 hard drive. Task symbols were created using

Microsoft Paint version 5.1 and saved as monochrome bitmaps. Bitmaps were converted to *.bma file format using MATLAB (version 6.1, release 12.1, 2001, The

MathWorks, Inc., Waltham, MA). These files were converted into *.img files format on the C++ compiler and displayed using an *.img display program.

Randomization of trial type followed the main specification of four (16%) matching and properly cued task trials per run of 25 trials. Randomization text files

were created using MATLAB (version 6.5, release 13, 2002, The MathWorks, Inc.,

Waltham, MA), for 32 runs for the first four participants and subsequently 48 runs, after the design was changed, for all the rest of the participants. Symbols differed, if not matching, by two or three details/degrees. Matching and non-matching symbols sets are shown in Figure 2.2g. The trial types can be broken down as follows. Right or left cues

(central arrows) occurred each in 50% of the trials (Figure 2.2b). Targets, or validly cued matches, which required a response, occurred 16% of the time, with 8% in the left hemifield and 8% in the right hemifield (Figure 2.2c). Thus, they were an oddball event. Non-targets included the following: invalidly cued, matching stimuli occurred

16% of the time, with 8% each in the left and right hemifields (Figure 2.2e). This was the same percentage as the validly cued matching/targets. Validly cued, non-matching stimuli occurred 34% of the time, with 17% each in the left and right hemifields (Figure

2.2d). Invalid, non-matching stimuli occurred 34% of the time, with 17% each in left and right hemifields (Figure 2.2f). Different events (i.e., cue left, cue right, validly cued matching and non-matching symbols, and invalidly cued matching and non-matching symbols) and responses were marked using different electronic bits, or combinations of these, via a data translation board and into MANSCAN 4.1 Microamps Recorder (SAM

Technology, Inc., San Francisco, CA) software package, allowing registration of EEG,

EOG, and stimulus marker recordings simultaneously.

Participants were instructed to keep their eyes fixated on the central fixation dot throughout the entire task. As cues appeared to either the left or right of the fixation point, participants were instructed to shift their attention, not to saccade, but to the appropriate quadrant. If symbols that were identical, i.e., a match occurred, appeared in the cued quadrant, participants were required to respond by tapping a touch sensor.

Thus, reaction times were recorded for subjective match to properly cued targets. If the symbols did not match, participants were instructed to refrain from responding. If symbols appeared in the opposite quadrant to the cue, whether matching or non- matching, participants were instructed to ignore them. Thus, there was a benefit to following the cue, as only validly cued matches were targets. These targets were present

16% of the time, or four per run of 25, two in the left hemifield and two in the right hemifield.

Figure 2.2h shows a schematic of the timing details in which each one of these trial types took place within the task. Each trial started with a central fixation dot that held constant throughout the entire task. However, this occurred alone for first 565 milliseconds. Next, an arrow cue was presented around the central fixation dot for 100 milliseconds pointing to the left or right of the fixation dot. This transitioned back solely to the central fixation dot for 900 milliseconds. Next, the target or non-target of two symbols appeared in the periphery either in the cued outlined quadrant or in the non-cued outlined quadrant, with the central fixation dot still present. This target lasted for 35 milliseconds (ms). The length of this presentation intentionally was short to prevent an orienting visual attention to saccade. If the participant perceived that the symbols were in the cued quadrant and were identical, then they were to respond by

tapping the touch sensor. If symbols did not match or they appeared in the non-cued quadrant, they were to be ignored. At the end of each trial, the central fixation dot was the sole focus for 1200 milliseconds. This progressed into the next trial, for a total of 25 trials per run. Experimental studies consisted of 32 runs (800 trials) for the first four participants, and a subsequent 48 runs (1,200 trials) for the rest. Each run was approximately 65 seconds in length. All stimuli were synchronized with the refresh rate.

For each participant, response hand was randomly assigned to the first half, in order to balance this response hand across participants. The response hand changed half way through the experiment for each participant.

2.3 Behavioral Analysis

2.3a Behavioral recording. Behavioral responses were expressed by tapping a custom touch sensor pad to perceived targets. This was powered by a DC power unit and fed into the EEG booth. The response traveled out of the booth and into a data translation bit board, thus encoding the response as a specific bit value. Additionally, the bit board (data translation board) was sent markers, via bit values, from the C++ code of the PC running the task about the various cue and target types to the Microamps

DSP (Sam Technology, Inc., San Francisco, CA). This went into the PC, as explained in detail in the EEG recording section, with MANSCAN 4.1 recording software (Sam

Technology, Inc., San Francisco, CA). Thus, all the makers for cue, target, and response were recorded time locked to the EEGs.

2.3b Behavioral data processing. Based on the timing of all the cue, target and response markers in relation to one another within a trial, reaction times to targets, false alarms to cue or non-targets, and omission of response to targets were garnered using

MATLAB (version 7.0.0.19920, release 14, 2004, The MathWorks, Inc., Waltham,

MA). Following the protocol by Hopf and Mangun (2000), only correct responses falling within the window of 150: 1,000 ms post-target were acceptable.

2.3c Behavioral statistical analysis. Reaction time averages at the participant and subsequent group level were calculated using MATLAB (version 6.5, release 13,

2002, The MathWorks, Inc., Waltham, MA). The following analyses were conducted using Microsoft Office Excel 2002 (version 10, 2001, Microsoft, Redmond, WA). The hit rate, or the percentage of correct responses to targets in relation to omissions was calculated at the participant and group level. Finally, the percent correct was calculated 44

at both the participant and subsequent group level by combining percentages of correctly refraining from responding to non-targets in relation to false alarms with the hit rate. False alarms were calculated at the participant and subsequent group level as follows: False Alarm A occurred when either a match or a non-match occurred in the invalidly cued hemifield and the participant responded; whereas False Alarm B occurred when a non-match occurred at a validly cued location and the participant responded. False Alarm A further was divided to look at the invalidly cued matches, which, in theory, could be an obvious false alarm should the participant not have shifted to the correctly cued hemifield at the exclusion of or while ignoring this invalidly cued hemifield.

An ANOVA and various t-tests were conducted using SPSS (15.01, 2006, SPSS,

Inc., Chicago, IL.). The ANOVA was performed to look for main effects and interactions of the factors cue validity, matches, and correct responses to targets. T- tests were done to find if there was a significant difference between these various false alarms and the hit rate from chance and vs. one another. A one-sample t-test was used to show the difference between False Alarm A and the sample value of zero (chance in a distribution). Also, the same one-sample t-test was used with False Alarm B.

Additionally, a paired t-test was conducted to see if there was a significant difference in

False Alarm A vs. False Alarm B. Finally, a paired t-test was done to examine whether the distribution of Hit Rate significantly was different than the distribution of total False

Alarms. This was done to show if there was a difference between mistakes (false alarms) and correct responding (hit rate).

2.4 EOG Analysis

2.4a EOG processing. Electro-oculograms were recorded via four bipolar electrodes in the standard locations of left and right canthi (for horizontal eye measures) and above and below the left eye (for vertical measures). Electrode impedances were less than 5 kΩ. The EOG control task was used to give a rough guide of excursions for each participant. The first run of artifact identification took place using the MANSCAN

Analysis StripChart version 4.1 (1999, SAM Technology, Inc., San Francisco, CA) automated HEO (horizontal electro-oculogram) and VEO (vertical electro-oculogram) detection. This was followed by a detailed inspection of the EOG in relation to frontal

EEG channels. Next, the second run of artifact identification took place within a

MATLAB based custom GUI. Here the data was epoched by condition (trial type, i.e., left cue locked data, right cue-locked data). Next, the linear trend was removed from the each trial by channel-wise linear detrending. The best fitting line over each trial was subtracted from each trial within each channel. This was a technique used to reduce sway. Next, the data was baseline corrected. This is a procedure where the pre-cue mean was deducted from the entire epoch for each trial. This allowed all differences that are revealed post-cue to be due to evoked responses vs. differing baselines. Finally, a low pass FIR (finite impulse response) filter at 30 Hz was used on the data. This was used to eliminate noise on all individual trials, yet still preserving the components of the

ERP. All of these procedures forced various excursions that were obscured in the raw continuous data to be more obvious. Excursions in the EOG channels were used to identify eye artifacts. Additionally, a thresholding technique was employed in which excursions greater than +/-30 μV from baseline in EOG channels were indicative of eye

artifacts and thus labeled so. This “30 μV” was based on a personal communication (A.

Delorme, personal email, July 28, 2005). This was more conservative than other studies employing a threshold of +/-60 μV for artifacts (Fu et al., 2001). However, this was less conservative than the default settings of the MANSCAN Analysis StripChart automated detection with 28 μV for VEMs and 25 μV for HEMs. Importantly, as mentioned previously, even with such criterion, the automated system did not function correctly based on the sway in continuous data. Next, EOG averages at the subject level were created. EOG averages with excursions greater than +/-2 μV suggested eye excursions were still present (van Velzen and Eimer, 2003). If this occurred, the

Woody Filter (Woody, 1967) was used within the window of the excursion in that average. Each individual trial that was still left, not classified yet as containing an artifact, was put through this Woody filter to find which ones cross-correlated best with this excursion in this average used as a template. Subsequently, such trials were pruned one by one from this average until the new average was under +/- 2 μV. At this point, it was assumed that any micro excursions remaining at the individual trial level were averaged out. Final inspection included plotting EOG averages (both HEO and VEO) based on all trials overlain with an average made from data classified as artifact-free.

The greater the difference between the two suggested that artifacts had indeed been removed.

2.5 EEG Analysis

2.5a EEG recording. EEGs were recorded using an elastic cap made of spandex-type fabric (Electro-Cap International, Inc., Eaton, OH) with a montage of 84 tin electrodes (Figure 2.5a) based on the 10-10 system (American Clinical

Neurophysiology Society, 2006). Each electrode was referenced-linked to left and right mastoids with a forehead ground. Reference, ground, and EOG electrodes were maintained at less than 5kΩ EEG. EEG electrode impedances were maintained at less than 20 kΩ. Signals were amplified using a Microamps amplifier (Sam Technology,

Inc., San Francisco, CA) and fed into a Microamps DSP (Sam Technology, Inc., San

Francisco, CA) box for digitization. There was a band pass filter of 0.05:100 Hz.

Digitized signals at 512 Hz were down-sampled to 256 Hz.

From here, EEG signals were fed into a Pentium III Dell Dimension XPS T450 using Windows NT and the software MANSCAN 4.1 Microamps Recorder (Sam

Technology, Inc., San Francisco, CA). Horizontal and vertical EOGs (electro- oculograms) were measured simultaneously to allow removal of eye movement artifacts and to guarantee central fixation. Stimulus markers and reaction times were recorded into the EEG/EOG recording files allowing for time locking within the analyses.

Additionally, participant gross motor behavior was monitored via use of an infrared camera and a PA system during the task.

2.5b EEG data processing. Each run of 25 trials was recorded in a separate file, which included EEG and EOG data, channel names and markers for cue, target and response. This information was opened using MANSCAN Analysis STRIPCHART.

Each file was run through the automated eye movement detection. Furthermore, artifacts, including eye movements missed by the automated system, etc., were hand marked. This was the first pass of artifact detection. Data was exported by the software, which saved them as text files. Artifact and marker information, i.e., channel, timing, type of artifact, cue, target, response, etc., were saved in text files separate from the raw EEG/EOG data. Next, these raw data files were manually opened and saved as *.dat files for easy importation into MATLAB (version 7.0.0.19920, release 14, 2004, The MathWorks, Inc., Waltham, MA). Once in MATLAB variable space, each raw data file was saved as a *.mat file. Processing of the text files with the artifacts, etc., was done to create a matrix at the individual level with all this information to be drawn upon on the fly to plot various cue and target types conditions as MATLAB figure files. A custom made GUI was built to allow one to see data from all EEG channels in montage format simultaneously with the EOG channels. Within this GUI, artifact marking at the trial level for each condition was possible. This was the second pass of artifact detection. Often, many artifacts that were not easily seen in the MANSCAN StripChart were far more visible in the MATLAB GUI. This is due to trend removal, filtering (low pass finite impulse response filter at 30 Hz.), and baseline correction, which were all done in the GUI for this very purpose. All artifacts found in the GUI were noted and appended to the previously created artifact matrix for each participant. Artifact-free data was used to make ERPs at the subject and group level.

Finally, a process of balancing the amount of trials per condition was put in place. This took the data and balanced it so that each participant contributed the same number of trials per condition to their own averages. This assured that the data was balanced at the subject and thus group level. Therefore, differences between conditions could not be attributed to an imbalance in trial number per condition. Next, this data was plotted at the group level. Also, difference waves were made for the CTI in the direction of left cue locked data minus right. This was plotted to help reveal differences between the two and components such as the EDAN. Finally, data was saved to new matrices in preparation for statistical analysis.

Grand-averaged across all participants for left and right cue locked data was plotted overlain for each electrode. Furthermore, this data was used to create difference waves (left minus right) with MATLAB. This was done to divulge any differences between left and right cue locked data. The baseline, precue, was used to find +/- 2 SD over the mean. This was shown plotted in two horizontal lines on each electrode. It was believed that anything greater than +/- 2 SD would represent significant difference between the two greater than the baseline as determined pre-cue.

The first round of ANOVAs was conducted using all channels, for both left and right cue conditions with 50 ms and 100 ms windows. ERP data for each subject was averaged or the root mean square (RMS) was taken, across these 50 ms and 100 ms windows. This created four sets of data for ANOVAs: 50 ms means, 50 ms RMS, 100 ms means, 100 ms RMS. This was done as there was no obvious choice which window or measure would give us the best information. Therefore, all windows and measures were used. In other cases, independent component analysis (ICA) was done within the

MATLAB based toolbox EEGLAB (version 6.03b, 2008, Swartz Center for

Computational Neuroscience, University of California San Diego, San Diego, CA).

ICA uses blind source decomposition, similar to PCA (principle component analysis) but based on kurtosis (peakedness, spikiness, the 4th standardized moment of the distribution) criteria; whereas PCA is based on orthogonality (eigenvalue decomposition) (Delorme and Makeig, 2004). This was used first to find the pattern of spatial regions of interest (collapsed across channels within these regions) and temporal windows (collapsed across the time window into a mean or RMS) on which to perform statistical analysis.

Visual inspection of the difference waves created by subtracting the right from the left cue-locked ERPs suggested an early cue-related difference in the N1 component, which will be referred to as Component 1 synonymously (Figures 3.3a and

3.3b). Independent Component Analysis (ICA) within EEGLAB then was used to identify left and right occipito-parietal regions having a prominent N1 component and the time window of that component negative 10 SD over the baseline mean as calculated in MATLAB. Since this temporal window and ROIs were determined using

ICA rather than visual inspection, they are presented in the results.

A similar analysis to that of the N1 was done to include the P1-N1-P2 module.

These same ICA components and their regions of interest (ROIs) from EEGLAB were used as from the N1 analysis. However, this was done with lowering the SD threshold to +/-3 to define the window based on the ICA components. Next, the RMS of the data, based on this window from ICA, was calculated with the intent of including both

positive and negative components (P1, N1, P2) as all positive data. This included what appeared to be the P1-N1-P2 complex with a window of 109.1:187.2 post-cue.

Close inspection of the ERPs in comparison with the P1-N1-P2 complex data as previously done revealed a P1 peak at approximately 110 ms and a P2 peak approximately 230 ms in the occipito-parietal regions. Thus, much of the P1 and all of the P2 were not included in the previous analysis. It is fair to say that the ICA components used to identify the N1 did not in fact contain the P1 or P2. Based on this information, it was decided to find differences in left and right cue data in left and right hemispheres based on an overlay of the two on the same montage. This is shown in

Figure 3.2e and Figure 3.2f. From this, it was determined that clear differences could be seen with the P1. The P1 was observed visually with greater positivity in the left cue data contralaterally in the right occipito-parietal electrodes (PO8, PO10, PO4, P6, P4,

P8, PO455, and O10) and the right cue data contralaterally in the left occipito-parietal electrodes (PO7, PO9, PO3, P5, P3, P7, PO355, and O9). As the peak of this P1 was at

110 ms, it was decided to keep the window narrow around this difference, using a window of 100:120 ms. The mean of the data was obtained within this time window, by collapsing across the aforementioned channels into regions of interest.

Next, the same process was used for the P2. Visual inspection revealed the P2 with greater positivity for the left cue data contralaterally in the right occipito-parietal electrodes (P8, PO8, PO10, O10) and the right cue data contralaterally in the left occipito-parietal electrodes (P7, PO7, PO9, O9). As the peak of this P2 was at 230 ms, it was decided to keep the window narrow on this difference, using a window of

220:240 ms. The mean data was obtained for this time window, collapsing across them mentioned channels into regions of interest (ROI).

Visual inspection of the difference waves divulged an early direction attention negativity (EDAN) component. In right hemisphere channels (C2, C4, C6, P2, P, P6,

CP4 and CP6) there is greater negativity contralateral to the cue than in the opposite hemisphere channels (C5, C3, C1, P5, P3, P1, CP3 and CP5). This exceeds the –2 SD over baseline in the right hemisphere, suggesting significance greater than baseline.

These regions of interest and this time window garnered from the difference waves were used to perform statistical analysis on the ERP data. Additionally, an analysis was done using the group difference waves with channels as repeated measures. However, as the true nature of the EDAN requires reverse polarity between the hemispheres within the difference wave (Harter et al., 1989), it was decided to examine the topographic brain maps to conduct further analysis. A different set of electrodes for an

ROI and time window was found. This resulted in an asymmetric laterality. The left hemisphere channels consisted of (P9, P7, P5, PO9, PO7, O9), whereas the right hemisphere electrodes consisted of (P2, P4, P6, P8, CP4, CP6). A narrow time window of 40 ms, 260:300 ms, was chosen following the methods of the discoverers of the

EDAN (Harter et al., 1989).

Component 2 was identified by examining the left and right cue locked data overlain montages (Figures 3.2e and 3.2f) and negative difference waves (Figures 3.3a and 3.3b). It appeared to have a peak at about 250 ms and may coincide with the P2.

However, as you will notice from the previous P2 analysis description of hemispheric differences, this component is dissimilar. The N1 peak in left and right cue locked data

group average topographies matched the ICA components associated with it. Based on the intricacies and confusion of this Component 2, it was decided to plot the presumed peak of this 250 ms post-cue in topos for left and right cue locked data group averages to attempt to match this with ICA components.

The alpha component in the time domain was examined using the TSE

(temporal spectral evolution) method first used by Salmelim and Hari (1994). Data went through the following process as outlined by the originators: (1) Artifact free data for left and right cue locked data was band pass filtered between 8:14 Hz. All previous studies used the Butterworth IIR (infinite impulse response) filter and cautioned about temporal smearing caused by this (for example, refer to Worden et al. 2000). We avoided this problem by using a FIR (finite impulse response) filter. (2) Next, EEG data was full-wave rectified; that is the absolute value was taken. Essentially, this doubles the frequency of the data to approximately 20 Hz in the beta range. However, this is necessary to prevent cancellation in averaging that would occur with the combination of positive and negative values. (3) An average at the participant level was taken for left and right cue locked data. (4) An average was taken at the group level for left and right cue locked data. [Note: Prior to this procedure as outlined in (1) through

(4), the data in this study, at the trial level, was baseline corrected in the exact way used with the ERP data.] Plots of left and right cue locked data overlain then were used to divulge the posterior channels showing a divergence in alpha power as shown by amplitude. ROIs were defined by the difference shown in the plots. Channels showing this divergence between cue types within hemisphere, included those in the left hemisphere (PO3, PO355, P1, P3, P5, and P7) and right hemisphere (PO4, PO455, P2,

P4 P6, and P8) ROIs. The first 100 ms (precue) of plots was trimmed due to a filter warm up artifact. This is similar to the protocol used by Thut et al. (1996). Statistical analysis was then conducted on channels and then ROIs to see if this difference was significant. First, PO3 vs. PO4 and PO355 with PO455 were tested. As one subject was missing PO4, significance testing only could be done using 11 participants.

Additionally, two participants were missing PO455 and thus significance testing was done on 10 participants. Next, the left and right ROIs were tested for significance across all 12 participants using a separate ANOVA across 50 ms and 100 ms windows for the period of 500:900 ms in which the divergence was visible in the plots. Finally, an analysis was done using the group data means and channels as repeated measures for the 500:900 ms period using the same ROIs.

2.5c EEG statistical analysis. Data was exported from MATLAB into Excel files or text files. This was pasted into SPSS (Statistical Package for Social Sciences, version 16.0, 2008, SPSS, Inc., Chicago, IL) to conduct ANOVAs and paired t-tests.

When an ANOVA divulged a significant interaction of Cue x Hemisphere, post-hoc paired t-tests were used to localize this significance. The previous section contains details of how the processing was done prior to this and how data was grouped for significance testing. Bonferroni correction for multiple comparisons was used when appropriate.

3.0 Results

3.1 Behavioral Data

Behavioral data for reaction times for left and right cued trials appears to show little difference in reaction times between the two at the group level. The mean reaction time for left cued targets was 683.35 ms, whereas the mean reaction time for right cued targets was 685.04 ms. Please refer to Table 3.1a.

The group behavioral means data is listed in Table 3.1b. The column labeled

“HR” (hit rate) reveals that the correct responses to validly cued matches were 74.67% at the group level. The column labeled “MIC” (match invalidly cued) false alarms shows us that at the group level there were approximately 4% of these false alarms took place. Thus, approximately 96% of the time participants were able to ignore matches on the invalidly cued hemifield. This is 31.73% of the total invalid trials, which comprise FA A (false-alarm type a). The “% Correct” column gives us an overall picture of performance, which is a combination of HR and FA. The overall percent correct performance rate was very high at the group level, 91.44%.

An ANOVA was performed using the factors of Validity (cue validity), Match

(matching target symbols) and Response-Non Response (correct response to target or

Hit Rate). There was a significant main effect of Response-Non Response (Hit rate: F1,

11, = 493.549, p < 0.001). This survived all other tests beyond Assuming Sphericity as significant with all the same values. Next, there was a significant two-way interaction of Validity x Response-Non Response (Cue validity x Hit Rate: F1, 11= 150.614, p <

0.001). This survived all other tests of significance beyond Assuming Sphericity with all the same values. Finally, there was a significant three-way interaction of Validity x

Match x Response-Non Response (Cue Validity x Matching Symbols x Hit Rate: F1, 11 =

150.614, p < 0.001). This survived all other forms of significance testing beyond

Assuming Sphericity with all the same values. Please refer to Table 3.1c. Next, the distributions of FA A and FA B were examined using a one-sample t-test to show the difference between their distributions and chance (using the test value of 0). FA A, or trials where the participant responded even when nothing appeared at the cued location, was insignificantly different from chance (t11 = 2.996; p = 0.12). Please refer to Table

3.1d. FA B, or trials where non-matches appeared at the cued location, was significantly different from chance (t11 = 3.564; p = 0.004). A paired samples t-test divulged a significant difference between these two types of false alarms (t11 = -3.52, p

= 0.005). A one-sample t-test (using the test value of 0) was performed to see if the hit rate (HR) was significantly different from chance. This divulged a significant difference chance (t11= 15.010, p < 0.001). Finally, a paired sample t-test was done between targets that were validly cued matches termed hit rate (HR) vs. non-targets,

which were invalidly cued matches (MIC). This divulged a significant difference between the two (t11= 12.27, p < 0.001).

3.2 Grand Averaged ERPs

EEG data was grand-averaged across 12 participants for 84 channels for both left and right cue locked data using MATLAB. This was plotted in brain map electrode montages using MATLAB. Please refer to Figures 3.2a, 3.2b, 3.2c, 3.2d, 3.2e, and 3.2f.

Furthermore, grand averaged ERPs were imported into EEGLAB, an open source

MATLAB based GUI. In EEGLAB, the latency of the peak of both left and right cue locked ERPs at the group level was plotted in brain map topographies. Please refer to

Figures 3.2g. and 3.2h.

3.3 Cue Contrast: Difference ERPS

Difference ERPs (left cue locked data minus right cue locked data) were plotted in Figures 3.3a and 3.3b. In these figures, diverging polarity of N1s can be seen clearly between left and right occipito-parietal. As this is left minus right, the N1 in the right hemisphere is negative between 100:200 ms in right channels (P6, P8, P10, PO4, PO8,

PO10, O2, and O10) contralateral to the cue. In contrast, the N1 is positive in this time window in left electrodes (P5, PO3, P7, PO7, O1, P9, PO9, and O9). The N1 (Figures

3.3c and d) in both hemispheres is greater than +/- 2 SD over baseline (represented as horizontal red lines) suggesting significance in both hemispheres. In the period of

200:400 ms post-cue, there is an early directing attention negativity-like (EDAN-like) component (Figure 3.3e). In right hemisphere channels (C2, C4, C6, P2, P4, P6, CP4, and CP6) there is greater negativity than in the left hemisphere channels (C1, C3, C5,

P1, P3, P5, CP3, and CP5). This exceeds the –2 SD over baseline in the right hemisphere, suggesting significance greater than baseline. Statistical analysis of the mean and RMS ERP data in this window for these two ROIs failed to show significance. However, using the group difference waves, a one-way ANOVA was performed. This revealed a significant interaction of Hemisphere (F 1, 7 = 85.073, p

<0.001). This survived all other significance testing in the ANOVA with all the same values. Please refer to Table 3.3a. Furthermore, post-hoc paired t-tests divulged a significant difference between the hemispheres (t7 = 9.223, p < 0.001). Please refer to

Table 3.3b. Figure 3.3f shows the significance histogram of this.

Topographic inspection of the difference waves as shown in Figure 3.3g appeared to show an asymmetric dipole of negative activity in the right hemisphere and positive activity in the left hemisphere at 275 ms post-cue. Regions of interest within each hemisphere were located with the lowest and highest values respectively in opposing hemispheres. Left hemisphere channels were more posterior (P9, P7, P5,

PO9, PO7, O9) than the right hemisphere channels (P2, P4, P6, P8, CP4, CP6). A clear

EDAN was seen in the period of 260:300 ms post-cue in these regions (Figure 3.3h). A one-way ANOVA (Table 3.3c) divulged a significant difference between left and right hemispheres (F1,5 = 115.153; p < 0.001). This survived all other testing of significance beyond Assuming Sphericity. A post-hoc paired t-test (Table 3.3d) confirmed a significant difference between hemispheres (t5 = 10.543; p < 0.001). Furthermore, this fits the typical EDAN with reversed polarity between hemispheres. This is shown in difference waves in Figure 3.3h and the significance histogram in Figure 3.3i.

3.4 Cue Contrast: ANOVAS

Grand-averaged left and right cue locked ERPs were was placed into four separate formats via MATLAB for statistical analysis to determine which would reveal the best temporal and spatial resolution of the differences between the two. This included 50 ms and 100 ms time windows of both the mean and root mean square

(RMS) over these windows. Statistical analysis was conducted using SPSS.

All grand-averaged ERPs were used as input variables in a 2 x N windows x

Channels repeated-measures ANOVA, with cue (L, R), window (either 11 windows for

100 ms windowed data or 22 for 50 ms windowed data), and channels (84) as factors.

Results of the 50 ms means data ANOVA (Table 3.4a) revealed a significant main effect for Window (F21, 84 = 8.88; p <0.001 with Sphericity Assumed).

Furthermore, this survived all other testing (Greenhouse-Geisser: F1.819, 7.274 = 8.88; p =

.012, Huynh-Feldt: F3.251, 13.006 = 8.88; p = .002 and Lower-bound: F1.000, 4.000 = 8.88; p =

.041). Channel had a significant main effect (F83, 332 = 2.72; p < 0.001) only with

Sphericity Assumed, and did not survive as significant with the further testing. Cue x

Channel had a significant two-way interaction (F83, 332 = 1.5; p = .007) with Sphericity

Assumed, but this did not survive further testing. Similarly, there was a significant

Window x Channel two-way interaction (F 1743, 6972 = 2.45; p < 0.001). Likewise, this did not survive more stringent testing.

Results of the 100 ms means data ANOVA (Table 3.4b) revealed a significant main effect for Window (F 10, 40 = 9.938; p < .001 Assuming Sphericity). Additionally this survived all other testing (Greenhouse-Geisser: F 1.572, 6.288 = 9.938; p = .013,

Huynh-Feldt: F 2.413, 9.653 = 9.938; p = .004 and Lower-bound: F 1.000, 4.000 = 9.938; p =

.034). The main effect of Channel was significant (F 83, 332 = p <.001 Sphericity

Assumed). However, this did not survive more stringent testing, suggesting sphericity should not have been assumed. There was significant two-way interaction of Cue x

Channel (F83, 332 = 1.51; p = 0.006) Sphericity Assumed; however, this interaction did not survive as significant with further testing. Similarly, there was a significant two-way interaction of Window x Channel (F830, 3320 = 2.55; p < 0.001 Sphericity Assumed).

Likewise, this two-way interaction did not survive more stringent testing, suggesting that sphericity should not have been assumed.

Results of the 50 ms RMS data ANOVA (Table 3.4c) revealed a significant main effect of Window (F 21, 84 = 8.18; p < .001) Sphericity Assumed. This survived all other testing as follows: Greenhouse-Geisser: F 1.955, 7.819 = 8.18; p = .012, Huynh-Feldt:

F 3.801, 15.202 = 8.18; p = .001 and Lower-bound: F 1.000, 4.000 = 8.18; p = .046.

Additionally, there was a significant main effect of Channel (F83, 332 = 6.59; p < .001

Sphericity Assumed). This survived significance with further testing as follows:

Greenhouse-Geisser: F2.340, 9.360 = 6.59; p = .014 and Huynh-Feldt: F5.844, 23.376 = 6.59; p

< .001. There was significant two-way interaction of Cue x Window (F21, 84 = 2.29; p

=.004) Sphericity Assumed. However, this interaction did not survive any of the more stringent testing. There was another significant two-way interaction of Window x

Channel (F1743, 6972 = 2.46; p < .001) Sphericity Assumed. Furthermore, this did survive significance using Huynh-Feldt (F9.529, 38.118 = 2.46; p = .024). Finally, there was a significant three-way interaction of Cue x Channel x Window (F 1743, 6972 = 1.17; p <

.001). However, this did not survive as significant with any of the other testing, which suggests sphericity should not have been assumed.

Finally, results of the 100 ms RMS data ANOVA (Table 3.4d) revealed a main effect for Window (F10, 40 = 9.058; p < .001 Sphericity Assumed). This was significant for all further testing as follows: Greenhouse-Geisser: F1.825, 7.300 = 9.058; p = .011,

Huynh-Feldt: F3.276, 13.105 = 9.058; p = .001 and Lower-bound: F1.000, 4.000 = 9.058; p

=.040. Additionally, there was a significant main effect for Channel (F 83, 332 = 6.564; p

<.001 Sphericity Assumed). This survived additional testing as follows: Greenhouse-

Geisser: F 2.419, 9.676 = 6.564; p = .013 and Huynh-Feldt: F 6.385, 25.538 = 6.564; p <.001.

There was a significant two-way interaction for Cue x Window (F 10, 40 = 2.91; p = .008

Sphericity Assumed). However, this did not survive further testing. Additionally, there was a significant two-way interaction of Window x Channel (F830, 332 = 2.76; p < .001 for Sphericity Assumed and F8.557, 34.228 = 2.76; p= .016 for Huynh-Feldt). Finally, there was a significant three-way interaction of Cue x Window x Channel (F830, 3320 = 1.27; p

<.001) Assuming Sphericity. However, this did not survive any further significance testing.

3.5 Cue Contrast Components: ANOVAS

Independent component analysis was performed on left and right cue locked data. A broad searching was conducted to locate components that resembled the N1’s spatial and temporal features as shown in the data. ICA revealed component1/N1 with a spatial region of interest for the left cue locked data component 8 of right occipito- parietal electrodes (P6, P8, P10, PO4, PO8, PO10, O2, and O10) contralateral to the cue

(Figure 3.5). This was within a time window of 139:164.3 ms post-cue, negative 10 SD over the baseline mean (Figure 3.5a). This accounted for 0.99% of the variance (as calculated in EEGLAB) in the left cue locked group average within the CTI. Intensity values/units for ICA topos in EEGLAB are arbitrary; however, the warmer the color, the greater the spatial correlation of that area with the component. For this component, a thresholding value of 50 or greater, all red, was used to define this ROI. Similarly,

ICA revealed a spatial region of interest for right cue locked data component 10 of left occipito-parietal electrodes (P5, P7, P9, PO3, PO7, PO9, O1, and O9) contralateral to the cue (Figure 3.5). This was within a time window of 141.1:170.3 ms post-cue, negative 10 SD over the baseline mean (Figure 3.5a). This accounted for 2.98% of the variance (calculated by EEGLAB) in the right cue locked group average within the CTI.

For this component, a thresholding value of 50 or greater, all red, was used to define this ROI. Data was collapsed across these regions of interest, within this time window, with the output being a root mean square (RMS) and a mean.

Component 1/N1 analysis was first conducted using the root mean square but failed to yield any significant results. Next, the mean was used, which did yield results.

Analysis of Variance (ANOVA) using SPSS was conducted with the factors of hemisphere (left and right) and cue (left and right), revealing a significant interaction of

Cue x Hemisphere (F1, 11 = 9.956, p = 0.009). This survived all other testing beyond

Sphericity Assumed with the same level of significance and all the same values (error, degrees of freedom, F, p, etc.). Please refer to Table 3.5a. Post-hoc paired t-tests showed that the left-cued N1 in the right hemisphere was significantly greater (more negative) than the right-cued N1 (t11= -3.006, p= .012), and that the right-cued N1 in the left hemisphere was significantly greater (more negative) than the left-cued N1 (t11 = -

2.311, p = .041). Please refer to Table 3.5b for statistics and Figure 3.5b for a significance histogram of the means.

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The results of the P1-N1-P2 using the same ICA components as the N1 analysis were performed. An ANOVA (Table 3.5c) revealed a borderline significant Cue x

Hemisphere interaction (F1,11= 4.991, p = 0.047 Assuming Sphericity). This survived all further testing (Greenhouse-Geisser, Huynh-Feldt and Lower bound) as significant in the ANOVA with all the same values (error, degrees of freedom, F, p, etc.).

However, paired t-tests did not reveal significant differences between left and right cue locked data in left and right ROIs.

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The results of the P1 ANOVA using the factors of Cue (left and Right) and

Hemispheres (Left and Right) revealed nothing significant using the mean. Thus, the

RMS was used for this same window and ROIs of data as well. An ANOVA divulged no significant main effects, but a significant interaction of Cue x Hemisphere (F1,11 =

5.414, p = 0.040). This survived all other tests beyond Assuming Sphericity with all the same F values, error, degrees freedom, and significance. Please refer to Table 3.5d.

However, post-hoc paired t-tests of this interaction failed to yield significance.

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The results of the P2 ANOVA using the factors of Cue (left and Right) and

Hemispheres (Left and Right) revealed a significant main effect of Hemisphere (F1,11 =

6.677, p = 0.025). This survived all other testing beyond Assuming Sphericity with all the same values. Furthermore, these was a significant interaction of Cue x Hemisphere

(F1,11= 5.917, p = 0.033), which survived all further testing beyond Assuming

Sphericity with all the same values. Please refer to Table 3.5e. However, post-hoc paired t-tests of this interaction divulged nothing of significance. Finally, it was decided to use the RMS of this P2 with the same time window and ROI. An ANOVA using the same factors as in the means data revealed nothing of significance.

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Component 2 was identified using the ERP topos plotted in Figure 3.5c. ICA components matching these topographies were found in left cue locked data ICA component 1 and right cue locked data ICA component 1. These are shown in Figure

3.5d. Please note the intensity units are arbitrary here and thus cannot be labeled. Left cue locked data ICA component 1 was responsible for 15.8348% of the variance of the

CTI. Right cue locked data ICA component 1 was associated with 14.6722% of the variance of the CTI. Figure 3.5e shows these two ICA components overlain. Using

+/- 3 SD over the ICA component baseline mean, the left data component 1 time window was divulged as 242.4: 285.2 ms post-cue. Similarly, the right data component

1 time window was determine by thresholding +/- 3 SD over baseline mean. This divulged a time window of 233.6:268.8 ms post-cue. This gives us a combined time window ranging from 233.6:285.2 ms post-cue. Data processing continued as these time windows overlap with the peak of 250 ms observed visually in the data and the ERP topos for that time period match the spatial topography of these ICA components.

Again, as intensity values/units for ICA topos in EEGLAB are arbitrary. Nevertheless, it is clear that the warmer the color in the ICA component topography regions, the greater the spatial correlation of that area with the component. For both left data ICA component 1 and right data ICA component 1, a thresholding value of 55 or greater, all dark red, was used to define this ROI to conduct statistical analysis. Left and right ICA component 1 both revealed a left hemisphere ROI (P1, P3, P5, PO355, and PO3) and a right hemisphere ROI (P2, P4, P6, PO455 and PO4) that are within black ovals of the

ICA component topo maps in Figure 3.5d. Data for statistical analysis was collapsed

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across these regions of interest, within the time window, and the mean and RMS were taken for input into SPSS.

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Statistical analysis (ANOVAs) of Component 2 with an input of the mean data failed to show significance. Furthermore, the RMS of the data of this in an ANOVA failed to reach significance as well.

3.6 Cue Contrast: α-Activity in the Time Domain

TSE (temporal spectral evolution) data is plotted here for left and right cue locked data overlain. In this TSE data, plots of amplitude are a measure of alpha activity. Both left and right cue locked data start at a high alpha amplitude precue and stay high until approximately 120 ms post-cue. Next, there is a clear dip or event- related desynchronization (ERD) of both lasting until approximately 500 ms. Finally, alpha increases again in an event-related synchronization (ERS). Here, the divergence

(in alpha amplitudes) between the two conditions (left and right cue) within each hemisphere is clear. Plotting of this was done to divulge the posterior channels showing this. Figures 3.6a and 3.6b show this overlay in montage format. Figure 3.6c and 3.6d show two electrode sets selected to show this difference. A clear divide in alpha amplitude can be seen between left and right cue locked data here between left and right hemisphere electrodes in occipito-parietal electrodes. In Figure 3.6c, the amplitude for left cue locked data in the left hemisphere channel PO3 is greater in amplitude ipsilateral to the cue and contralateral to the ignored hemifield in the window of

500:900 ms post-cue. The exact reverse can be seen in the right channel PO4.

Furthermore, this same divergence can be seen between the channels in Figure 3.6d.

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Table 3.6a shows the ANOVA done using PO3 and PO4 of the TSE mean data for both conditions within the window of 500:900 ms post-cue. (Note: one participant did not have channel PO4, so this participant was removed from the analysis. Thus, the analysis was done using 11 participants.) Hemisphere had a significant main effect (F1,

10= 5.129; p =0.047), which survived as significant for all further testing beyond

Assuming Sphericity. A two way interaction of Cue x Hemisphere was significant (F1,

10 = 7.653; p = 0.02), which survived all further testing beyond Assuming Sphericity.

Post-hoc paired t-tests failed to show significance between cue types within hemisphere.

However, right cue in the left hemisphere was significantly less than right cue in the right hemisphere (t10 = -2.811, p =.018). Please refer to Table 3.6b.

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Table 3.6c shows the ANOVA conducted using PO355 and PO455 of the TSE mean data for both conditions within the window of 500:900 ms post-cue. (Note: two participants did not have channel PO455, so they were removed from the analysis.

Thus, the analysis was done using 10 participants.) A two way interaction of Cue x

Hemisphere was significant (F1, 9 = 7.373; p = 0.024) which survived all further testing beyond Assuming Sphericity. Post-hoc paired t-tests failed to show significance between cue types within hemisphere. However, right cue in the left hemisphere was significantly less than right cue in the right hemisphere (t9 = -2.811, p =.027). Please refer to Table 3.6d.

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Testing combining TSE mean data for PO3 and PO355 as left hemisphere and

PO4 and PO455, two channel ROIs, as right hemisphere for the 500:900 ms window for both conditions revealed the following. (Note: As one participant was missing PO4 and

PO445, this analysis was done using 11 participants.) An ANOVA showed was a significant main effect of hemisphere (F1,10 = 5.314; p = .044). Testing was conducted by combining TSE mean data from both conditions using PO3 and PO355 (left hemisphere) and PO4 and PO455 (right hemisphere) within the window of 500:900 ms.

(Note: As one participant was missing PO4 and PO445, this analysis was done using 11 participants.) Analysis with an ANOVA revealed that there was a significant main effect of hemisphere (F1,10 = 5.314; p = .044). Please refer to the ANOVA Table 3.6e.

This survived all other testing beyond Assuming Sphericity with all the same values.

Cue x Hemisphere was a significant interaction (F1,10 = 7.489; p = .021). Again, this survived all other testing beyond Assuming Sphericity with all the same values. Only one post-hoc paired t-test was significant, that of right cue in the right cue in the left hemisphere being significantly less than that of the right cue in the right hemisphere (t10

= -2.753; p = .020). Please refer to the paired t-test table, Table 3.6f.

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Significance testing of the mean TSE data on the left and right ROIs (six channels each) using both conditions was done for the 500:900 ms post-cue window.

There was a significant main effect of hemisphere (F1,11 = 6.468; p = .027). Please refer to the ANOVA table, Table 3.6g. This survived all other testing beyond Assuming

Sphericity with all the same values. Cue x Hemisphere was a significant interaction

(F1,11 = 7.234; p = .021). Again, this survived all other testing beyond Assuming

Sphericity with all the same values. Only one post-hoc paired t-test was significant; that of right cue in the right cue in the left hemisphere being significantly less than that of the right cue in the right hemisphere (t11 = -2.889; p = .015). Please refer to the paired t-test table, Table 3.6h.

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The following analysis was done using TSE mean data for both conditions:

ANOVAs of 50 ms and100 ms windows of the period between 500:900 ms post-cue and the two ROIs. The following abbreviations will be used therein: main effect (ME), hemisphere (H), cue by hemisphere interaction (CxH), and paired t-test comparing right cue in the left hemisphere with right cue in the right hemisphere (RCLH-RCRH).

Furthermore, all significant values reported from the ANOVA survived all tests beyond

Assuming Sphericity with all the same values; therefore, they will be given only once in the text. However, all will be shown in the tables.

In the 50 ms windowed TSE mean data, the first window of 500:550, there was a significant CxH (F1,11 = 5.741; p = .035). Post hoc paired t-tests only revealed one significant pair of RCLH-RCRH (t11= -2.509; p = .029). The following window,

550:600 ms, has a significant CxH (F1,11 = 9.113; p = .012). Post hoc paired t-tests only revealed one significant pair of RCLH-RCRH (t11= -2.689; p = .021). In the 600:650 ms window, there was significant ME of H (F1,11 = 5.267; p = .042). Also, there was a significant CxH (F1,11 = 7.766; p = .018). Post hoc paired t-tests only revealed one significant pair of RCLH-RCRH (t11= -2.703; p = .021). In the 650:700 ms window, there was a significant ME of H (F1,11 = 6.557; p = .026). Also, there was a significant

CxH (F1,11 = 6.838; p = .024). Post hoc paired t-tests only revealed one significant pair of RCLH-RCRH (t11= -2.809; p = .017). In the 700:750 ms window, there was a significant ME of H (F1,11 = 7.090; p = .022). Also, there was a significant CxH (F1,11 =

4.924; p = .048). Post hoc paired t-tests only revealed one significant pair of RCLH-

RCRH (t11= -2.708; p = .020). The 750: 800 ms window had a significant ME of H

(F1,11 = 7.400; p = .020). Also, there was a significant CxH (F1,11 = 6.600; p = .026).

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Post hoc paired t-tests only revealed one significant pair of RCLH-RCRH (t11= -2.966; p = .013). The 750:800 ms window showed nothing of significance in the ANOVA.

Thus, no post hoc testing was done. In the final window, 850:900 ms post-cue, there was a significant ME of H (F1,11 = 6.639; p = .026). Also, there was a significant CxH

(F1,11 = 7.991; p = .016). Post hoc paired t-tests only revealed one significant pair of

RCLH-RCRH (t11= -3.058; p = .011). For ANOVA tables for the 50 ms data, please refer to Tables 3.6i to 3.6o. For paired t-tests, please refer to Table 3.6p.

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In the 100 ms TSE mean windowed data, the first window of 500:600 post-cue, there was a significant CxH (F1,11 = 7.474; p = .019). Post hoc paired t-tests only revealed one significant pair of RCLH-RCRH (t11= -2.626; p = .024). In the following window, 600:700 ms, there was a significant ME of H (F1,11 = 5.885; p = .034).

Additionally, there was a significant CxH (F1,11 = 7.423; p = .020). Post hoc paired t- tests only revealed one significant pair of RCLH-RCRH (t11= -2.771; p = .018). In the next window, 700: 800 ms, there was a significant ME of H (F1,11 = 7.290; p = .021).

Additionally, there was a significant CxH (F1,11 = 5.782; p = .035). Post hoc paired t- tests only revealed one significant pair of RCLH-RCRH (t11= -2.841; p = .016). In the final window, 800:900 ms, there was a significant ME of H (F1,11 = 6.724; p = .025).

Additionally, there was a significant CxH (F1,11 = 7.557; p = .019). Post hoc paired t- tests only revealed one significant pair of RCLH-RCRH (t11= -2.998; p = .012). For

ANOVA tables for the 100 ms data, please refer to Tables 3.6q to 3.6t. For paired t- tests, please refer to Table 3.6u.

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The final analysis of the alpha data using the TSE was conducted using the group data with a window of 500:900 ms for left hemisphere (PO3, PO355, P1, P3, P5, and P7) and right hemisphere (PO4, PO455, P2, P4, P6, and P8) ROIs. An ANOVA showed that there was a significant main effect of hemisphere (F1,5= 73.934, p < 0.001).

This survived all other significance testing in the ANOVA with the same values.

Furthermore, there was a significant Cue x Hemisphere interaction (F1,5 = 85.289, p <

0.001). This survived all other significance testing in the ANOVA with all the same values. Please refer to Table 3.6v. Post-hoc paired t-tests showed that there was a significant difference between left and right cues within left and right hemispheres

(right hemisphere differences LCRH-RCRH: t 5 = -7.513, p = 0.001 and left hemisphere differences LCLH-RCLH: t5 = 10.488, p <0.001). Please refer to Table

3.6w. Figure 3.6e shows a significance histogram of this.

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4.0 Discussion

4.1 Behavioral Results

In the behavioral data, the group means reaction times between left (683.35 ms) and right (685.04 ms) target responses was less than 2 ms, in fact exactly 1.69 ms in difference. Thus, there does not appear to be an obvious difference between the two.

The group hit rate (HR), or correct response to targets, was 74.6%. This appears to show the difficulty of the discrimination task, which most people could not do, in our extensive screening. Additionally, the overall group performance on the task was

91.44%. This includes correctly responding to targets and refraining from responding to anything else. This appears to show that participants were doing the task well. An

ANOVA divulged a significant three way interaction of Cue validity x matching symbols x hit rate (F1,11= 150.61, p<0.001). This was clear evidence that participants were doing the task well and shifting their attention, as valid cues and matching targets were associated with hit rate. In fact, that is an accurate definition of the task itself.

False alarm A (FAA), a response when nothing appeared in the cued location, was no different from chance (t11=2.996; p = 0.12). False alarm B (FAB), or responding to validly cued non-matches was significantly different from chance (t11=-3.564; p =

0.004). As would be expected, since FAA did not differ from chance and FAB did, there should be no difference between the two. There was a significant difference

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(t11= -3.52; p= 0.004). This was clear with the behavioral means as well, in that FAB was 10 times more likely to occur than FAA. This is evidence that participants were shifting their attention to the cued location, yet making discrimination mistakes at that location. Otherwise, this would not be the case. HR, or correct response to validly cued matching targets, was shown as significantly different from chance (t11=15.010; p <

0.001) and significantly different from matches that were not cued (t11=; p<0.001).

Again, this suggests that even though matches occurred at the side not cued, participants did not merely respond to matches. In fact, for the most part they ignored them in the uncued hemifield, as their attention was focused on the opposite hemifield at the time.

4.2 ERPS

Initial ANOVAs, using the mean and RMS of EEG data from all 84 channels and 11 or 22 time windows of both left and right cue-locked data, revealed no significant interactions surviving beyond Assuming Sphericity. Ideally, using such a model one would want a significant two way interaction of Cue x Channel or even a significant three-way interaction of Cue x Window x Channel. Unfortunately, by using that many levels within factors, i.e., all channels and multiple time windows, there was too much variability to find significance. Thus, this required focusing on specific time windows and regions of interest (ROI) that contained components that might be divergent between left and right cue locked data. As the ultimate goal was to find any differences between left and right cue-locked data, scouring the data for spatial differences (ROIs) within specific time windows was a way to decrease the variability.

This allowed us to focus directly on the differences themselves without losing statistical power due to variability.

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4.3 Early Components

Grand average data for left and right cues was overlain in a montage. This revealed clear differences visually in the N1 (Component 1), more negative contralateral to the cue. This was clear in the difference waves as well (left minus right) in which N1 to left cues was negative in the right posterior and polarity reversed as positive in the left posterior. An ANOVA of the mean EEG data, based on the temporal window and spatial regions of interest garnered from ICA, revealed a significant Cue x

Hemisphere (F1, 11 = 9.956, p = 0.009) for this N1. This survived all other testing beyond Sphericity Assumed. Post-hoc testing revealed significant differences between the N1, greater negativity, in the right hemisphere for left cue locked data vs. right cue locked data (t11= -3.006, p= .012), and the converse in the opposing hemisphere (t11 = -

2.311, p = .041). This difference suggests that participants were focused on the cue and deriving meaning from it in preparation to shift attention based on that meaning. The

N1 in this study is similar to that of van Velzen and Eimer (2003) and Brignani et al.

(2009) in timing (between 100:200 ms post-cue) and regions (greater negativity contralateral to cue in occipito-parietal). At this point in the CTI, cue processing and preparing to shift are underway.

The full P1-N1-P2 complex was not resolved in the ICA components that were associated with the N1. Thus, initial testing done on this module was in error using the components associated with the N1. Close inspection of the P1 and P2 in the data showed that the timing of these components did not match the positive peaks in the ICA components associated with the N1. Subsequent testing of the P1 alone via an

ANOVA revealed a significant two way interaction of Cue x Hemisphere (F1,11= 5.414;

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p = 0.040). Unfortunately, this did not survive post-hoc testing to find a difference between left and right cue locked data in left and right hemispheres. Similarly, testing of the P2 alone revealed a significant two-way interaction of Cue x Hemisphere (F1,11=

5.917; p = 0.033). This, as well, did not survive post-hoc testing. This suggests that the interactions shown in the ANOVA are not due to a difference between left and right cue locked data in left hemisphere and right hemisphere, but to a difference within cue of between hemispheres. For example, there may be a significant difference between left cues in the right hemisphere vs. the left hemisphere.

Statistical analysis of Component 2 produced nothing of significance. One interpretation of this is that within that time window with a peak at 250 ms post-cue for those regions of interest, there is no difference between left and right cue. Thus, the process occurring might be shared and one in the same, independent of lateralization associated with the cue types in other components.

In the difference waves, there was an EDAN-like potential seen between

200:400 ms. This is EDAN-like as there was no initial clear reversal of polarity between hemispheres. In fact, both means of the window were negative values. There was significant difference between the hemispheres in a one-way ANOVA (F1,7 = 85.073, p

< 0.001), which was confirmed with a post-hoc paired t-test (t7 = 9.223, p < 0.001).

Both of these negative means survived Confidence Interval testing, showing a statistically significant difference from zero. Despite the lack of reversal of polarity between hemispheres within an averaged time window of 200:400 ms, there was indeed a reversal within this very window. This is clearly visible in the window of 300:400 ms, as shown in Figure 3.3e. Thus, if a smaller window was used, significance paired

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with a reversal of polarity might have been found. However, it was decided that a better approach was to allow the topographical brain maps to divulge the ROIs and timing.

This revealed an asymmetric lateralization of this reversal of polarity and a time window 40 ms window. This is the same time length of time window as employed by the discoverers of the EDAN (Harter et al., 1989). A one-way ANOVA divulged a significant difference between left and right hemispheres (F1,5 = 115.153; p < 0.001).

Furthermore, a post-hoc paired t-test confirmed a significant difference between hemispheres (t5 = 10.543; p < 0.001). Thus, this fits the typical EDAN with reversed polarity between hemispheres. However, this positive mean value in the left hemisphere did not survive as statistically significant from zero using a Confidence

Interval test; whereas the right hemisphere mean negative value did. This suggests that there is no significant reversal of polarity since both are not statistically significant from zero. However, the EDAN-like component with a reversed polarity in the second half of the time window and this EDAN demonstrate significant difference between hemisphere in the same posterior region and within the timing (i.e., 200:400 ms post- cue) as mentioned in previous studies, and in the same direction based on the difference in difference waves (Harter et al., 1989; Harter and Anllo-Vento, 1991; Yamaguchi et al., 1994 and 1995; Eimer, 2000, Hopf and Mangun, 2000; van Velzen and Eimer,

2003; Talsma et al., 2005; and Dale et al., 2008).

In summary, the EDAN and EDAN-like features seen in the difference waves appear to show that, in both hemispheres, the ERP for the right cue is significantly greater, more positive, than the ERP for the left cue. Therefore, the contralateral/ipsilateral distinction between conditions and hemisphere that was present

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with the N1 no longer is present at this time point. At this point, the exact reason for this difference with the right cue-locked data being more positive in both hemispheres is not clear. The latest research suggests that the EDAN is related to cue processing in preparation to shift attention and not the attentional shift itself (van Velzen and Eimer,

2003). However, as this distinction between hemispheres is not seen in our data, this suggests it might be a later cognitive process taking place after cue processing, but still before the attentional shift. This could include preparing to shift and disengaging from the current foveal location. Alternatively, with a larger group of participants, perhaps the EDAN would reverse fully in polarity in our study, suggesting it is part of the cue processing as consistent with vanVelzen and Eimer. This could only be shown with further research continuing our study.

Most noteworthy, the progression of the N1 from the occipito-parietal regions into the EDAN which we see more anterior in the centro-parietal regions is unique.

Some studies see this EDAN in the occipito-parietal regions (Harter et al., 1989).

Yamaguchi et al. (1994) saw the EDAN emerge first in the posterior and continue soon thereafter in a more anterior region. A follow-up study with Yamaguchi et al. (1995) reported the emergence of an EDAN in the posterior temporal regions at 240 ms post- cue, which progressed to the posterior parietal regions at about 260 ms. Talsma et al.

(2005) saw an EDAN in the window of 240:400 ms post-cue in the TPO junction. One important question might be if these EDANs seen in neighboring areas from various studies are one in the same. It could be that different electrode montages with different coverage and lack of source localization across various studies could make this EDAN appear to be shifting more anterior or temporal in location between studies. In our

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study, the anterior shift we see from the N1 and subsequently into the EDAN may suggest a higher cognitive process with the progression. The N1 in our study may reflect cue processing from early sensory areas in the occipito-parietal regions (van

Velzen and Eimer, 2003; Brignani et al, 2009), whereas the EDAN in the centro-parietal region may be related to planning and interpretation of where to shift attention. Indeed, the anterior parietal regions are associated with enhanced perceptual processing

(Bradley, 2009), suggesting a top-down influence on early sensory regions.

Furthermore, the right centro-parietal cortex is associated with detecting local features of task cues and their meaning (Volberg, Kliegl, Hanslmayr, & Greenlee, 2009).

The alpha TSE data show two early components of a qualitative nature as shown in Figures 3.6c and 3.6d. As these components are overlapping very clearly between conditions, no quantitative analysis was performed to find a possible divergence. These neural components appear to be of importance as this shows a great similarity within both hemispheres between both cue conditions. Thus, the neural processes represented here appear the same for both conditions.

The first of these components commences in the precue baseline and extends until approximately 120 ms post-cue; this is high alpha amplitude bilaterally. Again, both left and right hemispheres show this to be heavily overlapping between cue types, with no clear divergence. Worden et al. (2000) suggested this was due to equal alpha suppression of both peripheral hemifields with the selective focus on the fovea. It is possible this is what is represented here in the data. However, as the baseline time window in our data is limited, we need to be cautious about making such claims. Most noteworthy, this does reflect the same baseline convergence, between cue conditions

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within hemisphere, which Worden and colleagues reported. Thus, an implication of equal bilateral high alpha could represent a suppression of all extrafoveal regions for both conditions. As the majority of the visual cortex represents the extrafovea, this does not contradict such an explanation. Furthermore, since participants scored well behaviorally, it suggests they did indeed do the task as instructed. As we know participants were most likely focused foveally, with the exclusion of the extrafovea at this point in the task, this is logical. The second of these two neural components transpires within the approximate window of 200: 500 ms post-cue. At this point, there is a clear drop in alpha activity as reflected in the amplitude. Again, data from both cue conditions heavily overlap with no clear divergence within hemisphere. Worden et al.

(2000) recognized this as an event-related desynchronization (ERD), similar to that shown in the review by Pfurtscheller and Lopes da Silva (1999). [Note: The definitions of event-related desynchronization and event-related synchronization (ERS) as used in this study are consistent with Pfurtscheller and Lopes da Silva (1999) and Worden et al.

(2000). Synchronization refers to the phenomenon of phase locking between sources oscillating with the same frequency. If a number of such oscillating sources become synchronized in a region of cortex at a given frequency, the consequent summation of their potentials leads to an increase in power (magnitude of activity) in the resulting

EEG at that frequency. Thus, with the TSE, there would be an increase in amplitude with synchronization and a decrease in amplitude with desynchronization. In simplest terms, high alpha activity/amplitude is termed ERS, whereas a decrease or low alpha activity/amplitude is referred to as ERD.] In the review, Pfurtscheller and Lopes da

Silva explain that the ERD is often related to perception and judgment, or active

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processing of information. Thus, the ERD in this study may be associated with extracting information from the cue as a perceptual process and judgment of how to proceed based on this. Additionally, we suggest here that this drop alpha from the baseline may be associated with the disengagement of attention from the fovea in addition to cue processing. If high bilateral alpha represents suppression of the extrafovea present during selective foveal attention, a drop in this alpha bilaterally may suggest that attention is no longer engaged in the same place or that a disengagement from that location is occurring at that point in time. Interpretation of this alpha data does seem to provide physiological evidence consistent with Kinsobourne’s reciprocal interaction model of spatial attention (Kinsbourne, 1977 and 1987; Thut et al., 2006).

In such a model, parietal alpha activity would counterbalance allowing attention to one hemispace while simultaneously inhibiting attention to another hemispace. These two parietal spatial attentional processors could balance with reciprocal interaction so that one hemispace could be attended while the other was ignored. Connections between the two, to allow for this interaction, could be at the level of the corpus callosum. The ERD shows a drop in alpha in both hemispheres. With this drop, the suppression is no longer present at the extrafoveal regions. Following Kinsbourne’s model, we know it is a possibility that suppression (of the unattended) always is paired with attention (to the attended stimulus). Thus, if the suppression has disappeared, attention is no longer engaged in the same location. Therefore, this ERD appears to represent the disengagement of attention from the fovea. Additionally, other processes have been associated in the literature with an ERD. The ERD may reflect a higher perceptual- cognitive process in which one derives meaning from the cue after the N1-cue

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processing. This drop in alpha (ERD) may represent a process of preparing to shift attention, based on already extracted meaning from the cue and disengagement of attention from the fovea. If this is the case in our study with the ERD, this might explain why our EDAN does not reverse polarity in the difference waves between hemispheres as well. The neural components occurring directly after the N1-associated cue processing, at approximately 200 ms, include both the EDAN and ERD. These two components appear to be happening simultaneously, but are represented in different analyses of the same data. As there is a loss of a contralateral/ipsilateral distinction between cue types within these neural components, this may indeed be occurring after cue processing. Therefore, these neural processes (EDAN, ERD) may reflect preparation to shift and disengagement from the foveal focus. In fact, Posner and

Petersen (1990) attributed the posterior parietal lobe as associated with disengaging attention within their model of covert visual shifting of attention. Therefore, the ERD within this region may be a neural representation of this.

In summation, the bilateral baseline high alpha suggests suppression of extrafoveal regions coinciding at the point in the task when there is foveal attention.

The ERD suggests that this extrafoveal suppression, perhaps in conjunction with foveal attention, has dissolved. This may include cue processing, or it may suggest a process just after the N1-associated early cue processing. We suggest that the ERD represents preparing to shift based on already extracted cue meaning and the disengagement of attention from the fovea in preparation for the shifting of attention to a new location.

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4.4 Late Components

The TSE of the data shows an increase in alpha with a clear quantitative and sustained divergence in alpha amplitude between left and right cues in the posterior regions between 500:900 ms post-cue. This was confirmed by a significant two-way interaction in an ANOVA of Cue x Hemisphere (F1,5= 85.289, p < 0.001). Furthermore, the difference between the amplitudes within hemisphere was clearly shown in post hoc paired t-tests such that the left cue-locked data amplitudes were significantly lower in the right hemisphere than right cue-locked data (t5 = -7.513, p = 0.001), whereas the right cue-locked data was significantly lower in amplitude in the left hemisphere than left cue-locked data (t5 = 10.488, p < 0.001). This increase in alpha activity/amplitude is termed an event-related synchronization. Worden et al. (2000) suggested that the higher alpha amplitude in the hemisphere contralateral to the ignored hemifield, in contrast to lower amplitude in contralateral to the attended hemispace, suggested suppression of the ignored hemispace. On the other side of the coin, Sauseng et al.

(2005) found the same results and made the claim that lower alpha amplitude contralateral to the attended hemispace, in contrast to the ignored hemispace, allowed attention to take place at the attended location. They called this alpha suppression.

Furthermore, the increase in alpha, contralateral to the ignored hemispace, they labeled alpha enhancement. Interestingly, Sauseng et al. (2005) claimed this alpha suppression was a neural sign that attention had shifted to the cued location. Together, these results suggest a balance of suppression of alpha to the attended hemispace paired with an enhancement of alpha to the ignored hemispace. Thut et al. (2006) found similar results and suggested a model of reciprocal interaction of two attentional processors akin to

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Kinsbourne’s model of spatial attention (Kinsbourne, 1977 and 1987). Again, we have two spatial attentional processors, one for each hemisphere, or perhaps even more, working in a balanced relationship to allow for attention to focus in one spatial location with suppressing potential distractors or irrelevant information at unattended loci. This could indeed be part of the fronto-parietal network that Kanwisher and Wojciulik

(2000) related to Rizzolatti et al.’s (1987) premotor theory of attention. However, as we did not see any differences in the frontal regions, we cannot say this for sure.

However, our results suggest that increased alpha activity, an event related synchronization (ERS) as shown by amplitude, could modulate the aforementioned cognitive processes. Thus, higher alpha contralateral to the to-be-ignored side

(unattended side) in concert with comparatively lower alpha activity contralateral to the attended hemispace could be a result of this. A similar model of alpha modulation by covert visual attention has been put to practical use by van Gerven and Jensen (2009) for use brain-computer interfaces.

In summation, our ERS data appears to show the shift in attention has occurred with a suppression of the ignored hemispace simultaneously. Finally, as this divergence in alpha within the ERS continues until target onset, this provides evidence of the maintenance of attention until the target.

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5.0 Conclusions

5.1 Main Conclusions

In conclusion, this study shows the CTI can be broken into various neural components that may be associated with underlying cognitive processes. Posner and

Petersen (1990) popularized a model of three cognitive stages to shifting attention in space. This included (1) disengaging attention, (2) shifting attention in space, and (3) reengaging attention at the new location. In our research, we expanded this to include more cognitive processes in chronological order. We do this with caution, however, as these processes may not be contiguous, but overlapping. Within our model, we include

Posner and Petersen’s (1) and (2) into our early period (precue until 500 ms post-cue) and (3) into our late period (500 ms post-cue until 900 ms post-cue/target). Our model of cognitive processes was elaborated on in the Motivation section and shown again here in Figure 5.1. Our goal in this study was to investigate the timing of these putative cognitive components through the study of event-related neural components in the CTI

(shown in Figure 5.1).

The first neural component we uncovered was the N1. This was found using

ICA between 100:200 ms post-cue. This was lateralized. Next, the EDAN was shown in difference waves from approximately 200:400 ms post-cue. The EDAN does not appear to be lateralized in our data. Additionally, alpha differences were clear with the

TSE: early high baseline alpha from precue to approximately 120 ms post-cue (not

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lateralized), the ERD from approximately 120 ms post-cue until 500 ms post-cue (not lateralized), and finally a divergence of alpha/ERS, 500: 900 ms post-cue (lateralized).

Our goal was to investigate the timing of perceptual and cognitive processes within the CTI by studying event-related neural components occurring in this period.

Please refer to Figure 5.1 for a schematic of these electrophysiological components and associated cognitive processes. The following is the outline of cognitive processes that were first mentioned in the Motivation section. We attempted to pair these cognitive components with their presumed electrophysiological components based on the data analysis and published literature. (1) Suppression of the extrafovea field at a time window in the task where there is foveal attention appears to be shown with the initial high alpha from precue until 120 ms post-cue. As there are equal amplitudes to both conditions in both hemispheres, with the majority of the visual cortex representing the extrafovea, this does seem to suggest the suppression of all extrafoveal regions. This conclusion was suggested by Worden et al. (2000). (2) Interpretation of meaning from the cue and (3) preparation for action or attentional shifting based on meaning from this cue appears to happen with the N1 (somewhere between 100:200 ms) and the EDAN approximately 200:400 ms. This is consistent with the interpretation by van Velzen and

Eimer (2003) and Brignani et al. (2009). Furthermore, the clear ERD, which is seen from approximately 120: 500 ms, is consistent with perception and judgment, or active processing of information of the cue as reported by Pfurtscheller and Lopes da Silva

(1999). (4) Disengagement of focus at the fovea appears to be associated with a drop in alpha with the ERD 120: 500 ms (Worden et al., 2000; Kinsbourne, 1977 and 1987).

(5) Shifting attention covertly to the cued location is not that obvious. We do not have a

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component that we can associate directly with the shifting of attention itself. Thus, we speculate where the attentional shift is taking place based on the other components we do have. Using deductive reasoning, we infer that attention has most likely shifted by the time we reach (6) with the alpha divergence between cue types within hemisphere as shown in the ERS. The EDAN, which lasts until approximately 400 ms, appears not to include the attentional shift. This was shown by van Velzen and Eimer (2003). Thus, hypothetically, the attentional shift might occur after the EDAN ends, around 400 ms, yet before the alpha divergence, at 500 ms. So we suggest that the attentional shift in this task might occur sometime between 400:500 ms post-cue. (6) Reengaging selective attention at the cued location (with exclusion of other loci) may occur at around 500 ms post-cue. This is when the lower alpha contralateral to cued hemifield allows attention to the cued location, as attention has shifted (Sauseng et al., 2005); whereas higher alpha contralateral to ignored hemispace might suppress that location by an inhibitory effect of alpha (Worden et al., 2000). Furthermore, as both of these alpha amplitudes remain divergent until the end of the CTI, this suggests that this is involved with (7) maintaining attention at this location in preparation for the anticipated target.

The chronological and anatomical shifts from the N1 (approximately 100:200 ms post-cue) in the occipito-parietal regions to the EDAN (approximately 200:400 ms post-cue) in the centro-parietal regions, suggests a higher cognitive process is taking place with the EDAN. Perhaps this means the EDAN is exerting a top-down influence on the early sensory regions. This is consistent with current literature (Bradley, 2009;

Volberg, Kliegl, Hanslmayr, & Greenlee, 2009). Indeed, the lateralization shown with the N1, which is no longer present in the EDAN, suggests a different process is taking

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place. At the same time as the EDAN, the ERD in the TSE/alpha data occurs. The ERD lacks lateralization as well. It is possible that the EDAN in the difference waves and the

ERD in the TSE/alpha data reflect the same process, as they occur concurrently. They both may represent the disengagement of attention from the fovea. This is what we suggest; however, future research is needed to confirm this possibility. Posner and

Petersen (1990) did speculate that the posterior parietal lobe was involved with disengaging attention. In fact, this may be a location from which the ERD arises.

Future research is needed to elucidate the neural network(s) involved with shifting visual spatial attention covertly within a complex task involving multiple perceptual and cognitive processes such as this.

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5.2 Conclusions in Relation to the Hypotheses

Hypothesis one and two were in regards to behavioral performance in the Posner task showing attention indeed did shift. More specifically, hypothesis one was concerning responses to matches on the attended side should be significantly greater than chance, whereas on the unattended side matches should not be greater than chance.

This was indeed the case. Hypothesis two was that there would be significant difference between hit rate and false alarm. This was the case as well. Thus, based on behavioral performance alone, it appears that participants were indeed shifting their attention based on the cue.

In regards to the cue contrast EEG data, there were three more hypotheses based on components. The first one was that there would be a significantly more negative N1 contralateral to the directed hemifield. This was indeed the case. The second hypothesis was the presence of an EDAN being significantly more negative in the right posterior (left minus right) difference waves with a reversal of polarity, a positive potential, in the left hemisphere. We found the negativity in the right hemisphere.

Additionally, we found evidence of a positive going potential in the left hemisphere.

However, this was not as clear cut and pronounced as we had expected. The third hypothesis was that the alpha suppression, as measured via amplitude, would be significantly greater contralateral to the cued direction, and enhanced contralateral to the ignored hemifield. Finally, this was found as well.

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