\chapter{Impact of Attention on Sensory Columnar Processing} \label{CH2}

\hangindent=0.75cm Westerberg, J. A., Sigworth, E. A., Schall, J. D., and Maier, A. (2021) Pop-out search instigates beta-gated feature selectivity enhancement across V4 layers. \textit{Proceedings of the National Academy of Sciences} 118(50):e2103702118.

\vspace{\baselineskip}
\centering{\textit{“Whatever she might become she would never be static.” \\- Sinclair Lewis}}

\raggedright
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\section{Summary}
Visual search is a work-horse for investigating how attention interacts with processing of sensory information. Attentional selection has been linked to altered cortical sensory responses and feature preferences (i.e., tuning). However, attentional modulation of feature selectivity during visual search is largely unexplored. Here we map the spatiotemporal profile of feature selectivity during a singleton search task. Monkeys performed search where a single pop-out feature determined the target of attention. We recorded laminar neural responses from mid-level visual area V4. We first identified “feature columns” which showed preference for individual colors. In the unattended condition with no target in the receptive field, V4 feature columns were significantly more selective in the superficial relative to the middle and deep layers. Attending a stimulus increased feature selectivity in all layers but not equally. Feature selectivity increased most in the deep layers, leading to higher selectivity in the extragranular layers as compared to the middle layer. This attention-induced enhancement was rhythmically gated in phase with the beta-band local field potential. Beta power dominated both extragranular laminar compartments, but current source density analysis pointed to an origin in superficial layers, specifically. While beta-band power was present regardless of attentional state, feature selectivity was only gated by beta in the attended condition with the target in the receptive field. Neither the beta oscillation nor its gating of feature selectivity varied with microsaccade production. Importantly, beta modulation of neural activity predicted response times, suggesting a direct link between attentional gating and behavioral output. Together, these findings suggest beta-range synaptic activation in V4's superficial layers rhythmically gates attentional enhancement of feature tuning in a way that affects the speed of attentional selection.

\section{Introduction}
Throughout cortex, sensory information is organized into maps. This phenomenon is readily observable in visual cortex where maps organize information in both the radial (e.g., within cortical columns) and tangential (e.g., across a cortical area) dimensions \citep{hubel_orientation_1977,hubel_receptive_1968,kaas_plasticity_1991,hubel_receptive_1962}. Importantly, sensory information attributed to these maps is malleable. For example, selective attention is linked to profound changes in neural activity organizing sensory information in both space and time \citep{cohen_using_2011,cohen_origin_2009,desimone_neural_1995,ferro_directed_2021,fries_modulation_2001,fries_effects_2008,kastner_mechanisms_2000,martinez-trujillo_attentional_2002,mcadams_effects_1999,mcadams_attention_2000,ni_spatially_2017,ni_neuronal_2019,pooresmaeili_separable_2010,poort_role_2012,recanzone_effects_2000,reynolds_competitive_1999,reynolds_attention_2000,reynolds_interacting_2003,reynolds_attentional_2004,reynolds_normalization_2009,roelfsema_different_2007,roelfsema_object-based_1998,rotermund_attention_2009,sani_temporally_2017,stanisor_unified_2013,thiele_additive_2009,treue_feature-based_1999,vinck_attentional_2013,wegener_influence_2004,williford_effects_2006}.

In visual cortex, cortical columnar microcircuits comprise many neurons that respond to the same location of visual space and similar stimulus features. For example, primary visual cortex (V1) features “orientation columns” consisting of neurons sharing response preference for the same stimulus orientation \citep{hubel_sequence_1974,vanduffel_organization_2002} and “ocular dominance columns” consisting of neurons that preferentially respond to the same eye \citep{levay_pattern_1975}. Similar columnar organization for feature selectivity has been described across many other visual cortical areas including area V2  \citep{tootell_functional_1989,tootell_columnar_2017,vanduffel_organization_2002}, area V3 \citep{adams_functional_2001}, middle temporal area (area MT) \citep{albrecht_spatial_1984,deangelis_organization_1999,liu_functional_2003}, and inferotemporal cortex \citep{fujita_columns_1992,tanaka_inferotemporal_1996,tanaka_columns_2003}. Mid-level visual cortical area V4, a well-studied area with respect to attentional modulation, follows suit with columnar organization of visual responses and feature preferences  \citep{conway_color-tuned_2009,kobatake_neuronal_1994,kotake_organization_2009,schein_is_1982,tootell_search_2004,yoshioka_neuronal_1996,zeki_color_1973,zeki_representation_1980}. Yet, we do not know the extent to which attention impacts feature preferences along columns. While canonical microcircuit models of cortex predicts laminar differences for attentional modulation (e.g., feedback-recipient extragranular layers modulating before granular layers \citep{douglas_canonical_1989,douglas_functional_1991,bastos_canonical_2012}), how this modulation interacts with columnar feature selectivity is largely unknown.

We sought to determine the spatiotemporal profile of feature preferences within the V4 laminar microcircuit during attentional selection. To address this question, we performed neurophysiological recordings along V4 layers in monkeys performing an attention-demanding pop-out search task. We identified feature columns demonstrating homogeneous feature preference along cortical depth. When the search array item presented in the column's receptive field was unattended, the upper cortical layers were most selective. However, when attended, feature selectivity in the deep layers enhanced the most, resulting in overall strongest feature selectivity in both extragranular compartments. We further found that the enhancement of feature selectivity associated with attention was rhythmically gated in the beta-range. While beta activity was measurable across both unattended and attended conditions, rhythmic gating of feature selectivity was only present with attention. Moreover, beta power modulating the neural response was predictive of response time, suggesting a link between attentional gating and behavior. Synaptic currents revealed the beta rhythm originates in superficial cortical layers, which is compatible with top-down influence.

\begin{figure}[!t]
\centering
\includegraphics[width=15.13cm]{Figures/ch2-1.pdf}
\caption{Task design and laminar recording technique}
\subcaption{A. Monkeys  fixated a dot. Following a variable delay, an array was presented. Monkeys were tasked to saccade to the oddball to receive a reward. B. Response times across all sessions for both monkeys (left, cyan: monkey Ca; right, magenta: He). C. Behavioral performance for both monkeys across all sessions (cyan: monkey Ca; magenta: He). D. Occipital view of a 3D render of a macaque brain. Area V4 is highlighted in red. The cortical structure is revealed by the section through the prelunate gyrus. This slice is magnified at the right, with labels indicating structural landmarks (lus, lunate sulcus; sts, superior temporal sulcus; ips, intraparietal sulcus; gm, gray matter; wm, white matter). The red box indicates a portion of V4 where linear electrode arrays can be introduced orthogonally to record across the layers of a cortical column. E. MR scans for monkey He (left) and monkey Ca (right). The section was taken orthogonal to the 19 mm diameter recording chamber (red outline) oriented 55° off vertical axis, 23 mm from the midline. The superior temporal sulcus can be seen running through the center of each chamber. A grid of possible penetration locations is overlaid with solid red dots indicating locations sampled in this study on the prelunate gyrus, ventroposterior to the superior temporal sulcus. F. Ex vivo image of the posterior half of the brain of monkey He with expanded view of the area between lus and sts. Red marks are diiodine deposited by the final two recordings in monkey He. G. Histological stains, depicting the laminar structure of V4. Labels are indicated at the far left. Neun stain (left center) highlights the distribution of neuronal cell bodies. SMI-32 (right center) highlights pyramidal cells. Cartoon probe shows laminar positioning. H. Receptive fields (RF) across recording sites of a single array penetration, indicating perpendicular penetration. I. CSD for the same session as H. Current sinks are indicated in red and sources in blue. The initial sink following visual stimulation was used as a functional marker to determine the L4/5 boundary. The black horizontal line indicates granular input sink. Data are smoothed along depth and across time for visualization. J. Mean CSD profile following alignment of the 30 sessions (21, monkey Ca; 9, He). Formatting identical to I. K. RF locations across sessions and monkeys (cyan, monkey Ca; magenta, He). RF centers determined online, and diameters estimated from previous reports.}
\label{fig:Fig2.1}
\end{figure}

\section{Results}

\subsection{Behavioral Performance}
Both monkeys performed a pop-out visual search task necessitating attentional selection of, followed by an accurate eye movement to, an oddball stimulus to receive a juice reward (\href{fig:Fig2.1}{Figure 2.1A}). Both monkeys performed the task well above chance (accuracy: monkey Ca, 88\%; He, 81\%) with response times (RT) consistent with previous reports of monkeys performing this task (\href{fig:Fig2.1}{Figure 2.1B-C}; median RT: monkey Ca, 243 ms; He, 224 ms) (see previous reports for comparable behavioral data from a different cohort of macaques \citep{purcell_supplementary_2012,westerberg_performance_2020}).

\subsection{Laminar Recordings in V4}
To evaluate the laminar profile of feature selectivity in primate area V4, we recorded neural activity throughout the layers of cortex in 2 macaque monkeys using a linear electrode array with 100 μm spacing between recording contacts (Plexon S-Probe). Prior to any surgical procedures, monkeys underwent a magnetic resonance (MR) scan to identify the location of V4 (\href{fig:Fig2.1}{Figure 2.1D}). Following implantation of a recording chamber, monkeys underwent a second MR scan with a gadolinium-filled recording grid to identify chamber locations where an electrode could be introduced orthogonally into the prelunate gyrus (\href{fig:Fig2.1}{Figure 2.1E}). On each recording, we positioned and oriented the linear electrode array to span all V4 layers (\href{fig:Fig2.1}{Figure 2.1G}). At the end of the experiment, penetration locations were verified anatomically (\href{fig:Fig2.1}{Figure 2.1F}).

Monkeys performed a fixation task during which we flashed randomized, monochromatic stimuli uniform in brightness to perform reverse-correlation-based receptive field (RF) mapping \citep{cox_temporal_2019,dougherty_binocular_2019,westerberg_v1_2019}. Overlapping RFs through vertically (radially) aligned recording sites were used to confirm the perpendicular orientation of the probe (\href{fig:Fig2.1}{Figure 2.1H}) \citep{cox_temporal_2019,nandy_laminar_2017}. The distribution of RFs across cortex was consistent with the previously established representation of visual space on the prelunate gyrus (\href{fig:Fig2.1}{Figure 2.1K}) \citep{gattass_visuotopic_1988}. To compare columns across sessions, we aligned individual recording sites relative to the granular input sink of the current source density (CSD) following visual stimulation, which identifies the boundary between the granular (L4) and infragranular layers (L5/6) of cortex (\href{fig:Fig2.1}{Figure 2.1I}) \citep{maier_distinct_2010,nandy_laminar_2017,schroeder_spatiotemporal_1998,westerberg_v1_2019}. 10 electrode contacts including and above the L4/5 boundary and 5 sites below were included for analysis. Relative to the population-based (grand average) evoked CSD profile (Fig. 1.1J), the topmost 5 sites corresponded to the supragranular sink, the middle 5 to the granular sink, and the deepest 5 to the infragranular source.

\subsection{V4 Feature Columns}
As a first step, we identified feature columns in V4. Columnar organization for color tuning has previously been reported for V4  \citep{conway_color-tuned_2009,kotake_organization_2009,tootell_search_2004,zeki_color_1973,zeki_representation_1980}. During performance of the visual search task, small, isoluminant red or green stimuli were presented within the population RF of the column \citep{westerberg_priming_2020}. We estimated neural activity by evaluating high gamma power (70-150 Hz) at each recording site as it reliably reflects feature selectivity in visual cortex \citep{ray_differences_2010}, and can be reliably measured at laminar V4 recording sites, unlike single- or multi-unit activity. To eliminate volume conducted signals, we recalculated the locally-generated gamma power from the columnar CSD \citep{kajikawa_how_2011,nicholson_field_1971}. Initially, and for the results reported in this section, these analyses were restricted to the ‘unattended’ condition. This condition was defined as the presence of a distractor stimulus within the receptive field of the cortical column regardless of the position of the target in the array (5 out of 6 array configurations where target is outside of the RF) and the trial was performed correctly.

To quantify the selectivity of each recording site for stimulus color, we computed the ratio of the red vs. green responses with values bounded between -1 (100\% green preference) and 1 (100\% red preference). Hereafter we will refer to this as the feature selectivity index (FSI). We then calculated the mean FSI along cortical depth to compute a columnar feature selectivity index (CFSI). The CFSI quantifies the degree of feature preference across the entire column. Qualitatively, we found a high degree of consistency of columnar feature selectivity, with some columns strongly preferring red or green and other columns not distinguishing red from green \href{fig:Fig2.2}{Figure 2.2A}. To quantify this observation, we performed Wilcoxon signed rank tests on the magnitudes of FSI along depth, where columns were deemed feature selective if $p<0.05$. We found significant feature selectivity in 19 of 30 columns (63.3\%) across both monkeys (monkey Ca, 12 of 21; He, 7 of 9).

\begin{figure}[!t]
\centering
\includegraphics[width=9.69cm]{Figures/ch2-2.pdf}
\caption{Identifying feature columns in V4}
\subcaption{A. Feature selectivity index (FSI) for each recording site (bottom). Each plotted column corresponds to one cortical column. Data are sorted from the most green- to most red-preferring columns. Hue indicates the preferred color of each recording site and saturation indicates the magnitude of the preference. Horizontal lines indicate laminar boundaries. Average FSI along each column (CFSI) indicated in bar chart (top). Each bar corresponds to the column underneath. Significant selectivity along depth indicated by an asterisk between plots. Significant consistency of color preference, or lack of color preference, is denoted by a carrot between the bar and color plots. Color of asterisks and carrots indicates monkey from which the column was recorded (cyan, monkey Ca; magenta, He). B. Dot plot of FSI for each recording site (top). Black data are 10 of 1000 shuffle distributions generated where FSI were picked at random from all columns (shuffle control). C. Scatter plot of the standard deviation along depth for each cortical column as well as for a randomly chosen sample of 10 of the 1000 shuffle control null distributions. Color of bar indicates data source (cyan, monkey Ca; magenta, He; black, shuffle). Vertical lines indicate the median standard deviations for the data and for the shuffle. The p value of a statistically significant result of a signed rank test is indicated at the bottom.}
\label{fig:Fig2.2}
\end{figure}

Note that the CFSI alone does not indicate how consistent feature selectivity is within a column. To evaluate the columnar consistency of feature selectivity, we performed Bartlett’s tests for the FSI variance of each column relative to the null distributions derived from 15 random samples of all measured FSIs, bootstrapped 1000 times. The median value was taken to determine significance with $p<0.05$. Feature selectivity was more consistent across columns than would be predicted by the null distribution in 23 of 30 columns (76.7\%) across both monkeys (monkey Ca, 17 of 21; He, 6 of 9). Only 4 of 30 columns exhibited no or inconsistent selectivity across depth through these measures. We further quantified the degree of similarity within a column relative to the variance across columns by computing intraclass correlation coefficients (ICC) \citep{shrout_intraclass_1979,mcgraw_forming_1996}. ICC reveals how consistent individual columns are in feature selectivity relative to the overall sample of V4 columns. Values towards 1 indicate a high degree of consistency within a column, and values towards 0 indicate little to no consistency. Across the sample, we found consistency within columns to be significantly greater than chance ($\rho=0.773, F(29, 406)=52.19, p<<0.001$) in both monkeys (monkey Ca, $\rho=0.848, F(20,280)=84.96, p<<0.001$; He, $\rho=0.550, F(8,112)=19.27, p<<0.001$) (\href{fig:Fig2.2}{Figure 2.2B}). For comparison, we generated shuffle control distributions of FSI values and computed the ICC. This control yielded the expected lack of consistency ($\rho=0.003, F(999,13986)=1.08, p=0.37$). These results suggest that most V4 columns are homogeneous in their feature selectivity. Lastly, we assessed the distribution of standard deviations of FSI across columns relative to that of the null distributions \href{fig:Fig2.2}{Figure 2.2C}. We performed a Wilcoxon signed rank test on these distributions. The result showed a significant difference ($Z=-4.02, p=5.79e^{-5}$), providing further evidence for the homogeneity of columnar feature selectivity in V4. 

\begin{figure}[!t]
\centering
\includegraphics[width=15.13cm]{Figures/ch2-3.pdf}
\caption{Consistency between gamma and MUA feature selectivity indices}
\subcaption{Feature selectivity measured with multiunit samples does not differ from feature selectivity measured with high gamma. A. Histogram of differences between feature selectivity of multiunits ($n=285$) and the corresponding column’s high gamma feature selectivity ($n=19$). Multiunits were sampled in cortical columns that exhibited feature selectivity in the unattended condition with a distractor in the receptive field. Columnar feature selectivity index for the high gamma was then subtracted from the feature selectivity index found for the multiunits found in each column. Prior to subtraction, FSI was made relative to the preferred color (as defined by the CFSI of the high gamma) to examine both red and green columns together. Values right of zero indicate greater selectivity in the multiunit activity and left indicate greater selectivity in the high gamma (at the column level). Result of Wilcoxon signed rank shown in upper righthand corner. B. Identical to panel A except instead of computing difference between a multiunit FSI and its corresponding column high gamma FSI, the difference between the multiunit FSI is taken against the high gamma FSI at the recording site where the multiunit was isolated. Again, no significant differences were observed as indicated by the Wilcoxon signed rank test show in the upper right hand corner of the panel.}
\label{fig:Fig2.3}
\end{figure}


We compared the feature selectivity of recorded multiunits ($n=285$) along cortical columns to the columnar feature selectivity of each multiunit’s corresponding column. We found no significant differences between the multiunit feature selectivity and their corresponding columns (Wilcoxon signed rank, $Z=1.21, p=0.22$) (\href{fig:Fig2.3}{Figure 2.3}), bolstering confidence that the locally generated gamma is a useful measure of columnar feature selectivity.

\begin{table*}
\centering
\resizebox{\linewidth}{!}{
\begin{tabularx}{\textwidth}{lXXXr}
\hline
\textit{\textbf{Model Identifier}} &
  \textbf{Model Statement} &
  \textbf{Parameter Priors} &
  \textbf{LOO-ELPD DIFF {[}SE{]}} &
  \textbf{Bayes Factor} \\ \\ \\
  \hline 
\multirow{2}{*}{\textit{\begin{tabular}[c]{@{}l@{}}COLUMN X \\ DEPTH X\\  ATTENTION\end{tabular}}} &
  \multirow{2}{*}{\begin{tabular}[c]{@{}l@{}}$FSI_{i,j,k} \sim N(\mu +$ \\ $d_i \cdot c_j + d_i \cdot a_k +$ \\ $d_i + c_j + a_k + e_{i,j,k}, \sigma^2_{i,j,k})$\end{tabular}} &
  \multirow{2}{*}{\begin{tabular}[c]{@{}l@{}}$\mu, d_i, c_j, a_k \sim N(0,\sigma)$\\ $\sigma \sim Half-t(3,0,1)$\end{tabular}} &
  \multirow{2}{*}{\textit{Reference}} &
   \\ \\ \\
 &
   &
   &
   &
  \multirow{2}{*}{$7.20e^{6}$} \\
\multirow{2}{*}{\textit{\begin{tabular}[c]{@{}l@{}}COLUMN X\\ DEPTH\end{tabular}}} &
  \multirow{2}{*}{\begin{tabular}[c]{@{}l@{}}$FSI_{i,j,k} \sim N(\mu +$ \\ $d_i \cdot c_j + d_i + c_j +$ \\ $e_{i,j,k}, \sigma^2_{i,j,k})$\end{tabular}} &
  \multirow{2}{*}{\begin{tabular}[c]{@{}l@{}}$\mu, d_i, c_j \sim N(0,\sigma)$\\ $\sigma \sim Half-t(3,0,1)$\end{tabular}} &
  \multirow{2}{*}{$-186.9 [12.9]*$} &
   \\ \\ \\
 &
   &
   &
   &
  \multirow{2}{*}{$0.0001$} \\
\multirow{2}{*}{\textit{\begin{tabular}[c]{@{}l@{}}COLUMN \\ \end{tabular}}} &
  \multirow{2}{*}{\begin{tabular}[c]{@{}l@{}}$FSI_{i,j,k} \sim N(\mu +$ \\ $c_j + e_{i,j,k}, \sigma^2_{i,j,k})$\end{tabular}} &
  \multirow{2}{*}{\begin{tabular}[c]{@{}l@{}}$\mu, c_j \sim N(0,\sigma)$\\ $\sigma \sim Half-t(3,0,1)$\end{tabular}} &
  \multirow{2}{*}{$-185.8 [13.0]*$} &
   \\ \\ \\
 &
   &
   &
   &
  \multirow{2}{*}{$1.01e^{69}$} \\
\multirow{2}{*}{\textit{\begin{tabular}[c]{@{}l@{}}DEPTH\\ \end{tabular}}} &
  \multirow{2}{*}{\begin{tabular}[c]{@{}l@{}}$FSI_{i,j,k} \sim N(\mu +$\\ $d_i + e_{i,j,k}, \sigma^2_{i,j,k})$\end{tabular}} &
  \multirow{2}{*}{\begin{tabular}[c]{@{}l@{}}$\mu, d_i \sim N(0,\sigma)$\\ $\sigma \sim Half-t(3,0,1)$\end{tabular}} &
  \multirow{2}{*}{$-371.3 [26.5]*$} &
   \\ \\ \\
 &
   &
   &
   &
  \multirow{2}{*}{$0.0019$} \\
\multirow{2}{*}{\textit{\begin{tabular}[c]{@{}l@{}}NULL\\ \end{tabular}}} &
  \multirow{2}{*}{\begin{tabular}[c]{@{}l@{}}$FSI_{i,j,k} \sim N(\mu +$ \\ $e_{i,j,k}, \sigma^2_{i,j,k})$\end{tabular}} &
  \multirow{2}{*}{\begin{tabular}[c]{@{}l@{}}$\mu \sim N(0,\sigma)$\\ $\sigma \sim Half-t(3,0,1)$\end{tabular}} &
  \multirow{2}{*}{$-370.5 [26.5]*$} &
   \\ \\
 &
   &
   &
   &
  
   \\ \hline
\end{tabularx}}
\caption{Bayesian multilevel models for spatial organization of feature selectivity in V4 and the influence of attention condition}
\subcaption{COLUMN x DEPTH x ATTENTION models feature selectivity as a function of cortical column, depth of recording site, and attention condition with a trio of interactions. \textit{COLUMN x DEPTH} models selectivity as a function of cortical column and depth of recording site with an interaction between the two. \textit{COLUMN} and \textit{DEPTH} only account for either cortical column or depth of recording site, respectively. NULL represents a model which assumes there is no spatial organization for feature selectivity and attention. Leave-One-Out cross-validation Expected Log Predicted Density (LOO-ELPD) is a measure of the predictive ability of the model, with larger values indicating higher predictive accuracy. LOO-ELPD difference values and the associated standard error are shown with respect to the best performing model. Bayes Factor describes support for one model over the another model, with larger values (>1) indicating greater support for the model above and smaller values (<1) indicating support for the model below. $c_j$ is the random effect due to column, $d_i$ is the depth of the recording site, $a_k$ is a binary coded attention condition, $\mu$ is the mean response, $e_{i,j,k}$ is random error, and $FSI_{i,j,k}$ is the feature selectivity index for cortical column $\textit{j}$ at depth $\textit{i}$ and attention condition $\textit{k}$; $\sigma^2_{i,j,k}$ is the variance in feature selectivity index and is assumed fixed. $*$ denotes a significant difference.}
\label{tab:Tab2.1}
\end{table*}

To further bolster our confidence in the columnar organization for feature selectivity, we turned to Bayesian modeling. Using a Bayesian framework, we directly evaluated and compared the likelihood of several models of the organization of feature representation. Each column contributed 15x2 data points – 1 per recording site for each attention condition (which will be elaborated on in a later section). Model comparisons were based on samples of the posterior distribution from each model. The results are summarized in \href{tab:Tab2.1}{Table 2.1}. Briefly, we find that the models utilizing columnar information are more accurate in predicting the observed feature selectivity. We quantified relative model performance using the so-called leave-one-out cross-validation expected log predictive density difference (LOO-ELPD DIFF). This measure was computed between increasingly more complex models (i.e., the intercept only model to the model incorporating all independent variables). The models without a column parameter (e.g., NULL and DEPTH) had the lowest values and therefore performed worse than models including a column parameter. It should be noted that depth alone does not improve accuracy as seen in the LOO-ELPD DIFF between NULL and DEPTH, indicating a lack of trends in feature selectivity within individual layers across columns. In other words, there was no observed tendency for any given layer to have a bias. These model comparisons confirm the results found through our other frequentist methods.

\begin{figure}[!t]
\centering
\includegraphics[width=15.13cm]{Figures/ch2-4.pdf}
\caption{Color columns and their modulation of selectivity with attention}
\subcaption{A. Comparing selectivity differences between unattended (top) and attended (bottom) conditions for each column. Each vertical color plot represents a column with the intensity of red or green denoting the strength in feature selectivity for that color. Columns are grouped by the change associated with attention: enhancement, no change, suppression, and elimination of feature selectivity from left to right. Columns highlighted with the gray bars were columns originally measured to have no significant feature selectivity. B. Box and whisker plot for the change in FSI for each recording site in each column corresponding to A directly above. C. Laminar profiles of strength of feature selectivity. Strength of feature selectivity is taken as the difference in feature selectivity relative to the column mean with higher values indicating stronger feature selectivity relative to the column and negative values indicating weaker feature selectivity than the column average. Recording sites were divvied into their corresponding laminar compartment. Normalized feature selectivity for each of laminar compartment shown as a violin plot for the unattended condition (left, $n=19$), attended (right, $n=24$), and the enhancement from unattended to attended (middle, $n=14$). Normalization was performed such that values ranged for each recording site from -1 to 1 for non-preferred to preferred feature such that red- and green-preferring columns could be analyzed together. Solid vertical line and dashed vertical line in each distribution denote the mean and median, respectively. Red cloud behind distributions shows 95\% confidence interval cloud for strength or change in strength along depth (rather than the agglomerated compartments). Brackets to the right of each plot denote significant differences between the distributions measured by Wilcoxon signed rank test ($p<0.05$).}
\label{fig:Fig2.4}
\end{figure}

\subsection{Laminar Variation in Strength of Feature Selectivity and its Modulation with Attention}
We next investigated whether changes in feature selectivity were observable with directed attention. To do so, we plotted the FSI for each recording site for both the unattended and attended conditions (\href{fig:Fig2.4}{Figure 2.4A}). The unattended condition inclusion criteria remained the same and the attended condition was restricted to trials where the search target stimulus was present in the column’s RF and the trial was performed correctly. We noticed several qualitative changes between the unattended and attended conditions (\href{fig:Fig2.4}{Figure 2.4A}). Many columns showed enhanced feature selectivity with attention ($n=14$, 47\%). We performed Wilcoxon signed rank tests on the difference between the FSI between conditions across recording sites within each column ($p<0.05$). This analysis confirmed that attending the stimulus in the RF mostly leads to enhancement of feature selectivity (\href{fig:Fig2.4}{Figure 2.4B}). This enhancement was so pronounced that some non-selective columns became significantly selective ($n=6$; 20\%) when attention was deployed. However, some columns did not appear to change in selectivity ($n=13$, 43\%). Fewer showed suppressed selectivity ($n=2$, 6\%) or loss of selectivity ($n=1$, 3\%). Given their small sample size, we disregard these outliers from further consideration and focus on the population of columns that were enhanced with attention.  

Next, we sought to investigate laminar differences for feature selectivity and whether the enhancement associated with attention follows a distinct laminar profile. We combined recording sites within their respective laminar compartments and measured feature selectivity. Specifically, we subtracted the nCFSI from the nFSI at each recording site to compute the deviation of individual V4 laminar compartments from the columnar mean (\href{fig:Fig2.4}{Figure 2.4C}). We performed pairwise Wilcoxon signed rank tests of the magnitude of FSIs between the laminar compartments. We found differences between layers (upper and middle: $Z=4.55, p=5.2e^{-6}$; upper and lower: $Z=5.19, p=2.1e^{-7}$; middle and lower: $Z=0.94, p=0.34$), with upper layers exhibiting stronger feature selectivity than middle and lower layers. We used the same method to investigate the laminar profile in the attention condition (\href{fig:Fig2.4}{Figure 2.4C}). In the attended condition, both extragranular compartments were significantly more feature selective than the middle layers (upper and middle: $Z=2.67, p=0.007$; upper and lower: $Z=-0.96, p=0.34$; middle and lower: $Z=-3.70, p=2.1e^{-4}$). Accordingly, we observed the largest attentional modulation of feature selectivity in the lower layers with the smallest in the middle and upper layers (upper and middle: $Z=-0.93, p=0.35$; upper and lower: $Z=-3.80, p=1.4e^{-4}$; middle and lower: $Z=--2.92, p=0.0035$). Nonetheless, attentional enhancement of feature selectivity was significant in all laminar compartments as measured through Wilcoxon signed rank tests on the differences between conditions for columns with feature selectivity (upper: $Z=2.24, p=0.02$; middle: $Z=2.15, p=0.03$; lower: $Z=4.06, p=4.9e^{-5}$).

\begin{figure}[!t]
\centering
\includegraphics[width=15.13cm]{Figures/ch2-5.pdf}
\caption{Testing different estimates of granular layer size}
\subcaption{Testing different middle layer channel counts to account for uncertainty about thickness of layer 4. A. Cartoon depicting number and positions of channels included in this analysis determining whether size of middle layer compartment affects results with respect to attentional modulation of feature selectivity. The number of channels used decreased from 5 (the configuration used throughout main text) to 1 by eliminating successive more superficial channels as indicated by the highlighted channels. This was done relative to the L4/5 boundary where we have a well-defined physiological indicator consistent with anatomy. B. Strength of feature selectivity (SFS) is taken as the difference in feature selectivity relative to the column mean with higher values indicating stronger feature selectivity relative to the column and negative values indicating weaker feature selectivity than the column average. Feature selectivities for each middle layer compartment size are shown as a violin plot for the unattended condition (left, $n=19$), attended (right, $n=24$), and the enhancement from unattended to attended (middle, $n=14$). Solid vertical line and dashed vertical line in each distribution denote the mean and median, respectively. Brackets to the right of each plot denote the result of an ANOVA. No significant differences were observed using differing numbers of channels for the middle layer.}
\label{fig:Fig2.5}
\end{figure}


As we do not have a reliable physiological indicator of the boundary between the middle and upper layers (like we do for middle and lower layers), one possibility might be that the 0.4-0.5 mm used for middle layer specification is not representative of the granular layer, specifically. V4’s granular layer has been estimated to be less extensive \citep{hof_neurofilament_1995} and effects limited to the granular layer might be masked, accordingly. To control for this possibility we measured the feature selectivity, and the impacts of attention, on increasingly conservative estimates (5 sites down to 1). This showed that the results described above are consistent between middle layer sizes (\href{fig:Fig2.5}{Figure 2.5}) and granular layer-specific effects are unlikely to be masked.

\begin{figure}[!t]
\centering
\includegraphics[width=9.69cm]{Figures/ch2-6.pdf}
\caption{Rhythmic feature selectivity across cortical layers}
\subcaption{A. Temporal profile of high gamma feature selectivity across sessions found to have significant enhancement of feature selectivity with attention (left, $n=14$). Traces are shown from time since stimulus array onset. Upper, middle, and lower layer compartments shown in blue, magenta, and green, respectively. Attended condition shown as opaque lines, unattended as translucent. 95\% confidence intervals shown as clouds around lines. Black trace shows example locally-generated high gamma power response for reference relative in time to the FSI traces. Orange bar at the bottom indicates response time periods including transient (60-250 ms) and excluding transient (100-250 ms) for subsequent analysis. B. Normalized power from Fourier transform (FFT) of the temporal profiles of FSI in the beta-frequency band for the attended (opaque) and unattended (translucent) conditions. Circles denote FFT on data including transient and square, data excluding transient. Dotted lines at bottom of each plot denote significant difference between attended and unattended conditions for the respective data sets ($p<0.05$). C. Summary differences between conditions and layers for the beta FSI power (15-30 Hz) taken as the average for respective conditions in B. Data in orange outline are including transient, data in orange fill are excluding transient. Brackets denoting p<0.05 in Wilcoxon signed rank test indicating differences between attention conditions and differences between relative power in beta-oscillating feature selectivity between layers. D. Difference in temporal profiles of feature selectivity between attention conditions and corresponding FFT in beta range indicating oscillation exists in attention condition and not unattended. E. Fourier fit of temporal profile of feature selectivity for the response excluding transient at the frequency with peak power across the laminar compartments taken from the difference measures in D. Clouds denote 90\% prediction intervals of the model fits.}
\label{fig:Fig2.6}
\end{figure}

Lastly, we considered Bayesian modeling again (\href{tab:Tab2.1}{Table 2.1}). Notably, addition of the attention condition, and the associated interactions, improves model performance as the complete model including the ATTENTION parameter has the highest LOO-ELPD. In \href{tab:Tab2.1}{Table 2.1} this is seen in the LOO-ELPD difference where all other models relative to the ATTENTION-sensitive model are negative (i.e., lower values). However, there are factors other than maximal model performance to consider in evaluating likelihood – model parsimony. To address model parsimony, the standard error (SE) of the LOO-ELPD difference between the complete model (COLUMN x DEPTH x ATTENTION) and all other models exceeds 5 – indicating all terms in the model are useful. Exceeding 5 times the standard error is a conservative threshold for evaluating the difference between models through this method \citep{bengio_no_2004}.

\subsection{Rhythmic Modulation of Feature Selectivity with Attention}
We next sought to determine whether there was temporal variability in feature selectivity. To do so, we computed the FSI as a function of time from the peak of the transient visual response, taken as the peak of the population response averaged across recording sites and columns, to 250 ms following the stimulus array onset. We limited this analysis to the sessions found to show significant enhancement of feature selectivity with attention. We clipped the calculation at the peak of the visual transient response as the lack of activity prior to the response results in highly variable FSI (presumably because the measure is based on a ratio). We further divvied the data into the individual laminar compartments. We noticed that there appears to be rhythmicity of selectivity along all layers - but only in the attention condition. Note that this is an oscillation in feature selectivity, not just neural activity which, if significant, would suggest feature preferences oscillate in time. Qualitatively, a beta-range (15-30 Hz) rhythm seems to only appear in the attention condition (\href{fig:Fig2.6}{Figure 2.6A}). This corresponds well to a beta oscillation apparent within the locally-generated high gamma signal. Quantitatively, a Fourier transform (FFT) of the temporal profile of feature selectivity in the beta range showed a significant boost in the attended condition relative to the unattended condition (\href{fig:Fig2.6}{Figure 2.6B}). This was observed in both a subset of the data including the transient response as well as a subset excluding the transient, suggesting that this boost is not solely an artifact of the transient peak. In comparing differences between the attention conditions and layers across the beta range we find that the beta boost linked to attention is present along all layers, albeit stronger in the middle and lower layers than the upper layers (Wilcoxon signed rank, $p<0.05$). All differences are depicted in \href{fig:Fig2.6}{Figure 2.6C}. Interestingly, the beta rhythm survives a difference computation (\href{fig:Fig2.6}{Figure 2.6D}) - meaning the same rhythmicity is not present, or at least mostly attenuated, in the unattended condition. Performing a Fourier fit on the temporal profiles of enhancement in feature selectivity at a beta frequency suggests the enhancement contains a beta oscillatory component as these model fits better explain the enhancement than a model without a timevarying component ($R^{2}$: Fourier fit, upper=$0.33$, middle=$0.45$, lower=$0.38$; intercept-only fit, upper=$-1.1e^{-13}$, middle=$4.9e^{-13}$, lower=$-5.3e^{-15}$). Importantly, the rhythmic modulation of feature selectivity did not depend on microsaccades (\href{fig:Fig2.7}{Figure 2.7}), suggesting this is not simply a result of motor-related activity.

\begin{figure}[!ht]
\centering
\includegraphics[width=15.13cm]{Figures/ch2-7.pdf}
\caption{Microsaccades cannot explain gated selectivity}
\subcaption{A. Example main sequence from correctly performed trials in a single session by monkey Ca. Detected microsaccades circled in blue. Note, microsaccades greater than 0.5 dva are not included in this analysis because they resulted in aborted trials. B. Beta power triggered to the time of microsaccades across both monkeys and all sessions where attention significantly enhanced feature selectivity ($n=14$). Microsaccades included in this analysis must have occurred 100-250 ms following array onset and on a correctly performed trial. Trials were sorted by attention condition and stimulus color to ensure no differences existed between those trial types. C. Temporal profile of high gamma feature selectivity across sessions found to have significant enhancement of feature selectivity with attention (left, $n=14$). Upper, middle, and lower layer compartments shown in blue, magenta, and green, respectively. Attended condition shown as opaque lines, unattended as translucent. 95\% confidence intervals shown as clouds around lines. D. Orange bars at the top indicate response timeperiods including transient (circle marker, 60-250 ms) and excluding transient (square marker, 100-250 ms) for subsequent analysis. Normalized power from Fourier transform (FFT) of the temporal profiles of FSI in the beta-frequency band for the attended (opaque) and unattended (translucent) conditions. Circles denote FFT on data including transient and square, data excluding transient. Dotted lines at bottom of each plot denote significant difference between attended and unattended conditions for the respective datasets ($p<0.05$). E. Summary differences between conditions and layers for the beta FSI power (15-30 Hz) taken as the average for respective conditions in D. Data in orange outline are including transient, data in orange fill are excluding transient. Brackets denote $p<0.05$ in Wilcoxon signed rank test indicating differences between conditions and differences between relative power in selectivity between layers. F. Difference in temporal profiles of feature selectivity between attention conditions. G. FFT in the beta range corresponding to F indicating oscillation exists in attention condition and not unattended.}
\label{fig:Fig2.7}
\end{figure}

\begin{figure}[!t]
\centering
\includegraphics[width=7.57cm]{Figures/ch2-8.pdf}
\caption{Laminar profile of beta-locked CSD across attention conditions}
\subcaption{Synaptic activation measured as CSD for the attended and unattended conditions locked to beta for each respective condition averaged across sessions ($n=30$). Beta rhythm shown here is taken as the average across recording channels along the cortical column with 95\% CI. Current sink is observable in the upper and middle layers of cortex coupled to beta. Difference between attended and unattended conditions shown at right. Greater activation in the upper layers is observable in the attention condition highlighted with a outline. Significant synaptic activation and difference between conditions shown below each color plot. Blue line represents upper layer CSD and magenta, middle layer CSD. Abscissa uniform throughout each plot.}
\label{fig:Fig2.8}
\end{figure}

\subsection{Laminar Origins for the Synaptic Currents Generating the Beta Rhythm}
As a next step, we sought to determine if there are changes in synaptic activity that might relate to the generation of the beta oscillation. To do so, we identified troughs in the non-recalculated beta oscillation (to avoid circular analysis) on a trial-by-trial level and extracted the time-locked CSD, as an estimate of synaptic activation, for both the attended and unattended condition (\href{fig:Fig2.8}{Figure 2.8}). We found a significant current sink in middle and upper layers, time-locked to beta troughs in both conditions. Specifically, we computed t-tests over time, where significance was defined as 10 consecutive ms of $p<0.05$. Electrode contacts within each laminar compartment were averaged for computing statistics. In computing the difference between the resultant spatiotemporal profiles of CSD, we found that the superficial current sink was significantly stronger in the attention condition using the same criteria. Moreover, this significant difference largely preceded the beta trough, perhaps indicative of a causal relationship. Thus, beta-driven activation seems to be linked to synaptic interactions in the upper layers of V4.

\subsection{Beta Coupling}
We sought to better characterize the rhythmic activity during this task. We first plotted the gamma power responses along depth, averaged across sessions for both the attended and unattended conditions (\href{fig:Fig2.9}{Figure 2.9B}). We next confirmed that these gamma responses were significantly modulated by attention by performing a t-test across time, where significance was defined as $p<0.05$ for 10 consecutive ms. We noticed a beta-range rhythm along all layers in both conditions. To confirm this observation, we filtered the gamma power in the beta frequency band (15-30 Hz) and plotted the result (\href{fig:Fig2.9}{Figure 2.9B}). This transformation yields beta modulation of gamma power, hereafter referred to as gamma-beta. We noticed that there was no significant difference between the gamma-beta in the attended and unattended condition (conditions detailed in \href{fig:Fig2.9}{Figure 2.9A}). Hence, although beta rhythmicity of feature selectivity is exclusive to the attention condition, beta modulation of the gamma response occured with or without attention.

\begin{figure}[!t]
\centering
\includegraphics[width=15.13cm]{Figures/ch2-9.pdf}
\caption{Laminar profile of gamma and beta activity}
\subcaption{A. Search array configurations and their respective attention condition assignment. B. Temporal profile of gamma power shown across recording sites averaged across cortical columns (left, $n=30$) from time since stimulus array onset. Upper, middle, and lower compartments shown as the top 5, middle 5, and bottom 5 traces, respectively. Attended condition shown as red lines, unattended in blue. Significant difference between attended and unattended conditions observed across all recording sites denoted by an asterisk next to each pair of traces measured through t-test through time where $p<0.05$ for more than 10 ms. Gamma power responses filtered in the beta band (15-30 Hz) shown at right, referred to hereafter as gamma-beta. Beta rhythm observable across both conditions along all recording sites with no measurable significant differences between traces through the same method. C. Strength of beta power (not gamma-beta) along recording sites for attended (red) and unattended (blue) conditions taken as the average 100-250 ms following array onset. Significant difference denoted at right between attended and unattended conditions through a Wilcoxon signed rank test ($p<0.05$). Brackets show significant differences in beta power strength between laminar compartments regardless of attention condition. D. Gamma power across recording sites (top) coupled to the upper layers beta oscillation (mean and 95\% confidence interval cloud) for the attended (left) and unattended (right) conditions taken as the average across sessions ($n=30$). Significant coupling shown as outline through t-test along recording sites and samples relative to beta trough with a Bonferroni correction applied. Significant coupling between gamma power and beta oscillation observed in both attention conditions around the time of the beta trough along all layers.}
\label{fig:Fig2.9}
\end{figure}

\begin{figure}[!t]
\centering
\includegraphics[width=9.69cm]{Figures/ch2-10.pdf}
\caption{Spatiotemporal profile of low-frequency power and coupling with gamma activity}
\subcaption{A. Power in the beta (top), alpha (middle), and high theta (bottom) low-frequency
bands (abscissa) for each depth (ordinate) averaged across sessions ($n=30$) for the attended (red) and unattended (blue) conditions. Solid lines denote means and translucent clouds are the 95\% confidence intervals. Brackets above each subplot indicate significant difference between attention conditions through a Wilcoxon signed rank test ($p<0.05$). Brackets to the right of each subplot indicate significant differences between laminar compartments through a Wilcoxon signed rank test ($p<0.05$). B. Gamma power across recording sites coupled to the upper layers beta (top), alpha (middle), and high theta (bottom) oscillations   for the attended (left) and unattended (right) conditions taken as the average across sessions ($n=30$). Significant coupling shown as outline through t-test along recording sites and samples relative to beta trough with a Bonferroni correction applied.}
\label{fig:Fig2.10}
\end{figure}

We then measured coupling between gamma power along V4 layers and beta. These analyses were limited to low-frequency oscillations during the sustained response (100-250 ms following array onset) to eliminate confounds related the transient response. First, we observed beta power along layers for both attention conditions (\href{fig:Fig2.9}{Figure 2.9C}). We then performed a Wilcoxon signed rank test between each of the possible combinations of pairs of laminar compartments to determine if there was a significant difference in beta ($p<0.05$). We found that beta power was stronger in the extragranular compartments than the granular and strongest in the lower layers, coinciding with the strongest beta modulation of feature selectivity. At the same time, we determined whether there was a significant difference in beta power along the layers between conditions (Wilcoxon signed rank; $p<0.05$). This analysis revealed a significant difference between attention conditions across all layers. We then computed the coupling between gamma and beta within the upper layers of cortex – where the beta-generating CSD was found (\href{fig:Fig2.9}{Figure 2.9D}). We found that gamma power along all layers was coupled to the upper layer beta oscillation to a significant degree. However, there was no observable difference in the spatiotemporal profiles between attention conditions. In other words, attention does not seem to modulate beta-frequency coupling of V4 neuronal responses, which eliminates a simple, response timing-related explanation for attentional gating of feature selectivity. To ensure this finding was beta-specific, we also measured the spatiotemporal profile of power in other low-frequency bands – specifically, alpha (8-15 Hz) and high theta (7-8 Hz) – as well as their coupling with the gamma activity (\href{fig:Fig2.9}{Figure 2.10}). We found the profiles of power were not identical across frequency bands nor were the coupling profiles similar. This suggests that our findings are specific to the beta band and not due to generalized broadband low-frequency coupling. 

\begin{table*}[!t]
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}{lcccccc}
 &
  \multicolumn{1}{l}{} &
  \multicolumn{3}{c}{\textit{Frequentist}} &
  \multicolumn{2}{c}{\textit{Bayesian}} \\ \cline{2-7} 
\multicolumn{1}{l|}{} &
  \multicolumn{1}{c|}{} &
   &
   &
  \multicolumn{1}{c|}{} &
   &
  \multicolumn{1}{c|}{} \\
\multicolumn{1}{l|}{\multirow{-2}{*}{\textbf{Model Term}}} &
  \multicolumn{1}{c|}{\multirow{-2}{*}{\textbf{Value [SE]}}} &
  \multirow{-2}{*}{\textbf{t}} &
  \multirow{-2}{*}{\textbf{p}} &
  \multicolumn{1}{c|}{\multirow{-2}{*}{\textbf{95\% Confidence Interval}}} &
  \multirow{-2}{*}{\textbf{Median}} &
  \multicolumn{1}{c|}{\multirow{-2}{*}{\textbf{95\% Credible Interval}}} \\ \hline
\multicolumn{1}{|l|}{$\gamma-\theta$:Unattend} &
  \multicolumn{1}{c|}{-81.8 [97.1]} &
  \multicolumn{1}{c|}{-0.84} &
  \multicolumn{1}{c|}{0.40} &
  \multicolumn{1}{c|}{-272.1, 108.4} &
  \multicolumn{1}{c|}{-82.9} &
  \multicolumn{1}{c|}{-599.9, 431.3} \\
\rowcolor[HTML]{EFEFEF} 
\multicolumn{1}{|l|}{\cellcolor[HTML]{EFEFEF}$\gamma-\alpha$:Unattend} &
  \multicolumn{1}{c|}{\cellcolor[HTML]{EFEFEF}-8.8 [77.6]} &
  \multicolumn{1}{c|}{\cellcolor[HTML]{EFEFEF}-0.11} &
  \multicolumn{1}{c|}{\cellcolor[HTML]{EFEFEF}0.91} &
  \multicolumn{1}{c|}{\cellcolor[HTML]{EFEFEF}-160.9, 143.9} &
  \multicolumn{1}{c|}{\cellcolor[HTML]{EFEFEF}-64.2} &
  \multicolumn{1}{c|}{\cellcolor[HTML]{EFEFEF}-642.2, 520.6} \\
\multicolumn{1}{|l|}{$\gamma-\beta$:Unattend} &
  \multicolumn{1}{c|}{40.5 [59.3]} &
  \multicolumn{1}{c|}{0.68} &
  \multicolumn{1}{c|}{0.49} &
  \multicolumn{1}{c|}{-75.7, 156.7} &
  \multicolumn{1}{c|}{70.9} &
  \multicolumn{1}{c|}{-323.1, 467.5} \\ \hline
\multicolumn{7}{l}{} \\ \hline
\multicolumn{1}{|l|}{$\gamma-\theta$:Attend} &
  \multicolumn{1}{c|}{-29.9 [92.5]} &
  \multicolumn{1}{c|}{-0.31} &
  \multicolumn{1}{c|}{0.75} &
  \multicolumn{1}{c|}{-210.3, 152.4} &
  \multicolumn{1}{c|}{78.6} &
  \multicolumn{1}{c|}{-548.9, 702.1} \\
\rowcolor[HTML]{EFEFEF} 
\multicolumn{1}{|l|}{\cellcolor[HTML]{EFEFEF}$\gamma-\alpha$:Attend} &
  \multicolumn{1}{c|}{\cellcolor[HTML]{EFEFEF}326.9 [73.1]} &
  \multicolumn{1}{c|}{\cellcolor[HTML]{EFEFEF}4.47} &
  \multicolumn{1}{c|}{\cellcolor[HTML]{EFEFEF}{\color[HTML]{FE0000} \textbf{7.7$e^{-4}$}}} &
  \multicolumn{1}{c|}{\cellcolor[HTML]{EFEFEF}183.7, 470.3} &
  \multicolumn{1}{c|}{\cellcolor[HTML]{EFEFEF}284.6} &
  \multicolumn{1}{c|}{\cellcolor[HTML]{EFEFEF}-357.1, 930.8} \\
\multicolumn{1}{|l|}{$\gamma-\beta$:Attend} &
  \multicolumn{1}{c|}{-342.9 [40.49]} &
  \multicolumn{1}{c|}{-8.47} &
  \multicolumn{1}{c|}{{\color[HTML]{FE0000} \textbf{2.4$e^{-17}$}}} &
  \multicolumn{1}{c|}{-422.3, -263.6} &
  \multicolumn{1}{c|}{-330.1} &
  \multicolumn{1}{c|}{{\color[HTML]{FE0000} \textbf{-605.4, -65.1}}} \\ \hline
\end{tabular}}
\caption{Strength of oscillatory activity modulating locally-generated high gamma predicts behavioral responses when attended}
\subcaption{Results of multiple linear regression in the form of proportional odds logistic regression (POLR). Both the frequentist and Bayesian forms of the POLR were tested. Model was designed to fit vincentized response time (RT) using 3 coefficients: power in theta ($\theta$), alpha ($\alpha$), and beta ($\beta$) derived from a Fourier transform (FFT) of the locally-generated gamma power ($\gamma$). These were interacted with attention condition. Data from both monkeys ($n=2$) across all sessions ($n=30$) were included. Each recording site ($n=450$) contributed 24 data points (4 vincentized bins x 3 modulating frequencies x 2 attention conditions). Significant coefficients displayed in red (frequentist form, p column; Bayesian form, 95\% Credible Interval column), with direction of relationship between coefficient and outcome indicated as negative or positive numbers in the value column.}
\label{tab:Tab2.2}
\end{table*}

\subsection{Gamma-Beta Strength Predicts Behavior}
Low-frequency oscillations have been linked to performance during attention tasks \citep{landau_attention_2012,saalmann_pulvinar_2012,fiebelkorn_rhythmic_2013,fiebelkorn_dynamic_2018,fiebelkorn_mediodorsal_2019,helfrich_neural_2018,kienitz_theta_2018,ferro_directed_2021}. We thus investigated whether low frequencies, specifically those measureable in the locally generated gamma response, were linked to the animals’ behavioral output. Given the coupling between gamma and beta (\href{fig:Fig2.9}{Figure 2.9}), we suspected beta as the most promising candidate for a relationship with behavior. To see if this was the case, we extended our investigation to other low frequencies (theta and alpha). For this analysis, we used the power estimate from the FFT of high gamma as predictors, and RT (i.e., initiation time of saccade to target) to measure behavioral outcome. Only correct trials were considered and trials in the unattended condition were subsampled to match the attended condition. To evaluate these potential relationships, we turned to a form of multiple linear regression.

We used a proportional odds logistic regression (POLR) model to assess the relationship between low frequency power in the gamma response and behavioral outcome. This structure allowed us to vincentize the response time data, and corresponding neural data, into quartiles (i.e., the fastest 25\%, the faster 25\%, the slower 25\%, and the slowest 25\% of trials), and treat it as an ordered factor outcome. In this structure, each recording site contributed 4 data points (1 for each vincentized bin) for each evaluated condition. Results of this approach are summarized in \href{tab:Tab2.2}{Table 2.2}. The model was fit with the 3 (theta, alpha, beta) low-frequency power measures as independent variables, each interacted with attention condition and tested under frequentist and Bayesian frameworks. A negative coefficient indicates that stronger magnitude of that frequency modulating the locally generated gamma response is associated with faster response times and vice versa. We found that both beta power and alpha power were significantly predictive of response time in the attended condition. As beta power increased, response time decreased. As alpha power increased, response time increased. In the unattended case, none of the low frequency powers were associated with RT. As we have found laminar dependencies in previous analyses, we also tested models including depth as an interaction term with each of the low-frequency oscillatory independent variables, however they did not improve model performance (likelihood ratio tests of ordinal regression models: $LR(20)=22.82, p=0.30$), which is why we report the more parsimonious model here. Of most import to this study, these findings demonstrate the beta rhythm modulating the neural response is associated with behavior, but only with attention – paralleling our finding regarding the beta-gated feature selectivity.

\section{Discussion}
Sensory responses in visual cortical microcircuits are modified with attention \citep{desimone_neural_1995,mcadams_effects_1999,mcadams_attention_2000,treue_feature-based_1999,kastner_mechanisms_2000,recanzone_effects_2000,reynolds_competitive_1999,reynolds_attention_2000,martinez-trujillo_attentional_2002,martinez-trujillo_feature-based_2004,reynolds_interacting_2003,reynolds_attentional_2004,williford_effects_2006,reynolds_normalization_2009,nandy_laminar_2017,ni_spatially_2017,ni_neuronal_2019,sani_temporally_2017}. We sought to understand how feature selectivity is modified in an attention task. To do so, we focused on the columnar microcircuit of V4. We found that enhancement of feature selectivity occurred across the layers of cortex, albeit with variable magnitude. Interestingly, this enhancement was coupled to several cycles of a beta oscillation in the local field potential. Notably, the coupling of feature selectivity to beta oscillations in the neural response was not present in the unattended condition. In other words, feature selectivity only oscillated in synchrony with the beta rhythm when attention was deployed, reminiscent of prior reports where attention-related behavior was linked to low-frequency oscillations in neural activity  \citep{fries_modulation_2001,womelsdorf_modulation_2007,lakatos_entrainment_2008,gregoriou_cell-type-specific_2012,landau_attention_2012,fiebelkorn_rhythmic_2013,fiebelkorn_mediodorsal_2019,zalta_natural_2020}. Our findings are the first to demonstrate mechanistically that low frequency rhythms in attentional performance are not just linked to response magnitude, but also to periodic changes in feature selectivity (tuning) in visual cortex.

As an initial step in our study, we identified columnar cortical representations for feature selectivity in area V4. Previous work testing for the existence of color columns yielded conflicting results \citep{zeki_color_1973,zeki_representation_1980,schein_is_1982,yoshioka_neuronal_1996}, however the more recent studies ubiquitously support their existence \citep{tootell_search_2004,kotake_organization_2009,conway_color-tuned_2009}. Our findings confirm these recent conclusions. It is perhaps worth noting the value in our approach to this question. That is, none of the previous studies recorded neurons simultaneously along the layers of V4 as orthogonal penetrations of V4 are notoriously difficult. Thus, virtually all prior findings relied on indirect assessments of columnar functional architecture. Using MR-guided linear array recordings, we were able to measure simultaneous activity along V4 columns and thus confirm the existence of color columns. Moreover, our results shed some light on discrepancies in previous results. Specifically, we found that different columns prefer different colors, and that they do so at different relative strengths. This finding reconciles both previous functional imaging studies \citep{conway_specialized_2007,wade_fmri_2008,tanigawa_functional_2010}, as well as prior neurophysiological studies on color selectivity in V4 \citep{zeki_color_1973,zeki_representation_1980,schein_is_1982,schein_spectral_1990,roe_toward_2012,liebe_dissociable_2011,bushnell_shape_2012}. The variability we found at the laminar level might explain the conflicting results regarding the magnitude of selectivity across studies using differing methodological approaches. That is, we found that despite the existence of color columns, the strength of selectivity for a specific color along depth is not uniform. Instead, V4’s upper layers generally show stronger selectivity than the lower layers – but only in the unattended condition. This is consistent with previous findings along a different feature dimension - namely, orientation tuning \citep{pettine_laminar_2019} – perhaps suggesting a common attribute of feature processing in the absence of attention. Taken together, our results demonstrate that despite the existence of color columns, V4 color selectivity varies considerably in both the tangential and the radial dimension. 

One important limitation of the current study when considering precision in these color representations is that we were limited to testing only two colors out of the entire gamut available to trichromats. Moreover, our determination of isoluminance was based on a human ideal observer rather than psychophysical testing of each animal. Many of our conclusions thus are based 1) on the assumption that there were little to no luminance differences between our stimuli that could explain our results in the absence of hue selectivity and 2) that the evidence for red and green color columns can be extended to other parts of the color spectrum as well. We believe that assumption 1) can be justified by the fact that the absorption spectra of macaque and human L and M cones differ negligibly (\citep{nunn_spectral_1984}, but see \citep{dobkins_comparison_2000}) and that direct psychophysical evaluation suggests that humans and macaques perceive colors similarly \citep{gagin_color-detection_2014}. Importantly, evidence that we gathered from indirect measures suggested that luminance varied negligibly. Both response time \citep{tanaka_visual_2013} and neural response latencies \citep{geisler_responses_2007} vary with luminance of stimuli. In our dataset, we find no variability in these measures between the red and green stimuli (\href{tab:Tab2.3}{Table 2.3}). Furthermore, previous work has found no support for a functional organization for luminance in V4 \citep{tootell_search_2004} making our interpretation that the columnar organization we observe is due to color-selectivity rather than luminance, the more parsimonious conclusion. Nonetheless, it is important to note that it cannot entirely be ruled out that at least some of the results we obtained are due to differences in luminance rather than hue. Similarly, while assumption 2) remains largely a conjecture, it rests on our direct findings for colors red and green as well as the array of previous studies providing indirect evidence for V4 color columns across a wider area of the gamut \citep{zeki_color_1973,zeki_representation_1980,conway_color-tuned_2009,kotake_organization_2009}. Further investigation is necessary to determine whether the laminar differences we found extend to different colors. Furthermore, given the representation and tuning for color varies across the cortical sheet \citep{bohon_representation_2016} and the limited portion of V4 where orthogonal laminar recordings can easily be achieved, additional variability might exist beyond what we observed here. While more general questions regarding the nuances of the laminar organization of color selectivity remain, the finding that V4 columns act as functional microcircuits for the processing of different color stimuli is consistent with the principles of a canonical cortical microcircuit \citep{douglas_canonical_1989,douglas_functional_1991,bastos_canonical_2012}.	

\begin{table}[]
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}{cccccccccc}
\multicolumn{1}{l|}{}                                                                                                                                   & \textit{\textbf{Dataset}}         & \textit{\textbf{n}}         & \textit{\textbf{\begin{tabular}[c]{@{}c@{}}Mean\\ Diff. (ms)\end{tabular}}} & \textit{\textbf{SD (ms)}}    & \textit{\textbf{\begin{tabular}[c]{@{}c@{}}95\% CI\\ (ms)\end{tabular}}} & \textit{\textbf{\begin{tabular}[c]{@{}c@{}}Median\\ Diff. (ms)\end{tabular}}} & \textit{\textbf{\begin{tabular}[c]{@{}c@{}}Signed\\ Rank\end{tabular}}} & \textit{\textbf{z}}          & \textit{\textbf{p}}                               \\ \hline
\multicolumn{1}{c|}{}                                                                                                                                   & Complete                          & 30                          & 0.30                                                                        & 5.21                         & -1.56, 2.17                                                             & -0.77                                                                         & 242                                                                     & 0.20                         & \multicolumn{1}{c|}{0.85}                         \\
\multicolumn{1}{c|}{}                                                                                                                                   & \cellcolor[HTML]{EFEFEF}Monkey Ca & \cellcolor[HTML]{EFEFEF}21  & \cellcolor[HTML]{EFEFEF}0.43                                                & \cellcolor[HTML]{EFEFEF}4.17 & \cellcolor[HTML]{EFEFEF}-2.22, 1.35                                     & \cellcolor[HTML]{EFEFEF}-0.98                                                 & \cellcolor[HTML]{EFEFEF}123.5                                           & \cellcolor[HTML]{EFEFEF}0.28 & \multicolumn{1}{c|}{\cellcolor[HTML]{EFEFEF}0.78} \\
\multicolumn{1}{c|}{\multirow{-3}{*}{\textit{\textbf{\begin{tabular}[c]{@{}c@{}}Behavioral RT Difference\\ (Red-Green Target Trials)\end{tabular}}}}}   & Monkey He                         & 9                           & 0.58                                                                        & 5.94                         & -3.30, 4.46                                                             & 1.02                                                                          & 23                                                                      & 0.06                         & \multicolumn{1}{c|}{0.95}                         \\ \cline{2-10} 
\multicolumn{1}{l}{}                                                                                                                                    & \multicolumn{1}{l}{}              & \multicolumn{1}{l}{}        & \multicolumn{1}{l}{}                                                        & \multicolumn{1}{l}{}         & \multicolumn{1}{l}{}                                                    & \multicolumn{1}{l}{}                                                          & \multicolumn{1}{l}{}                                                    & \multicolumn{1}{l}{}         & \multicolumn{1}{l}{}                              \\ \cline{2-10} 
\multicolumn{1}{c|}{}                                                                                                                                   & Complete                          & 445                         & -0.28                                                                       & 10.33                        & -1.24, 0.68                                                             & 0                                                                             & 45057                                                                   & 0.69                         & \multicolumn{1}{c|}{0.49}                         \\
\multicolumn{1}{c|}{}                                                                                                                                   & \cellcolor[HTML]{EFEFEF}Monkey Ca & \cellcolor[HTML]{EFEFEF}310 & \cellcolor[HTML]{EFEFEF}0.17                                                & \cellcolor[HTML]{EFEFEF}9.90 & \cellcolor[HTML]{EFEFEF}-0.93, 1.28                                     & \cellcolor[HTML]{EFEFEF}-1                                                    & \cellcolor[HTML]{EFEFEF}22067                                           & \cellcolor[HTML]{EFEFEF}0.06 & \multicolumn{1}{c|}{\cellcolor[HTML]{EFEFEF}0.95} \\
\multicolumn{1}{c|}{\multirow{-3}{*}{\textit{\textbf{\begin{tabular}[c]{@{}c@{}}Neural Response Latency\\ Difference (Red-Green in RF)\end{tabular}}}}} & Monkey He                         & 135                         & -1.26                                                                       & 11.21                        & -3.15, 0.62                                                             & 0                                                                             & 4106                                                                    & 1.07                         & \multicolumn{1}{c|}{0.28}                         \\ \cline{2-10} 
\end{tabular}}
\caption{Comparisons of behavioral RT and neural response latency between red and green stimuli show no evidence for differing luminance between stimuli}
\subcaption{Measurements and statistics of the differences in response time (RT) for red vs. green target trials (top) and the differences in neural response latency for red vs. green stimuli in the column’s receptive field (RF) (bottom). Results reported for the complete dataset as well as each monkey individually. Behavioral RT taken as the initiation time of the saccadic response. Neural response latency taken as the time at which the multiunit spiking activity exceeded 30\% of its maximum response. Mean difference, standard deviation (SD), 95\% confidence interval (CI), and median difference. Statistical differences, or a lack thereof, reported using a Wilcoxon signed rank test yielding a signed rank statistic, z value, and p value.}
\label{tab:Tab2.3}
\end{table}

Perhaps our most surprising result was not just that the attention enhanced feature selectivity fluctuated in a beta-rhythm, but also that the beta-rhythm was present in our neural activity regardless of attention condition. This result might suggest that the beta-rhythm observed in our data is not necessarily generated through a cognitive process, but is rather more associated with sensory or perceptual processing. Support for this conclusion can be found in other work. Notably, direct modification of low-frequency neural oscillations in V4 through optogenetics impairs perception \citep{nandy_optogenetically_2019}. However, other work suggests that these low-frequency oscillations are top-down in origin and regulate the enhancement of bottom-up responses \citep{richter_top-down_2017} and are not dependent on early visual cortical areas \citep{schmiedt_beta_2014}. Other studies more generally suggest a top-down role for beta activity with respect to attention \citep{lopes_da_silva_dynamic_1970,bekisz_attention-dependent_2003,buschman_top-down_2007,bosman_attentional_2012,grothe_switching_2012}, subjective visual detection \citep{wilke_local_2006,maier_divergence_2008}, working memory \citep{tallon-baudry_attention_2005,salazar_content-specific_2012}, and other phenomena involving feedback \citep{bao_perceptual_2010}. As our data is limited to V4, we cannot directly test for a source for the beta oscillations. It is noteworthy, however, that the beta rhythm in both attention conditions has associated current sinks in both the granular, feedforward input layer and in the superficial layers of cortex. Perhaps more insight can be gained from exploring the putative functional architecture that would be supporting these findings as well as the instantiating mechanism.

One consideration is the array of afferent and efferent connections of V4. Area V4 seemingly assumes an important position in the visual cortical hierarchy with respect to attentional selection and modulation when considering its anatomical connections. It shows attentional modulation across various tasks and has connections to both earlier and later visual cortical areas as well as connections to frontal attentional control structures \citep{schall_topography_1995,roe_toward_2012,ungerleider_cortical_2008,pouget_visual_2009}. Moreover, these frontal structures have a demonstrable impact on sensory processing in V4 \citep{moore_selective_2003}. For example, inactivation of frontal eye field (FEF) at sites corresponding retinotopically to recorded V4 neurons reveals that FEF contributes to the presaccadic specification of visual target features \citep{noudoost_distinct_2014}. Also of note, inactivation of ventral prearcuate areas (VPA) in frontal cortex, eliminates effects of feature attention in V4 \citep{bichot_role_2019}. 

Assuming the beta-oscillation we describe has a prefrontal origin, the target of frontal feedback connections to V4 would likely be the extragranular layers \citep{douglas_canonical_1989,douglas_functional_1991,anderson_pathways_2011,bastos_canonical_2012}, which coincides with where we observe the difference in beta-generating CSD. However, it is worth noting that the deep layers of V4 have previously been shown to be most associated with behavioral outcome \citep{pettine_laminar_2019} whereas the behavior-associated synaptic activity in our study was located to the upper layers of V4. This apparent discrepancy could be reconciled by upper-layer synaptic activity perhaps driving apical dendrites from deep-layer visual cortical neurons \citep{mountcastle_columnar_1997}. Regardless, the extragranular nature of this signal might suggest that top-down connections play a role in our findings.

Models of visual attentional selection promote an important role for area V4 interacting with other visual cortical and frontal areas \citep{hamker_reentry_2005}. With these connections and interactions in mind, the connections between frontal areas like FEF or VPA and V4 are one likely source for the mechanism manifesting the frequency-gating we observe in sensory processing and seem a good candidate for further investigation. Specifically, frontal attentional control structures might excite V4 in a beta-rhythmic fashion and with laminar specificity such that targets of attention are enhanced only with that rhythm. That is, frontal structures could regulate the bottom-up neural response such that the recipients of V4 output are more sensitive to behaviorally-relevant information. However, recent work also provides causal evidence of a role of posterior parietal cortex in pop-out modulation and the saccadic selection of salient stimuli \citep{chen_parietal_2020}. Given the interconnectivity of V4 and parietal areas \citep{ungerleider_cortical_2008} coupled with these findings, posterior parietal cortex becomes another possible source for the results reported here.

Recent reports have demonstrated a link between spiking activity and low-frequency oscillations that is predictive of behavioral outcome in fronto-parietal attention networks \citep{fiebelkorn_spike_2021}. Given the similarity with our findings, it seems a link could be hypothesized between this network and the sensory processing stage we document here. However, it is important to note that their findings were restricted to visuomovement neurons in FEF. In another attention task visual neurons in FEF, but not movement or visuomovement neurons, showed enhanced gamma frequency synchronization with activity in V4 \citep{gregoriou_cell-type-specific_2012}. Yet, it may also be that the populations of functional cell types in FEF are more nuanced than previously thought, so alternative labeling of these populations might reveal a link through functional mappings \citep{lowe_functional_2018}. While speculative, this hypothesis can be tested empirically to determine whether there is a direct link between the behavior-associated beta found in the oculomotor system and that found at the sensory processing stage documented here.

In considering possible instantiating mechanism neural pathways, it is also useful to consider the nature and conditions of the attention allocation. Specifically, do these findings arise from an endogenous, goal-directed process or from and exogenous, stimulus-driven mechanism. Pop-out singleton stimuli are known to capture attention \citep{theeuwes_visual_1993,folk_involuntary_1992,luck_progress_2021,cosman_prefrontal_2018}. The exogenous factors of the task used in this study are known to modulate V4 activity when endogenous factors are controlled for \citep{burrows_influence_2009}, but endogenous saccadic preparation can also modulate V4 activity \citep{steinmetz_eye_2014}. Thus, the findings of this paper could result through exogenous or endogenous means.

Speculatively, an exogenous mechanism seems most plausible. This rests on three independent observations: 1) The animals’ task was efficient pop-out search, which is thought to involve exogenous, stimulus-driven attention. 2) The V4 response to the array of visual stimuli comprises a beta-component regardless of attention condition, suggesting that attention-related feedback is not necessary for beta-range rhythmicity to emerge in V4. 3) The beta-modulated feature selectivity arises within the initial transient response, before feedback can evolve. To bolster support for point \#3, enhancement of feature selectivity should be significant before feedback activity (about 100 ms following array onset \citep{mehta_intermodal_2000,mehta_intermodal_2000-1}, see Fig. S6). Sensory cortex may be sufficient to select the attentional target in this task without frontal or parietal help. Models of exogenous selection can locate salient items solely through local lateral inhibition (e.g., \citep{itti_saliency-based_2000,soltani_visual_2010}). Taken together, these factors are consistent with the conjecture that the beta-gated enhancement with attention arises through a bottom-up process. It is noteworthy that slow, rhythmic fluctuations of V4 responses coupled to attention have previously been shown to emerge through competitive interactions between neighboring neurons \citep{kienitz_theta_2018}. However, as noted, the dissociation of exogenous and endogenous attentional components in this task is indirect, so further work is required to disentangle them.

\begin{figure}[!t]
\centering
\includegraphics[width=7.69cm]{Figures/ch2-11.pdf}
\caption{Feature selectivity is enhanced early in response regardless of behavioral response timing}
\subcaption{Correctly performed trials were median split such that they were divvied into the ’fast 50\%’ of trials (purple) and the ’slow 50\%’ of trials (orange). Feature selectivity was computed for each recording site for both the fast and slow trials independently. Data contributing to the feature selectivity computation were restricted to the time 60 ms following array onset to 100 ms following array onset as to only capture the transient feedforward visual response. The 14 columns which showed significant enhancement of feature selectivity with attention, totaling 210 recordings sites, were included in the comparison. Normalized feature selectivity was used to accommodate data from both red- and green-preferring columns. Significant enhancement of feature selectivity with attention was found for both fast ($Z=6.82$, $p=9.4e^{−12}$) and slow ($Z=6.02$, $p=1.7^{e−9}$) trials through a Wilcoxon signed rank test during this early response epoch - as indicated by the asterisk next to each histogram. No significant difference was found between the fast and slow trials ($Z=1.64$, $p=0.10$).}
\label{fig:Fig2.11}
\end{figure}

\section{Methods}
\subsection{Surgical Procedures and MRI}
Monkeys were implanted with a custom head post and MR-compatible recording chamber (Crist Instruments) concurrent with a craniotomy over V4. Chamber placement was confirmed with structural MRI (Fig. 1). Recording sites were also confirmed postmortem in one monkey using DiI injections \citep{westerberg_priming_2020} (He). A third monkey (Z, \textit{Macaca mulatta}, 12.0 kg), exclusively contributing MR scans for 3D renders, also underwent similar surgical procedures. Tranquilization was performed under aseptic conditions with the animals under N$_{2}$0/0$_{2}$, isoflurane (1–5\%) anesthesia mixture. Vital signs were monitored continuously. Expired PCO$_{2}$ was maintained at about 4\%. Postoperative antibiotics and analgesics were administered. Further elaboration of surgical methods is documented elsewhere \citep{westerberg_priming_2020,westerberg_performance_2020}. MR images for chamber localization and guiding linear electrode penetrations perpendicular to the cortical surface were taken from anesthetized animals placed inside a 3T MRI scanner (Philips). Vital signs were monitored continuously. T1-weighted 3D MPRAGE scans were acquired with a 32-channel head coil equipped for SENSE imaging. Images were acquired using a 0.5 mm isotropic voxel resolution with the following parameters: repetition time 5 s, echo time 2.5 ms, and ﬂip angle 7°. 2D and 3D renders were performed offline using the multi-image analysis GUI, “Mango” (Research Imaging Institute, UTHSCSA).

\subsection{V4 Localization}
Recordings took place on the convexity of the prelunate gyrus in approximately the dorso-lateral, rostral aspect of the V4 complex, where receptive fields are located at 2–10 dva eccentricity in the lower contralateral visual hemifield \citep{gattass_visuotopic_1988}. Laminar recordings must take place at a location where the array can be positioned orthogonal to the cortical surface, which limits potential locations. Recording sites were confirmed through staining the recording site locations by dipping the electrode arrays in DiI on the final recordings in monkey He (Fig. 1). Recording locations are highlighted in Fig. 1 on MR scans for each monkey. 

\subsection{Stimuli and Task}
Monkeys viewed arrays of stimuli presented on a CRT monitor with 60 Hz refresh rate, at 57 cm distance. Stimulus presentations and task timing were achieved using TEMPO (Reflective Computing). Visual presentations were monitored with a photodiode positioned on the CRT monitor so that electrophysiological signals could be reliably aligned offline. Due to study constraints, we were limited to red (CIE coordinates: $x=0.648$, $y=0.331$) and green circles (CIE coordinates: $x=0.321$, $y=0.598$), rendered isoluminant to a human observer at 2.8 cd/m\textsuperscript{2} on a uniform gray background. As the stimuli were rather limited and determination of isoluminance was based on a human ideal observer rather than individual measurements for each of the monkeys included in the study, we define our test features as the red and green stimulus. Because we are limited to two colors and cannot account for potential differences in perceived luminance between the macaques, we qualify our two stimuli as distinct ‘features’ as the intersection of color and luminance information. Nonetheless, we report the information regarding the colors used in this study for the ideal human observer. Cone excitation was computed from the CIE coordinates and luminance \citep{cole_computation_1992}: 

\begin{eqnarray*}
\epsilon_L&=&Y\cdot\left[0.15514\cdot\frac{x}{y}+0.54312-0.03286\cdot\frac{(1-x-y)}{y}\right] \\
\epsilon_M&=&Y\cdot\left[-0.15514\cdot\frac{x}{y}+0.45684-0.03286\cdot\frac{(1-x-y)}{y}\right] \\
\epsilon_S&=&Y\cdot\left[0.00801\cdot\frac{(1-x-y)}{y}\right]
\end{eqnarray*}

\noindent where the following cone excitations were measured for red stimulus: $\epsilon_L=2.37$, $\epsilon_M = 0.43$, $\epsilon_S = 0.0014$; green stimulus: $\epsilon_L = 1.74$, $\epsilon_M = 1.06$, $\epsilon_S = 0.0030$; and the background: $\epsilon_L = 1.86$, $\epsilon_M = 0.94$, $\epsilon_S = 0.023$. Cone contrasts were then computed by:

\begin{eqnarray*}
C_L&=&\frac{(\epsilon_{L,Stimulus}-\epsilon_{L,Background})}{\epsilon_{L,Background}} \\
C_M&=&\frac{(\epsilon_{M,Stimulus}-\epsilon_{M,Background})}{\epsilon_{M,Background}} \\
C_S&=&\frac{(\epsilon_{S,Stimulus}-\epsilon_{S,Background})}{\epsilon_{S,Background}}
\end{eqnarray*}

\noindent where cone contrasts for red stimulus were found to be: $C_L = 0.27$, $C_M = -0.54$, $C_S = -0.94$; and the green stimulus: $C_L = -0.06$, $C_M = 0.13$, $C_S = -0.87$. 

Trials were initiated when monkeys fixated within 0.5 degree of visual angle (dva) a small, white fixation dot (diameter=0.3 dva). The time between fixation acquisition and array presentation varied between 750–1250 ms, taken from a function designed to produce a nonaging foreperiod as to eliminate any potential effect of stimulus expectation \citep{nickerson_response_1969,naatanen_diminishing_1970,naatanen_non-aging_1971}. Taking fixation period times from a nonaging foreperiod function results in sets of trials with a non-uniform probability of a given foreperiod duration with a bias towards shorter foreperiods with a reduction in probability as duration increases within the range. Following the fixation period, the stimulus array consisting of 6 items was presented. Stimuli were scaled with eccentricity at 0.3 dva per 1 dva eccentricity so that they were smaller than the estimated V4 receptive field size (0.84 dva per 1 dva eccentricity \citep{freeman_metamers_2011}). The polar angle positioning of the items relative to fixation varied from session to session so that one item of the stimulus array was positioned at the center of the receptive field. Items were spaced such that only one item was in the V4 column receptive field, with uniform spacing in polar angle and equal eccentricity. Monkeys engaged in a task while viewing the stimulus array. One item in the array was a different feature (red or green) from the others. The oddball item could be in any one of the six positions of the stimulus array. Position of the oddball on each trial was randomly chosen with equal probability for any of the positions (16.6\%). Monkeys earned fluid reward for shifting gaze directly to the oddball item within 1000 ms of array presentation and maintaining fixation within a 2–5 dva window around the oddball for 500 ms. Additionally, eye movements were monitored continuously at 1 kHz using an infrared corneal reflection system (SR Research). If the monkey failed to look at the oddball, no reward was given and a 1-5 s timeout ensued.

\subsection{Neurophysiological Procedure}
Laminar V4 neurophysiology data were acquired at 24.4 kHz resolution using a 128-channel PZ5 Neurodigitizer and RZ2 Bioamp processor (Tucker-Davis). Raw signals were filtered online and output between 0.1 Hz and 12 kHz. Data were collected from 2 monkeys (left hemisphere, monkey Ca; right hemisphere, He) across 70 recording sessions (31, monkey Ca; 39, He) using 32 channel linear microelectrode arrays with 0.1 mm interelectrode spacing (Plexon). Each recording session, electrode arrays were introduced into the prelunate gyrus through the intact dura mater using a custom micromanipulator (Narishige). Electrode arrays were positioned so they spanned all layers of V4 and had a subset of electrodes positioned outside of cortex. 30 (21, monkey Ca; 9, He) of 70 sessions were included in the final analysis. The remaining 40 sessions were found to either not have a discernable CSD profile for laminar alignment or not be orthogonal to the cortical surface and were thus removed from analysis as to only include electrode penetrations where we had a high degree of confidence that they were positioned precisely.

\subsection{Receptive Field Mapping}
To determine the orientation and eccentricity of the visual receptive field, monkeys performed a receptive field mapping task prior to the main (feature search) item task. Monkeys fixated for 400–7000 ms while a series of 1 – 7 stimuli were presented that spanned the visual field contralateral to the recording chamber. Stimuli were five high-contrast concentric white and black circles that scaled in size with eccentricity (0.3 dva per 1 dva eccentricity). In all recording sessions, stimuli could appear in any location spanning the lower visual quadrant contralateral to the recording chamber placement in 5° angular increments relative to fixation and in eccentricities ranging from 2 dva to 10 dva in 1 dva increments. Each stimulus was presented for 200–500 ms with an interstimulus interval of 200–500 ms. If the monkey maintained fixation for the duration of the stimulus presentation sequence, they received a juice reward. During this receptive field mapping task, multiunit activity, gamma power, and evoked local field potentials (LFPs, 1–100 Hz) were measured across all recording sites \citep{cox_temporal_2019,cox_receptive_2013,dougherty_binocular_2019,westerberg_v1_2019}. Online, we measured the response across visual space for each recording site. Recordings proceeded to the feature search task if there was qualitative homogeneity of receptive fields along depth (Fig. 1) similar to those described previously \citep{nandy_laminar_2017}. The receptive field center was chosen to be the stimulus location that evoked the largest response along the depth of recording sites. Following receptive field identification, the stimulus array in the feature search task was then oriented so that its eccentricity coincided with the location of the receptive field (eccentricity: 3–10 dva) and a single array item was placed at the center of the receptive field (size: 0.9–3 dva).  

\subsection{CSD and Laminar Alignment}
Positions of the individual recording sites relative to the layers of V4 were determined using current source density (CSD) analysis. CSD reflects local synaptic currents resulting from excitatory and inhibitory postsynaptic potentials \citep{mitzdorf_current_1985}. CSD was computed from the raw neurophysiological signal by taking the second spatial derivative along the electrode contacts \citep{nicholson_theory_1975,schroeder_spatiotemporal_1998,maier_distinct_2010,westerberg_v1_2019}. CSD activation following presentation of a visual stimulus reliably produces a specific pattern of activation which can be observed in primate visual cortex \citep{schroeder_spatiotemporal_1998,maier_distinct_2010}, including V4 \citep{givre_contribution_1994,bollimunta_neuronal_2008,nandy_laminar_2017}. Specifically, current sinks following visual stimulation first appear in the granular input layers of cortex, and then ascend and descend to the extragranular compartments. To compute the CSD from the LFP, we used the previously described procedure \citep{nicholson_theory_1975}:

$$CSD_{t,d}=-\sigma\left(\frac{x_{t,d-z}+x_{t,d+z}-2x_{t,d}}{z^2}\right)$$

\noindent where the CSD at timepoint $t$ and at cortical depth $d$ is the sum of voltages $x$ at electrodes immediately above and below ($z$ is the interelectrode distance) minus 2 times the voltage at $d$ divided by the interelectrode-distance-squared. That yields the voltage local to $d$. To transform the voltage to current, we multiplied that by $-\sigma$, where $\sigma$ is a previously reported estimate of the conductivity of cortex \citep{logothetis_vivo_2007}. For each recording session, we computed the CSD and identified the initial granular input sink following visual stimulation. Sessions were aligned using the bottom of the initial feedforward input sink as a functional marker. We defined the size of individual laminar compartments uniformly relative to space. Throughout, ‘middle’ refers to the estimate of the granular input layer 4 (0.5 mm space above the CSD functional marker), ‘upper’ refers to the estimate of supragranular layers 2 and 3 (0.5 mm space above the L4 compartment), and ‘lower’ refers to the estimate of infragranular layers 5 and 6 (0.5 mm space below the L4 compartment). CSD was then also used to recalculate the gamma activity as our measure of the localized neural activity.

\subsection{Locally Generated Gamma Recalculation}
LFP measured from an electrode contains far field, volume-conducted signals which challenge the interpretability of results concerning localized function. To understand local circuit interactions, such as columnar organization of feature preferences, additional steps can be taken to account for the volume-conducted signals. The configuration of microelectrodes in the linear array provides the opportunity to recalculate the low-frequency LFP signal without volume-conducted components. Specifically, we calculated the locally generated component of the LFP from the measured laminar CSD ($LFP_{calc}$) using a previously described model \citep{nicholson_field_1971,kajikawa_how_2011}:

$$LFP_{calc}(d_i,t)=A\sum_j\frac{CSD_{d_j,t}}{\sqrt{h^2+|d_j-d_i|^2}}$$

\noindent where $LFP_{calc}$ at depth $i$ ($d_i$) for each timepoint $t$ is taken as the sum of CSD at depths $j$ ($d_j$) for each timepoint divided by the Euclidean distance to account for the attenuating impact of local currents on distant field potentials. The factor value of $A$ acts is only as a scaling factor. Because we cannot accurately estimate the magnitude of the one-dimensional CSD-derived waveform, we eliminate this parameter from the calculation. This omission is consistent with previous reports \citep{kajikawa_how_2011} but limits the interpretability of locally generated $LFP_{calc}$ signals as the absolute magnitude of the response is only an estimation. However, relative magnitude differences can readily be observed between conditions with this method. Also, for our purposes, we set $h$ to 0 as we assume that our observed CSD and the recalculated LFP are colocalized.

From the recalculated LFP, gamma power was extracted using previously reported filtering methods \citep{maier_distinct_2010}. Briefly, the recalculated LFP signal at each recording site was band-pass filtered for the desired frequency band (gamma, 70–150 Hz). This signal was also filtered for high theta (7-8 Hz), alpha (8-15 Hz), and beta (15-30 Hz) where necessary, but did not go through the subsequent power conversion. For the gamma, the signal was then full-wave rectified and low-pass filtered at half the frequency of the cut off of the original high-pass filter to obtain power. 4\textsuperscript{th} order Butterworth filters were used for both steps. Gamma power was used for both the online receptive field mapping and for offline analysis of neural activity along cortical depth. Gamma responses were baseline corrected at the trial level by subtracting the average activation in the 300 ms preceding search array presentation from the response at all timepoints. This baseline correction procedure was performed prior to any further analysis of the data. Data were clipped 10 ms prior to the saccade at the trial level to eliminate any potential influence of eye movements.


\subsection{Feature Selectivity Index}
Feature (red vs. green) selectivity was derived from the gamma power observed along recording sites. Responses were taken when a red stimulus was presented to the receptive field (RF) of the cortical column and when a green stimulus was present in the receptive field. Only trials where the stimulus was unattended (e.g., not the array oddball/saccade target) were included in the initial analysis of feature selectivity. For later analyses where comparisons of attended and unattended conditions were made, trials where the oddball feature stimulus was in the RF or outside the RF were used, respectively. The responses were taken as the average activation 50–150 ms following array presentation. An index was computed from these responses, referred to hereafter as the feature selectivity index (FSI). Feature selectivity index for recording site $i$ from cortical column $j$ and attention condition $k$ ($FSI_{i,j,k}$) was defined as the difference between the mean response $x$ for the recording site on trials where the stimulus was red ($x_{red,i,j,k}$) and when the stimulus was green ($x_{green,i,j,k}$) divided by the sum of those two responses:

$$FSI_{i,j,k}=\frac{x_{red,i,j,k}-x_{green,i,j,k}}{x_{red,i,j,k}+x_{green,i,j,k}}, \ \ \ where \ i=1,\ldots ,15, \ j=1,\ldots ,30, \ k=1,2$$

\noindent this provided a value bounded between -1 and 1 where larger magnitude indices indicate a greater degree of selectivity for either green (towards -1) or red (towards 1). A columnar feature selectivity index (CFSI) was taken as the average of FSI across the column. 

For certain computations, feature preference is unimportant because the interpretation of the result depends on the presence not the quality of the preference. In those circumstances, we normalized FSI ($nFSI_{i,j,k}$) and CFSI ($nCFSI_{j,k}$) to change the sign of each feature selectivity index so that +1 indicates selectivity towards the preferred color of the column and -1, the non-preferred color of the column. Being based on a specific feature preference, this adjusted index was only useful for columns selective for a color feature.

\subsection{Feature Selectivity Index Statistics}
To evaluate whether columns showed significant feature selectivity and whether they were consistent along depth at the single session level, we generated null distributions for which to compare the experimental data to. We did so by taking the FSI measured for all recording sites ($n=15$) across all columns ($n=30$), totaling 450 indices, and randomly selected 15 values, 1000 times. In this way, we ended up with a large number of distributions that comprised measured data, but that did not retain the effect of the individuality of a column. That is, these null distributions represent columns where the variability within and across columns is identical. That way any difference observed when comparing to these bootstrap columns is a result of the effect of the column. To evaluate whether individual columns showed significant feature selectivity for either red or green stimuli, we employed Wilcoxon signed rank tests. The Wilcoxon signed rank test does not make any assumption of homogeneity of variance like standard t-tests or ANOVAs, which is relevant given our second test. We performed this test 1000 times for each column, comparing each observed column to the bootstrap columns. We took the median $p$-value resulting from those repeated tests to determine whether a column had significant feature selectivity. To evaluate whether individual columns were consistent in their FSI’s along depth, we employed Bartlett’s test. We used the same procedure as the Wilcoxon signed rank tests to evaluate significant consistency along depth for each session ($p<0.05$).

\subsection{Intraclass Correlation Coefficient}
To assess the consistency of feature selectivity along a cortical column relative to the variability that might exist across columns, we computed an intraclass correlation coefficient (ICC) \citep{shrout_intraclass_1979} of type ICC(C,1) \citep{mcgraw_forming_1996}. Under this type of ICC, we are interested in the degree of consistency among measurements made under the fixed levels of recording site locations (e.g. their individual positions along cortical depth). We used a 2-way mixed effects model without an interaction: 

$$FSI_{i,j,k}=\mu+c_{j,k}+d_{i,j,k}+e_{i,j,k}, \ \ \ where \ i=1,\ldots ,15, \ j=1,\ldots ,30, \ k=1,2$$

\noindent where $FSI_{i,j,k}$ is the FSI for column $j$ at depth/recording site $i$ and attention condition $k$, $\mu$ is the population mean for all observations and is constant. $c_{j,k}$ is the cortical column effect which is random, independent, and normally distributed with mean 0 and variance $\sigma^2_c$. $d_{i,j,k}$ is the effect of the recording site depth, which is fixed so that $\sum{d_{i,j,k}}=0$ and has variance $\theta_{d}^{2}=\sum{d_{i,j,k}^{2}/(m-1)}$ where $m$ is the number of recording sites for each column, 15. $e_{i,j,k}$ are the residual effects which are random, independent, and normally distributed with mean 0 and variance $\sigma_e^2$. All effects are pairwise independent. For ICC(C,1) the true value of $\rho$ is defined as: 

$$\rho=\frac{\sigma^2_c}{\sigma^2_c+\sigma^2_e}$$

\noindent where $\rho$ is bounded between 0 and 1. 0 indicates a lack of consistency within the cortical column relative to the variability that exists across columns and 1 indicates the recording sites along depth are highly consistent relative to the variability across columns. We estimate $\rho$ with $\hat{\rho}$ as: 

$$\hat{\rho}=\frac{MS_C-MS_E}{MS_C+(m-1)MS_E}$$

\noindent where $MS_C$ is the mean square for cortical columns and $MS_E$ is the mean square error. We computed $\hat{\rho}$ for both monkeys individually as well as for the data spanning both monkeys. We also computed $\hat{\rho}$ for a shuffle control where null distributions for feature selectivity along depth were taken at random from all experimental data 1000 times. In computing $\hat{\rho}$ from this distribution, we gain a sense of the consistency in feature selectivity along depth if there was no specificity to column. 

\subsection{Layer Comparisons}
While we observed consistency in feature preference along depth within a cortical column, we sought to investigate whether laminar differences exist with respect to the strength of selectivity. We did so by computing an index for each recording site that indicated whether the selectivity at that recording site was greater or less than the mean FSI for the cortical column in which it was recorded from. Hereafter we refer to this index as the strength of feature selectivity (SFS) where the strength of feature selectivity for recording site $i$ in cortical column $j$ ($SFS_{i,j,k}$) is taken as the difference between the unsigned FSI for recording site ($nFSI_{i,j,k}$) and the mean unsigned feature selectivity index for the cortical column which contains it ($nCFSI_{j,k}$):

$$SFS_{i,j,k}=nFSI_{i,j,k}-nCFSI_{j,k}, \ \ \ where \ i=1,\ldots ,15, \ j=1,\ldots ,30, \ k=1,2$$

Pairwise Wilcoxon signed rank tests were performed between each of the laminar compartments where all recording sites located within a laminar compartment contributed ($n=75$ per compartment). This was done to evaluate whether there was a significant shift in SFS with depth. The same test methodology was used to evaluate the relative enhancement.

\subsection{Bayesian Modeling}
To further determine the likelihood of a columnar structure to feature representations in V4, we modeled feature selectivity as a function of attention, column, and/or depth of recording site by generating five models and performing direct model comparisons using a Bayesian multilevel framework. Specifically, four Bayesian mixed effects models as well as a null Bayesian model were computed for comparisons. All Bayesian modeling was performed in R using the brms and performance packages and evaluated with the loo package. Model statements, prior distributions, and resultant statistics are reported in \href{tab:Tab2.1}{Table 2.1}. All models began with a non-informative prior distribution. For model evaluation, 5000 samples were taken from the posterior distribution from each of 4 independent chains using STAN’s Hamiltonian Monte Carlo algorithm via the RStan interface. For model comparison, we computed the leave-one-out cross-validation expected log predictive density (LOO-ELPD) and Bayes factor for each experimental model relative to another.

\subsection{Fourier Transform of Feature Selectivity Index and Gamma Response}
To estimate the temporal profile of feature selectivity, a feature selectivity index (FSI) for each recording site was computed at each timepoint. The timeseries of FSI was then subjected to a Fourier transform (FFT) to estimate the power of the oscillation. The FFT was performed on two epochs, one including the transient visual response (60-250 ms following search array onset) and the other omitting the transient visual response (100-250 ms following search array onset) to control for contamination in the Fourier space by the transient response. Where shown (e.g., Fig. 3), 95\% confidence intervals were estimated by subsampling 80\% of the data, 1000 times. 

FFT was also used to estimate the power of certain frequencies (7-30 Hz) found in the high gamma response. Where applicable (e.g., \href{tab:Tab2.2}{Table 2.2} \href{fig:FigS5}{Fig. S5}), the power datapoints used for modeling were taken as the average power in that frequency band ($\theta$, 7-8 Hz; $\alpha$, 8-15 Hz; $\beta$, 15-30 Hz). Only the response excluding the transient (100-250 ms following search array onset) were included for these analyses.

\subsection{Microsaccade Detection}
Variation in the production of microsaccades is one potential explanation for the observations. To address this possibility, we traced microsaccades using a openly available toolbox utilizing unsupervised clustering \citep{otero-millan_unsupervised_2014}. Using this method we identified trials in which a microsaccade occurred between search array onset and saccadic response time as well as the times in which they occurred.

\subsection{Proportional Odds Logistic Regression Modeling}
To model the relationship between low frequencies ($\theta$, 7-8 Hz; $\alpha$, 8-15 Hz; $\beta$, 15-30 Hz) embedded in the locally generated high gamma response and behavioral response time (RT), we fit a proportional odds logistic regression (POLR), also referred to as an ordered logit model. Under this model framework, we group the outcome (RT) into four ordered categories based on their quartiles, namely ‘fastest’, ‘faster’, ‘slower’, and ‘slowest’, yielding 4 vincentized response time bins ($RT_{vin}$) For convenience, we refer to these quartiles as 1, 2, 3, and 4 respectively, forming the $V$ outcome categories. The POLR model uses the log odds of the ordinal outcome $RT_{vin}$ being less than or equal to a particular category $v=1,\ldots,V-1$ as the outcome, which can be simplified to the logit as:

$$log\left(\frac{P(RT_{vin}\le v)}{P(RT_{vin}>v)}\right)=logit(P(RT_{vin}\le v))$$

\noindent The full model parameterization in this case is then:

$$logit(P(RT_{vin}\le v))=\omega_v+\phi_1\theta+\phi_2\alpha+\phi_3\beta+\phi_4\theta A+\phi_5\alpha A+\phi_6\beta A$$

\noindent where $\omega_v$ is the intercept estimate specific to category $v$, and $\phi_1,\ldots,\phi_6$ are the coefficient estimates used to describe each category comparison. $A$ is an indicator variable for the attention condition. This model structure makes the assumption that the data meets the proportional odds assumption. More specifically, suppose that we define the proportions of the data that fall into each of these categories as $v_1$, $v_2$, $v_3$, and $v_4$ respectively. The proportional odds assumption says that the log odds of being in these categories are:

\begin{eqnarray*}
logit(P(RT_{vin}\le 1))&=&log\left(\frac{v_1}{v_2+v_3+v_4}\right) \\
logit(P(RT_{vin}\le 2))&=&log\left(\frac{v_1+v_2}{v_3+v_4}\right) \\
logit(P(RT_{vin}\le 3))&=&log\left(\frac{v_1+v_2+v_3}{v_4}\right) \\
\end{eqnarray*}

To meet the proportional odds assumption, the difference between each row of the above array needs to be the same number \citep{mccullagh_regression-models_1980}. Since we assigned the outcome based on the quartiles of the observed data, we know that $v_1=v_2=v_3=v_4=0.25$. Thus, the difference between rows 1 and 2 is $log(1)-log(0.25/0.75)=0.477$, and the difference between rows 2 and 3 is $log(3)-log(1)=0.477$. Thus, the assumption holds in this case. To further confirm that this assumption holds, we used Brant’s test \citep{brant_assessing_1990}, which fits a generalized ordered logistic regression that allows different regression coefficients $\phi_{1,v},\ldots,\phi_{6,v}$ to be estimated for each group comparison. It then tests the joint hypothesis that $\phi_{1,v}=\phi_1,\ldots,\phi_{6,v}=\phi_6$ for $v =1,2,3$. In both the attended and unattended model cases these tests were non-significant, further demonstrating that the proportional odds assumption is met by these data. 

The regression coefficients $\phi_1,\ldots,\phi_6$ represent the change in the log odds of the outcome $RT_{vin}$ being less than or equal to a particular category $v$ for every one unit increase in the corresponding covariate. By exponentiating these coefficients, we get the multiplicative proportional odds ratios, which have a similar interpretation to the odds ratios produced by a binary logistic regression.  

We fit the POLR model both under a frequentist framework, with coefficients estimated using maximum likelihood, and, for the sake of consistency with other models in the manuscript, under a Bayesian framework with coefficients estimated using MCMC sampling and non-informative priors. From the frequentist model, mean and 95\% confidence intervals for parameters are reported, and from the Bayesian model we report medians and 95\% credible intervals.

\subsection{Cross-frequency Coupling and Beta-Locked CSD}
We computed the coupling between our low-frequency (beta) and our high-frequency responses (gamma power). To do so we first detected troughs in each of the band-limited beta signals \citep{dougherty_ongoing_2017}. We triggered the time-varying gamma response from all recording contacts of the laminar probe to the time of beta troughs. More specifically, we averaged the gamma power response 100 ms preceding to 100 ms following the lowest point of the trough, the duration of more than one full beta frequency cycle. Only beta troughs during the time of the visual response leading up to the time of behavioral response, on a trial-by-trial basis, were included in this analysis. This procedure resulted in multiple beta-locked gamma response events for each trial and recording site. We averaged the trough-locked gamma response within each recording site and each trial. To account for possible differences in the magnitude of the gamma response between the layers of V4 (as exists in other visual cortical areas, \citep{maier_distinct_2010}), we calculated the difference between the average beta-locked gamma response and the average gamma response amplitude across the epoch for each recording site (e.g., depth) in each of the recorded cortical columns. Significance of coupling was determined by computing a t-score for each time point for each recording site. To calculate this t-score, we compared our results against a randomly shuffled control, obtained by shuffling procedure where we paired the beta signal from one trial with the gamma power response from a different, randomly selected trial, and did so for all trials and sessions in the data set. All statistics for cross-frequency coupling analyses were Bonferroni-corrected for multiple comparisons. We also extracted CSD around the time of the beta troughs in the same window as the coupling analysis.

\subsection{Multiunit Activity}
Spiking activity at the level of population multiunits was used for control analyses as it reliably reflects neural population dynamics \citep{trautmann_accurate_2019}. Detection of multiunit activity was done through previously described means \citep{legatt_averaged_1980}. This method has proved useful across brain areas and research groups \citep{logothetis_neurophysiological_2001,self_distinct_2013,shapcott_correlated_2016,tovar_stimulus_2020,westerberg_priming_2020,xing_laminar_2012}. Briefly, broadband neural activation was filtered between 0.3-3 kHz – the range were spiking activity predominates. The signal was then full-wave rectified and refiltered at half the original high-pass filter (0.15 kHz) thereby estimating the power of the multiunit activity. For filtering steps, we used a 4th-order Butterworth filter.