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# Decoding Covert Visual Attention in Space and Time from Neural Signals

Suliann Ben Hamed

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Suliann Ben Hamed. Decoding Covert Visual Attention in Space and Time from Neural Signals. Annual Review of Vision Science, 2025, 11, pp.495-520. 10.1146/annurev-vision-101322-011902. hal-05403577

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*Annual Review of Vision Science*

# Decoding Covert Visual Attention in Space and Time from Neural Signals

Suliann Ben Hamed

Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, University of Lyon 1,
Bron, France; email: benhamed@isc.cnrs.fr

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Annu. Rev. Vis. Sci. 2025. 11:495–520

First published as a Review in Advance on
August 6, 2025

The *Annual Review of Vision Science* is online at
vision.annualreviews.org

https://doi.org/10.1146/annurev-vision-101322-011902

Copyright © 2025 by the author(s). This work is
licensed under a Creative Commons Attribution 4.0
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## Keywords

attention spotlight, attention selection, distractor suppression, sustained attention, decoding, rhythmic attention

## Abstract

Visual attention prioritizes relevant stimuli in complex environments through top-down (goal-directed) and bottom-up (stimulus-driven) mechanisms within cortical networks. This review explores the neural mechanisms underlying visual attention, focusing on how attentional control is encoded and decoded from prefrontal signals in both spatial and temporal domains. Decoding methods enable real-time tracking of covert visual attention from prefrontal activity with high spatial and temporal resolution, as a neurophysiological proxy of the attentional spotlight. This research provides insights into stimulus selection mechanisms, proactive and reactive suppression of irrelevant stimuli, the rhythmic nature of attentional shifts and attentional saccades, the balance between focus and flexibility, and the variation of these processes along epochs of sustained attention. Additionally, the review highlights how recurrent neural networks in the prefrontal cortex contribute to supporting these attention dynamics. These findings collectively offer a comprehensive model of attention that integrates dynamic prioritization processes at short and longer timescales.
# 1. VISUAL ATTENTION AND ASSOCIATED CONCEPTS

## 1.1. Visual Attention and Associated Network Mechanisms

When one is observing a visual scene, attentional mechanisms prioritize specific objects, features, or areas for detailed processing while suppressing irrelevant information, which ensures that the brain focuses its limited cognitive resources on pertinent stimuli (Carrasco 2011, James 1890). This selective attention is fundamental to various cognitive processes such as perception, learning, memory, and decision-making (Petersen & Posner 2012, Posner & Petersen 1990). Attention can operate in different sensory modalities, allowing for selective focus across visual, auditory, and tactile domains to enhance perceptual processing and cognitive efficiency (Macaluso & Driver 2005) in complex multisensory environments (Talsma et al. 2010). In this article, we specifically focus on visual attention and its role in efficient processing of visual information.

Visual attention is crucial for directing our gaze and optimizing the processing of visual information. Overt attention and covert attention represent two different ways of allocating attention to visual stimuli. Overt attention involves the physical movement of the eyes toward a stimulus, directing the gaze to the focus of attention, while covert attention allows individuals to focus on objects or locations without moving their eyes (Carrasco 2011). The premotor theory of attention initially proposed that shifts in attention are inherently linked to the motor planning processes involved in eye movements, except for the very last eye movement execution signals (Rizzolatti et al. 1987). This view has, however, been nuanced. Indeed, the functional networks associated with eye movement and attention have been shown to be only partially overlapping (Corbetta 1998, Corbetta et al. 1998, Moore & Zirnsak 2017, Wardak et al. 2006). In spite of this, eye movements not only are guided by attention but also actively attract attention to the endpoint of the saccade, indicating a tight coupling between attentional shifts and oculomotor control (Deubel & Schneider 1996, Kowler et al. 1995). Both forms of attention are crucial for navigating complex environments by enabling detailed processing of relevant stimuli either directly or peripherally.

In the following, we specifically focus on covert visual attention and its underlying mechanisms. Two types of factors, each engaging distinct neuronal and network mechanisms, guide covert visual attention (Buschman & Kastner 2015, Corbetta & Shulman 2002). Bottom-up attention is driven by the inherent physical properties of stimuli, such as color, motion, and contrast, and is largely automatic and stimulus driven (Itti & Koch 2001). In contrast, top-down attention is governed by task demands, goals, or intentions and allows individuals to focus on specific objects or features while ignoring distracting elements (Desimone & Duncan 1995). Voluntary, goal-directed attention is mediated by the dorsal attention network, involving the frontal and parietal cortices, while automatic, stimulus-driven attention is managed by the ventral attention network, particularly in the temporoparietal junction and ventral frontal cortex (Corbetta & Shulman 2002). These networks are not entirely independent. The ventral attention network is described as a breaker circuit interrupting ongoing top-down attention when unexpected stimuli require immediate focus (Corbetta et al. 2008). The degree of functional coupling between these two networks depends on task demands (Buschman & Kastner 2015, Vossel et al. 2014). In particular, information propagates from the occipital cortex and the parietal cortex to the prefrontal cortex during bottom-up attention and from the prefrontal cortex to the parietal cortex and occipital cortex during top-down attention control (Buschman & Miller 2007, Gregoriou et al. 2009, Ibos et al. 2013, Katsuki & Constantinidis 2012) through cell type-specific coordination (Gregoriou et al. 2012), and the prefrontal cortex plays a key role in goal-directed attention and task-dependent processing (Ibos et al. 2013, Katsuki et al. 2014). In addition, when attention is guided by task goals, there is increased gamma-band synchronization between the prefrontal cortex and the posterior parietal cortex, prioritizing goal-relevant stimuli across different cortical areas (Buschman &
Miller 2007). When attention is driven by external stimuli, neuronal synchronization shifts toward lower frequencies, in the beta-band range, facilitating automatic, stimulus-driven processing.

While electrophysiological recordings, such as single-unit activity, multiunit activity, or local field potentials (LFPs), have illuminated rich patterns of attentional modulation across brain regions (e.g., the prefrontal and parietal cortex), these findings do not in themselves confirm that such activity is necessary for the behavior observed. Indeed, neurophysiological signals can reflect downstream correlates or epiphenomena, as demonstrated by instances in which attentional modulations in the visual cortex fail to affect behavior when the upstream source is perturbed (Moore & Zirnsak 2017, Schall 2004). In contrast, causal manipulations—including microstimulation (Ekstrom et al. 2008, Moore & Fallah 2004); inactivation (Wardak et al. 2004, 2006, 2012); and, more recently, optogenetics and chemogenetics (Hüer et al. 2024, Mendoza-Halliday et al. 2024) in animal models—provide direct evidence for the necessity of specific regions in attentional control. For example, reversible inactivation of the frontal eye fields disrupts covert attentional allocation and impairs visual performance (Wardak et al. 2004), whereas microstimulation can enhance perception at attended locations (Moore & Fallah 2004), strongly implicating the prefrontal cortex in causal control of attention. While parietal regions such as the lateral intraparietal area are also modulated during attention tasks, their inactivation tends to produce spatial neglect-like deficits, suggesting a more spatially localized or integrative role (Wardak et al. 2006). Causal methods thus allow one to precisely interrogate the functional role of each cortical area in attentional control.

## 1.2. Target Selection and Priority Maps

Target selection refers to the brain's ability to identify and prioritize stimuli that are relevant to the ongoing task in a cluttered environment. This process is typically associated with top-down attention. This selection process can operate either in physical space (Where's my cup?) or in feature space (Where's my red cup?). Attentional target selection is thought to rely on neuronal priority maps. These maps correspond to neural representations that combine sensory input with task relevance, helping to guide attention by highlighting locations or objects of importance in a scene on the basis of both bottom-up saliency and top-down goals (Fecteau & Munoz 2006). In the parietal cortex, particularly the lateral intraparietal area, priority maps are thought to integrate spatial and sensory saliency (Bisley & Goldberg 2010, Constantinidis & Steinmetz 2005). In the prefrontal cortex, priority maps are thought to prioritize task-relevant information to guide behavior, allowing for effective attentional target selection (Thompson & Bichot 2005). It is proposed that targets are selected from the priority maps through competitive interactions between stimuli (Desimone & Duncan 1995), where the most behaviorally relevant or salient items, as determined by both bottom-up sensory inputs and top-down goals, are enhanced for attentional focus while others are suppressed (Bisley & Goldberg 2010). Neuronal activity in the salience priority map predicts the goal and latency of eye movements in visual search tasks in which targets are presented embedded in distractors (Ipata et al. 2006).

In contrast, distractor suppression refers to the process by which irrelevant or distracting stimuli are inhibited to maintain focus on task-relevant information. Initial neurophysiological investigations showed that distractors are filtered out at the perceptual level, such that their representation does not propagate into the visual system (Luck & Hillyard 1994). It is proposed that the dorsal attention network is crucial for maintaining task goals and suppressing distractions, while the ventral attention network, including the anterior insula, can be triggered by unexpected distractors, requiring further suppression mechanisms (Vossel et al. 2014). The suppression of distractors can be implemented either reactively, i.e., following an initial processing stage, or
proactively, such that they are inhibited in anticipation before they occur (Di Bello et al. 2022, Gaspar & McDonald 2014).

**1.3. Attention Spotlight and Shifts in Attention**

The attentional spotlight is a psychological construct that refers to the mechanism by which attention is directed to a specific region or object in the visual field, enhancing the processing of information in that focused area while filtering out and suppressing irrelevant stimuli. Much like a spotlight in a dark room, it illuminates the selected portion of the environment, allowing for more detailed perception and cognitive processing, while leaving the surrounding areas in relative darkness (Posner 1980). This metaphor is widely used in cognitive psychology to explain how selective attention operates, and it facilitates efficient resource allocation in tasks. This attention spotlight is hypothesized to shift across the visual field, prioritizing certain features for detailed processing, in response to environmental changes, either under voluntary, top-down, endogenous or automatic, bottom-up, exogenous control. The attention spotlight is also shown to adjust to the structure of the environment in size, for example, to include multiple grouped objects, thus impacting the spatial resolution of attention (Eriksen & St. James 1986, Eriksen & Yeh 1985).

Behavioral evidence suggest that a highly dynamic attentional spotlight samples space rhythmically, operates at roughly 8 Hz, and critically shapes our perception (Dugué et al. 2015; Fiebelkorn et al. 2018, 2013; Gaillard et al. 2020; Landau & Fries 2012; VanRullen et al. 2007). Modeling of psychometric responses in humans has led to the proposal of a framework of a blinking attentional spotlight (VanRullen et al. 2007). Neurophysiological evidence supports these behavioral observations. Shifts in the attentional spotlight are thought to be orchestrated by coordinated changes in neuronal population activity. A first view proposes that, during top-down attentional shifts, there is rhythmic neural activity in the prefrontal cortex, particularly in the beta frequency range, which supports serial, covert visual search and target selection by discrete attentional shifts (Buschman & Miller 2009, 2010). A contrasting view provides evidence for a continuous exploration of space by the attentional spotlight, showing that attention moves rhythmically across the visual field at a default alpha frequency, regardless of task complexity (Gaillard & Ben Hamed 2022, Gaillard et al. 2020), and implements both selection and suppression mechanisms (Di Bello et al. 2022, Gaillard et al. 2020).

**2. DECODING VISUAL ATTENTION FROM NEURONAL SIGNALS**

**2.1. The Challenge of Decoding a Covert Cognitive Function**

Decoding covert cognitive functions, such as attention, from neural signals poses significant challenges due to their covert nature. Indeed, unlike overt motor actions that have clear external observable markers such as hand configuration or arm trajectory, cognitive processes are internal. As a result, time-locked events that can serve as reference points for decoding are missing (Gaillard & Ben Hamed 2021). Therefore, precisely tracking these processes in real time is difficult. One can only infer a temporal relationship between brain signals and covert attention on the basis of external observable events such as task instruction (orienting attention to a specific location) and successful perception (indicating successful attention orientation). Another challenge posed by the decoding of covert cognitive functions is due to the variability of their associated brain activity. For instance, noise correlations among neurons can limit the efficacy of decoding attention from the prefrontal cortex (Ben Hadj Hassen & Ben Hamed 2020, Tremblay et al. 2015b). Moreover, cognitive signals, such as those related to covert attention, are often distributed across large neuronal populations and fluctuate in time, at both short (Gaillard & Ben Hamed 2022, Gaillard et al. 2020) and longer (Amengual et al. 2022, Gaillard et al. 2021) timescales, further complicating efforts to
decode them on a single-trial basis. Such signals are also often multiplexed with other information such as engagement in the task (Amengual et al. 2022) or temporal discounting (Amengual et al. 2023), such that they appear noisy if these confounding signals are not properly modeled (Amengual & Ben Hamed 2021). Last, assessing the behavioral relevance of decoding readouts of cognitive information is critical in evaluating its validity and utility. The primary goal of this validation is to determine whether the decoded neural signals accurately reflect the target cognitive states and thus have real-world significance. Despite these challenges, temporally and spatially resolved tracking of covert attention has contributed to the advancement of our understanding of the neural bases of attention. We first summarize the main methods used for the decoding of attention from different types of neuronal signals. Then, we provide a critical assessment of the behavioral relevance of the decoded signals.

## 2.2. Decoding Methods

Decoding methods provide the computational tools needed to extract interpretable information from complex neural signals. This section outlines discrete, continuous, and two-step decoding strategies used to infer cognitive variables such as attention from neuronal activity on the basis of behavioral outcome.

### 2.2.1. Discrete decoding methods.
Discrete decoding methods aim to classify neural activity into distinct categories or predefined states (Figure 1). In the context of decoding motor control signals, such discrete decoders are, for example, designed to associate neuronal activity from the motor or premotor cortex, with observed hand configuration categories, such as flexion of a given digit or the shape of a specific grasp (Agudelo-Toro et al. 2024, Ben Hamed et al. 2007). In the context of decoding cognitive information, such discrete decoders are, for example, designed to associate neuronal activity from the prefrontal cortex with a categorical decision outcome, whether a yes/no, a left/right localization, or a match/nonmatch to sample response is provided (Astrand et al. 2016, Tremblay et al. 2015b). Discrete decoding typically involves training a classifier, such as a support vector machine (Cortes & Vapnik 1995) or a neural network (Hopfield 1982), to map neural signals like spiking activity or LFPs to discrete observable behavioral states. This approach is useful in applications where the goal is to detect specific decision outcomes or movement categories that can be mapped onto categorical labels. However, one limitation of discrete decoding is that it does not capture the full dynamic range of the outcomes and how they vary in time.

### 2.2.2. Continuous decoding methods.
Continuous decoding methods seek to map neural signals onto a continuous variable, allowing for the real-time tracking of ongoing cognitive or motor processes. These methods are essential when the goal is to continuously monitor the position of attention or the trajectory of a limb in motor control tasks, for example, to control a robotic arm. In contrast to discrete approaches, continuous decoding uses algorithms such as Kalman filters (Wu et al. 2002) and linear regression models (Paninski et al. 2007) to continuously estimate a variable from neural activity (Figure 1). For instance, Astrand et al. (2016) utilized continuous decoding to track the (x,y) spatial location of covert attention in macaque monkeys, showing that the decoded attentional spotlight could be mapped in real time across a two-dimensional space. The challenge in continuous decoding lies in achieving high spatial and temporal resolution while maintaining accuracy, especially when dealing with noisy and high-dimensional neural data. This challenge is addressed by increasing the size of the dataset on which the continuous decoders are initially trained, as well as optimizing the model fitting of the neuronal data to capture as much signal as possible from the data.
<figure><img src="image_7.png" /><figcaption>Figure 1</figcaption></figure>

Decoding visual attention from prefrontal cortex. (a) Dense multielectrode recordings from the prefrontal cortex (frontal eye fields). (b) Example of spiking activity from ten different neurons (N1 to N10) for four different attentional conditions (attention oriented down left, down right, up left, and up right) and five repetitions of each condition. (c) Single-trial responses of the ten neurons and discrete and continuous decoding outcome of this specific trial.

2.2.3. **Two-step continuous decoding methods.** Given the fact that covert attention signals are inherently challenging to train a decoder, due to the lack of time-locking observable events and their multiplexing with other information such as engagement in the task (Amengual et al. 2022) or temporal discounting (Amengual et al. 2023), a two-step decoding method has been introduced (De Sousa et al. 2021). This procedure involves first using a machine learning decoder to estimate the spatial location of the attentional spotlight from neuronal signals (spiking activity, or LFPs). This first step decodes the attention-related signals to track the (x,y) coordinates of the attentional spotlight. In the second step, the procedure improves upon the first by identifying trials where the decoded attention signals closely match the instruction received by the subjects [e.g., attention
oriented to a location of coordinates (8°,8°) on the screen, and decoded spotlight within 1° from this location]. These trials are referred to as high-information-content trials and are hypothesized to be rich in attention-related information. These trials contrast with low-information-content trials, in which the decoded attention signals deviate from the instruction received by the subjects; such trials are hypothesized to be noisy and to not precisely associate neuronal activity with the instructed location of attention. In this second step, the high-information-content trials are used to retrain the decoder, improving overall decoding accuracy and its correlation with behavioral performance (see Section 2.4). Such retraining procedures prevent underfitting by the decoder (Kitamura & Deible 2020, Niu et al. 2020). In addition, identifying representative samples in the training dataset (i.e., high-information-content trials) reduces the impact of the training dataset on overall decoding performance (Ghadikolaei et al. 2019).

## 2.3. Decoding Visual Attention from Population Neuronal Signals

With the development of increasingly sophisticated decoding methods, attention can now be tracked in real time from various types of neuronal signals. This section explores how different neuronal signals—ranging from single-unit to local field potentials—contribute to the decoding of covert visual attention.

### 2.3.1. Single-unit activity.
The prefrontal cortex and in particular the frontal eye fields are at the core of attention control (Buschman & Miller 2007, Ekstrom et al. 2008, Gregoriou et al. 2009, Ibos et al. 2013, Wardak et al. 2006). The inactivation of the frontal eye field impairs attention orientation (Wardak et al. 2006). Microstimulations of the frontal eye field enhance attention orientation, subsequent visual perception (Moore & Fallah 2001, 2004; Moore et al. 2003), and visual responses in the striate and extrastriate visual cortex (Ekstrom et al. 2008). In this context, a prerequisite for the decoding of cognitive information such as attention from neuronal population activity is the existence of an overall spatially stable neuronal code. Cross-temporal decoding approaches allow one (a) to identify the decoder that best accounts for the relationship between the neuronal responses and spatial attention instruction on any given trial and a given time in the trial from a reference event such as cue presentation and (b) to test this decoder at other times in the trial (Astrand et al. 2015). Using this type of approach, researchers showed that, in cortical areas such as the lateral intraparietal area, spatial attention is encoded in a time-varying code, making the real-time tracking of attention extremely challenging when the intrinsic connectivity of the recurrent network implementing this variable is not clearly understood. In contrast, in the prefrontal cortex, spatial attention is encoded in a globally stable code, making this area an ideal target for the real-time tracking of attention from the underlying neuronal signals (Astrand et al. 2015). Accordingly, prefrontal cortex single-unit activity can be used to decode the allocation of covert attention with high accuracy on a trial-by-trial basis (Astrand et al. 2014a,b; Tremblay et al. 2015b), including when very local single-unit or multiunit activity is used (Schafer & Moore 2011).

### 2.3.2. Multiunit activity.
Multiunit activity reflects the combined activity of multiple neurons recorded from the same electrode, providing a broader but less granular view of neural activity compared to single-unit activity (Perel et al. 2015). Multiunit activity of neuronal ensembles has been described to capture the low-dimensional information structure of cortical regions or manifolds with the same degree of reliability as single-unit activity (Trautmann et al. 2019). It is also shown to provide reliable time-resolved tracking of neuronal information (Stark & Abeles 2007), although this information is distinct from both single-unit activity and LFPs (Perel et al. 2015). Astrand et al. (2016) and Gaillard et al. (2020) demonstrated that multiunit activity from the prefrontal cortex can be used to track the (x,y) spatial locus of covert attention, offering direct
<figure><img src="image_8.png" /><figcaption>Figure 2</figcaption></figure>

Behavioral relevance of decoding readout. Correlation between hit rate and distance between attentional spotlight and expected location of the target, when using (a) MUA signals or (b) LFPs for decoding. Correlations were performed following a one-step continuous decoding procedure or following a two-step continuous decoding procedure. Abbreviations: LFP, local field potential; MUA, multiunit activity. Data from De Sousa et al. (2021).

access to the two-dimensional coordinates of the attentional spotlight (Figure 2a, left). This decoding was achieved in real time, showing that multiunit activity is capable of providing both spatial and temporal information about attention shifts. This work extends that of Tremblay et al. (2015b), who reported categorical decoding of attention from prefrontal multiunit activity with an accuracy comparable to that achieved using single-unit population activity. Because this approach implements a continuous decoding strategy, it allows one to achieve an unprecedented spatial ( < 0.5°) and temporal ( < 50 ms) tracking resolution of decoded attention (Gaillard et al. 2020). De Sousa et al. (2021) used the two-step decoding procedure described in Section 2.2.3 to further enhance the prediction strength between the decoding of the attentional spotlight from prefrontal multiunit activity and behavioral outcome, at the single-trial level (Figure 2a, right).

### 2.3.3. Sampling population and advances in recording technologies.
The accuracy of attention decoding from neuronal activity is critically shaped by both the size of the sampled neural population and the spatial resolution of cortical coverage. Decoding performance improves when recordings capture a functionally diverse and spatially distributed set of neurons, particularly in areas with topographic or columnar organization such as the visual cortex (Averbeck et al. 2006, Graf et al. 2011). In these contexts, attentional signals are often embedded within population codes, whose fidelity depends not only on the number of recorded units but also on how well they reflect dynamic changes across spatial priority maps (Astrand et al. 2016, Sprague et al. 2015). High-density neural recordings have significantly enhanced our ability to sample such populations, thanks to advances in probe technology such as Neuropixels, which allow for stable, long-term, and large-scale recordings across multiple brain regions (Jun et al. 2017, Steinmetz et al. 2021). These technologies have enabled researchers to resolve fine-grained spatial organization and correlations in neural activity that are essential for decoding attentional states (Panzeri et al. 2017, Yuste 2015). By leveraging this spatial and temporal richness, it becomes increasingly feasible to reconstruct attentional dynamics with high precision, offering deeper insights into the distributed nature of cognitive processing in the brain. Such developments have the potential to refine and validate decoding models in humans, bridging the translational gap between nonhuman primate research and clinical or cognitive neuroscience applications (Eisenkolb et al. 2023). Future studies should leverage these modern tools to further interrogate how population-level dynamics support covert attention and how decoding approaches scale with larger and more heterogeneous neural populations.
### 2.3.4. Impact of noise correlations.
Neuronal correlations, particularly noise correlations (i.e., shared variability between neurons), significantly impact the accuracy of attention decoding from both multiunit activity and single-unit activity. Indeed, when neurons are weakly correlated, each unit contributes more independent information, enhancing the decoder's ability to distinguish attentional states. In contrast, high levels of noise correlation between neurons can lead to redundancy, as the neurons are not providing independent signals, thereby decreasing the amount of unique information the population can transmit about attentional states (Averbeck et al. 2006). When neurons exhibit high levels of correlated noise, the potential benefits of recording from larger neuronal populations thus diminish because the additional neurons do not increase the overall information content proportionally. Accordingly, studies have shown that reducing noise correlations among neurons can significantly improve the accuracy of single-unit attention decoding (Cohen & Maunsell 2009, Tremblay et al. 2015b). However, this observation varies as a function of the specific recording configuration of any specific recording session (Shi et al. 2022; S. Ben Hamed, E. Astrand & S. Ben Hadj Hassen, unpublished observation); noise correlations either enhance or degrade the ability to decode attention from neural activity, depending on their structure and the nature of the signals being recorded. This is due to the fact that levels of noise correlation vary as a function of cortical layers as well as a function of functional preference (Ben Hadj Hassen & Ben Hamed 2020, Shi et al. 2022). In particular, units located in superficial cortical layers show higher noise correlations among cells sharing the same functional specificities (e.g., the same spatial preferred location), while the reverse is observed for units located in deep cortical layers (Ben Hadj Hassen & Ben Hamed 2020, Hansen et al. 2012).

### 2.3.5. Decoding attention from local field potential signals.
LFPs reflect the summed electrical activity from a large population of neurons in a given brain region, capturing both incoming and local processes as well as oscillatory dynamics, all of which play a key role in attentional processes. Attention decoding from LFPs can be achieved using both the temporal domain and the frequency domain, each providing unique insights into how attention is processed in the brain. In the temporal domain, decoding of attention relies on analyzing the raw LFP signal over time. De Sousa et al. (2021) demonstrated that averaging LFP signals over varying time windows before target presentation allowed for the successful decoding of the attentional spotlight in terms of spatial location, with categorization accuracies only slightly lower than those achieved using multiunit activity (average 71% accuracy for LFP versus 77% for multiunit activity; chance level 25%). These researchers additionally demonstrate continuous (x,y) estimation of the attentional spotlight from LFPs similarly to what has been achieved using multiunit activity (Astrand et al. 2016, De Sousa et al. 2021, Gaillard et al. 2020) and a correlation between this real-time decoding and behavioral outcome in the task, such that the closer the decoded attentional spotlight from the expected location of the target as instructed at the beginning of the trial, the higher is the subject's performance (Figure 2b, left). In the frequency domain, attention-related signals are extracted by analyzing the power of LFPs across different frequency bands. Tremblay et al. (2015a) and De Sousa et al. (2021) reported that gamma-band activity (30–250 Hz), particularly in the mid-gamma (60–120 Hz) and high-gamma (120–250 Hz) ranges, contains the most attention-related information and is more strongly associated with the precise decoding of attention location compared to lower-frequency bands such as delta, theta, and alpha. The two-step decoding procedure introduced by De Sousa et al. (2021) further enhances attention decoding by distinguishing between high- and low-information-content trials. This approach significantly improves the accuracy of the decoding, especially in the frequency domain, by focusing on the most informative LFP signals (Figure 2b, right). The method not only boosts decoding accuracy but also strengthens the
correlation between the decoded attentional spotlight and behavioral performance, as discussed
next.

**2.4. Assessing the Behavioral Relevance of Decoding Readout**

While relevance of decoding readout can be directly assessed when decoding motor information
from neuronal signals by observing the actual motor behavior of the subject, assessing the be-
havioral relevance of decoding readouts involves linking the decoding accuracy of neural activity
to observable behavioral outcomes, such as task performance, decision-making, and attentional
shifts. Astrand et al. (2016) demonstrated that decoding attention from the prefrontal cortex is
highly predictive of overt behavior in macaque monkeys, in tasks requiring selective attention,
such that ensemble neural activity can reliably predict the allocation of attention in space as in-
ferred from subsequent behavioral responses, such as target detection performance and speed of
response. Specifically, on correct trials, spatial attention orientation to one of four quadrants could
be predicted with an accuracy of more than 70% when LFPs were used and more than 75% when
multiunit activity was used, while on incorrect trials, classification accuracies decreased by up to
10% (De Sousa et al. 2021). Classification accuracies also decreased by more than 10% between an
easy and a more difficult attentional task. This thus demonstrated a correlation between decoding
accuracies and overt behavior. These measures are, however, average measures. The continuous
$(x,y)$ decoding of attention allows one to reach a more quantitative assessment of the behavioral
relevance of the decoded attentional spotlight. Astrand et al. (2016) show that missed target de-
tections were more frequent when the decoded attentional spotlight was further away from target
location (Figure 2). Conversely, false alarms (i.e., inappropriate responses to nontarget stimuli)
were more frequent when the decoded attentional spotlight was close to the location where the
distractor was presented. In both conditions, for stimuli of identical features and locations, reac-
tion times were faster the closer the decoded attentional spotlight was to the stimulus triggering
the response. Gaillard et al. (2020) confirmed this finding at a higher temporal resolution. Like-
wise, the $(x,y)$ location of the attentional spotlight decoded from prefrontal LFPs was predictive
of the monkey's performance, such that hit rates were higher for trials in which the spotlight was
decoded closest to the target, whether decoding was achieved in the temporal or the frequency
domains. Overall, these findings indicate that the attentional decoding readouts align with and
influence behavior in real time.

**2.5. Translational and Clinical Value of Attention Decoding**

The real-time tracking of attention opens broad and promising avenues for attentional training, neurofeedback applications, and closed-loop experimental designs (Weiskopf 2012). The demonstrated stability of attention decoding across sessions enables a practical pipeline wherein participants can first undergo one or two calibration sessions to train individualized decoders and assess decoding performance. On the basis of these results, previous work has successfully implemented fMRI-based neurofeedback for cognitive constructs such as categorical attention, perceptual confidence, and working memory, showing both behavioral and neurophysiological effects (Cortese et al. 2016; deBettencourt et al. 2015; Loriette et al. 2021a,b). In such neurofeedback protocols, real-time single-trial decoding of the spatial focus of attention from individual subjects is used to drive feedback signals such as a cursor on a screen during fMRI scanning. Such efforts have typically relied on binary classification approaches (e.g., focused versus unfocused, category A versus B). By contrast, a more fine-grained and continuous neurofeedback paradigm can be achieved on the basis of decoding the spatial location of covert attention (S. Dali, C. Loriette, D. Ibarrola, F. Lamberton & S. Ben Hamed, manuscript in preparation). This
provides a new framework for precise attentional training protocols and could significantly
enhance the functional specificity and efficacy of neurofeedback. In the long term, this approach
holds promise not only for optimizing attentional performance in healthy individuals but also for
clinical interventions aimed at attentional remediation—for example, in patients with attention
deficit hyperactivity disorder, traumatic brain injury, or stroke-induced spatial neglect.

3. TRACKING THE ATTENTIONAL SPOTLIGHT AND THE
ASSOCIATED SPATIAL AND TEMPORAL NEURONAL DYNAMICS

As discussed in Section 1.3, the attentional spotlight is a metaphorical psychological concept that proposes that attention functions similarly to a beam of light (a) that enhances perceptual sensitivity in a selected area and (b) that can move voluntarily or involuntarily to prioritize relevant information, enhancing task performance and perceptual accuracy (Carrasco 2011). While most attention-related studies hypothesize that, on average, the attentional spotlight in any given cognitive task is centered at the instructed location, Astrand et al. (2016) show that attention is not stable but moves around the visual scene. Although the attentional spotlight is, on average, close to the instructed location of attention, this is not the case at the single-trial level. The analysis of the spatial and temporal structure of the attentional spotlight across trials allows for a precise characterization of the neural basis of attention and an understanding of how the attentional spotlight drives perception and decision-making in real time.

**3.1. Spatial Dynamics of the Attentional Spotlight**

The spatial deployment of covert attention is not static but dynamically shaped by both behavioral
goals and contextual expectations. This section examines how the attentional spotlight evolves
across space and time, under the influence of voluntary instructions and experience-driven priority
signals.

**3.1.1. Attentional selection and top-down control.** By using the decoded attentional spot-
light metric, it is possible to reconstruct the density probability map of attention allocation just
prior to target presentation across multiple targets (Figure 3a). Before cue presentation, the den-
sity map of the decoded attentional spotlight position is centered on the fixation point. Following
an instruction orienting attention to a specific location in space, the density map shifts toward the
cued location (Gaillard et al. 2020) (Figure 3a). The decoded attentional spotlight is thus prob-
abilistically under the influence of a voluntary control and implements the ongoing behavioral
demands. Global context can also influence the spatial deployment of the attentional spotlight.
Indeed, in tasks requiring the monitoring of only two spatial locations, the attentional spotlight
mostly explores these two locations and the space in between, while in tasks requiring the moni-
toring of four spatial locations distributed over the four visual quadrants, the attentional spotlight
explores the entire visual field (Gaillard et al. 2020) (Figure 3b).

The attentional spotlight is hypothesized to be Gaussian shaped, such that visual perception is
maximal at its center and gets weaker and weaker as one moves away from its center. A classical
way to probe this is by instructing subjects to orient their attention to a given location in space
and respond as fast as possible to a target presentation. When nontarget stimuli are presented at
unexpected locations around the expected location of the target, the closer the distractor is to the
expected target location, the higher the rate of false alarm (Di Bello et al. 2022, Suzuki & Gottlieb
2013). This evidence has been taken as a confirmation of the Gaussian shape of the attentional
spotlight (Figure 3c). However, this approach does not take into account the dynamics of the
attentional spotlight and possibly conflates attentional effects with other spatial bias effects such
as statistical learning of environmental structure (Beffara et al. 2022, 2023; Dolci et al. 2023, 2024;
<figure><img src="image_9.png" /><figcaption>Figure 3</figcaption></figure>

Dynamics of decoded attentional spotlight. (a) Density probability map of the decoded attention spotlight when attention is oriented at the center or up to the right of the visual field. (b) Trajectory of the attentional spotlight when the subject is monitoring one out of two possible locations or one out of four possible locations. (c) Percentage of false alarms as a function of unexpected distractor to target distance in visual degrees. (d) Percentage of hits (**right axis**) and normalized population peak spiking rate (**left axis**) as a function of attention spotlight to target distance in visual degrees. (e) Percentage of false alarms (**right axis**, red) and normalized population peak spiking rate (**left axis**) as a function of attention spotlight to expected distractor distance in visual degrees. (f) Percentage of false alarms (**right axis**) and normalized population peak spiking rate (**left axis**) as a function of attention spotlight to unexpected distractor distance in visual degrees. Abbreviations: AD, attention to expected distractor distance; Ad, attention to unexpected distractor distance; AT, attention to target distance; FA, false alarm response; Td, target to unexpected distractor distance. Panels a and b adapted from Gaillard et al. (2020) (CC BY 4.0). Data in panels c–f from Di Bello et al. (2022).

Rashal et al. 2023). As discussed above in Section 2.4, the closer the decoded attentional spotlight to the target at the time it is presented, the higher is the detection rate by the subject (Astrand et al. 2016, 2020; Di Bello et al. 2022) (Figure 3d). At the same time, the closer the decoded attentional spotlight to the target at the time it is presented, the higher are the neuronal responses in the prefrontal cortex (Di Bello et al. 2022) (Figure 3d). This is a direct experimental confirmation that the attentional spotlight impacts perception in a Gaussian-like manner.

**3.1.2. Priority maps and dynamic attention.** When orienting our attention in the environment, we usually use prior information about the environment: We know where we usually place our coffee cup, where the fridge is located, and where we are supposed to leave our keys. Priority maps based on reward and statistical learning reflect the brain's ability to adapt attentional resources toward the most behaviorally relevant or predictive elements of the environment. Accordingly, reward-based learning has been shown to strengthen neural representations within priority maps, enhancing sensitivity to high-value targets in areas like the parietal and prefrontal cortices (Chelazzi et al. 2014). Similarly, statistical learning allows individuals to detect and prioritize stimuli patterns, increasing efficiency in visual search by enhancing responses to frequently encountered stimuli or spatial locations (Summerfield & Egner 2009). Thus, reward and statistical cues shape attentional priorities, making behavior more efficient and adaptive in complex environments. In such maps, attention is dynamically allocated on the basis of past experiences, where stimuli associated with higher rewards or greater statistical regularity gain priority in perceptual processing (Beffara et al. 2023). Accordingly, when subjects are instructed to respond as fast as
possible to the presentation of a target at a given location, in a task that involves four possible
target locations, subjects produce high false alarm rates at the expected target location, but also
at the locations where targets can be presented on different trials (Di Bello et al. 2022). This in-
dicates that the spatial priority map guides perceptual decisions in a similar manner as top-down
voluntary control over the attentional spotlight. However, this prioritization interacts with the
selection process implemented by the decoded attentional spotlight, as is discussed next (Beffara
et al. 2023, Di Bello et al. 2022).

**3.2. Distractor Suppression**

Distractor suppression is a fundamental component of attentional control, enabling the brain to maintain focus in the presence of competing stimuli. Here, we distinguish between proactive and reactive suppression processes and examine how they are dynamically implemented through prefrontal activity and the decoded attentional spotlight.

**3.2.1. Proactive suppression.** Proactive distractor suppression is a mechanism by which attention preemptively reduces the influence of irrelevant stimuli, facilitating the processing of relevant information. This type of suppression operates through top-down control mechanisms, as damage to the prefrontal cortex results in a loss of the ability to acquire and use behavior-guiding rules to prioritize behaviorally significant stimuli and filter out distractions (Duncan 1986, Passingham & Passingham 1995, Shallice et al. 1997). Studies demonstrate that, when the attentional spotlight is far from a distractor, multiunit activity and LFPs in the frontal eye fields show marked suppression of neural responses to irrelevant stimuli (Di Bello et al. 2022). Suzuki & Gottlieb (2013) demonstrated that both the dorsolateral prefrontal cortex (dlPFC) and the lateral intraparietal area contribute to this suppression, with the dlPFC exhibiting stronger, more sustained suppression of distractors. This proactive suppression is implemented early in the visual processing stream, likely within the prefrontal cortex or upstream regions, ensuring that attentional resources are allocated efficiently (Cosman et al. 2018).

Di Bello et al. (2022) demonstrated that this suppression is particularly pronounced when the attentional spotlight is directed away from task-irrelevant distractors, leading to a significant reduction in neural responses. In contrast, when the decoded attentional spotlight is on the distractor when presented, it gets selected according to the same mechanism as that described for target selection. Specifically, the closer the spotlight to the distractor at the time it is presented, the higher is the false alarm rate by the subject (Astrand et al. 2016, 2020; Di Bello et al. 2022). At the same time, the closer the decoded attentional spotlight to the distractor at the time it is presented, the higher are the neuronal responses in the prefrontal cortex (Di Bello et al. 2022) (Figure 3e). In other words, failed suppression of a distractor results from a selection by the attentional spotlight. This proactive suppression is mostly observed for expected distractors, the feature or the location of which can be anticipated.

**3.2.2. Reactive suppression.** Reactive distractor suppression enables the brain to deal with distractions that have already been perceived, preventing them from leading to goal-directed behavior. Unlike proactive suppression, which operates by preemptively reducing the salience of irrelevant stimuli before they are processed, reactive suppression engages after a distractor has already captured attention. This suppression halts the further processing of the distractor and interrupts any potential responses that might be triggered by its presence. Di Bello et al. (2022) provided neuronal evidence for reactive suppression in the frontal eye fields by demonstrating that unexpected distractors that were initially processed produced a marked phasic response in multiunit activity and LFPs. However, this initial response was quickly followed by a sharp decrease in activity—indicative of the brain's attempt to suppress the irrelevant stimulus after
its perception. This suppression occurred through a filtering-out process, implemented by the attentional spotlight, halting its further processing. Specifically, in contrast with the target and the expected distractors, the closer the decoded attentional spotlight was to the unexpected distractor at time of presentation, the lower the rate of false alarms and the stronger the reactive suppression of the initial processing neuronal response (Figure 3f). This reactive suppression process was particularly efficient when unexpected distractors were presented within 3° from the expected location of the target, supporting a role of the attentional spotlight in fine-grained perceptual discrimination, as reported, at both the behavioral (Carrasco 2011) and neurophysiological (Desimone & Duncan 1995, Maunsell & Cook 2002) levels.

In summary, the decoded attentional spotlight implements a Gaussian-shaped filter with two distinct operations. The first operation is a filter that selects relevant information or information-sharing physical features with the relevant information. This operation allows for target selection and the incorrect selection of distractors sharing a feature with the target, including a spatial feature. The second operation is a filter that reactively suppresses information, interrupting its processing. This operation allows for the reactive suppression of information after an initial processing stage, presumably allowing for the evaluation of its relevance. This is further discussed in Section 5.

## 3.3. Dynamic Attention in Time

Attention is not only spatially selective but also temporally dynamic, fluctuating in rhythmic patterns that shape perception and behavior. This section explores how the attentional spotlight evolves over time through oscillatory sampling and alternates between exploratory and exploitative modes.

### 3.3.1. Rhythmic exploration of space and attentional saccades.
As discussed in Section 1.3, behavioral evidence indicates that attention samples space rhythmically (Dugué et al. 2015; Fiebelkorn et al. 2013, 2018; Gaillard et al. 2020; Landau & Fries 2012; VanRullen et al. 2007). A dynamic interplay within the macaque frontoparietal network is proposed to coordinate two rhythmically alternating prefrontal theta (3–8Hz) states accounting for the rhythmic properties of spatial attention (Fiebelkorn et al. 2018, VanRullen 2018). A first state is defined by a parietal gamma-band activity, associated with enhanced visual processing and better behavioral performance. A second state is defined by parietal alpha-band activity, associated with attenuated visual processing and worse behavioral performance. Gaillard et al. (2020) provide direct neurophysiological evidence for a continuous sampling of spatial locations in a cyclic manner by the decoded attentional spotlight from prefrontal cortex population activity. Rather than focusing solely on one fixed point at a time, attention is proposed to dynamically alternate between task-relevant and task-irrelevant locations at a frequency typically in the alpha range (7–12 Hz). This rhythmic exploration is not limited to precued target locations but extends to uncued, unpredictable distractor locations, supporting the flexibility of attention in dynamically adjusting to environmental demands. This oscillatory nature of attentional shifts is linked to perceptual cycles that guide behavioral responses, such that stimuli presented at an optimal phase relative to this rhythmic activity of the attentional spotlight are better detected and result in faster and more accurate responses. These oscillations are thought to reflect an intrinsic alpha-clock mechanism originating within the prefrontal cortex, which synchronizes attentional sampling with external task demands. The concept of attentional saccades is introduced as a new way of understanding how the attentional spotlight explores visual space (Gaillard et al. 2020). Unlike traditional models in which attention is fixed or shifts discretely, Gaillard and colleagues suggest that attention operates rhythmically, engaging in continuous spatial sampling at an alpha frequency (7–12 Hz). These attentional
saccades involve prefrontal population decoding of the attentional spotlight, showing that it shifts
rhythmically across space, much like saccadic eye movements but without actual gaze shifts.

**3.3.2. Exploration and exploitation.** Attentional saccades are proposed to allow for efficient spatial sampling in dynamic environments by alternating between exploration phases, where attention broadly scans space, and exploitation phases, where it focuses on specific, task-relevant locations (Gaillard et al. 2020). VanRullen (2018) proposes a blinking attentional spotlight model in which attention rhythmically alternates between collecting information and concentrating on detected targets. This model suggests that rhythmic sampling, or attentional saccades, enables optimal information processing by aligning attentional resources with salient environmental features at optimal time intervals, contributing to the balance between efficient exploration of novel stimuli and focused exploitation of relevant items in the visual field (VanRullen 2018). Overall, this describes a highly flexible attention spotlight that moves around the visual environment.

**3.4. Sustained Attention in Task**

Sustained attention, often referred to as vigilance, is the ability to maintain focus and remain attent-
tive to stimuli or tasks over extended periods of time. Mackworth (1948) introduced the concept
through the development of the clock test, which demonstrated the decline of vigilance, a phe-
nomenon now known as the vigilance decrement. This work established that sustained attention
is critical in tasks requiring continuous monitoring over long durations, such as air traffic control
and military surveillance. Posner & Petersen (1990) expanded on this research by distinguishing
sustained attention as part of a broader attentional system, involving both alertness and executive
control. Recent advances have explored the neural basis of sustained attention, with studies show-
ing that the right frontoparietal network plays a central role in maintaining attention over time
(Esterman et al. 2013, 2015). This network includes the dlPFC and the inferior parietal lobule,
which are activated during prolonged tasks requiring sustained effort. The vigilance decrement
model, developed by Mackworth (1948), suggests that performance generally declines as task du-
ration increases. This model indicates that the decrease in vigilance and responsiveness, along
with the increase in mental fatigue, contributes to the vigilance decrement. The overload model
(Thomson et al. 2015) introduces the idea that sustained attention can be compromised when
tasks become too numerous or too complex, exceeding the cognitive system's processing capac-
ity. Thomson et al. (2015) suggest that, when the brain is overloaded, there is a reduction in the
ability to maintain attention, leading to decreased performance and an increase in errors. The op-
portunity cost model by Esterman & Rothlein (2019) proposes that sustained attention involves
an opportunity cost, such that maintaining attention on a specific task prevents the completion
of other potentially more rewarding or interesting tasks. This opportunity cost can influence the
motivation and efficiency with which a person maintains attention, especially when the ongoing
task is perceived as less engaging or less motivating. Thus, sustained attention is easier if the effort
is lower (Kurzban 2016) or if the reward is higher (Massar et al. 2016). Furthermore, emerging
research describes fluctuations in sustained attention, impacting performance (Esterman et al.
2013, 2014). Rosenberg et al. (2016) used machine learning to predict lapses in sustained atten-
tion on the basis of patterns of brain activity. The exact mechanisms of attentional lapses are
unclear.

**3.4.1. States of distractibility and impulsivity.** States of distractibility represent periods during which the visual attentional system is more prone to being captured by irrelevant stimuli, leading to a decrease in the ability to focus on task-relevant information. Distractibility arises from fluctuations in cognitive control and arousal, often linked to internal states such as fatigue, boredom, or diminished motivation. These fluctuations reduce the brain's capacity to filter out
irrelevant information, making the brain more vulnerable to environmental distractions. States of impulsivity reflect another dimension of fluctuating cognitive control. Impulsivity is characterized by errors or inappropriate responses due to speeded responses at the cost of sufficient deliberation. Impulsivity and distractibility represent distinct cognitive states. Amengual et al. (2022) identified a continuum between distractibility, optimal response, and impulsivity, showing that these states are encoded by overlapping but distinct neuronal subpopulations in the prefrontal cortex. They are described by two distinct neural states (Figure 4i): a U-shaped and a linear neural state. The U-shaped state reflects a pattern in which both low and high levels of neural excitability

<figure><img src="image_10.png" /><figcaption>Figure 4</figcaption></figure>

Prioritization processes at the basis of the prefrontal neuronal priority map. (a-c) Spatial statistical map (a), current attentional goals (b), and resultant stable attention priority map (c). (d) Dynamic attention spotlight implementing selection of target features (right) and suppression of nontarget features (left). (e) Attentional selection resulting from the combination of the attention priority map with the dynamic attentional spotlight implementing a selection process. (f) Attentional suppression resulting from the combination of the attention priority map with the dynamic attentional spotlight implementing a suppression process. (g,h) Selection and suppression can take place proactively (g) or reactively (h) following an initial processing stage by the attentional spotlight. (i) States of distractibility and impulsivity control the decision processes of the prefrontal cortex following attentional selection. (j) Fluctuations in sustained attention influence the efficacy of attentional orientation.
lead to poor attentional performance. At low neural activity, individuals are prone either to inattention and distractions or to impulsivity, with optimal focus occurring in the middle of the curve. In contrast, the linear neural state relates to impulsivity, showing a direct relationship between neural activity and response control. High neural activity leads to impulsive actions, with insufficient control over responses, while lower activity improves inhibition, resulting in more deliberate actions. However, too weak neural activity can cause overinhibition, leading to missed responses and increased distraction and inattention. Together, these two states associate different neural patterns with attention lapses and impulsive behavior and directly account for variations in how visual information is processed. Aston-Jones & Cohen (2005) described the locus coeruleus-norepinephrine (LC-NE) system as a critical regulator of arousal states that influence distractibility and impulsivity. The LC-NE system is responsible for controlling the balance between task engagement and disengagement. When arousal is optimal, the LC-NE system enhances focus on task-relevant stimuli, maintaining a state of focused attention. However, during periods of low arousal and excessive arousal, attentional control wanes, increasing distractibility and impulsivity, respectively. This system operates in a phasic mode when arousal levels are high, promoting task-oriented behavior, but shifts to a tonic mode during low arousal, increasing sensitivity to distractions (Aston-Jones & Cohen 2005). The LC-NE system is suggested to control the distractibility and impulsivity neural states identified in the prefrontal cortex and their outcome in visual processing. An important question is how these states of impulsivity, distractibility, and optimal behavior organize in time.

### 3.4.2. Intrinsic fluctuations in sustained visual attention.
Esterman et al. (2014) describe, on the basis of continuous evaluation of reaction time variability, in-the-zone epochs, during which the processing of distractor images is more stable and less error prone, and out-of-the-zone epochs, during which the processing of distractor images is unstable and more error prone. Reaction time variability was positively correlated with activity in task-positive networks and was negatively correlated with activity in the task-negative default mode network. In other words, in-the-zone epochs are characterized by higher default mode network activity, but during these epochs, subjects are at risk of distraction when network activity increases beyond a specific level beyond a specific threshold. More effortful out-of-the-zone processing, less optimal for sustained performance, relies on activity in the dorsal attention network. These intrinsic fluctuations in sustained attention between in-the-zone and out-of-the-zone states are described to take place every minute (Esterman et al. 2013).

Slower attentional fluctuations in attentional states are also described. Gaillard et al. (2021) examined fluctuations in attentional performance and neural activity, demonstrating that both humans and nonhuman primates experience rhythmic fluctuations in attentional efficacy, with cycles occurring approximately 5-6 times per hour. These fluctuations manifest as alternating periods of optimal and suboptimal performance. Epochs of optimal performance are associated with higher rates of correct detections and lower rates of false alarms. Epochs of suboptimal performance are characterized by lower performance in the task and higher distractibility. These fluctuations coincide with changes in pupil diameter suggesting a tight coupling with changes in vigilance state and the noradrenergic system. These very slow fluctuations in attention not only affect behavioral performance but also are correlated with changes in neural markers of attentional focus. Gaillard et al. (2021) found that, during high-performance periods, attentional information was more precisely encoded in prefrontal multiunit activity and LFPs. Attentional spotlight orientation was more focused and aligned with cue instruction (Figure 4j). Last, spike-field coherence in the theta and alpha ranges was enhanced, suggesting that these fluctuations are coordinated through a broader cortical network. Conversely, during periods of low attentional
focus, neural encoding was less accurate, leading to poorer task performance, attentional spotlight
was less accurate, and spike-field coherence was reduced. Overall, these fluctuations in visual
attention demonstrate that visual processing is not stable, reflecting the brain's ability to manage
prolonged cognitive effort through dynamic shifts in attentional focus.

4. RECURRENT NETWORKS AND ATTENTION CONTROL

The prefrontal cortex is known for its ability to maintain and manipulate information over time, a hallmark of recurrent processing. Recurrent connections within neural populations in the prefrontal cortex allow for sustained activity patterns that can encode information even in the absence of continuous external inputs, closely resembling working memory functions (Wang 2013). These recurrent dynamics support persistent neural firing, which has been observed experimentally in the prefrontal cortex during tasks requiring memory retention or attentional focus (Amengual & Ben Hamed 2021, Miller & Cohen 2001). Recurrent networks capture the flexibility of the prefrontal cortex, where neural activity patterns dynamically shift in response to changing task demands, a phenomenon that aligns with the context-dependent processing characteristic of the prefrontal cortex (Sussillo 2014). In addition, recurrent architectures can integrate visual information over time and generate behaviorally relevant outputs (Song et al. 2016). Bistability is another key property of the prefrontal cortex that is successfully modeled by neural network recurrency. Bistability involves the prefrontal cortex's ability to switch between stable states. This property is not solely due to synaptic inputs but is deeply rooted in intrinsic neuronal properties, such as slow potassium currents and persistent sodium conductance (Delord et al. 1997). This enables neurons to maintain activity after external visual stimuli have disappeared, a fundamental feature of visual attention, allowing for sustained activity through feedback loops and supporting both persistent and flexible cognitive processing (Fontanier et al. 2022, Sarazin et al. 2021). These bistable circuits enable the prefrontal cortex to function as a dynamical system, capable of managing cognitive demands through a balance of sustained and transient activity states (Miller & Cohen 2001, Sarazin et al. 2021).

In addition to exploring the role of persistent firing and bistability, recent work highlights the importance of spatiotemporal dynamics and oscillatory activity in cognitive processes such as attention. Chen & Gong (2022) propose a novel mechanism for attention sampling based on theta oscillations and superdiffusive motion, revealing that neural circuits can flexibly switch between different attention states. This model proposes that, in recurrent networks, theta rhythmic activity can support flexible switching between exploration and exploitation of attention, closely mimicking the cognitive flexibility observed in decision-making tasks. The findings suggest that spatiotemporal neural activity patterns, facilitated by recurrent networks, provide functional advantages in processing complex environments by efficiently sampling multiple locations, particularly when distractions are present. In addition, this work proposes a role of recurrent networks in managing the fluctuations between attentional states and the oscillatory coupling between theta and gamma rhythms, which are essential for maintaining attentional focus on salient objects. The properties of the recurrent network proposed by Chen & Gong (2022) very closely match the description of the neurophysiological bases of attentional dynamics (Fiebelkorn et al. 2018, Gaillard et al. 2020). These oscillatory patterns provide a dynamic, time-based framework for understanding how the brain balances persistent task-relevant focus with the need for exploratory shifts in attention.

Altogether, modeling and experimental evidence suggest that the intrinsic architecture of the
prefrontal cortex is at the origin of visual attentional control and captures the diversity of neuronal
signatures that underpin attentional dynamics.
# 5. TOWARD AN INTEGRATED MODEL OF ATTENTION

The association of the different bodies of work described in this review allows one to propose an integrated highly dynamic model of attentional prioritization. **Figure 4** captures this integrated model in the spatial dimension. Two different types of influences contribute to the spatial priority map. The first type of influences results from the combination of statistical maps incorporating knowledge about the environment (**Figure 4a**) with current goals (**Figure 4b**). Statistical knowledge about the environment incorporates physical regularities, such as spatial location, color, and shape (Beffara et al. 2023, Dolci et al. 2023). These can be altered by reinforcement mechanisms, including learning- and reward-based reinforcement (Chelazzi et al. 2014). Current goals, although dynamic at long timescales (when preparing a meal, I will look first for the pan, then for the onions, then for the raisins), can be considered static and can arise from top-down executive cognitive control mechanisms at short timescales (Miller & Cohen 2001). Executive control over current goals incorporates the selection of specific goal-oriented features (onion: white, round object, in lower cupboard shelf) and the proactive suppression of non-goal-oriented features (nononions). These two combine to define a spatial priority map recapitulating internal goals and subjective knowledge of the environment (Beffara et al. 2022, Rashal et al. 2023) (**Figure 4c**). The second type of influences results from dynamic stochastic properties of the prefrontal cortex, due to its recurrent connectivity (Song et al. 2016, Wang 2013) (**Figure 4d**). The attentional spotlight dynamically biases the attentional priority map and automatically implements the discrimination between goals (target selection; **Figure 4e**) and nongoals (distractor suppression; **Figure 4f**). This selection/suppression mechanism can take place proactively when goals and nongoals are predictable and clearly defined (**Figure 4g**). A selection/suppression mechanism can take place reactively, following a rapid initial stage allowing for the discrimination between goals and nongoals (Di Bello et al. 2022) (**Figure 4h**). The decision process that is implemented on the basis of the resultant priority map leads to the selection of the target of perception. This decision process is determined by a decision threshold that varies from high to low as a function of the subject's responsiveness and associated neural states of distractibility and impulsivity (**Figure 4i**). This decision process is also impacted by fluctuations between epochs of optimality and epochs of suboptimality during sustained attention (**Figure 4j**). **Figure 4** represents this integrated model of attention in space. This model highlights multiple levels of dynamics in visual attention and its associated neural substrates.

This model could be formulated in a higher-dimensional feature space, such that statistical learning, goals, and dynamic selection and suppression could operate in both space and any feature dimensions (color, shape, etc.), thus leading to the concept of a higher-dimensional priority map dynamically arising from the coordination of prefrontal recurrent networks specialized in spatial and object-based prioritization.

## SUMMARY POINTS

1. Machine learning decoding techniques allow for real-time tracking of the attentional spotlight with a high spatial and temporal resolution.

2. The decoded attentional spotlight implements stimulus selection of both correctly identified behavioral targets and erroneously identified distractors.

3. The decoded attentional spotlight implements reactive stimulus suppression.

4. The decoded attentional spotlight is highly dynamic and flexibly deploys in attentional saccades in the visual scene at an alpha rhythm to explore and exploit visual information.
5. Outcomes of visual attention processes change as a function of whether subjects are in a state of distractibility, optimal behavior, or impulsivity, possibly under noradrenergic modulation control.

6. Attentional processes fluctuate at a timescale of a few minutes between epochs of high neuronal and behavioral efficiency and epochs of lower efficiency.

7. The recurrent neural networks within the prefrontal cortex account for the observed attentional dynamics, sustaining and modulating attentional focus over time and space and facilitating flexible goal-directed behavior.

## FUTURE ISSUES

1. Current real-time tracking of visual attention involves tracking its location. Future work should enhance this tracking with feature-related content as well as with information on focus and size.

2. Future research could benefit from combining data from various neural sources (e.g., fMRI, EEG, single-cell recordings) to create a more comprehensive model of attentional control across different brain regions.

3. Studies often use controlled, static settings, but future research should focus on how attention operates in dynamic, real-world contexts to better understand attentional prioritization under complex, real-life conditions.

4. While rhythmic shifts in attention are now accepted, further research is needed to determine how specific neural oscillations contribute to attention cycles and how they are modulated by internal and external stimuli.

5. The efficacy of visual attention processes varies with time as a function of distractibility and impulsivity states. The mechanisms underlying these fluctuations under sustained engagement are yet unknown.

6. Exploring individual differences in the multiple facets of attentional functions could reveal personalized aspects of attention control, which may have implications for both diagnostics and treatments of attentional disorders.

## DISCLOSURE STATEMENT

The author is not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review.

## ACKNOWLEDGMENTS

I would like to thank all previous lab members that have cumulatively contributed to this work: C. Wardak, E. Astrand, S. Ben Hadj Hassen, F. Di Bello, J. Amengual, C. Gaillard, and C. De Sousa.

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