Long-term priors influence visual perception through recruitment of long-range feedback

The Administration of Ketamine Is Associated with Dose-Dependent Stabilization of Cortical Dynamics in Humans

During wakefulness, external stimuli elicit conscious experiences. In contrast, dreams and drug-induced dissociated states are characterized by vivid internally generated conscious experiences and reduced ability to perceive external stimuli. Understanding the physiological distinctions between normal wakefulness and dissociated states may therefore disambiguate signatures of responsiveness to external stimuli from those that underlie conscious experience. The hypothesis that conscious experiences are associated with brain criticality has received considerable theoretical and experimental support. Consistent with this hypothesis, statistical signatures of criticality are similar in normal wakefulness and dissociative states but are abolished in dreamless sleep and under anesthesia. Thus, while statistical measures of criticality are associated with the ability to have conscious experience, they do not readily distinguish between perception of the external world from internally generated percepts. Here, we investigate distinct, dynamical, signatures of criticality during escalating ketamine doses in high-density EEG in human male volunteers. We show that during normal wakefulness, EEG is found at a critical point between damped and exploding oscillations. With increasing doses of ketamine, as dissociative symptoms intensify, activity is progressively stabilized-most prominently at higher frequencies. We also show that stabilization is a more reliable marker of the effects of ketamine than conventional measures such as power spectra. These findings suggest that stabilization of cortical dynamics correlates with decreased ability to respond to and perceive external stimuli rather than the ability to have conscious experiences per se. Altogether, these results suggest that combining statistical and dynamical criticality measures may distinguish wakefulness, dissociation, and unconsciousness.

The predictive nature of spontaneous brain activity across scales and species

Emerging research suggests the brain operates as a "prediction machine," continuously anticipating sensory, motor, and cognitive outcomes. Central to this capability is the brain's spontaneous activity-ongoing internal processes independent of external stimuli. Neuroimaging and computational studies support that this activity is integral to maintaining and refining mental models of our environment, body, and behaviors, akin to generative models in computation. During rest, spontaneous activity expands the variability of potential representations, enhancing the accuracy and adaptability of these models. When performing tasks, internal models direct brain regions to anticipate sensory and motor states, optimizing performance. This review synthesizes evidence from various species, from C. elegans to humans, highlighting three key aspects of spontaneous brain activity's role in prediction: the similarity between spontaneous and task-related activity, the encoding of behavioral and interoceptive priors, and the high metabolic cost of this activity, underscoring prediction as a fundamental function of brains across species.

Susceptibility to spatial illusions does not depend on visual experience: Evidence from sighted and blind children

Visuospatial illusions may be a by-product of learned regularities in the environment or they may reflect the recruitment of sensory mechanisms that, in some contexts, provide an erroneous spatial estimate. Young children experience visual illusions, and blind adults are susceptible using touch alone, suggesting that the perceptual inferences influencing illusions are amodal and rapidly acquired. However, other evidence, such as visual illusions in the newly sighted, points to the involvement of innate mechanisms. To help tease apart cognitive from sensory influences, we investigated susceptibility to the Ebbinghaus, Müller-Lyer and Vertical-Horizontal illusions in children aged 6-14 years following visual-only, haptic-only and bimodal exploration. Consistent with previous findings, children of all ages were susceptible to all three visual illusions. In addition, illusions of extent but not of size were experienced using haptics alone. We then tested 17 congenitally blind children to investigate whether illusions were mediated by vision. Similar to their sighted counterparts, blind children were also susceptible to illusions following haptic exploration suggesting that early visual experience is not necessary for spatial illusions to be perceived. Reduced susceptibility in older children to some illusions further implies that explicit or formal knowledge of spatial relations is unlikely to mediate these experiences. Instead, the results are consistent with previous evidence for cross-modal interactions in 'visual' brain regions and point to the possibility that illusions may be driven by innate developmental processes that are not entirely dependent on, although are refined by, visual experience.

Unpacking the complexities of consciousness: Theories and reflections

As the field of consciousness science matures, the research agenda has expanded from an initial focus on the neural correlates of consciousness, to developing and testing theories of consciousness. Several theories have been put forward, each aiming to elucidate the relationship between consciousness and brain function. However, there is an ongoing, intense debate regarding whether these theories examine the same phenomenon. And, despite ongoing research efforts, it seems like the field has so far failed to converge around any single theory, and instead exhibits significant polarization. To advance this discussion, proponents of five prominent theories of consciousness-Global Neuronal Workspace Theory (GNWT), Higher-Order Theories (HOT), Integrated Information Theory (IIT), Recurrent Processing Theory (RPT), and Predictive Processing (PP)-engaged in a public debate in 2022, as part of the annual meeting of the Association for the Scientific Study of Consciousness (ASSC). They were invited to clarify the explananda of their theories, articulate the core mechanisms underpinning the corresponding explanations, and outline their foundational premises. This was followed by an open discussion that delved into the testability of these theories, potential evidence that could refute them, and areas of consensus and disagreement. Most importantly, the debate demonstrated that at this stage, there is more controversy than agreement between the theories, pertaining to the most basic questions of what consciousness is, how to identify conscious states, and what is required from any theory of consciousness. Addressing these core questions is crucial for advancing the field towards a deeper understanding and comparison of competing theories.

Beyond task response-Pre-stimulus activity modulates contents of consciousness

The current discussion on the neural correlates of the contents of consciousness (NCCc) focuses mainly on the post-stimulus period of task-related activity. This neglects the substantial impact of the spontaneous or ongoing activity of the brain as manifest in pre-stimulus activity. Does the interaction of pre- and post-stimulus activity shape the contents of consciousness? Addressing this gap in our knowledge, we review and converge two recent lines of findings, that is, pre-stimulus alpha power and pre- and post-stimulus alpha trial-to-trial variability (TTV). The data show that pre-stimulus alpha power modulates post-stimulus activity including specifically the subjective features of conscious contents like confidence and vividness. At the same time, alpha pre-stimulus variability shapes post-stimulus TTV reduction including the associated contents of consciousness. We propose that non-additive rather than merely additive interaction of the internal pre-stimulus activity with the external stimulus in the alpha band is key for contents to become conscious. This is mediated by mechanisms on different levels including neurophysiological, neurocomputational, neurodynamic, neuropsychological and neurophenomenal levels. Overall, considering the interplay of pre-stimulus intrinsic and post-stimulus extrinsic activity across wider timescales, not just evoked responses in the post-stimulus period, is critical for identifying neural correlates of consciousness. This is well in line with both processing and especially the Temporo-spatial theory of consciousness (TTC).

Attentional control influence habituation through modulation of connectivity patterns within the prefrontal cortex: Insights from stereo-EEG

Attentional control, guided by top-down processes, enables selective focus on pertinent information, while habituation, influenced by bottom-up factors and prior experiences, shapes cognitive responses by emphasizing stimulus relevance. These two fundamental processes collaborate to regulate cognitive behavior, with the prefrontal cortex and its subregions playing a pivotal role. Nevertheless, the intricate neural mechanisms underlying the interaction between attentional control and habituation are still a subject of ongoing exploration. To our knowledge, there is a dearth of comprehensive studies on the functional connectivity between subsystems within the prefrontal cortex during attentional control processes in both primates and humans. Utilizing stereo-electroencephalogram (SEEG) recordings during the Stroop task, we observed top-down dominance effects and corresponding connectivity patterns among the orbitofrontal cortex (OFC), the middle frontal gyrus (MFG), and the inferior frontal gyrus (IFG) during heightened attentional control. These findings highlighting the involvement of OFC in habituation through top-down attention. Our study unveils unique connectivity profiles, shedding light on the neural interplay between top-down and bottom-up attentional control processes, shaping goal-directed attention.

Autonomous navigation and collision prediction of port channel based on computer vision and lidar

This study aims to enhance the safety and efficiency of port navigation by reducing ship collision accidents, minimizing environmental risks, and optimizing waterways to increase port throughput. Initially, a three-dimensional map of the port's waterway, including data on water depth, rocks, and obstacles, is generated through laser radar scanning. Visual perception technology is adopted to process and identify the data for environmental awareness. Single Shot MultiBox Detector (SSD) is utilized to position ships and obstacles, while point cloud data create a comprehensive three-dimensional map. In order to improve the optimal navigation approach of the Rapidly-Exploring Random Tree (RRT), an artificial potential field method is employed. Additionally, the collision prediction model utilizes K-Means clustering to enhance the Faster R-CNN algorithm for predicting the paths of other ships and obstacles. The results indicate that the RRT enhanced by the artificial potential field method reduces the average path length (from 500 to 430 m), average time consumption (from 30 to 22 s), and maximum collision risk (from 15 to 8%). Moreover, the accuracy, recall rate, and F1 score of the K-Means + Faster R-CNN collision prediction model reach 92%, 88%, and 90%, respectively, outperforming other models. Overall, these findings underscore the substantial advantages of the proposed enhanced algorithm in autonomous navigation and collision prediction in port waterways.

Neural Mechanisms Determining the Duration of Task-free, Self-paced Visual Perception

Humans spend hours each day spontaneously engaging with visual content, free from specific tasks and at their own pace. Currently, the brain mechanisms determining the duration of self-paced perceptual behavior remain largely unknown. Here, participants viewed naturalistic images under task-free settings and self-paced each image's viewing duration while undergoing EEG and pupillometry recordings. Across two independent data sets, we observed large inter- and intra-individual variability in viewing duration. However, beyond an image's presentation order and category, specific image content had no consistent effects on spontaneous viewing duration across participants. Overall, longer viewing durations were associated with sustained enhanced posterior positivity and anterior negativity in the ERPs. Individual-specific variations in the spontaneous viewing duration were consistently correlated with evoked EEG activity amplitudes and pupil size changes. By contrast, presentation order was selectively correlated with baseline alpha power and baseline pupil size. Critically, spontaneous viewing duration was strongly predicted by the temporal stability in neural activity patterns starting as early as 350 msec after image onset, suggesting that early neural stability is a key predictor for sustained perceptual engagement. Interestingly, neither bottom-up nor top-down predictions about image category influenced spontaneous viewing duration. Overall, these results suggest that individual-specific factors can influence perceptual processing at a surprisingly early time point and influence the multifaceted ebb and flow of spontaneous human perceptual behavior in naturalistic settings.

Perceptual reorganization from prior knowledge emerges late in childhood

Human vision relies heavily on prior knowledge. Here, we show for the first time that prior-knowledge-induced reshaping of visual inputs emerges gradually in late childhood. To isolate the effects of prior knowledge on perception, we presented 4- to 12-year-olds and adults with two-tone images - hard-to-recognize degraded photos. In adults, seeing the original photo triggers perceptual reorganization, causing mandatory recognition of the two-tone version. This involves top-down signaling from higher-order brain areas to early visual cortex. We show that children younger than 7-9 years do not experience this knowledge-guided shift, despite viewing the original photo immediately before each two-tone. To assess computations underlying this development, we compared human performance to three neural networks with varying architectures. The best-performing model behaved much like 4- to 5-year-olds, displaying feature-based rather than holistic processing strategies. The reconciliation of prior knowledge with sensory input undergoes a striking age-related shift, which may underpin the development of many perceptual abilities.

Perceptual transitions between object rigidity and non-rigidity: Competition and cooperation among motion energy, feature tracking, and shape-based priors

Why do moving objects appear rigid when projected retinal images are deformed non-rigidly? We used rotating rigid objects that can appear rigid or non-rigid to test whether shape features contribute to rigidity perception. When two circular rings were rigidly linked at an angle and jointly rotated at moderate speeds, observers reported that the rings wobbled and were not linked rigidly, but rigid rotation was reported at slow speeds. When gaps, paint, or vertices were added, the rings appeared rigidly rotating even at moderate speeds. At high speeds, all configurations appeared non-rigid. Salient features thus contribute to rigidity at slow and moderate speeds but not at high speeds. Simulated responses of arrays of motion-energy cells showed that motion flow vectors are predominantly orthogonal to the contours of the rings, not parallel to the rotation direction. A convolutional neural network trained to distinguish flow patterns for wobbling versus rotation gave a high probability of wobbling for the motion-energy flows. However, the convolutional neural network gave high probabilities of rotation for motion flows generated by tracking features with arrays of MT pattern-motion cells and corner detectors. In addition, circular rings can appear to spin and roll despite the absence of any sensory evidence, and this illusion is prevented by vertices, gaps, and painted segments, showing the effects of rotational symmetry and shape. Combining convolutional neural network outputs that give greater weight to motion energy at fast speeds and to feature tracking at slow speeds, with the shape-based priors for wobbling and rolling, explained rigid and non-rigid percepts across shapes and speeds (R2 = 0.95). The results demonstrate how cooperation and competition between different neuronal classes lead to specific states of visual perception and to transitions between the states.

Abnormal higher-order network interactions in Parkinson's disease visual hallucinations

Visual hallucinations in Parkinson's disease can be viewed from a systems-level perspective, whereby dysfunctional communication between brain networks responsible for perception predisposes a person to hallucinate. To this end, abnormal functional interactions between higher-order and primary sensory networks have been implicated in the pathophysiology of visual hallucinations in Parkinson's disease, however the precise signatures remain to be determined. Dimensionality reduction techniques offer a novel means for simplifying the interpretation of multidimensional brain imaging data, identifying hierarchical patterns in the data that are driven by both within- and between-functional network changes. Here, we applied two complementary non-linear dimensionality reduction techniques-diffusion-map embedding and t-distributed stochastic neighbour embedding (t-SNE)-to resting state functional MRI data, in order to characterize the altered functional hierarchy associated with susceptibility to visual hallucinations. Our study involved 77 people with Parkinson's disease (31 with hallucinations; 46 without hallucinations) and 19 age-matched healthy control subjects. In patients with visual hallucinations, we found compression of the unimodal-heteromodal gradient consistent with increased functional integration between sensory and higher order networks. This was mirrored in a traditional functional connectivity analysis, which showed increased connectivity between the visual and default mode networks in the hallucinating group. Together, these results suggest a route by which higher-order regions may have excessive influence over earlier sensory processes, as proposed by theoretical models of hallucinations across disorders. By contrast, the t-SNE analysis identified distinct alterations in prefrontal regions, suggesting an additional layer of complexity in the functional brain network abnormalities implicated in hallucinations, which was not apparent in traditional functional connectivity analyses. Together, the results confirm abnormal brain organization associated with the hallucinating phenotype in Parkinson's disease and highlight the utility of applying convergent dimensionality reduction techniques to investigate complex clinical symptoms. In addition, the patterns we describe in Parkinson's disease converge with those seen in other conditions, suggesting that reduced hierarchical differentiation across sensory-perceptual systems may be a common transdiagnostic vulnerability in neuropsychiatric disorders with perceptual disturbances.

Modelling phenomenological differences in aetiologically distinct visual hallucinations using deep neural networks

Visual hallucinations (VHs) are perceptions of objects or events in the absence of the sensory stimulation that would normally support such perceptions. Although all VHs share this core characteristic, there are substantial phenomenological differences between VHs that have different aetiologies, such as those arising from Neurodegenerative conditions, visual loss, or psychedelic compounds. Here, we examine the potential mechanistic basis of these differences by leveraging recent advances in visualising the learned representations of a coupled classifier and generative deep neural network-an approach we call 'computational (neuro)phenomenology'. Examining three aetiologically distinct populations in which VHs occur-Neurodegenerative conditions (Parkinson's Disease and Lewy Body Dementia), visual loss (Charles Bonnet Syndrome, CBS), and psychedelics-we identified three dimensions relevant to distinguishing these classes of VHs: realism (veridicality), dependence on sensory input (spontaneity), and complexity. By selectively tuning the parameters of the visualisation algorithm to reflect influence along each of these phenomenological dimensions we were able to generate 'synthetic VHs' that were characteristic of the VHs experienced by each aetiology. We verified the validity of this approach experimentally in two studies that examined the phenomenology of VHs in Neurodegenerative and CBS patients, and in people with recent psychedelic experience. These studies confirmed the existence of phenomenological differences across these three dimensions between groups, and crucially, found that the appropriate synthetic VHs were rated as being representative of each group's hallucinatory phenomenology. Together, our findings highlight the phenomenological diversity of VHs associated with distinct causal factors and demonstrate how a neural network model of visual phenomenology can successfully capture the distinctive visual characteristics of hallucinatory experience.

From neural noise to co-adaptability: Rethinking the multifaceted architecture of motor variability

In the last decade, the source and the functional meaning of motor variability have attracted considerable attention in behavioral and brain sciences. This construct classically combined different levels of description, variable internal robustness or coherence, and multifaceted operational meanings. We provide here a comprehensive review of the literature with the primary aim of building a precise lexicon that goes beyond the generic and monolithic use of motor variability. In the pars destruens of the work, we model three domains of motor variability related to peculiar computational elements that influence fluctuations in motor outputs. Each domain is in turn characterized by multiple sub-domains. We begin with the domains of noise and differentiation. However, the main contribution of our model concerns the domain of adaptability, which refers to variation within the same exact motor representation. In particular, we use the terms learning and (social)fitting to specify the portions of motor variability that depend on our propensity to learn and on our largely constitutive propensity to be influenced by external factors. A particular focus is on motor variability in the context of the sub-domain named co-adaptability. Further groundbreaking challenges arise in the modeling of motor variability. Therefore, in a separate pars construens, we attempt to characterize these challenges, addressing both theoretical and experimental aspects as well as potential clinical implications for neurorehabilitation. All in all, our work suggests that motor variability is neither simply detrimental nor beneficial, and that studying its fluctuations can provide meaningful insights for future research.

Perceptual Transitions between Object Rigidity & Non-rigidity: Competition and cooperation between motion-energy, feature-tracking and shape-based priors

Why do moving objects appear rigid when projected retinal images are deformed non-rigidly? We used rotating rigid objects that can appear rigid or non-rigid to test whether shape features contribute to rigidity perception. When two circular rings were rigidly linked at an angle and jointly rotated at moderate speeds, observers reported that the rings wobbled and were not linked rigidly but rigid rotation was reported at slow speeds. When gaps, paint or vertices were added, the rings appeared rigidly rotating even at moderate speeds. At high speeds, all configurations appeared non-rigid. Salient features thus contribute to rigidity at slow and moderate speeds, but not at high speeds. Simulated responses of arrays of motion-energy cells showed that motion flow vectors are predominantly orthogonal to the contours of the rings, not parallel to the rotation direction. A convolutional neural network trained to distinguish flow patterns for wobbling versus rotation, gave a high probability of wobbling for the motion-energy flows. However, the CNN gave high probabilities of rotation for motion flows generated by tracking features with arrays of MT pattern-motion cells and corner detectors. In addition, circular rings can appear to spin and roll despite the absence of any sensory evidence, and this illusion is prevented by vertices, gaps, and painted segments, showing the effects of rotational symmetry and shape. Combining CNN outputs that give greater weight to motion energy at fast speeds and to feature tracking at slow, with the shape-based priors for wobbling and rolling, explained rigid and nonrigid percepts across shapes and speeds (R2=0.95). The results demonstrate how cooperation and competition between different neuronal classes leads to specific states of visual perception and to transitions between the states.

Linguistic Priors for Perception

In this commentary, we approach the topic of linguistic relativity from a predictive coding perspective. Discussing the role of "priors" in shaping perception, we argue that language creates an important set of priors for humans, which can affect how sensory information is processed and interpreted. Namely, languages create conventionalized conceptual systems for their speakers, mirroring and reinforcing what is behaviorally important in a society. As such, they create collective conceptual convergence on how to categorize the world and thus "streamline" what people rely on to guide their perception.

Towards a pluralistic neurobiological understanding of consciousness

Theories of consciousness are often based on the assumption that a single, unified neurobiological account will explain different types of conscious awareness. However, recent findings show that, even within a single modality such as conscious visual perception, the anatomical location, timing, and information flow of neural activity related to conscious awareness vary depending on both external and internal factors. This suggests that the search for generic neural correlates of consciousness may not be fruitful. I argue that consciousness science requires a more pluralistic approach and propose a new framework: joint determinant theory (JDT). This theory may be capable of accommodating different brain circuit mechanisms for conscious contents as varied as percepts, wills, memories, emotions, and thoughts, as well as their integrated experience.

Perceptual bias contextualized in visually ambiguous stimuli

The visual appearance of an object is a function of stimulus properties as well as perceptual biases imposed by the observer. The context-specific trade-off between both can be measured accurately in a perceptual judgment task, involving grouping by proximity in ambiguous dot lattices. Such grouping depends lawfully on a stimulus parameter of the dot lattices known as their aspect ratio (AR), whose effect is modulated by a perceptual bias representing the preference for a cardinal orientation. In two experiments, we investigated how preceding context can lead to bias modulation, either in a top-down fashion via visual working memory (VWM) or bottom-up via sensory priming. In Experiment 1, we embedded the perceptual judgment task in a change detection paradigm and studied how the factors of VWM load (complexity of the memory array) and content (congruency in orientation to the ensuing dot lattice) affect the prominence of perceptual bias. A robust vertical orientation bias was observed, which was increased by VWM load and modulated by congruent VWM content. In Experiment 2, dot lattices were preceded by oriented primes. Here, primes regardless of orientation elicited a vertical orientation bias in dot lattices compared to a neutral baseline. Taken together, the two experiments demonstrate that top-down context (VWM load and content) effectively controls orientation bias modulation, while bottom-up context (i.e., priming) merely acts as an undifferentiated trigger to perceptual bias. These findings characterize the temporal context sensitivity of Gestalt perception, shed light on the processes responsible for different perceptual outcomes of ambiguous stimuli, and identify some of the mechanisms controlling perceptual bias.

Visual numerosity perception shows no advantage in real-world scenes compared to artificial displays

While the human visual system is sensitive to numerosity, the mechanisms that allow perception to extract and represent the number of objects in a scene remains unknown. Prominent theoretical approaches posit that numerosity perception emerges from passive experience with visual scenes throughout development, and that unsupervised deep neural network models mirror all characteristic behavioral features observed in participants. Here, we derive and test a novel prediction: if the visual number sense emerges from exposure to real-world scenes, then the closer a stimulus aligns with the natural statistics of the real world, the better number perception should be. But - in contrast to this prediction - we observe no such advantage (and sometimes even a notable impairment) in number perception for natural scenes compared to artificial dot displays in college-aged adults. These findings are not accounted for by the difficulty in object identification, visual clutter, the parsability of objects from the rest of the scene, or increased occlusion. This pattern of results represents a fundamental challenge to recent models of numerosity perception based in experiential learning of statistical regularities, and instead suggests that the visual number sense is attuned to abstract number of objects, independent of their underlying correlation with non-numeric features. We discuss our results in the context of recent proposals that suggest that object complexity and entropy may play a role in number perception.

Cortical response variability is driven by local excitability changes with somatotopic organization

Identical sensory stimuli can lead to different neural responses depending on the instantaneous brain state. Specifically, neural excitability in sensory areas may shape the brain´s response already from earliest cortical processing onwards. However, whether these dynamics affect a given sensory domain as a whole or occur on a spatially local level is largely unknown. We studied this in the somatosensory domain of 38 human participants with EEG, presenting stimuli to the median and tibial nerves alternatingly, and testing the co-variation of initial cortical responses in hand and foot areas, as well as their relation to pre-stimulus oscillatory states. We found that amplitude fluctuations of initial cortical responses to hand and foot stimulation - the N20 and P40 components of the somatosensory evoked potential (SEP), respectively - were not related, indicating local excitability changes in primary sensory regions. In addition, effects of pre-stimulus alpha (8-13 Hz) and beta (18-23 Hz) band amplitude on hand-related responses showed a robust somatotopic organization, thus further strengthening the notion of local excitability fluctuations. However, for foot-related responses, the spatial specificity of pre-stimulus effects was less consistent across frequency bands, with beta appearing to be more foot-specific than alpha. Connectivity analyses in source space suggested this to be due to a somatosensory alpha rhythm that is primarily driven by activity in hand regions while beta frequencies may operate in a more hand-region-independent manner. Altogether, our findings suggest spatially distinct excitability dynamics within the primary somatosensory cortex, yet with the caveat that frequency-specific processes in one sub-region may not readily generalize to other sub-regions.

Frequency-specific neural signatures of perceptual content and perceptual stability

In the natural environment, we often form stable perceptual experiences from ambiguous and fleeting sensory inputs. Which neural activity underlies the content of perception and which neural activity supports perceptual stability remains an open question. We used a bistable perception paradigm involving ambiguous images to behaviorally dissociate perceptual content from perceptual stability, and magnetoencephalography to measure whole-brain neural dynamics in humans. Combining multivariate decoding and neural state-space analyses, we found frequency-band-specific neural signatures that underlie the content of perception and promote perceptual stability, respectively. Across different types of images, non-oscillatory neural activity in the slow cortical potential (<5 Hz) range supported the content of perception. Perceptual stability was additionally influenced by the amplitude of alpha and beta oscillations. In addition, neural activity underlying perceptual memory, which supports perceptual stability when sensory input is temporally removed from view, also encodes elapsed time. Together, these results reveal distinct neural mechanisms that support the content versus stability of visual perception.

Theories of consciousness

Recent years have seen a blossoming of theories about the biological and physical basis of consciousness. Good theories guide empirical research, allowing us to interpret data, develop new experimental techniques and expand our capacity to manipulate the phenomenon of interest. Indeed, it is only when couched in terms of a theory that empirical discoveries can ultimately deliver a satisfying understanding of a phenomenon. However, in the case of consciousness, it is unclear how current theories relate to each other, or whether they can be empirically distinguished. To clarify this complicated landscape, we review four prominent theoretical approaches to consciousness: higher-order theories, global workspace theories, re-entry and predictive processing theories and integrated information theory. We describe the key characteristics of each approach by identifying which aspects of consciousness they propose to explain, what their neurobiological commitments are and what empirical data are adduced in their support. We consider how some prominent empirical debates might distinguish among these theories, and we outline three ways in which theories need to be developed to deliver a mature regimen of theory-testing in the neuroscience of consciousness. There are good reasons to think that the iterative development, testing and comparison of theories of consciousness will lead to a deeper understanding of this most profound of mysteries.

Neural oscillations promoting perceptual stability and perceptual memory during bistable perception

Ambiguous images elicit bistable perception, wherein periods of momentary perceptual stability are interrupted by sudden perceptual switches. When intermittently presented, ambiguous images trigger a perceptual memory trace in the intervening blank periods. Understanding the neural bases of perceptual stability and perceptual memory during bistable perception may hold clues for explaining the apparent stability of visual experience in the natural world, where ambiguous and fleeting images are prevalent. Motivated by recent work showing the involvement of the right inferior frontal gyrus (rIFG) in bistable perception, we conducted a transcranial direct-current stimulation (tDCS) study with a double-blind, within-subject cross-over design to test a potential causal role of rIFG in these processes. Subjects viewed ambiguous images presented continuously or intermittently while under EEG recording. We did not find any significant tDCS effect on perceptual behavior. However, the fluctuations of oscillatory power in the alpha and beta bands predicted perceptual stability, with higher power corresponding to longer percept durations. In addition, higher alpha and beta power predicted enhanced perceptual memory during intermittent viewing. These results reveal a unified neurophysiological mechanism sustaining perceptual stability and perceptual memory when the visual system is faced with ambiguous input.