The normalization model of attention

Linking cortical surface area to computational properties in human visual perception

Cortical structure and function are closely linked, shaping the neural basis of human behavior. This study explores how cortical surface area (SA), a structural feature, influences computational properties in human visual perception. Using a combination of psychophysical, neuroimaging, and computational modeling approaches, we find that variations in SA across the parietal and frontal cortices are linked to distinct behavioral patterns in a motion perception task. These differences in behavior correspond to specific parameters within a divisive normalization model, indicating a unique contribution of SA to the spatial organization of cortical circuitry. This work highlights the importance of cortical architecture in modifying computational processes that underlie perception, enhancing our understanding of how structural differences can influence neural function and behavior.

Conflicting Sensory Information Sharpens the Neural Representations of Early Selective Visuospatial Attention

Adaptive behaviors require the ability to resolve conflicting information caused by the processing of incompatible sensory inputs. Prominent theories of attention have posited that early selective attention helps mitigate cognitive interference caused by conflicting sensory information by facilitating the processing of task-relevant sensory inputs and filtering out behaviorally irrelevant information. Surprisingly, many recent studies that investigated the role of early selective attention on conflict mitigation have failed to provide positive evidence. Here, we examined changes in the selectivity of early visuospatial attention in male and female human subjects performing an attention-cueing Eriksen flanker task, where they discriminated the shape of a visual target surrounded by congruent or incongruent distractors. We used the inverted encoding model to reconstruct spatial representations of visual selective attention from the topographical patterns of amplitude modulations in alpha band oscillations in scalp EEG (∼8-12 Hz). We found that the fidelity of the alpha-based spatial reconstruction was significantly higher in the incongruent compared with the congruent condition. Importantly, these conflict-related modulations in the reconstruction fidelity occurred at a much earlier time window than those of the lateralized posterior event-related potentials associated with target selection and distractor suppression processes, as well as conflict-related modulations in the frontocentral negative-going wave and midline-frontal theta oscillations (∼3-7 Hz), thought to track executive control functions. Taken together, our data suggest that conflict resolution is supported by the cascade of neural processes underlying early selective visuospatial attention and frontal executive functions that unfold over time.

Rethinking simultaneous suppression in visual cortex via compressive spatiotemporal population receptive fields

When multiple visual stimuli are presented simultaneously in the receptive field, the neural response is suppressed compared to presenting the same stimuli sequentially. The prevailing hypothesis suggests that this suppression is due to competition among multiple stimuli for limited resources within receptive fields, governed by task demands. However, it is unknown how stimulus-driven computations may give rise to simultaneous suppression. Using fMRI, we find simultaneous suppression in single voxels, which varies with both stimulus size and timing, and progressively increases up the visual hierarchy. Using population receptive field (pRF) models, we find that compressive spatiotemporal summation rather than compressive spatial summation predicts simultaneous suppression, and that increased simultaneous suppression is linked to larger pRF sizes and stronger compressive nonlinearities. These results necessitate a rethinking of simultaneous suppression as the outcome of stimulus-driven compressive spatiotemporal computations within pRFs, and open new opportunities to study visual processing capacity across space and time.

Adaptation in the visual system: Networked fatigue or suppressed prediction error signalling?

Our brains are constantly adapting to changes in our visual environments. Neural adaptation exerts a persistent influence on the activity of sensory neurons and our perceptual experience, however there is a lack of consensus regarding how adaptation is implemented in the visual system. One account describes fatigue-based mechanisms embedded within local networks of stimulus-selective neurons (networked fatigue models). Another depicts adaptation as a product of stimulus expectations (predictive coding models). In this review, I evaluate neuroimaging and psychophysical evidence that poses fundamental problems for predictive coding models of neural adaptation. Specifically, I discuss observations of distinct repetition and expectation effects, as well as incorrect predictions of repulsive adaptation aftereffects made by predictive coding accounts. Based on this evidence, I argue that networked fatigue models provide a more parsimonious account of adaptation effects in the visual system. Although stimulus expectations can be formed based on recent stimulation history, any consequences of these expectations are likely to co-occur (or interact) with effects of fatigue-based adaptation. I conclude by proposing novel, testable hypotheses relating to interactions between fatigue-based adaptation and other predictive processes, focusing on stimulus feature extrapolation phenomena.

The role of binocular disparity and attention in the neural representation of multiple moving stimuli in the visual cortex

A fundamental process of vision involves segmenting visual scenes into distinct objects and surfaces. Stereoscopic depth and visual motion are important cues for segmentation. However, the mechanisms by which the visual system utilizes depth and motion cues to segment multiple objects are not fully understood. We investigated how neurons in the middle-temporal (MT) cortex of macaque monkeys represented overlapping surfaces located at different depths and moved simultaneously in different directions. We recorded neuronal activities in the MT of three male monkeys while they performed discrimination tasks under different attention conditions. We found that neuronal responses to overlapping surfaces showed a robust bias toward the horizontal binocular disparity of one of the two surfaces. Across all animals, the disparity bias of a neuron in response to two surfaces positively correlated with the neuron's disparity preference for a single surface. For two animals, neurons that preferred near disparities of single surfaces (near neurons) showed a near bias to overlapping stimuli, whereas neurons that preferred far disparities (far neurons) showed a far bias. For the third animal, both near and far neurons displayed a near bias, although the near neurons showed a stronger near bias than the far neurons. Interestingly, for all three animals, both near and far neurons exhibited an initial near bias relative to the average of the responses to the individual surfaces. Although attention can modulate neuronal response to better represent the attended surface, the disparity bias was not due to attention. We also found that the effect of attention modulation on MT responses was consistent with object-based rather than feature-based attention. We proposed a model in which the pool size of the neuron population that weighs the responses to individual stimulus components can be variable. This model is a novel extension of the standard normalization model and provides a unified explanation for the disparity bias observed across animals. Our results revealed the neural encoding rule for multiple stimuli located at different depths and presented new evidence of response modulation by object-based attention in MT. The disparity bias allows subgroups of neurons to preferentially represent individual surfaces of multiple stimuli at different depths, thereby facilitating segmentation.

Eight challenges in developing theory of intelligence

A good theory of mathematical beauty is more practical than any current observation, as new predictions about physical reality can be self-consistently verified. This belief applies to the current status of understanding deep neural networks including large language models and even the biological intelligence. Toy models provide a metaphor of physical reality, allowing mathematically formulating the reality (i.e., the so-called theory), which can be updated as more conjectures are justified or refuted. One does not need to present all details in a model, but rather, more abstract models are constructed, as complex systems such as the brains or deep networks have many sloppy dimensions but much less stiff dimensions that strongly impact macroscopic observables. This type of bottom-up mechanistic modeling is still promising in the modern era of understanding the natural or artificial intelligence. Here, we shed light on eight challenges in developing theory of intelligence following this theoretical paradigm. Theses challenges are representation learning, generalization, adversarial robustness, continual learning, causal learning, internal model of the brain, next-token prediction, and the mechanics of subjective experience.

Endogenous attention enhances contrast appearance regardless of stimulus contrast

There has been enduring debate on how attention alters contrast appearance. Recent research indicates that exogenous attention enhances contrast appearance for low-contrast stimuli but attenuates it for high-contrast stimuli. Similarly, one study has demonstrated that endogenous attention heightens perceived contrast for low-contrast stimuli, yet none have explored its impact on high-contrast stimuli. In this study, we investigated how endogenous attention alters contrast appearance, with a specific focus on high-contrast stimuli. In Experiment 1, we utilized the rapid serial visual presentation (RSVP) paradigm to direct endogenous attention, revealing that contrast appearance was enhanced for both low- and high-contrast stimuli. To eliminate potential influences from the confined attention field in the RSVP paradigm, Experiment 2 adopted the letter identification paradigm, deploying attention across a broader visual field. Results consistently indicated that endogenous attention increased perceived contrast for high-contrast stimuli. Experiment 3 employed equiluminant chromatic letters as stimuli in the letter identification task to eliminate potential interference from contrast adaption, which might have occurred in Experiment 2. Remarkably, the boosting effect of endogenous attention persisted. Combining the results from these experiments, we propose that endogenous attention consistently enhances contrast appearance, irrespective of stimulus contrast levels. This stands in contrast to the effects of exogenous attention, suggesting that mechanisms through which endogenous attention alters contrast appearance may differ from those of exogenous attention.

Reading Reshapes Stimulus Selectivity in the Visual Word Form Area

Reading depends on a brain region known as the "visual word form area" (VWFA) in the left ventral occipitotemporal cortex. This region's function is debated because its stimulus selectivity is not absolute, it is modulated by a variety of task demands, and it is inconsistently localized. We used fMRI to characterize the combination of sensory and cognitive factors that activate word-responsive regions that we precisely localized in 16 adult humans (4 male). We then presented three types of character strings: English words, pseudowords, and unfamiliar characters with matched visual features. Participants performed three different tasks while viewing those stimuli: detecting real words, detecting color in the characters, and detecting color in the fixation mark. There were three primary findings about the VWFA's response: (1) It preferred letter strings over unfamiliar characters even when the stimuli were ignored during the fixation task. (2) Compared with those baseline responses, engaging in the word reading task enhanced the response to words but suppressed the response to unfamiliar characters. (3) Attending to the stimuli to judge their color had little effect on the response magnitudes. Thus, the VWFA is uniquely modulated by a cognitive signal that is specific to voluntary linguistic processing and is not additive. Functional connectivity analyses revealed that communication between the VWFA and a left frontal language area increased when the participant engaged in the linguistic task. We conclude that the VWFA is inherently selective for familiar orthography, but it falls under control of the language network when the task demands it.

Confirmation Bias through Selective Use of Evidence in Human Cortex

Decision-makers often process new evidence selectively, depending on their current beliefs about the world. We asked whether such confirmation biases result from biases in the encoding of sensory evidence in the brain, or alternatively in the utilization of encoded evidence for behavior. Human participants estimated the source of a sequence of visual-spatial evidence samples while we measured cortical population activity with magnetoencephalography (MEG). Halfway through the sequence, participants were prompted to judge the more likely source category. Their processing of subsequent evidence depended on its consistency with the previously chosen category, but the encoding of evidence in cortical activity did not. Instead, the encoded evidence in parietal and primary visual cortex contributed less to the estimation report when that evidence was inconsistent with the previous choice. We conclude that confirmation bias originates from the way in which decision-makers utilize information encoded in the brain. This provides room for deliberative control.

Perceptual Resolution of Ambiguity: Can Tuned, Divisive Normalization Account for both Interocular Similarity Grouping and Difference Enhancement

Our visual system usually provides a unique and functional representation of the external world. At times, however, the visual system has more than one compelling interpretation of the same retinal stimulus; in this case, neural populations compete for perceptual dominance to resolve ambiguity. Spatial and temporal context can guide perceptual experience. Recent evidence shows that ambiguous retinal stimuli are sometimes resolved by enhancing either similarity or differences among multiple percepts. Divisive normalization is a canonical neural computation that enables context-dependent sensory processing by attenuating a neuron's response by other neurons. Experiments here show that divisive normalization can account for perceptual representations of either similarity enhancement (so-called grouping) or difference enhancement, offering a unified framework for opposite perceptual outcomes.

Causal evidence for cholinergic stabilization of attractor landscape dynamics

There is substantial evidence that neuromodulatory systems critically influence brain state dynamics; however, most work has been purely descriptive. Here, we quantify, using data combining local inactivation of the basal forebrain with simultaneous measurement of resting-state fMRI activity in the macaque, the causal role of long-range cholinergic input to the stabilization of brain states in the cerebral cortex. Local inactivation of the nucleus basalis of Meynert (nbM) leads to a decrease in the energy barriers required for an fMRI state transition in cortical ongoing activity. Moreover, the inactivation of particular nbM sub-regions predominantly affects information transfer in cortical regions known to receive direct anatomical projections. We demonstrate these results in a simple neurodynamical model of cholinergic impact on neuronal firing rates and slow hyperpolarizing adaptation currents. We conclude that the cholinergic system plays a critical role in stabilizing macroscale brain state dynamics.

Transcranial Alternating Current Stimulation over Frontal Eye Fields Mimics Attentional Modulation of Visual Processing

Attentional control over sensory processing has been linked to neural alpha oscillations and related inhibition of cerebral cortex. Despite the wide consensus on the functional relevance of alpha oscillations for attention, precise neural mechanisms of how alpha oscillations shape perception and how this top-down modulation is implemented in cortical networks remain unclear. Here, we tested the hypothesis that alpha oscillations in frontal eye fields (FEFs) are causally involved in the top-down regulation of visual processing in humans (male and female). We applied sham-controlled, intermittent transcranial alternating current stimulation (tACS) over bilateral FEF at either 10 Hz (alpha) or 40 Hz (gamma) to manipulate attentional preparation in a visual discrimination task. Under each stimulation condition, we measured psychometric functions for contrast perception and introduced a novel linear mixed modeling approach for statistical control of neurosensory side effects of the electric stimulation. tACS at alpha frequency reduced the slope of the psychometric function, resulting in improved subthreshold and impaired superthreshold contrast perception. Side effects on the psychometric functions were complex and showed large interindividual variability. Controlling for the impact of side effects on the psychometric parameters by using covariates in the linear mixed model analysis reduced this variability and strengthened the perceptual effect. We propose that alpha tACS over FEF mimicked a state of endogenous attention by strengthening a fronto-occipitoparietal network in the alpha band. We speculate that this network modulation enhanced phasic gating in occipitoparietal cortex leading to increased variability of single-trial psychometric thresholds, measurable as a reduction of psychometric slope.

Reading reshapes stimulus selectivity in the visual word form area

Reading depends on a brain region known as the "visual word form area" (VWFA) in left ventral occipito-temporal cortex. This region's function is debated because its stimulus selectivity is not absolute, it is modulated by a variety of task demands, and it is inconsistently localized. We used fMRI to characterize the combination of sensory and cognitive factors that activate word-responsive regions that we precisely localized in 16 adult humans (4 male). We then presented three types of character strings: English words, pseudowords, and unfamiliar characters with matched visual features. Participants performed three different tasks while viewing those stimuli: detecting real words, detecting color in the characters, and detecting color in the fixation mark. There were three primary findings about the VWFA's response: (1) It preferred letter strings over unfamiliar characters even when the stimuli were ignored during the fixation task; (2) Compared to those baseline responses, engaging in the word reading task enhanced the response to words but suppressed the response to unfamiliar characters. (3) Attending to the stimuli to judge their font color had little effect on the response magnitudes. Thus, the VWFA is uniquely modulated by a cognitive signal that is specific to voluntary linguistic processing and is not additive. Functional connectivity analyses revealed that communication between the VWFA and a left frontal language area increased when the participant engaged in the linguistic task. We conclude that the VWFA is inherently selective for familiar orthography, but it falls under control of the language network when the task demands it.

Distinct Yet Proximal Face- and Body-Selective Brain Regions Enable Clutter-Tolerant Representations of the Face, Body, and Whole Person

Faces and bodies are processed in separate but adjacent regions in the primate visual cortex. Yet, the functional significance of dividing the whole person into areas dedicated to its face and body components and their neighboring locations remains unknown. Here we hypothesized that this separation and proximity together with a normalization mechanism generate clutter-tolerant representations of the face, body, and whole person when presented in complex multi-category scenes. To test this hypothesis, we conducted a fMRI study, presenting images of a person within a multi-category scene to human male and female participants and assessed the contribution of each component to the response to the scene. Our results revealed a clutter-tolerant representation of the whole person in areas selective for both faces and bodies, typically located at the border between the two category-selective regions. Regions exclusively selective for faces or bodies demonstrated clutter-tolerant representations of their preferred category, corroborating earlier findings. Thus, the adjacent locations of face- and body-selective areas enable a hardwired machinery for decluttering of the whole person, without the need for a dedicated population of person-selective neurons. This distinct yet proximal functional organization of category-selective brain regions enhances the representation of the socially significant whole person, along with its face and body components, within multi-category scenes.

Representation and computation in visual working memory

The ability to sustain internal representations of the sensory environment beyond immediate perception is a fundamental requirement of cognitive processing. In recent years, debates regarding the capacity and fidelity of the working memory (WM) system have advanced our understanding of the nature of these representations. In particular, there is growing recognition that WM representations are not merely imperfect copies of a perceived object or event. New experimental tools have revealed that observers possess richer information about the uncertainty in their memories and take advantage of environmental regularities to use limited memory resources optimally. Meanwhile, computational models of visuospatial WM formulated at different levels of implementation have converged on common principles relating capacity to variability and uncertainty. Here we review recent research on human WM from a computational perspective, including the neural mechanisms that support it.

Multimodal gradients of basal forebrain connectivity across the neocortex

The cholinergic innervation of the cortex originates almost entirely from populations of neurons in the basal forebrain (BF). Structurally, the ascending BF cholinergic projections are highly branched, with individual cells targeting multiple different cortical regions. However, it is not known whether the structural organization of basal forebrain projections reflects their functional integration with the cortex. We therefore used high-resolution 7T diffusion and resting state functional MRI in humans to examine multimodal gradients of BF cholinergic connectivity with the cortex. Moving from anteromedial to posterolateral BF, we observed reduced tethering between structural and functional connectivity gradients, with the most pronounced dissimilarity localized in the nucleus basalis of Meynert (NbM). The cortical expression of this structure-function gradient revealed progressively weaker tethering moving from unimodal to transmodal cortex, with the lowest tethering in midcingulo-insular cortex. We used human [ 18 F] fluoroethoxy-benzovesamicol (FEOBV) PET to demonstrate that cortical areas with higher concentrations of cholinergic innervation tend to exhibit lower tethering between BF structural and functional connectivity, suggesting a pattern of increasingly diffuse axonal arborization. Anterograde viral tracing of cholinergic projections and [ 18 F] FEOBV PET in mice confirmed a gradient of axonal arborization across individual BF cholinergic neurons. Like humans, cholinergic neurons with the highest arborization project to cingulo-insular areas of the mouse isocortex. Altogether, our findings reveal that BF cholinergic neurons vary in their branch complexity, with certain subpopulations exhibiting greater modularity and others greater diffusivity in the functional integration of their cortical targets.

Sustained attention operates via dissociable neural mechanisms across different eccentric locations

In primates, foveal and peripheral vision have distinct neural architectures and functions. However, it has been debated if selective attention operates via the same or different neural mechanisms across eccentricities. We tested these alternative accounts by examining the effects of selective attention on the steady-state visually evoked potential (SSVEP) and the fronto-parietal signal measured via EEG from human subjects performing a sustained visuospatial attention task. With a negligible level of eye movements, both SSVEP and SND exhibited the heterogeneous patterns of attentional modulations across eccentricities. Specifically, the attentional modulations of these signals peaked at the parafoveal locations and such modulations wore off as visual stimuli appeared closer to the fovea or further away towards the periphery. However, with a relatively higher level of eye movements, the heterogeneous patterns of attentional modulations of these neural signals were less robust. These data demonstrate that the top-down influence of covert visuospatial attention on early sensory processing in human cortex depends on eccentricity and the level of saccadic responses. Taken together, the results suggest that sustained visuospatial attention operates differently across different eccentric locations, providing new understanding of how attention augments sensory representations regardless of where the attended stimulus appears.

Thalamic contributions to the state and contents of consciousness

Consciousness can be conceptualized as varying along at least two dimensions: the global state of consciousness and the content of conscious experience. Here, we highlight the cellular and systems-level contributions of the thalamus to conscious state and then argue for thalamic contributions to conscious content, including the integrated, segregated, and continuous nature of our experience. We underscore vital, yet distinct roles for core- and matrix-type thalamic neurons. Through reciprocal interactions with deep-layer cortical neurons, matrix neurons support wakefulness and determine perceptual thresholds, whereas the cortical interactions of core neurons maintain content and enable perceptual constancy. We further propose that conscious integration, segregation, and continuity depend on the convergent nature of corticothalamic projections enabling dimensionality reduction, a thalamic reticular nucleus-mediated divisive normalization-like process, and sustained coherent activity in thalamocortical loops, respectively. Overall, we conclude that the thalamus plays a central topological role in brain structures controlling conscious experience.

Visual context affects the perceived timing of tactile sensations elicited through intra-cortical microstimulation

Intra-cortical microstimulation (ICMS) is a technique to provide tactile sensations for a somatosensory brain-machine interface (BMI). A viable BMI must function within the rich, multisensory environment of the real world, but how ICMS is integrated with other sensory modalities is poorly understood. To investigate how ICMS percepts are integrated with visual information, ICMS and visual stimuli were delivered at varying times relative to one another. Both visual context and ICMS current amplitude were found to bias the qualitative experience of ICMS. In two tetraplegic participants, ICMS and visual stimuli were more likely to be experienced as occurring simultaneously when visual stimuli were more realistic, demonstrating an effect of visual context on the temporal binding window. The peak of the temporal binding window varied but was consistently offset from zero, suggesting that multisensory integration with ICMS can suffer from temporal misalignment. Recordings from primary somatosensory cortex (S1) during catch trials where visual stimuli were delivered without ICMS demonstrated that S1 represents visual information related to ICMS across visual contexts.

Neural mechanisms of resource allocation in working memory

To mitigate capacity limits of working memory, people allocate resources according to an item's relevance. However, the neural mechanisms supporting such a critical operation remain unknown. Here, we developed computational neuroimaging methods to decode and demix neural responses associated with multiple items in working memory with different priorities. In striate and extrastriate cortex, the gain of neural responses tracked the priority of memoranda. Higher-priority memoranda were decoded with smaller error and lower uncertainty. Moreover, these neural differences predicted behavioral differences in memory prioritization. Remarkably, trialwise variability in the magnitude of delay activity in frontal cortex predicted differences in decoded precision between low and high-priority items in visual cortex. These results suggest a model in which feedback signals broadcast from frontal cortex sculpt the gain of memory representations in visual cortex according to behavioral relevance, thus, identifying a neural mechanism for resource allocation.

Spatial attention alters visual cortical representation during target anticipation

Attention enables us to efficiently and flexibly interact with the environment by prioritizing some image features in preparation for responding to a stimulus. Using a concurrent psychophysics- fMRI experiment, we investigated how covert spatial attention affects responses in human visual cortex prior to target onset, and how it affects subsequent behavioral performance. Performance improved at cued locations and worsened at uncued locations, relative to distributed attention, demonstrating a selective tradeoff in processing. Pre-target BOLD responses in cortical visual field maps changed in two ways: First, there was a stimulus-independent baseline shift, positive in map locations near the cued location and negative elsewhere, paralleling the behavioral results. Second, population receptive field centers shifted toward the attended location. Both effects increased in higher visual areas. Together, the results show that spatial attention has large effects on visual cortex prior to target appearance, altering neural response properties throughout and across multiple visual field maps.

A dynamic neural resource model bridges sensory and working memory

Probing memory of a complex visual image within a few hundred milliseconds after its disappearance reveals significantly greater fidelity of recall than if the probe is delayed by as little as a second. Classically interpreted, the former taps into a detailed but rapidly decaying visual sensory or 'iconic' memory (IM), while the latter relies on capacity-limited but comparatively stable visual working memory (VWM). While iconic decay and VWM capacity have been extensively studied independently, currently no single framework quantitatively accounts for the dynamics of memory fidelity over these time scales. Here, we extend a stationary neural population model of VWM with a temporal dimension, incorporating rapid sensory-driven accumulation of activity encoding each visual feature in memory, and a slower accumulation of internal error that causes memorized features to randomly drift over time. Instead of facilitating read-out from an independent sensory store, an early cue benefits recall by lifting the effective limit on VWM signal strength imposed when multiple items compete for representation, allowing memory for the cued item to be supplemented with information from the decaying sensory trace. Empirical measurements of human recall dynamics validate these predictions while excluding alternative model architectures. A key conclusion is that differences in capacity classically thought to distinguish IM and VWM are in fact contingent upon a single resource-limited WM store.

Representational structures as a unifying framework for attention

Our visual system consciously processes only a subset of the incoming information. Selective attention allows us to prioritize relevant inputs, and can be allocated to features, locations, and objects. Recent advances in feature-based attention suggest that several selection principles are shared across these domains and that many differences between the effects of attention on perceptual processing can be explained by differences in the underlying representational structures. Moving forward, it can thus be useful to assess how attention changes the structure of the representational spaces over which it operates, which include the spatial organization, feature maps, and object-based coding in visual cortex. This will ultimately add to our understanding of how attention changes the flow of visual information processing more broadly.

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.

Are neuronal mechanisms of attention universal across human sensory and motor brain maps?

One's experience of shifting attention from the color to the smell to the act of picking a flower seems like a unitary process applied, at will, to one modality after another. Yet, the unique and separable experiences of sight versus smell versus movement might suggest that the neural mechanisms of attention have been separately optimized to employ each modality to its greatest advantage. Moreover, addressing the issue of universality can be particularly difficult due to a paucity of existing cross-modal comparisons and a dearth of neurophysiological methods that can be applied equally well across disparate modalities. Here we outline some of the conceptual and methodological issues related to this problem and present an instructive example of an experimental approach that can be applied widely throughout the human brain to permit detailed, quantitative comparison of attentional mechanisms across modalities. The ultimate goal is to spur efforts across disciplines to provide a large and varied database of empirical observations that will either support the notion of a universal neural substrate for attention or more clearly identify the degree to which attentional mechanisms are specialized for each modality.

We know what attention is!

Attention is one of the most thoroughly investigated psychological phenomena, yet skepticism about attention is widespread: we do not know what it is, it is too many things, there is no such thing. The deficiencies highlighted are not about experimental work but the adequacy of the scientific theory of attention. Combining common scientific claims about attention into a single theory leads to internal inconsistency. This paper demonstrates that a specific functional conception of attention is incorporated into the tasks used in standard experimental paradigms. In accepting these paradigms as valid probes of attention, we commit to this common conception. The conception unifies work at multiple levels of analysis into a coherent scientific explanation of attention. Thus, we all know what attention is.

The mediodorsal thalamus in executive control

Executive control, the ability to organize thoughts and action plans in real time, is a defining feature of higher cognition. Classical theories have emphasized cortical contributions to this process, but recent studies have reinvigorated interest in the role of the thalamus. Although it is well established that local thalamic damage diminishes cognitive capacity, such observations have been difficult to inform functional models. Recent progress in experimental techniques is beginning to enrich our understanding of the anatomical, physiological, and computational substrates underlying thalamic engagement in executive control. In this review, we discuss this progress and particularly focus on the mediodorsal thalamus, which regulates the activity within and across frontal cortical areas. We end with a synthesis that highlights frontal thalamocortical interactions in cognitive computations and discusses its functional implications in normal and pathological conditions.

Rationality, preferences, and emotions with biological constraints: it all starts from our senses

Is the role of our sensory systems to represent the physical world as accurately as possible? If so, are our preferences and emotions, often deemed irrational, decoupled from these 'ground-truth' sensory experiences? We show why the answer to both questions is 'no'. Brain function is metabolically costly, and the brain loses some fraction of the information that it encodes and transmits. Therefore, if brains maximize objective functions that increase the fitness of their species, they should adapt to the objective-maximizing rules of the environment at the earliest stages of sensory processing. Consequently, observed 'irrationalities', preferences, and emotions stem from the necessity for our early sensory systems to adapt and process information while considering the metabolic costs and internal states of the organism.

New Evidence for Retrospectively Cued Perception

Past research suggests a continuity between perception and memory, as reflected in influences of orienting of spatial attention by cues presented after a visual target offset (post-target cues) on target perception. Conducting two experiments, we tested and confirmed this claim. Our study revealed an elevated reliance on post-target cues for target detection with diminishing target visibility, leading to better performance in validly versus invalidly cued trials, indicative of contrast gain. We demonstrated this post-target cueing impact on target perception without a postcue response prompt, meaning that our results truly reflected a continuity between perception and memory rather than a task-specific impact of having to memorize the target due to a response prompt. While previous studies found an improvement in accuracy through valid compared to invalid cues using liminal targets, in Experiment 1, we further showed an influence of attention on participants' response time by the post-target cues with cues presented away from a clearly visible target. This suggests that visual interactions at the target location provided no better explanation of post-target cueing effects. Our results generalize prior research with liminal targets and confirm the view of a perception-memory continuum so that visual target processing is not shielded against visuospatial orienting of attention elicited by events following the offset of the visual target.

Emergent human-like covert attention in feedforward convolutional neural networks

Covert attention allows the selection of locations or features of the visual scene without moving the eyes. Cues and contexts predictive of a target's location orient covert attention and improve perceptual performance. The performance benefits are widely attributed to theories of covert attention as a limited resource, zoom, spotlight, or weighting of visual information. However, such concepts are difficult to map to neuronal populations. We show that a feedforward convolutional neural network (CNN) trained on images to optimize target detection accuracy and with no explicit incorporation of an attention mechanism, a limited resource, or feedback connections learns to utilize cues and contexts in the three most prominent covert attention tasks (Posner cueing, set size effects in search, and contextual cueing) and predicts the cue/context influences on human accuracy. The CNN's cueing/context effects generalize across network training schemes, to peripheral and central pre-cues, discrimination tasks, and reaction time measures, and critically do not vary with reductions in network resources (size). The CNN shows comparable cueing/context effects to a model that optimally uses image information to make decisions (Bayesian ideal observer) but generalizes these effects to cue instances unseen during training. Together, the findings suggest that human-like behavioral signatures of covert attention in the three landmark paradigms might be an emergent property of task accuracy optimization in neuronal populations without positing limited attentional resources. The findings might explain recent behavioral results showing cueing and context effects across a variety of simple organisms with no neocortex, from archerfish to fruit flies.

Modulation of motor surround inhibition during motor tasks

Surround inhibition (SI) in the motor system is important in individuation of actions, but is sometimes difficult to demonstrate. It has also not been evaluated in real life tasks. In this study, we use real life tasks and a new method where excitability of the surround muscle is assessed with respect to its current activity level rather than when it is at rest. Motor evoked potential (MEP) amplitudes were measured in the abductor digiti minimi (ADM) muscle while participants performed several motor tasks: "writing" on paper, "holding a pen" precisely and, "holding a water bottle" against gravity. These MEPs were compared to ADM MEPs amplitudes measured during a fifth finger abduction (ADM being the center muscle). SI was also measured in the traditional way, by comparing ADM MEPs during an index finger flexion and at rest. For the "writing" and "holding a pen" tasks, but not the "holding bottle" task, the MEP amplitudes were significantly smaller when compared to MEP amplitudes when the ADM was the center muscle with the same level of activation. The ADM MEP amplitudes were not different between rest and during index finger flexion. The new method employed here shows, that motor SI can be measured during tonic movements. The findings also show motor SI during two real-life motor tasks: "writing" and "holding a pen". The lack of modulation of MEP amplitude during "holding bottle" task seems to indicate that SI is action specific rather than muscle specific.

The dynamics of statistical learning in visual search and its interaction with salience processing: An EEG study

Visual attention can be guided by statistical regularities in the environment, that people implicitly learn from past experiences (statistical learning, SL). Moreover, a perceptually salient element can automatically capture attention, gaining processing priority through a bottom-up attentional control mechanism. The aim of our study was to investigate the dynamics of SL and if it shapes attentional target selection additively with salience processing, or whether these mechanisms interact, e.g. one gates the other. In a visual search task, we therefore manipulated target frequency (high vs. low) across locations while, in some trials, the target was salient in terms of colour. Additionally, halfway through the experiment, the high-frequency location changed to the opposite hemifield. EEG activity was simultaneously recorded, with a specific interest in two markers related to target selection and post-selection processing, respectively: N2pc and SPCN. Our results revealed that both SL and saliency significantly enhanced behavioural performance, but also interacted with each other, with an attenuated saliency effect at the high-frequency target location, and a smaller SL effect for salient targets. Concerning processing dynamics, the benefit of salience processing was more evident during the early stage of target selection and processing, as indexed by a larger N2pc and early-SPCN, whereas SL modulated the underlying neural activity particularly later on, as revealed by larger late-SPCN. Furthermore, we showed that SL was rapidly acquired and adjusted when the spatial imbalance changed. Overall, our findings suggest that SL is flexible to changes and, combined with salience processing, jointly contributes to establishing attentional priority.

Integrating Social Cognition Into Domain-General Control: Interactive Activation and Competition for the Control of Action (ICON)

Social cognition differs from general cognition in its focus on understanding, perceiving, and interpreting social information. However, we argue that the significance of domain-general processes for controlling cognition has been historically undervalued in social cognition and social neuroscience research. We suggest much of social cognition can be characterized as specialized feature representations supported by domain-general cognitive control systems. To test this proposal, we develop a comprehensive working model, based on an interactive activation and competition architecture and applied to the control of action. As such, we label the model "ICON" (interactive activation and competition model for the control of action). We used the ICON model to simulate human performance across various laboratory tasks. Our simulations emphasize that many laboratory-based social tasks do not require socially specific control systems, such as those that are argued to rely on neural networks associated with theory-of-mind. Moreover, our model clarifies that perceived disruptions in social cognition, even in what appears to be disruption to the control of social cognition, can stem from deficits in social representation instead. We advocate for a "default stance" in social cognition, where control is usually general, but representation is specific. This study underscores the importance of integrating social cognition within the broader realm of domain-general control processing, offering a unified perspective on task processing.

Information representation in an oscillating neural field model modulated by working memory signals

We study how stimulus information can be represented in the dynamical signatures of an oscillatory model of neural activity-a model whose activity can be modulated by input akin to signals involved in working memory (WM). We developed a neural field model, tuned near an oscillatory instability, in which the WM-like input can modulate the frequency and amplitude of the oscillation. Our neural field model has a spatial-like domain in which an input that preferentially targets a point-a stimulus feature-on the domain will induce feature-specific activity changes. These feature-specific activity changes affect both the mean rate of spikes and the relative timing of spiking activity to the global field oscillation-the phase of the spiking activity. From these two dynamical signatures, we define both a spike rate code and an oscillatory phase code. We assess the performance of these two codes to discriminate stimulus features using an information-theoretic analysis. We show that global WM input modulations can enhance phase code discrimination while simultaneously reducing rate code discrimination. Moreover, we find that the phase code performance is roughly two orders of magnitude larger than that of the rate code defined for the same model solutions. The results of our model have applications to sensory areas of the brain, to which prefrontal areas send inputs reflecting the content of WM. These WM inputs to sensory areas have been established to induce oscillatory changes similar to our model. Our model results suggest a mechanism by which WM signals may enhance sensory information represented in oscillatory activity beyond the comparatively weak representations based on the mean rate activity.

Computational model links normalization to chemoarchitecture in the human visual system

A goal of cognitive neuroscience is to provide computational accounts of brain function. Canonical computations-mathematical operations used by the brain in many contexts-fulfill broad information-processing needs by varying their algorithmic parameters. A key question concerns the identification of biological substrates for these computations and their algorithms. Chemoarchitecture-the spatial distribution of neurotransmitter receptor densities-shapes brain function. Here, we propose that local variations in specific receptor densities implement algorithmic modulations of canonical computations. To test this hypothesis, we combine mathematical modeling of brain responses with chemoarchitecture data. We compare parameters of divisive normalization obtained from 7-tesla functional magnetic resonance imaging with receptor density maps obtained from positron emission tomography. We find evidence that serotonin and γ-aminobutyric acid receptor densities are the biological substrate for algorithmic modulations of divisive normalization in the human visual system. Our model links computational and biological levels of vision, explaining how canonical computations allow the brain to fulfill broad information-processing needs.

Terms of debate: Consensus definitions to guide the scientific discourse on visual distraction

Hypothesis-driven research rests on clearly articulated scientific theories. The building blocks for communicating these theories are scientific terms. Obviously, communication - and thus, scientific progress - is hampered if the meaning of these terms varies idiosyncratically across (sub)fields and even across individual researchers within the same subfield. We have formed an international group of experts representing various theoretical stances with the goal to homogenize the use of the terms that are most relevant to fundamental research on visual distraction in visual search. Our discussions revealed striking heterogeneity and we had to invest much time and effort to increase our mutual understanding of each other's use of central terms, which turned out to be strongly related to our respective theoretical positions. We present the outcomes of these discussions in a glossary and provide some context in several essays. Specifically, we explicate how central terms are used in the distraction literature and consensually sharpen their definitions in order to enable communication across theoretical standpoints. Where applicable, we also explain how the respective constructs can be measured. We believe that this novel type of adversarial collaboration can serve as a model for other fields of psychological research that strive to build a solid groundwork for theorizing and communicating by establishing a common language. For the field of visual distraction, the present paper should facilitate communication across theoretical standpoints and may serve as an introduction and reference text for newcomers.

Attentional Modulation in Early Visual Cortex: A Focused Reanalysis of Steady-state Visual Evoked Potential Studies

Steady-state visual evoked potentials (SSVEPs) are a powerful tool for investigating selective attention. Here, we conducted a combined reanalysis of multiple studies employing this technique in a variety of attentional experiments to, first, establish benchmark effect sizes of attention on amplitude and phase of SSVEPs and, second, harness the power of a large data set to test more specific hypotheses. Data of eight published SSVEP studies were combined, in which human participants (n = 135 in total) attended to flickering random dot stimuli based on their defining features (e.g., location, color, luminance, or orientation) or feature conjunctions. The reanalysis established that, in all the studies, attention reliably enhanced amplitudes, with color-based attention providing the strongest effect. In addition, the latency of SSVEPs elicited by attended stimuli was reduced by ∼4 msec. Next, we investigated the modulation of SSVEP amplitudes in a subset of studies where two different features were attended concurrently. Although most models assume that attentional effects of multiple features are combined additively, our results suggest that neuronal enhancement provided by concurrent attention is better described by multiplicative integration. Finally, we used the combined data set to demonstrate that the increase in trial-averaged SSVEP amplitudes with attention cannot be explained by increased synchronization of single-trial phases. Contrary to the prediction of the phase-locking account, the variance across trials of complex Fourier coefficients increases with attention, which is more consistent with boosting of a largely phase-locked signal embedded in non-phase-locked noise.

Divisively normalized neuronal processing of uncertain visual feedback for visuomotor learning

When encountering a visual error during a reaching movement, the motor system improves the motor command for the subsequent trial. This improvement is impaired by visual error uncertainty, which is considered evidence that the motor system optimally estimates the error. However, how such statistical computation is accomplished remains unclear. Here, we propose an alternative scheme implemented with a divisive normalization (DN): the responses of neuronal elements are normalized by the summed activity of the population. This scheme assumes that when an uncertain visual error is provided by multiple cursors, the motor system processes the error conveyed by each cursor and integrates the information using DN. The DN model reproduced the patterns of learning response to 1-3 cursor errors and the impairment of learning response with visual error uncertainty. This study provides a new perspective on how the motor system updates motor commands according to uncertain visual error information.

Prefrontal Cortex Codes Representations of Target Identity and Feature Uncertainty

Many objects in the real world have features that vary over time, creating uncertainty in how they will look in the future. This uncertainty makes statistical knowledge about the likelihood of features critical to attention demanding processes such as visual search. However, little is known about how the uncertainty of visual features is integrated into predictions about search targets in the brain. In the current study, we test the idea that regions prefrontal cortex code statistical knowledge about search targets before the onset of search. Across 20 human participants (13 female; 7 male), we observe target identity in the multivariate pattern and uncertainty in the overall activation of dorsolateral prefrontal cortex (DLPFC) and inferior frontal junction (IFJ) in advance of the search display. This indicates that the target identity (mean) and uncertainty (variance) of the target distribution are coded independently within the same regions. Furthermore, once the search display appears the univariate IFJ signal scaled with the distance of the actual target from the expected mean, but more so when expected variability was low. These results inform neural theories of attention by showing how the prefrontal cortex represents both the identity and expected variability of features in service of top-down attentional control.SIGNIFICANCE STATEMENT Theories of attention and working memory posit that when we engage in complex cognitive tasks our performance is determined by how precisely we remember task-relevant information. However, in the real world the properties of objects change over time, creating uncertainty about many aspects of the task. There is currently a gap in our understanding of how neural systems represent this uncertainty and combine it with target identity information in anticipation of attention demanding cognitive tasks. In this study, we show that the prefrontal cortex represents identity and uncertainty as unique codes before task onset. These results advance theories of attention by showing that the prefrontal cortex codes both target identity and uncertainty to implement top-down attentional control.

Little engagement of attention by salient distractors defined in a different dimension or modality to the visual search target

Singleton distractors may inadvertently capture attention, interfering with the task at hand. The underlying neural mechanisms of how we prevent or handle distractor interference remain elusive. Here, we varied the type of salient distractor introduced in a visual search task: the distractor could be defined in the same (shape) dimension as the target, a different (color) dimension, or a different (tactile) modality (intra-dimensional, cross-dimensional, and, respectively, cross-modal distractor, all matched for physical salience); and besides behavioral interference, we measured lateralized electrophysiological indicators of attentional selectivity (the N2pc, Ppc, PD , CCN/CCP, CDA, and cCDA). The results revealed the intra-dimensional distractor to produce the strongest reaction-time interference, associated with the smallest target-elicited N2pc. In contrast, the cross-dimensional and cross-modal distractors did not engender any significant interference, and the target-elicited N2pc was comparable to the condition in which the search display contained only the target singleton, thus ruling out early attentional capture. Moreover, the cross-modal distractor elicited a significant early CCN/CCP, but did not influence the target-elicited N2pc, suggesting that the tactile distractor is registered by the somatosensory system (rather than being proactively suppressed), without, however, engaging attention. Together, our findings indicate that, in contrast to distractors defined in the same dimension as the target, distractors singled out in a different dimension or modality can be effectively prevented to engage attention, consistent with dimension- or modality-weighting accounts of attentional priority computation.

Statistical learning of target and distractor spatial probability shape a common attentional priority computation

Converging evidence recently put forward the notion that dedicated neurocognitive mechanisms do exist for the suppression of salient, but irrelevant distractors. Along this line, it is plausible to hypothesize that, in appropriate contexts, experience-dependent forms of attentional learning might selectively induce plastic changes within this dedicated circuitry, thus allowing an independent shaping of priorities at the service of attentional filtering. Conversely, previous work suggested that statistical learning (SL) of both target and distractor spatial probability distributions converge in adjusting only the overall attentional priority of locations: in fact, in the presence of an independent manipulation, either related to the target or to the distractor only, SL induces indirect effects (e.g., changes in filtering efficiency due to an uneven distribution of targets), suggesting that SL-induced plastic changes affect a shared neural substrate. Here we tested whether, when (conflicting) target- and distractor-related manipulations are concurrently applied to the very same locations, dedicated mechanisms might support the selective encoding of spatial priority in relation to the specific attentional operation involved. In three related experiments, human healthy participants discriminated the direction of a target arrow, while ignoring a salient distractor, if present; both target and distractor spatial probability distributions were concurrently manipulated in relation to each single location. Critically, the selection bias produced by the target-related SL was marginally reduced by an adverse distractor contingency, and the suppression bias generated by the distractor-related SL was erased, or even reversed, by an adverse target contingency. Our results suggest that even conflicting target- and distractor-related SL manipulations result in the adjustment of a unique spatial priority computation, likely because the process directly relies on direct plastic alterations of shared spatial priority map(s).

Silences, spikes and bursts: Three-part knot of the neural code

When a neuron breaks silence, it can emit action potentials in a number of patterns. Some responses are so sudden and intense that electrophysiologists felt the need to single them out, labelling action potentials emitted at a particularly high frequency with a metonym - bursts. Is there more to bursts than a figure of speech? After all, sudden bouts of high-frequency firing are expected to occur whenever inputs surge. The burst coding hypothesis advances that the neural code has three syllables: silences, spikes and bursts. We review evidence supporting this ternary code in terms of devoted mechanisms for burst generation, synaptic transmission and synaptic plasticity. We also review the learning and attention theories for which such a triad is beneficial.

The Random Step Method for Measuring the Point of Subjective Equality

Points of Subjective Equality (PSE) are commonly measured using staircase or constant stimuli methods. However, the staircase method is highly dependent on the step size, and the constant stimuli method is time-consuming. Thus, we wanted to develop an efficient and quick method to estimate both the PSE and the slope of the psychometric function. We developed a random-step algorithm in which a one-up-one-down rule is followed but with a random step size in a pre-defined range of test levels. Each stimulus would be chosen depending on the previous response of the subject. If the subject responded "up", any random level in the lower range would be picked for the next trial. And if the subject responded "down", any random level in the upper range would be picked for the next trial. This procedure would result in a bell-shaped distribution of the test levels around the estimated PSE, while a substantial amount of trials would still be dispersed at both bounds of the range. We then compared this method with traditional constant stimuli procedure on a task based on the Pulfrich phenomenon while the PSEs of participants could be varied using different neutral density filters. Our random-step method provided robust estimates of both the PSE and the slope under various noise levels with small trial counts, and we observed a significant correlation between the PSEs obtained with the two methods. The random-step method is an efficient way to measure the full psychometric function when testing time is critical, such as in clinical settings.

Laminar compartmentalization of attention modulation in area V4 aligns with the demands of visual processing hierarchy in the cortex

Attention selectively enhances neural responses to low contrast stimuli in visual area V4, a critical hub that sends projections both up and down the visual hierarchy. Veridical encoding of contrast information is a key computation in early visual areas, while later stages encoding higher level features benefit from improved sensitivity to low contrast. How area V4 meets these distinct information processing demands in the attentive state is unknown. We found that attentional modulation in V4 is cortical layer and cell-class specific. Putative excitatory neurons in the superficial layers show enhanced boosting of low contrast information, while those of deep layers exhibit contrast-independent scaling. Computational modeling suggested the extent of spatial integration of inhibitory neurons as the mechanism behind such laminar differences. Considering that superficial neurons are known to project to higher areas and deep layers to early visual areas, our findings suggest that the interactions between attention and contrast in V4 are compartmentalized, in alignment with the demands of the visual processing hierarchy.

Sustained attention operates via dissociable neural mechanisms across different eccentric locations

In primates, foveal and peripheral vision have distinct neural architectures and functions. However, it has been debated if selective attention operates via the same or different neural mechanisms across eccentricities. We tested these alternative accounts by examining the effects of selective attention on the steady-state visually evoked potential (SSVEP) and the fronto-parietal signal measured via EEG from human subjects performing a sustained visuospatial attention task. With a negligible level of eye movements, both SSVEP and SND exhibited the heterogeneous patterns of attentional modulations across eccentricities. Specifically, the attentional modulations of these signals peaked at the parafoveal locations and such modulations wore off as visual stimuli appeared closer to the fovea or further away towards the periphery. However, with a relatively higher level of eye movements, the heterogeneous patterns of attentional modulations of these neural signals were less robust. These data demonstrate that the top-down influence of covert visuospatial attention on early sensory processing in human cortex depends on eccentricity and the level of saccadic responses. Taken together, the results suggest that sustained visuospatial attention operates differently across different eccentric locations, providing new understanding of how attention augments sensory representations regardless of where the attended stimulus appears.

Modeling the diverse effects of divisive normalization on noise correlations

Divisive normalization, a prominent descriptive model of neural activity, is employed by theories of neural coding across many different brain areas. Yet, the relationship between normalization and the statistics of neural responses beyond single neurons remains largely unexplored. Here we focus on noise correlations, a widely studied pairwise statistic, because its stimulus and state dependence plays a central role in neural coding. Existing models of covariability typically ignore normalization despite empirical evidence suggesting it affects correlation structure in neural populations. We therefore propose a pairwise stochastic divisive normalization model that accounts for the effects of normalization and other factors on covariability. We first show that normalization modulates noise correlations in qualitatively different ways depending on whether normalization is shared between neurons, and we discuss how to infer when normalization signals are shared. We then apply our model to calcium imaging data from mouse primary visual cortex (V1), and find that it accurately fits the data, often outperforming a popular alternative model of correlations. Our analysis indicates that normalization signals are often shared between V1 neurons in this dataset. Our model will enable quantifying the relation between normalization and covariability in a broad range of neural systems, which could provide new constraints on circuit mechanisms of normalization and their role in information transmission and representation.

Attentional effects on local V1 microcircuits explain selective V1-V4 communication

Selective attention implements preferential routing of attended stimuli, likely through increasing the influence of the respective synaptic inputs on higher-area neurons. As the inputs of competing stimuli converge onto postsynaptic neurons, presynaptic circuits might offer the best target for attentional top-down influences. If those influences enabled presynaptic circuits to selectively entrain postsynaptic neurons, this might explain selective routing. Indeed, when two visual stimuli induce two gamma rhythms in V1, only the gamma induced by the attended stimulus entrains gamma in V4. Here, we modelled induced responses with a Dynamic Causal Model for Cross-Spectral Densities and found that selective entrainment can be explained by attentional modulation of intrinsic V1 connections. Specifically, local inhibition was decreased in the granular input layer and increased in the supragranular output layer of the V1 circuit that processed the attended stimulus. Thus, presynaptic attentional influences and ensuing entrainment were sufficient to mediate selective routing.

Stimulus expectations do not modulate visual event-related potentials in probabilistic cueing designs

Humans and other animals can learn and exploit repeating patterns that occur within their environments. These learned patterns can be used to form expectations about future sensory events. Several influential predictive coding models have been proposed to explain how learned expectations influence the activity of stimulus-selective neurons in the visual system. These models specify reductions in neural response measures when expectations are fulfilled (termed expectation suppression) and increases following surprising sensory events. However, there is currently scant evidence for expectation suppression in the visual system when confounding factors are taken into account. Effects of surprise have been observed in blood oxygen level dependent (BOLD) signals, but not when using electrophysiological measures. To provide a strong test for expectation suppression and surprise effects we performed a predictive cueing experiment while recording electroencephalographic (EEG) data. Participants (n=48) learned cue-face associations during a training session and were then exposed to these cue-face pairs in a subsequent experiment. Using univariate analyses of face-evoked event-related potentials (ERPs) we did not observe any differences across expected (90% probability), neutral (50%) and surprising (10%) face conditions. Across these comparisons, Bayes factors consistently favoured the null hypothesis throughout the time-course of the stimulus-evoked response. When using multivariate pattern analysis we did not observe above-chance classification of expected and surprising face-evoked ERPs. By contrast, we found robust within- and across-trial stimulus repetition effects. Our findings do not support predictive coding-based accounts that specify reduced prediction error signalling when perceptual expectations are fulfilled. They instead highlight the utility of other types of predictive processing models that describe expectation-related phenomena in the visual system without recourse to prediction error signalling.

Flexible information representation to stabilize sensory perception despite minor external input variations

Sensory information about the environment constantly changes or varies depending on circumstances. However, once we repeatedly experience objects, our brain can perceive and recognize them as identical, even if they are slightly altered or include some diversity. We can stably perceive things without interference from minor external changes or variety. Our recent study focusing on visual perception showed that repeatedly viewing the same oriented grating stimuli enables information representation for low-contrast (or weak-intensity) orientations in the primary visual cortex. We observed low contrast-preferring neurons, whose firing rates increased by reducing the luminance contrast. The number of such neurons increased after the experience, and the neuronal population, including such neurons, can represent even low-contrast orientations. This study indicated that experience leads to flexible information representations that continuously respond to inputs of various strengths at the neuronal population level in the primary sensory cortex. In this perspective article, in addition to the above mechanism, I would discuss alternative mechanisms for perceptual stabilization. The primary sensory cortex represents external information faithfully without alterations, as well as in a state distorted by experience. Both sensory representations may cooperatively and dynamically affect hierarchical downstream, resulting in stable perception.

Attention preserves the selectivity of feature-tuned normalization

Attention and divisive normalization both contribute to making visual processing more efficient. Attention selectively increases the neural gain of relevant information in the early visual cortex, resulting in stronger perceived salience for attended regions or features. Divisive normalization improves processing efficiency by suppressing responses to homogeneous inputs and highlighting salient boundaries, facilitating sparse coding of inputs. Theoretical and empirical research suggest a tight link between attention and normalization, wherein attending to a stimulus results in a release from normalization, thereby allowing for an increase in neural response gain. In the present study, we address whether attention alters the qualitative properties of normalization. Specifically, we examine how attention influences the feature-tuned nature of normalization, whereby suppression is stronger between visual stimuli whose orientation contents are similar, and weaker when the orientations are different. Ten human observers viewed stimuli that varied in orientation content while we acquired fMRI BOLD responses under two attentional states: attending toward or attending away from the stimulus. Our results indicate that attention does not alter the specificity of feature-tuned normalization. Instead, attention seems to enhance visuocortical responses evenly, regardless of the degree of orientation similarity within the stimulus. Since visuocortical responses exhibit adaptation to statistical regularities in natural scenes, we conclude that while attention can selectively increase the gain of responses to attended items, it does not appear to alter the ecologically relevant correspondence between orientation differences and strength of tuned normalization.NEW & NOTEWORTHY The magnitude of visuocortical BOLD responses scales with orientation differences in visual stimuli, with the strongest response suppression for collinear stimuli and least suppression for orthogonal, in a way that appears to match natural scene statistics. We examined the effects of attention on this feature-tuned property of suppression and found that while attending to a stimulus increases the overall gain of visuocortical responses, the qualitative properties of feature-tuning remain unchanged, suggesting attention preserves tuned normalization properties.