Prefrontal attentional saccades explore space rhythmically

Evidence for a constant occipital spotlight of attention using MVPA on EEG data

While traditional behavioural and electroencephalographic studies claim that visuospatial attention stays fixed at one location at a time, recent research has rather shown that attention rhythmically fluctuates between locations at different rates. However, little is known about the temporal dynamics of this fluctuation and whether it changes over time. We addressed this question by investigating how the neural pattern of visuospatial attention behaves over space and time by employing classification and conventional analysis of occipito-parietal EEG activity. Furthermore, we simulated data with the attentional electrophysiological correlates to control for the ground truth that would give rise to certain classification patterns. We analysed two visuospatial cueing tasks, with a peripheral and a central cue to control for sensory-driven processes, where attention was covertly oriented to the left or right hemifield. First, to decode the spatial locus of attention from neural activity, we trained and tested a classifier on every timepoint from the attentional cue to the stimulus onset. This resulted in one temporal generalization matrix per participant, which was time-frequency decomposed to identify the sampling rhythm. Independently, we calculated a lateralization index based on ERPs and alpha-band power and correlated these indices with classifier performance. Eventually, we simulated two dataset, with ERPs and alpha-band attentional modulations, and employed the same decoding approach. Our results show that attention settled on the cued hemifield in a late time window, but an early and rhythmic sampling of both hemifields exclusively after the peripheral cue. Only the ERP lateralization index correlated with classifier performance in the periperhal cue dataset, suggesting that the early rhythmic state did not reflect attentional sampling but instead was driven by the cue location, idea also supported by our simulations. Together, our results characterise the occipital attentional sampling as a constant process slightly delayed after the cue.

rTCT: Rodent Triangle Completion Task to facilitate translational study of path integration

Path integration is navigation in the absence of environmental landmarks and is a primary cognitive mechanism underlying spatial memory. Path integration performance is primarily assessed in humans using the Triangle Completion Task (TCT). In humans, TCT has shown promise for the early diagnosis of Alzheimer's disease. In rodents, however, path integration is assessed using a wide variety of tasks but none of which currently provide a homologue for the TCT. As rodents are routinely used as preclinical models, homologous path integration tasks that result in comparable performance metrics between species are important. In the present study we developed and tested a novel rodent version of the triangle completion task to enhance cross species comparability of path integration performance. Rats were able to comprehend and perform the task. A group of Alzheimer's disease model rats (TgF344-AD) exhibited similar path integration performance to their wild-type littermates; however, analysis of behavioural structure suggests use of differing behavioural strategies. This work establishes a novel rodent homologue of the triangle completion task, facilitating enhanced reverse translational study of human path integration.

Increased visual alpha-band activity during self-paced finger tapping does not affect early visual stimulus processing

Alpha-band activity is thought to be involved in orchestrating neural processing within and across brain regions relevant to various functions such as perception, cognition, and motor activity. Across different studies, attenuated alpha-band activity has been linked to increased neural excitability. Yet, there have been conflicting results concerning the consequences of alpha-band modulations for early sensory processing. We here examined whether movement-related alterations in visual alpha-band activity affected the early sensory processing of visual stimuli. For this purpose, in an EEG experiment, participants were engaged in a voluntary finger-tapping task while passively viewing flickering dots. We found extensive and expected movement-related amplitude modulations of motor alpha- and beta-band activity with event-related-desynchronization (ERD) before and during, and event-related-synchronization (ERS) after single voluntary finger taps. Crucially, while a visual alpha-band ERS accompanied the motor alpha-ERD before and during each finger tap, flicker-evoked Steady-State-Visually-Evoked-Potentials (SSVEPs), as a marker of early visual sensory gain, were not modulated in amplitude. As early sensory stimulus processing was unaffected by amplitude-modulated visual alpha-band activity, this argues against the idea that alpha-band activity represents a mechanism by which early sensory gain modulation is implemented. The distinct neural dynamics of visual alpha-band activity and early sensory processing may point to distinct and multiplexed neural selection processes in visual processing.

Visual Processing by Hierarchical and Dynamic Multiplexing

The complexity of natural environments requires highly flexible mechanisms for adaptive processing of single and multiple stimuli. Neuronal oscillations could be an ideal candidate for implementing such flexibility in neural systems. Here, we present a framework for structuring attention-guided processing of complex visual scenes in humans, based on multiplexing and phase coding schemes. Importantly, we suggest that the dynamic fluctuations of excitability vary rapidly in terms of magnitude, frequency and wave-form over time, i.e., they are not necessarily sinusoidal or sustained oscillations. Different elements of single objects would be processed within a single cycle (burst) of alpha activity (7-14 Hz), allowing for the formation of coherent object representations while separating multiple objects across multiple cycles. Each element of an object would be processed separately in time-expressed as different gamma band bursts (>30 Hz)-along the alpha phase. Since the processing capacity per alpha cycle is limited, an inverse relationship between object resolution and size of attentional spotlight ensures independence of the proposed mechanism from absolute object complexity. Frequency and wave-shape of those fluctuations would depend on the nature of the object that is processed and on cognitive demands. Multiple objects would further be organized along the phase of slower fluctuations (e.g., theta), potentially driven by saccades. Complex scene processing, involving covert attention and eye movements, would therefore be associated with multiple frequency changes in the alpha and lower frequency range. This framework embraces the idea of a hierarchical organization of visual processing, independent of environmental temporal dynamics.

Simultaneous oscillatory encoding of "hot" and "cold" information during social interactions in the monkey medial prefrontal cortex

Social interactions in primates require social cognition abilities such as anticipating the partner's future choices as well as pure cognitive skills involving processing task-relevant information. The medial prefrontal cortex (mPFC) has been implicated in these cognitive processes. Here, we investigated the neural oscillations underlying the complex social behaviors involving the interplay of social roles (Actor vs. Observer) and interaction types (whether working with a "Good" or "Bad" partner). We found opposite power modulations of the beta and gamma bands by social roles, indicating dedicated processing for task-related information. Concurrently, the interaction type was conveyed by lower frequencies, which are commonly associated with neural circuits linked to performance and reward monitoring. Thus, the mPFC exhibits parallel coding of both "cold" processes (purely cognitive) and "hot" processes (reward and social-related). This allocation of neural resources gives the mPFC a key neural node, flexibly integrating multiple sources of information during social interactions.

Distractor inhibition by alpha oscillations is controlled by an indirect mechanism governed by goal-relevant information

The role of alpha oscillations (8-13 Hz) in cognition is intensively investigated. While intracranial animal recordings demonstrate that alpha oscillations are associated with decreased neuronal excitability, it is been questioned whether alpha oscillations are under direct control from frontoparietal areas to suppress visual distractors. We here point to a revised mechanism in which alpha oscillations are controlled by an indirect mechanism governed by the load of goal-relevant information - a view compatible with perceptual load theory. We will outline how this framework can be further tested and discuss the consequences for network dynamics and resource allocation in the working brain.

Orchestration of saccadic eye-movements by brain rhythms in macaque Frontal Eye Field

Visual perception has been suggested to operate on temporal 'chunks' of sensory input, rather than on a continuous stream of visual information. Saccadic eye movements impose a natural rhythm on the sensory input, as periods of steady fixation between these rapid eye movements provide distinct temporal segments of information. Ideally, the timing of saccades should be precisely locked to the brain's rhythms of information processing. Here, we investigated such locking of saccades to rhythmic neural activity in rhesus monkeys performing a visual foraging task. We found that saccades are phase-locked to local field potential oscillations (especially, 9-22 Hz) in the Frontal Eye Field, with the phase of oscillations predictive of the saccade onset as early as 100 ms prior to these movements. Our data also indicate a functional role of this phase-locking in determining the direction of saccades. These findings show a tight-and likely important-link between oscillatory brain activity and rhythmic behavior that imposes a rhythmic temporal structure on sensory input, such as saccadic eye movements.

Enhanced behavioral performance through interareal gamma and beta synchronization

Cognitive functioning requires coordination between brain areas. Between visual areas, feedforward gamma synchronization improves behavioral performance. Here, we investigate whether similar principles hold across brain regions and frequency bands, using simultaneous electrocorticographic recordings from 15 areas of two macaque monkeys during performance of a selective attention task. Short behavioral reaction times (RTs), suggesting efficient interareal communication, occurred when occipital areas V1, V2, V4, and DP showed gamma synchronization, and fronto-central areas S1, 5, F1, F2, and F4 showed beta synchronization. For both area clusters and corresponding frequency bands, deviations from the typically observed phase relations increased RTs. Across clusters and frequency bands, good phase relations occurred in a correlated manner specifically when they processed the behaviorally relevant stimulus. Furthermore, the fronto-central cluster exerted a beta-band influence onto the occipital cluster whose strength predicted short RTs. These results suggest that local gamma and beta synchronization and their inter-regional coordination jointly improve behavioral performance.

The effect of familiarity on behavioral oscillations in face perception

Studies on behavioral oscillations demonstrate that visual sensitivity fluctuates over time and visual processing varies periodically, mirroring neural oscillations at the same frequencies. Do these behavioral oscillations reflect fixed and relatively automatic sensory sampling, or top-down processes such as attention or predictive coding? To disentangle these theories, the current study used a dual-target rapid serial visual presentation paradigm, where participants indicated the gender of a face target embedded in streams of distractors presented at 30 Hz. On critical trials, two identical targets were presented with varied stimulus onset asynchrony from 200 to 833 ms. The target was either familiar or unfamiliar faces, divided into different blocks. We found a 4.6 Hz phase-coherent fluctuation in gender discrimination performance across both trial types, consistent with previous reports. In addition, however, we found an effect at the alpha frequency, with behavioral oscillations in the familiar blocks characterized by a faster high-alpha peak than for the unfamiliar face blocks. These results are consistent with the combination of both a relatively stable modulation in the theta band and faster modulation of the alpha oscillations. Therefore, the overall pattern of perceptual sampling in visual perception may depend, at least in part, on task demands. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 16/08/2022. The protocol, as accepted by the journal, can be found at: https://doi.org/10.17605/OSF.IO/A98UF .

Rhythmic temporal coordination of neural activity prevents representational conflict during working memory

Selective attention¹ is characterized by alternating states associated with either attentional sampling or attentional shifting, helping to prevent functional conflicts by isolating function-specific neural activity in time.^2,3,4,5 We hypothesized that such rhythmic temporal coordination might also help to prevent representational conflicts during working memory.⁶ Multiple items can be simultaneously held in working memory, and these items can be represented by overlapping neural populations.^7,8,9 Traditional theories propose that the short-term storage of to-be-remembered items occurs through persistent neural activity,^10,11,12 but when neurons are simultaneously representing multiple items, persistent activity creates a potential for representational conflicts. In comparison, more recent, "activity-silent" theories of working memory propose that synaptic changes also contribute to short-term storage of to-be-remembered items.^13,14,15,16 Transient bursts in neural activity,¹⁷ rather than persistent activity, could serve to occasionally refresh these synaptic changes. Here, we used EEG and response times to test whether rhythmic temporal coordination helps to isolate neural activity associated with different to-be-remembered items, thereby helping to prevent representational conflicts. Consistent with this hypothesis, we report that the relative strength of different item representations alternates over time as a function of the frequency-specific phase. Although RTs were linked to theta (∼6 Hz) and beta (∼25 Hz) phases during a memory delay, the relative strength of item representations only alternated as a function of the beta phase. The present findings (1) are consistent with rhythmic temporal coordination being a general mechanism for preventing functional or representational conflicts during cognitive processes and (2) inform models describing the role of oscillatory dynamics in organizing working memory.^{13,18,19,20,21}.

Rhythmic attentional scanning

Sensory processing, short-term memory, and decision-making often deal with multiple items, or options, simultaneously. I review evidence suggesting that the brain handles such multiple items by "rhythmic attentional scanning (RAS)": each item is processed in a separate cycle of the theta rhythm, involving several gamma cycles, to reach an internally consistent representation in the form of a gamma-synchronized neuronal group. Within each theta cycle, items that are extended in representational space are scanned by traveling waves. Such scanning might go across small numbers of simple items linked into a chunk.

Long-Range α-Synchronization as Control Signal for BCI: A Feasibility Study

Shifts in spatial attention are associated with variations in α band (α, 8-14 Hz) activity, specifically in interhemispheric imbalance. The underlying mechanism is attributed to local α-synchronization, which regulates local inhibition of neural excitability, and frontoparietal synchronization reflecting long-range communication. The direction-specific nature of this neural correlate brings forward its potential as a control signal in brain-computer interfaces (BCIs). In the present study, we explored whether long-range α-synchronization presents lateralized patterns dependent on voluntary attention orienting and whether these neural patterns can be picked up at a single-trial level to provide a control signal for active BCI. We collected electroencephalography (EEG) data from a cohort of healthy adults (n = 10) while performing a covert visuospatial attention (CVSA) task. The data show a lateralized pattern of α-band phase coupling between frontal and parieto-occipital regions after target presentation, replicating previous findings. This pattern, however, was not evident during the cue-to-target orienting interval, the ideal time window for BCI. Furthermore, decoding the direction of attention trial-by-trial from cue-locked synchronization with support vector machines (SVMs) was at chance level. The present findings suggest EEG may not be capable of detecting long-range α-synchronization in attentional orienting on a single-trial basis and, thus, highlight the limitations of this metric as a reliable signal for BCI control.

Interacting rhythms enhance sensitivity of target detection in a fronto-parietal computational model of visual attention

Even during sustained attention, enhanced processing of attended stimuli waxes and wanes rhythmically, with periods of enhanced and relatively diminished visual processing (and subsequent target detection) alternating at 4 or 8 Hz in a sustained visual attention task. These alternating attentional states occur alongside alternating dynamical states, in which lateral intraparietal cortex (LIP), the frontal eye field (FEF), and the mediodorsal pulvinar (mdPul) exhibit different activity and functional connectivity at α, β, and γ frequencies-rhythms associated with visual processing, working memory, and motor suppression. To assess whether and how these multiple interacting rhythms contribute to periodicity in attention, we propose a detailed computational model of FEF and LIP. When driven by θ-rhythmic inputs simulating experimentally-observed mdPul activity, this model reproduced the rhythmic dynamics and behavioral consequences of observed attentional states, revealing that the frequencies and mechanisms of the observed rhythms allow for peak sensitivity in visual target detection while maintaining functional flexibility.

There Is More Evidence of Rhythmic Attention than Can Be Found in Behavioral Studies: Perspective on Brookshire, 2022

Recent research indicates periodicity in attention-related sampling and switching, with some of the initial findings coming from behavioral studies. Brookshire [Brookshire, G. Putative rhythms in attentional switching can be explained by aperiodic temporal structure. Nature Human Behaviour, 2022, https://doi.org/10.1038/s41562-022-01364-0], points out that widely used approaches to testing for rhythms in behavioral times series can misclassify consistent aperiodic patterns in temporal structure as periodic patterns. Evidence for rhythmic attention, however, is not limited to behavioral data. Here, I briefly discuss (i) issues with differentiating periodic and aperiodic structure in both behavioral and neural time series, (ii) findings from neural data that are consistent with rhythmic sampling and switching during attentional deployment, and (iii) whether alternative approaches to establishing periodicity in behavioral time series, recommended by Brookshire are appropriate for this particular research topic.

Beyond the brain-computer interface: Decoding brain activity as a tool to understand neuronal mechanisms subtending cognition and behavior

One of the major challenges in system neurosciences consists in developing techniques for estimating the cognitive information content in brain activity. This has an enormous potential in different domains spanning from clinical applications, cognitive enhancement to a better understanding of the neural bases of cognition. In this context, the inclusion of machine learning techniques to decode different aspects of human cognition and behavior and its use to develop brain-computer interfaces for applications in neuroprosthetics has supported a genuine revolution in the field. However, while these approaches have been shown quite successful for the study of the motor and sensory functions, success is still far from being reached when it comes to covert cognitive functions such as attention, motivation and decision making. While improvement in this field of BCIs is growing fast, a new research focus has emerged from the development of strategies for decoding neural activity. In this review, we aim at exploring how the advanced in decoding of brain activity is becoming a major neuroscience tool moving forward our understanding of brain functions, providing a robust theoretical framework to test predictions on the relationship between brain activity and cognition and behavior.

Putative rhythms in attentional switching can be explained by aperiodic temporal structure

The neural and perceptual effects of attention were traditionally assumed to be sustained over time, but recent work suggests that covert attention rhythmically switches between objects at 3-8 Hz. Here I use simulations to demonstrate that the analysis approaches commonly used to test for rhythmic oscillations generate false positives in the presence of aperiodic temporal structure. I then propose two alternative analyses that are better able to discriminate between periodic and aperiodic structure in time series. Finally, I apply these alternative analyses to published datasets and find no evidence for behavioural rhythms in attentional switching after accounting for aperiodic temporal structure. The techniques presented here will help clarify the periodic and aperiodic dynamics of perception and of cognition more broadly.

Distractibility and impulsivity neural states are distinct from selective attention and modulate the implementation of spatial attention

In the context of visual attention, it has been classically assumed that missing the response to a target or erroneously selecting a distractor occurs as a consequence of the (miss)allocation of attention in space. In the present paper, we challenge this view and provide evidence that, in addition to encoding spatial attention, prefrontal neurons also encode a distractibility-to-impulsivity state. Using supervised dimensionality reduction techniques in prefrontal neuronal recordings in monkeys, we identify two partially overlapping neuronal subpopulations associated either with the focus of attention or overt behaviour. The degree of overlap accounts for the behavioral gain associated with the good allocation of attention. We further describe the neural variability accounting for distractibility-to-impulsivity behaviour by a two dimensional state associated with optimality in task and responsiveness. Overall, we thus show that behavioral performance arises from the integration of task-specific neuronal processes and pre-existing neuronal states describing task-independent behavioral states.

Failing to detect relevant information has been assumed to be a consequence of misallocation of attention. Here, the authors present findings showing that optimal behavioral performance results from the absence of interference between internal neural states and attention control.

Phasic modulation of visual representations during sustained attention

Sustained attention has long been thought to benefit perception in a continuous fashion, but recent evidence suggests that it affects perception in a discrete, rhythmic way. Periodic fluctuations in behavioral performance over time, and modulations of behavioral performance by the phase of spontaneous oscillatory brain activity point to an attentional sampling rate in the theta or alpha frequency range. We investigated whether such discrete sampling by attention is reflected in periodic fluctuations in the decodability of visual stimulus orientation from magnetoencephalographic (MEG) brain signals. In this exploratory study, human subjects attended one of the two grating stimuli, while MEG was being recorded. We assessed the strength of the visual representation of the attended stimulus using a support vector machine (SVM) to decode the orientation of the grating (clockwise vs. counterclockwise) from the MEG signal. We tested whether decoder performance depended on the theta/alpha phase of local brain activity. While the phase of ongoing activity in the visual cortex did not modulate decoding performance, theta/alpha phase of activity in the frontal eye fields and parietal cortex, contralateral to the attended stimulus did modulate decoding performance. These findings suggest that phasic modulations of visual stimulus representations in the brain are caused by frequency-specific top-down activity in the frontoparietal attention network, though the behavioral relevance of these effects could not be established.

Periodic attention operates faster during more complex visual search

Attention has been found to sample visual information periodically, in a wide range of frequencies below 20 Hz. This periodicity may be supported by brain oscillations at corresponding frequencies. We propose that part of the discrepancy in periodic frequencies observed in the literature is due to differences in attentional demands, resulting from heterogeneity in tasks performed. To test this hypothesis, we used visual search and manipulated task complexity, i.e., target discriminability (high, medium, low) and number of distractors (set size), while electro-encephalography was simultaneously recorded. We replicated previous results showing that the phase of pre-stimulus low-frequency oscillations predicts search performance. Crucially, such effects were observed at increasing frequencies within the theta-alpha range (6-18 Hz) for decreasing target discriminability. In medium and low discriminability conditions, correct responses were further associated with higher post-stimulus phase-locking than incorrect ones, in increasing frequency and latency. Finally, the larger the set size, the later the post-stimulus effect peaked. Together, these results suggest that increased complexity (lower discriminability or larger set size) requires more attentional cycles to perform the task, partially explaining discrepancies between reports of attentional sampling. Low-frequency oscillations structure the temporal dynamics of neural activity and aid top-down, attentional control for efficient visual processing.

A spatiotemporal mechanism of visual attention: Superdiffusive motion and theta oscillations of neural population activity patterns

Recent evidence has demonstrated that during visual spatial attention sampling, neural activity and behavioral performance exhibit large fluctuations. To understand the origin of these fluctuations and their functional role, here, we introduce a mechanism based on the dynamical activity pattern (attention spotlight) emerging from neural circuit models in the transition regime between different dynamical states. This attention activity pattern with rich spatiotemporal dynamics flexibly samples from different stimulus locations, explaining many key aspects of temporal fluctuations such as variable theta oscillations of visual spatial attention. Moreover, the mechanism expands our understanding of how visual attention exploits spatially complex fluctuations characterized by superdiffusive motion in space and makes experimentally testable predictions. We further illustrate that attention sampling based on such spatiotemporal fluctuations provides profound functional advantages such as adaptive switching between exploitation and exploration activities and is particularly efficient at sampling natural scenes with multiple salient objects.

The role of temporal cortex in the control of attention

Attention is an indispensable component of active vision. Contrary to the widely accepted notion that temporal cortex processing primarily focusses on passive object recognition, a series of very recent studies emphasize the role of temporal cortex structures, specifically the superior temporal sulcus (STS) and inferotemporal (IT) cortex, in guiding attention and implementing cognitive programs relevant for behavioral tasks. The goal of this theoretical paper is to advance the hypothesis that the temporal cortex attention network (TAN) entails necessary components to actively participate in attentional control in a flexible task-dependent manner. First, we will briefly discuss the general architecture of the temporal cortex with a focus on the STS and IT cortex of monkeys and their modulation with attention. Then we will review evidence from behavioral and neurophysiological studies that support their guidance of attention in the presence of cognitive control signals. Next, we propose a mechanistic framework for executive control of attention in the temporal cortex. Finally, we summarize the role of temporal cortex in implementing cognitive programs and discuss how they contribute to the dynamic nature of visual attention to ensure flexible behavior.

Providing Task Instructions During Motor Training Enhances Performance and Modulates Attentional Brain Networks

Learning a new motor task is a complex cognitive and motor process. Especially early during motor learning, cognitive functions such as attentional engagement, are essential, e.g., to discover relevant visual stimuli. Drawing participant's attention towards task-relevant stimuli-e.g., with task instructions using visual cues or explicit written information-is a common practice to support cognitive engagement during training and, hence, accelerate motor learning. However, there is little scientific evidence about how visually cued or written task instructions affect attentional brain networks during motor learning. In this experiment, we trained 36 healthy participants in a virtual motor task: surfing waves by steering a boat with a joystick. We measured the participants' motor performance and observed attentional brain networks using alpha-band electroencephalographic (EEG) activity before and after training. Participants received one of the following task instructions during training: (1) No explicit task instructions and letting participants surf freely (implicit training; IMP); (2) Task instructions provided through explicit visual cues (explicit-implicit training; E-IMP); or (3) through explicit written commands (explicit training; E). We found that providing task instructions during training (E and E-IMP) resulted in less post-training motor variability-linked to enhanced performance-compared to training without instructions (IMP). After training, participants trained with visual cues (E-IMP) enhanced the alpha-band strength over parieto-occipital and frontal brain areas at wave onset. In contrast, participants who trained with explicit commands (E) showed decreased fronto-temporal alpha activity. Thus, providing task instructions in written (E) or using visual cues (E-IMP) leads to similar motor performance improvements by enhancing activation on different attentional networks. While training with visual cues (E-IMP) may be associated with visuo-attentional processes, verbal-analytical processes may be more prominent when written explicit commands are provided (E). Together, we suggest that training parameters such as task instructions, modulate the attentional networks observed during motor practice and may support participant's cognitive engagement, compared to training without instructions.

An oscillatory pipelining mechanism supporting previewing during visual exploration and reading

Humans have a remarkable ability to efficiently explore visual scenes and text using eye movements. Humans typically make eye movements (saccades) every ~250 ms. Since saccade initiation and execution take 100 ms, this leaves only ~150 ms to recognize the fixated object (or word) while simultaneously previewing candidates for the next saccade goal. We propose a pipelining mechanism where serial processing occurs within a specific brain region, whereas parallel processing occurs across different brain regions. The mechanism is timed by alpha oscillations that coordinate the saccades, visual recognition, and previewing in the cortical hierarchy. Consequently, the neuronal mechanism supporting natural vision and saccades must be studied in unison to uncover the brain mechanisms supporting visual exploration and reading.

A neuro-computational model of visual attention with multiple attentional control sets

In numerous activities, humans need to attend to multiple sources of visual information at the same time. Although several recent studies support the evidence of this ability, the mechanism of multi-item attentional processing is still a matter of debate and has not been investigated much by previous computational models. Here, we present a neuro-computational model aiming to address specifically the question of how subjects attend to two items that deviate defined by feature and location. We simulate the experiment of Adamo et al. (2010) which required subjects to use two different attentional control sets, each a combination of color and location. The structure of our model is composed of two components "attention" and "decision-making". The important aspect of our model is its dynamic equations that allow us to simulate the time course of processes at a neural level that occur during different stages until a decision is made. We analyze in detail the conditions under which our model matches the behavioral and EEG data from human subjects. Consistent with experimental findings, our model supports the hypothesis of attending to two control settings concurrently. In particular, our model proposes that initially, feature-based attention operates in parallel across the scene, and only in ongoing processing, a selection by the location takes place.

Prefrontal Control of Proactive and Reactive Mechanisms of Visual Suppression

In everyday life, we are continuously struggling at focusing on our current goals while at the same time avoiding distractions. Attention is the neuro-cognitive process devoted to the selection of behaviorally relevant sensory information while at the same time preventing distraction by irrelevant information. Distraction can be prevented proactively, by strategically prioritizing task-relevant information at the expense of irrelevant information, or reactively, by suppressing the ongoing processing of distractors. The distinctive neuronal signature of these suppressive mechanisms is still largely unknown. Thanks to machine-learning decoding methods applied to prefrontal cortical activity, we monitor the dynamic spatial attention with an unprecedented spatial and temporal resolution. We first identify independent behavioral and neuronal signatures for long-term (learning-based spatial prioritization) and short-term (dynamic spatial attention) mechanisms. We then identify distinct behavioral and neuronal signatures for proactive and reactive suppression mechanisms. We find that while distracting task-relevant information is suppressed proactively, task-irrelevant information is suppressed reactively. Critically, we show that distractor suppression, whether proactive or reactive, strongly depends on the implementation of both long-term and short-term mechanisms of selection. Overall, we provide a unified neuro-cognitive framework describing how the prefrontal cortex deals with distractors in order to flexibly optimize behavior in dynamic environments.

Attentional sampling of visual and auditory objects is captured by theta-modulated neural activity

Recent evidence suggests that visual attention alternately samples two behaviourally relevant objects at approximately 4 Hz, rhythmically shifting between the objects. Whether similar attentional rhythms exist in other sensory modalities, however, is not yet clear. We therefore adapted and extended an established paradigm to investigate visual and potential auditory attentional rhythms, as well as possible interactions, on both a behavioural (detection performance, N = 33) and a neural level (EEG, N = 18). The results during unimodal attention demonstrate that both visual- and auditory-target detection fluctuate at frequencies of approximately 4-8 Hz, confirming that attentional rhythms are not specific to visual processing. The EEG recordings provided evidence of oscillatory activity that underlies these behavioural effects. At right and left occipital EEG electrodes, we detected counter-phasic theta-band activity (4-8 Hz), mirroring behavioural evidence of alternating sampling between the objects presented right and left of central fixation, respectively. Similarly, alpha-band activity as a signature of relatively suppressed sensory encoding showed a theta-rhythmic, counter-phasic change in power. Moreover, these theta-rhythmic changes in alpha power were predictive of behavioural performance in both sensory modalities. Overall, the present findings provide a new perspective on the multimodal rhythmicity of attention.

Rhythmic sampling revisited: Experimental paradigms and neural mechanisms

Sampling of information is thought to be an important aspect of explorative behaviour. Evidence for it has been gained in behavioural assessments of a variety of overt and covert cognitive domains, including sensation, attention, memory, eye movements and dexterity. A common aspect across many findings is that sampling tends to exhibit a rhythmicity at low frequencies (theta, 4-8 Hz; alpha, 9-12 Hz). Neurophysiological investigations in a wide range of species, including rodents, non-human primates and humans have demonstrated the presence of sampling related neural oscillations in a number of brain areas ranging from early sensory cortex, hippocampus to high-level cognitive areas. However, to assess whether rhythmic sampling represents a general aspect of exploratory behaviour one must critically evaluate the task parameters, and their potential link with neural oscillations. Here we focus on sampling during attentive vision to present an overview on the experimental conditions that are used to investigate rhythmic sampling and associated oscillatory brain activity in this domain. This review aims to (1) provide guidelines to efficiently quantify behavioural rhythms, (2) compare results from human and non-human primate studies and (3) argue that the underlying neural mechanisms of sampling can co-occur in both sensory and high-level areas.

A state space modeling approach to real-time phase estimation

Brain rhythms have been proposed to facilitate brain function, with an especially important role attributed to the phase of low-frequency rhythms. Understanding the role of phase in neural function requires interventions that perturb neural activity at a target phase, necessitating estimation of phase in real-time. Current methods for real-time phase estimation rely on bandpass filtering, which assumes narrowband signals and couples the signal and noise in the phase estimate, adding noise to the phase and impairing detections of relationships between phase and behavior. To address this, we propose a state space phase estimator for real-time tracking of phase. By tracking the analytic signal as a latent state, this framework avoids the requirement of bandpass filtering, separately models the signal and the noise, accounts for rhythmic confounds, and provides credible intervals for the phase estimate. We demonstrate in simulations that the state space phase estimator outperforms current state-of-the-art real-time methods in the contexts of common confounds such as broadband rhythms, phase resets, and co-occurring rhythms. Finally, we show applications of this approach to in vivo data. The method is available as a ready-to-use plug-in for the Open Ephys acquisition system, making it widely available for use in experiments.

Shifting expectations: Lapses in spatial attention are driven by anticipatory attentional shifts

Attention is dynamic, constantly shifting between different locations - sometimes imperfectly. How do goal-driven expectations impact dynamic spatial attention? A previous study (Dowd & Golomb, Psychological Science, 30(3), 343-361, 2019) explored object-feature binding when covert attention needed to be either maintained at a single location or shifted from one location to another. In addition to revealing feature-binding errors during dynamic shifts of attention, this study unexpectedly found that participants sometimes made correlated errors on trials when they did not have to shift attention, mistakenly reporting the features and location of an object at a different location. The authors posited that these errors represent "spatial lapses" attention, which are perhaps driven by the implicit sampling of other locations in anticipation of having to shift attention. To investigate whether these spatial lapses are indeed anticipatory, we conducted a series of four experiments. We first replicated in Psychological Science, 30(3), the original finding of spatial lapses, and then showed that these spatial lapses were not observed in contexts where participants are not expecting to have to shift attention. We then tested contexts where the direction of attentional shifts was spatially predictable, and found that participants lapse preferentially to more likely shift locations. Finally, we found that spatial lapses do not seem to be driven by explicit knowledge of likely shift locations. Combined, these results suggest that spatial lapses of attention are induced by the implicit anticipation of making an attentional shift, providing further insight into the interplay between implicit expectations, dynamic spatial attention, and visual perception.

Revisiting Persistent Neuronal Activity During Covert Spatial Attention

Persistent activity has been observed in the prefrontal cortex (PFC), in particular during the delay periods of visual attention tasks. Classical approaches based on the average activity over multiple trials have revealed that such an activity encodes the information about the attentional instruction provided in such tasks. However, single-trial approaches have shown that activity in this area is rather sparse than persistent and highly heterogeneous not only within the trials but also between the different trials. Thus, this observation raised the question of how persistent the actually persistent attention-related prefrontal activity is and how it contributes to spatial attention. In this paper, we review recent evidence of precisely deconstructing the persistence of the neural activity in the PFC in the context of attention orienting. The inclusion of machine-learning methods for decoding the information reveals that attention orienting is a highly dynamic process, possessing intrinsic oscillatory dynamics working at multiple timescales spanning from milliseconds to minutes. Dimensionality reduction methods further show that this persistent activity dynamically incorporates multiple sources of information. This novel framework reflects a high complexity in the neural representation of the attention-related information in the PFC, and how its computational organization predicts behavior.

Lévy walk dynamics explain gamma burst patterns in primate cerebral cortex

Lévy walks describe patterns of intermittent motion with variable step sizes. In complex biological systems, Lévy walks (non-Brownian, superdiffusive random walks) are associated with behaviors such as search patterns of animals foraging for food. Here we show that Lévy walks also describe patterns of oscillatory activity in primate cerebral cortex. We used a combination of empirical observation and modeling to investigate high-frequency (gamma band) local field potential activity in visual motion-processing cortical area MT of marmoset monkeys. We found that gamma activity is organized as localized burst patterns that propagate across the cortical surface with Lévy walk dynamics. Lévy walks are fundamentally different from either global synchronization, or regular propagating waves, because they include large steps that enable activity patterns to move rapidly over cortical modules. The presence of Lévy walk dynamics therefore represents a previously undiscovered mode of brain activity, and implies a novel way for the cortex to compute. We apply a biophysically realistic circuit model to explain that the Lévy walk dynamics arise from critical-state transitions between asynchronous and localized propagating wave states, and that these dynamics yield optimal spatial sampling of the cortical sheet. We hypothesise that Lévy walk dynamics could help the cortex to efficiently process variable inputs, and to find links in patterns of activity among sparsely spiking populations of neurons.

No evidence of rhythmic visuospatial attention at cued locations in a spatial cuing paradigm, regardless of their behavioural relevance

Recent evidence suggests that visuospatial attentional performance is not stable over time but fluctuates in a rhythmic fashion. These attentional rhythms allow for sampling of different visuospatial locations in each cycle of this rhythm. However, it is still unclear in which paradigmatic circumstances rhythmic attention becomes evident. First, it is unclear at what spatial locations rhythmic attention occurs. Second, it is unclear how the behavioural relevance of each spatial location determines the rhythmic sampling patterns. Here, we aim to elucidate these two issues. Firstly, we aim to find evidence of rhythmic attention at the predicted (i.e. cued) location under moderately informative predictor value, replicating earlier studies. Secondly, we hypothesise that rhythmic attentional sampling behaviour will be affected by the behavioural relevance of the sampled location, ranging from non-informative to fully informative. To these aims, we used a modified Egly-Driver task with three conditions: a fully informative cue, a moderately informative cue (replication condition), and a non-informative cue. We did not find evidence of rhythmic sampling at cued locations, failing to replicate earlier studies. Nor did we find differences in rhythmic sampling under different predictive values of the cue. The current data does not allow for robust conclusions regarding the non-cued locations due to the absence of a priori hypotheses. Post-hoc explorative data analyses, however, clearly indicate that attention samples non-cued locations in a theta-rhythmic manner, specifically when the cued location bears higher behavioural relevance than the non-cued locations.

Time-resolved estimation of strength of motor imagery representation by multivariate EEG decoding

Objective. Multivariate decoding enables access to information encoded in multiple brain activity features with high temporal resolution. However, whether the strength, of which this information is represented in the brain, can be extracted across time within single trials remains largely unexplored.Approach.In this study, we addressed this question by applying a support vector machine (SVM) to extract motor imagery (MI) representations, from electroencephalogram (EEG) data, and by performing time-resolved single-trial analyses of the multivariate decoding. EEG was recorded from a group of healthy participants during MI of opening and closing of the same hand.Main results.Cross-temporal decoding revealed both dynamic and stationary MI-relevant features during the task. Specifically, features representing MI evolved dynamically early in the trial and later stabilized into a stationary network of MI features. Using a hierarchical genetic algorithm for selection of MI-relevant features, we identified primarily contralateral alpha and beta frequency features over the sensorimotor and parieto-occipital cortices as stationary which extended into a bilateral pattern in the later part of the trial. During the stationary encoding of MI, by extracting the SVM prediction scores, we analyzed MI-relevant EEG activity patterns with respect to the temporal dynamics within single trials. We show that the SVM prediction score correlates to the amplitude of univariate MI-relevant features (as documented from an extensive repertoire of previous MI studies) within single trials, strongly suggesting that these are functional variations of MI strength hidden in trial averages.Significance.Our work demonstrates a powerful approach for estimating MI strength continually within single trials, having far-reaching impact for single-trial analyses. In terms of MI neurofeedback for motor rehabilitation, these results set the ground for more refined neurofeedback reflecting the strength of MI that can be provided to patients continually in time.

Distinct contributions of alpha and theta rhythms to perceptual and attentional sampling

Accumulating evidence suggests that visual perception operates in an oscillatory fashion at an alpha frequency (around 10 Hz). Moreover, visual attention also seems to operate rhythmically, albeit at a theta frequency (around 5 Hz). Both rhythms are often associated to "perceptual snapshots" taken at the favorable phases of these rhythms. However, less is known about the unfavorable phases: do they constitute "blind gaps," requiring the observer to guess, or is information sampled with reduced precision insufficient for the task demands? As simple detection or discrimination tasks cannot distinguish these options, we applied a continuous report task by asking for the exact orientation of a Landolt ring's gap to estimate separate model parameters for precision and the amount of guessing. We embedded this task in a well-established psychophysical protocol by densely sampling such reports across 20 cue-target stimulus onset asynchronies in a Posner-like cueing paradigm manipulating involuntary spatial attention. Testing the resulting time courses of the guessing and precision parameters for rhythmicities using a fast Fourier transform, we found an alpha rhythm (9.6 Hz) in precision for invalidly cued trials and a theta rhythm (4.8 Hz) in the guess rate across validity conditions. These results suggest distinct roles of the perceptual alpha and the attentional theta rhythm. We speculate that both rhythms result in environmental sampling characterized by fluctuating spatial resolution, speaking against a strict succession of blind gaps and perceptual snapshots.

Behavioral validation of novel high resolution attention decoding method from multi-units & local field potentials

The ability to access brain information in real-time is crucial both for a better understanding of cognitive functions and for the development of therapeutic applications based on brain-machine interfaces. Great success has been achieved in the field of neural motor prosthesis. Progress is still needed in the real-time decoding of higher-order cognitive processes such as covert attention. Recently, we showed that we can track the location of the attentional spotlight using classification methods applied to prefrontal multi-unit activity (MUA) in the non-human primates. Importantly, we demonstrated that the decoded (x,y) attentional spotlight parametrically correlates with the behavior of the monkeys thus validating our decoding of attention. We also demonstrate that this spotlight is extremely dynamic. Here, in order to get closer to non-invasive decoding applications, we extend our previous work to local field potential signals (LFP). Specifically, we achieve, for the first time, high decoding accuracy of the (x,y) location of the attentional spotlight from prefrontal LFP signals, to a degree comparable to that achieved from MUA signals, and we show that this LFP content is predictive of behavior. This LFP attention-related information is maximal in the gamma band (30-250 Hz), peaking between 60 to 120 Hz. In addition, we introduce a novel two-step decoding procedure based on the labelling of maximally attention-informative trials during the decoding procedure. This procedure strongly improves the correlation between our real-time MUA and LFP based decoding and behavioral performance, thus further refining the functional relevance of this real-time decoding of the (x,y) locus of attention. This improvement is more marked for LFP signals than for MUA signals. Overall, this study demonstrates that the attentional spotlight can be accessed from LFP frequency content, in real-time, and can be used to drive high-information content cognitive brain-machine interfaces for the development of new therapeutic strategies.

The neural bases of spatial attention and perceptual rhythms

Attentional processes allow the brain to overcome its processing capacities limitations by enhancing relevant visual information and suppressing irrelevant information. Thus attention plays a critical role, shaping our perception of the world. Several models have been proposed to describe the neuronal bases of attention and its mechanistic underlyings. Recent electrophysiological evidence show that attentional processes rely on oscillatory brain activities that correlate with rhythmic changes in cognitive performance. In the present review, we first take a historical perspective on how attention is viewed, from the initial spotlight theory of attention to the recent dynamic view of attention selection and we review their supporting psychophysical evidence. Based on recent prefrontal electrophysiological evidence, we refine the most recent models of attention sampling by proposing a rhythmic and continuous model of attentional sampling. In particular, we show that attention involves a continuous exploration of space, shifting within and across visual hemifield at specific alpha and theta rhythms, independently of the current attentional load. In addition, we show that this prefrontal attentional spotlight implements conjointly selection and suppression mechanisms, and is captured by salient incoming items. Last, we argue that this attention spotlight implements a highly flexible alternation of attentional exploration and exploitation epochs, depending on ongoing task contingencies. In a last part, we review the local and network oscillatory mechanisms that correlate with rhythmic attentional sampling, describing multiple rhythmic generators and complex network interactions.

Dynamics of Visual Perceptual Echoes Following Short-Term Visual Deprivation

The visual impulse-response function to random input as measured by EEG is dominated by the perceptual echo, a reverberation of stimulus information in the alpha range believed to represent active rhythmic sampling. How this response is generated on a cortical level is unknown. To characterize the underlying mechanisms, we investigated the echoes’ dynamics following short-term visual deprivation, which is known to modify the excitation/inhibition balance in visual cortex. We subjected observers to 150 min of light deprivation (LD) and monocular contrast deprivation (MD). Perceptual echoes were measured by binocular and dichoptic stimulation, respectively, and compared with a baseline condition. Our results show that the echo response is enhanced after LD, but not affected in temporal frequency or spatial propagation. Consistent with previous studies, MD shifted early response (0–150 ms) amplitudes in favor of the deprived eye, but had no systematic effect on the echoes. Our findings demonstrate that the echoes’ synchrony scales with cortical excitability, adding to previous evidence that they represent active visual processing. Their insensitivity to modulation at the monocular level suggests they are generated by a larger region of visual cortex. Our study provides further insight into how mechanisms of rhythmic sampling are implemented in the visual system.