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\chapter{Introduction}\label{CH1_INTRO}

\centering{\textit{“I define nothing. Not beauty, not patriotism. I take each thing as it is, without prior rules about what it should be.” \\- Bob Dylan}}

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\section{What Are We Looking For?}
In a world with an ever-growing enormity of useful and engaging information, and likewise increasing distractions, we are constantly guiding our attention to explore our environment. By directing our attention to specific people, places, and things, we can more carefully gather information and engage meaningfully with the world around us. While the usefulness of this attentional selection as a core cognitive function is not in doubt, an understanding of how our brains realize this mechanism is by no means complete. This dissertation serves to build on our understanding of attentional selection in the brain by investigating how certain neural circuits engage in attentional selection of useful visual information. Along the way, we can determine how these circuits produce signals across a range of spatial scales, from populations of neurons to electric fields outside the brain, yielding important discoveries towards linking attention research across species and techniques.

\section{Visual Search and Attentional Selection}
To reiterate, our environment is filled with information, some is useful to our goals, and some of it is not. To focus on useful information we deploy selective attention. From the psychological perspective, selective attention is the process through which we scrutinize items in our environment \citep{pashler_psychology_1998}. In deploying selective attention, we effectively prioritize the processing of specific stimuli, perhaps at the expense of processing others. A number of well-controlled behavioral tasks have been developed throughout the history of psychological research to investigate selective attention, one of which is known as visual search. Visual search is a simple task where the subject is required to identify the presence or exact location of a target object in a visual display. In the broadest sense, visual search is the process by which we explore our environment to gather information regarding the present stimuli. The "Where's Waldo?" series of children's books and "I Spy With My Little Eye" game exemplify the visual search task and its behavioral requirements. Visual search is an especially effective attention task in investigating how items are \textit{selected} for directed attention, a process known as attentional selection. Experimental work on selective attention began in the mid-twentieth century \citep{cherry_experiments_1953, broadbent_perception_1958} as did research on visual search \citep{ellis_effects_1947, carpenter_rate_1948, mackworth_breakdown_1948}. Since these earliest studies, decades of research has produced thousands of works on visual search, attentional selection, and their intersection. As such, these research programs taken a variety of directions, pursuing differing aspects of visual search and attentional selection. For instance, you can imagine versions of visual search that are more or less difficult. Searching for Waldo when there are dozens of other individuals wearing red-and-white striped clothing is substantially more difficult than when every other person is wearing solid green and Waldo is the lone red-striped individual. For the purposes of this dissertation, we will focus on the latter, a form of visual search known as 'pop-out'. With pop-out we are afforded a window into the attentional selection process in a simple task which has a substantial history to contextualize findings.

\section{Pop-out, Clearly}
Pop-out visual search, perhaps the simplest form of visual search, is characterized by identification of a conspicuous target. That is, pop-out refers to search conditions where an object is noticeably different in one feature dimension relative to the objects surrounding it, as for example is a red berry among green leaves. With the simplicity comes a notable behavioral observation. In the early 1950’s, the first study was performed investigating the speed at which items in visual search displays are selected through careful manipulation of the features making up the objects participants searched through \citep{eriksen_location_1952}. It was found that targets distinguished by single features (i.e., pop-outs) were identified faster than conjunctions of features. Conjunctions of features refers to items that are comprised of more than one notable feature such as a red square or green circle. From here, indexing attentional selection in time through behavioral response times (RTs) in visual search has become a staple of psychological research and, as will be discussed in detail later on, have become immensely powerful in relating selection times measured in the brain to the psychological measures of attentional selection.  

Another seminal moment in pop-out search investigation was when direct comparisons were made between pop-out search response times and conjunction search response times as a function of the number of items in the search array \citep{treisman_feature-integration_1980}. It was noted that while increasing the number of items in conjunction search linearly increased response time, response times in pop-out search were relatively invariant. Ultimately this supported the idea that a single feature dimension could be processed in parallel, no matter the number of items in the display. Therefore, in the case of single-feature pop-out, it might therefore be that the process of attentional selection is unfolding without the need for multiple shifts of directed spatial selective attention that would be necessary for scrutinizing items made up of several useful features. One key takeaway is that this suggests that the timing of attentional selection in pop-out search can perhaps be indexed more reliably due to this relative invariance. Another takeaway was given the seemingly 'parallel' nature of single-feature pop-out processing and the rapid RTs that were associated with these searches, pop-out could be resolved at a relatively low-level. In fact, modeling work puts forward that these simple feature contrasts can be resolved in a purely feedforward network \citep{itti_saliency-based_2000}. In other words, pop-out visual search could be resolved even in the earliest stages of sensory processing. To summarize, pop-out search is unique in that the task itself is rather simple visual search, but the processing required to resolve it has the potential to be either high- or low-level \citep{chun_taxonomy_2011}.

From these highlights of the otherwise extensive literature that is pop-out research, we gain key insights moving forward. With our ultimate goal of understanding circuits supporting attentional selection across measurement scales, pop-out (1) allows us to evoke attentional selection in (2) a relatively simple (and reliably trainable) task which (3) utilizes individual features and (4) has the potential of being accomplished through anything from early sensory processing to higher-level cognitive control. To contextualize, these points have allowed previous investigations, and the work to be presented here, into attentional selection in nonhuman primates with the ability to manipulate search conditions (e.g., individual features) in ways useful for pursuits into understanding the requisite underlying neural activity.

\section{A Primer on Neuronal Attentional Selection in Visual Search}
Following extensive behavioral studies, and once the technology had become sufficiently sophisticated, neuroscientific investigations into attention ensued. Early studies focused on attentional modulation of sensory responses with directed cuing \citep{moran_selective_1985}. In other words, how do responses of neurons representing items in space change as a function of whether or not the subject has been informed to attend to the part of space where that item exists. In short, these early studies discovered that neurons representing items in the part of space being attended to responded more vigorously than those outside the area of attention. With this seminal finding demonstrating neural correlates of attention, soon after the neuronal mechanisms of attentional selection in visual search, as well as other tasks, became a heavily pursued topic of investigation.

Neural correlates of visual search performance have been identified across the brain. Visual, temporal, parietal, frontal, thalamic, and midbrain areas have all been shown to modulate their activity as a function of behavioral requirements in searching visual displays (see \citep{schall_visuomotor_2015, saalmann_cognitive_2011, carrasco_visual_2011, moore_neural_2017, reynolds_normalization_2009, krauzlis_superior_2013, colby_space_1999} for reviews). As our primary objective in this dissertation is obtain a greater understanding of visual information processing and selection rather than, say, motor planning \citep{schall_visuomotor_2015} or performance monitoring \citep{schall_monitoring_2002}), we can narrow our focus to certain findings. 

Visual cortical areas have been a focus of intense investigation in the realm of resolving the attentional selection component of visual search. At a 30,000 foot view, this is because visual search arrays are comprised of multiple alternatives that can be represented in retinotopic visual space with the sensible spatial and featural representations that exist in visual cortex \citep{kaas_topographic_1997}. With respect to specific areas in visual cortex, one area is especially pertinent to the overall aims of the dissertation and have also been the primary focus of visual search research: area V4. 

V4 is classified as a mid-level area in the ventral visual pathway \citep{felleman_distributed_1991}. Originally termed the V4 complex and thought to contain multiple representations of the visual field \citep{zeki_representation_1969}, it was later determined that V4 proper is only a subset of the originally described area with a single visual field representation. V4 itself appears to be modular in organization featuring side-by-side topographical representations for color and form among other visual features \citep{yoshioka_intrinsic_1992, deyoe_concurrent_1988, deyoe_multiple_1994, felleman_modular_1997, xiao_segregation_1999, tootell_search_2004}. 

In the time since these anatomical descriptions, V4 has been investigated with respect to visual search. One pair of studies investigated the selection of features and behaviorally relevant stimuli in V4 during visual search. Monkeys identified a target stimulus composed of two features (shape and color) in conjunction. The target stimulus was defined by one of the features cued prior to trial onset. For example, if the objective of the trial was to identify the white stimulus among black stimuli, the monkey would identify the white circle or square among black circles and squares. This work demonstrated that the individual V4 neurons had feature selectivity and primarily reflected that selectivity rather than the behavioral relevance of that stimulus in their receptive field during task performance \citep{ogawa_target_2004}. However, at the population level, behavioral relevance of stimuli could be inferred. While V4 does primarily represent feature selectivity initially, closer in time to when the response is made to the target stimulus, the activity of those same V4 neurons more closely represents the behavioral relevance of the stimulus in their receptive field \citep{ogawa_neuronal_2006}. Together these findings suggest that V4 initially represents features at the individual neuron level and behavioral relevance at the population level. Ultimately, in taken with consideration to other works on visual search in V4 \citep{bichot_parallel_2005, zhou_feature-based_2011}, we have an exemplar area that seemingly maps the visual and behavioral information pertinent to the resolution of visual search.

\section{The Emergence of Laminar Physiology in Attention Research}
While an enormous literature on the neural mechanisms of attention has been produced in the last several decades, like the investigations described in the previous section, it has in some ways been limited by technology. With respect to neurophysiological recordings, one limiting factor has been the design and complexity of electrodes used to measure the neural activity. Relatively recent advances in recording electrode technologies have opened up a new avenue for attention research: laminar physiology of attention through linear multielectrode arrays (LMAs). LMAs are a line of equally spaced recording contacts that can each independently measure neural activity. This linear design is useful in measuring cortical columns, in particular. These cortical columns, a mesoscopic unit of cortical structure and where the bulk of attention research has been conducted, is a layered structure. That is, if you view the anatomy of a slice of cortex under a microscope, you will immediately notice distinct bands with varying types and densities of neurons. 

With some exceptions (e.g., agranualar cortex \citep{shepherd_intracortical_2009}), granular laminar structure is largely preserved across a wide range of functionally distinct brain areas. This has led to an influential model of cortical function: the canonical cortical microcircuit (CCM) \citep{douglas_canonical_1989, douglas_functional_1991}. The CCM puts forward the layered structure of cortex as a fundamental unit of cortical computation with a prescribed pattern of activation. Crucially, these patterns of activation arise from putative patterns of connectivity between cortical layers as well as external connections. In having this guiding framework, one can begin to speculate on the origins of and direction of flow throughout the brain for certain neural computations given observed patterns of neural activation. Among researchers of other neuroscience disciplines, this has provided attention researchers a hypothesis-generating mechanism for research at the mesoscopic circuit level with well-defined predictions for top-down vs. bottom-up contributions to attention given the expected laminar patterns of the CCM. 

With this in mind, does attention influence processing precisely within cortical columns and how is attentional selection computed across cortical layers? Some evidence already exists for cortical layers playing an important role in attention with differential effects of attention across layers in visual cortex \citep{nandy_laminar_2017, van_kerkoerle_layer-specificity_2017, ferro_directed_2021, cox_spiking_2019, mehta_intermodal_2000-1}. However, given the recent rise of laminar recordings, we do not know exactly the role of this structure in attentional selection during visual search. Laminar insights could be particularly interesting for pop-out search. As mentioned earlier, the mechanism giving rise to the attentional selection of pop-out stimuli could stem from a bottom-up or top-down mechanism given its relative simplicity with respect to types of visual search, and its cognitive demands \citep{chun_taxonomy_2011}. The putative connectivity patterns of the CCM afford a window into these top-down vs. bottom-up contributions for attentional selection of pop-out stimuli.

While the laminar scope of investigation for attention research promises crucial insight into how attentional selection comes about in neural circuits, it seemingly has a curious side-effect making it an exciting prospect for translational neuroscience: Cortical columns can generate EEG, one of the most commonly used measures in human neuroscience. 

\section{The Consequences of Laminar Patterns of Activation - EEG Generation}
Of course, neuroscientific research has not been limited to intracranial investigations. Beyond just attention research, human neuroscience has flourished through the use of noninvasive methods. One ubiquitous method is electroencephalography (EEG), a noninvasive electrophysiological measure which benefits from its high temporal resolution but suffers from its comparatively poor spatial resolution. That is, while invasive neurophysiological measures such as single-unit extracellular recordings are very finely spatially localized (to the level of individual neurons), noninvasive EEG is considered a 'global' signal. Because EEG is measured some distance from its intracranial generators, and the signal must propagate through several intermediaries (such as the skull), the signal is effectively filtered and integrated such that it reflects the activity of multiple brain areas. Even so, numerous indices of sensory, perceptual, cognitive, and motor processes have been identified in the global EEG signal. And while the spatial localization of EEG signals leaves something to be desired, understanding something about where specific signals are generated is by no means futile. while most EEG indices are observed across multiple recording positions across the scalp, the relative magnitude of these indices can vary sensibly. As a result, hypotheses have been formulated as to which brain areas generate which EEG indices and what patterns of neural activity are necessary to yield a signal that can be measured outside the skull. This has been an especially trendy topic as knowing where an EEG signal is generated exactly has the potential to better inform which neural processes these signals index and moreover bridge noninvasive and invasive programs of research.

One popular method for investigating the putative neural generators for EEG signal has become inverse source modeling \citep{grech_review_2008, michel_eeg_2004}. This procedure utilizes the spatial distribution of EEG voltage over the scalp to predict the intracranial source of the signal. However, the method has a major shortcoming. The inverse method, from a mathematical perspective, is indefinite. That is, there are multiple configurations of neural activity that can give rise to the same distribution of EEG which this method cannot distinguish between to determine the empirical generator. Unfortunately, this means that definite determination of the neural generators of specific EEG components has eluded the field. To obtain a definite answer and reveal these neural sources requires directly recording the generator, something that cannot be accomplished through noninvasive methods alone. Nonetheless, this has not prevented theorists and biophysicists to speculate as to what patterns of neural activity would give to EEG signals.

The cortical column seems to be a fundamental unit for the generation of EEG signals \citep{nunez_electric_2006}. The structure and orientation of the neurons within cortical columns is such that they can feasibly generate electric fields that would be measurable outside the brain. Crucially, cortical columns are heterogeneous structures with distinct layers and densities of cell bodies and dendritic arbors along said layers. In fact, studies in the rat have already demonstrated that the biophysical geometry and activation of cortical columns can produce EEG potentials \citep{riera_pitfalls_2012}. However, these studies have been primarily conducted through computational modeling with the empirical measurement of cortical columns generating EEG signals being relatively limited. Moreover, to date there have been no direct demonstrations of laminar activity within cortical columns of primates leading to the generation of EEG signals. However, there have been other ways researchers have thus far begun to better understand EEG signals and their biological substrates even if direct measurement has thus far eluded us.

\section{The Relationship Between Intra- and Extracranial Signals in Primate Electrophysiology}
Parallels have been drawn between patterns of neural activity and the EEG, often in comparing EEG and single-unit spiking \citep{snyder_stimulus-dependent_2015, sandhaeger_monkey_2019, sajad_cortical_2019} or EEG and LFP \citep{ng_eeg_2013, musall_effects_2014, sandhaeger_monkey_2019}. These studies have demonstrated that like-signals can be observed across measurement scales. While finding neurons that seemingly indicate the same psychological process as an EEG signal is a step towards functional localization of EEG components, there is a crucial gap between the two presenting a interpretive pitfall. As mentioned in the previous section, it is thought the the patterns of activity across cortical columns generates EEG. And while cortical columns are comprised of distinct layers of neurons and the activation of neurons is what generates EEG, it is not their action potentials that yield it. That is to say, while many parallels have been drawn between EEG activity and single-unit neuronal spiking, this spiking is one step removed from the putative generator activity. Instead of spiking, it is predominantly the synaptic activations of neurons across the cortical column that produces local current fluctuations and the ultimate production of electric fields comprising EEG \citep{nunez_electric_2006}. What this means is that while these previous studies have no doubt provided crucial insight into functional localization of processes indexed by EEG components, they are perhaps not directly measuring the exact activity giving rise to the EEG. Therefore, there is a gap in primate physiology beyond just relating patterns of laminar activity to EEG: We must also empirically measure the putative generating activity within those columns. And while this would ideally be performed for each and every EEG index of sensation, perception, cognition, and action, we can start by investigating the one pertinent to this dissertation, attentional selection and the EEG's 'N2pc' index of it.

\section{Understanding Attentional Selection with the N2pc}
While significant progress in attention research has been achieved through intracranial neurophysiology, it is by no means the only method being used. For instance, a substantial fraction of neuroscientific research into attention is done with human participants through use of noninvasive methods. One method proven as effective as it is popular is through EEG. In human research, EEG is commonly performed by placing electrodes on the scalp and recording electrical potentials from the brain propagating through the skull. Originally developed in animals \citep{caton_electrical_1875}, EEG was popularized in the early twentieth century as a way to reliably measure brain activity in human participants \citep{berger_uber_1929}. In combination with its more invasive sister-method, electocorticography (ECoG), the field of human electrophysiology research has blossomed in recent decades. Crucially, many insights have been gained in attention research through these methods.

While several EEG indices of attention have been identified, one in particular seems especially relevant to attentional selection. The N2pc is a event-related potential (ERP) of the EEG signal which indicates where spatial selective attention is deployed \citep{luck_electrophysiological_1994, eimer_n2pc_1996}. Physically, the N2pc manifests as a difference in polarization of the N2 component of the event-related potential of the EEG when visual selective attention is covertly directed contra- or ipsilateral relative to the position of the recording electrode. It is also important to note that the N2pc is primarily observed over the posterior half of the brain. For example, you would observe an N2pc over the back of your head as the difference between hemispheres if you were to maintain gaze at the center of this page and covertly read the word in the top-left corner. The N2pc was originally described in the early 1990's, and hundreds of studies have since been performed using the N2pc as a primary indicator of attention. The N2pc has even shown to be modulated in clinical cases of attentional disruptions such as in schizophrenia \citep{luck_speed_2006, verleger_neurophysiological_2013, fuggetta_electrophysiological_2015} and attention deficit/hyperactivity disorder \citep{wang_attentional_2016, wang_attentional_2017, shushakova_electrophysiological_2018, rodriguez-martinez_neurophysiological_2021}. And while these works have yielded significant findings into how attentional processing is instantiated, there is a substantial gap in knowledge associated with the N2pc: What neural activity gives rise to this signal?

In evaluating the parallels between neural activity and the N2pc specifically, a couple of crucial observations have been made. First, the N2pc seemingly mimics neural activity present in the frontal cortex. That is, the frontal eye fields (FEF) and the N2pc indicate the same patterns of attentional selection \citep{cohen_origin_2009, purcell_origin_2013}. FEF is a prefrontal cortical area found bilaterally along the bank of the arcuate sulcus. Defined as a portion of Brodmann area 8 \citep{brodmann_vergleichende_1909} and first described in the 19\textsuperscript{th} century by David Ferrier, it was characterized by its ability to evoke saccadic eye movements when the area was electrically stimulated \citep{ferrier_experiments_1875}. In the years since, FEF has served as a target for visual attention research \citep{schall_visuomotor_2015}. As attentional selection unfolds in visual search, spiking activity in FEF indicates first, followed in time by LFP in FEF, and lastly the N2pc. This temporal sequence seemingly indicates a putative role for frontal cortex in computationally generating the selection signal that ultimately leads to the N2pc. However, the biophysical geometry of FEF makes it an unlikely neural source for the N2pc. Second, a magnetoencephalographic (MEG) investigation into the N2pc found putative biophysical generators in posterior cortex. Specifically, dipole sources in posterior parietal and extrastriate visual cortex \citep{hopf_neural_2000}. Given these findings, we can begin to formulate more concrete hypotheses about which brain areas performing attentional selection might be at the heart of N2pc generation. In combination, an area like V4, which is functionally connected to FEF, is attentionally modulated, and exists in the posterior part of the brain, could serve as a plausible source.

\section{Linking Attentional Selection, Cortical Columns, and the N2pc}
Thus far we have introduced three key topics in neuroscience research: attention, laminar cortical circuitry, and EEG. We focused on highlighting their overlap. For instance, cortical columns in certain brain areas are implicated in attentional modulation. Cortical columns putatively generate EEG signals. Indices of attentional selection manifest in the EEG. And while there are these clear connections between the topics, a comprehensive description of the intersection of the three has not been demonstrated empirically. By linking the psychological process of attentional selection to its neural substrates within cortical columns and exploring the patterns of activation given rise, we can perhaps understand what the N2pc is. Ultimately, better understanding the neural generation of the N2pc provides interpretive value. With an understanding of how the N2pc is generated, researchers can more readily speculate as to the underlying neural processes when measuring the EEG signal alone. Therefore, linking cognitive processing in cortical circuits to the biophysical generation of EEG has the potential to provide key insight for the field of human neuroscience and yield a greater understanding of the function of cortical circuits in attentional selection along the way.

\section{What to Expect}
In the following five chapters we elaborate four studies designed to further our understanding of attentional selection across spatial scales. Specifically, this dissertation seeks to accomplish two primary aims. For one, we sought to characterize the changes present in visual cortical columns (specifically area V4) associated with attentional selection of pop-out stimuli. And two, we sought to determine whether the hypothesized changes in the same visual cortical columns lead to the generation of the EEG signal known as N2pc. Together, resolving these aims significantly advances our understanding of the circuits producing attentional selection and reveal what can be understood from attention-associated EEG from a neurobiological level. In chapter two, you will read on our initial search for whether attentional modulations of sensory processing are present along the layers of visual cortex during visual search. If V4 is generating the N2pc index of attention, we expect to observe attention-induced changes in cortical columnar processing. We found layer-specific changes in sensory processing associated with the attentional selection of targets in visual search. In chapter three, we describe the temporal evolution of attentional selection along the layers of visual cortex during visual search. If V4 is generating the N2pc, selection signals should be present at or before the time we would expect the N2pc. We found the timing of attentional selection in visual cortex preceded the usual timing of the N2pc. In chapter four, we sought to describe the parallels and putative relationship between activity generated across visual cortical columns and the overlying N2pc. If V4 is generating the N2pc, we expect simultaneous signaling of attentional selection in the cortical columnar activity and the EEG which should covary on a trial-by-trial basis. We found layer-specific relationships between attentional selection in visual cortex and the N2pc. In chapter five, we go beyond simple comparisons of intra- and extracranial signals and demonstrate that V4 can biophysically generate the N2pc. If V4 is generating the N2pc, we should see patterns of simulated EEG resembling the N2pc from forward modeled activity of visual cortical columns. We demonstrate that the synaptic currents across cortical layers in visual cortex are capable of regenerating the canonical N2pc distribution. In the final chapter, we will integrate the findings of these studies and discuss what their implications are towards our ultimate goal of understanding attentional selection across multiple scales.