Functional split brain in a driving/listening paradigm

Exploring complex and integrated information during sleep

The Integrated Information Theory is a theoretical framework that aims to elucidate the nature of consciousness by postulating that it emerges from the integration of information within a system, and that the degree of consciousness depends on the extent of information integration within the system. When consciousness is lost, the core complex of consciousness proposed by the Integrated Information Theory disintegrates, and Φ measures, which reflect the level of integrated information, are expected to diminish. This study examined the predictions of the Integrated Information Theory using the global brain network acquired via functional magnetic resonance imaging during various tasks and sleep. We discovered that the complex located within the frontoparietal network remained constant regardless of task content, while the regional distribution of the complex collapsed in the initial stages of sleep. Furthermore, Φ measures decreased as sleep progressed under limited analysis conditions. These findings align with predictions made by the Integrated Information Theory and support its postulates.

A measure centrality index for systematic empirical comparison of consciousness theories

Consciousness science is marred by disparate constructs and methodologies, making it challenging to systematically compare theories. This foundational crisis casts doubts on the scientific character of the field itself. Addressing it, we propose a framework for systematically comparing consciousness theories by introducing a novel inter-theory classification interface, the Measure Centrality Index (MCI). Recognizing its gradient distribution, the MCI assesses the degree of importance a specific empirical measure has for a given consciousness theory. We apply the MCI to probe how the empirical measures of the Global Neuronal Workspace Theory (GNW), Integrated Information Theory (IIT), and Temporospatial Theory of Consciousness (TTC) would fare within the context of the other two. We demonstrate that direct comparison of IIT, GNW, and TTC is meaningful and valid for some measures like Lempel-Ziv Complexity (LZC), Autocorrelation Window (ACW), and possibly Mutual Information (MI). In contrast, it is problematic for others like the anatomical and physiological neural correlates of consciousness (NCC) due to their MCI-based differential weightings within the structure of the theories. In sum, we introduce and provide proof-of-principle of a novel systematic method for direct inter-theory empirical comparisons, thereby addressing isolated evolution of theories and confirmatory bias issues in the state-of-the-art neuroscience of consciousness.

Influencing dreams through sensory stimulation: A systematic review

Sleep is typically considered a state of disconnection from the environment, yet instances of external sensory stimuli influencing dreams have been reported for centuries. Explaining this phenomenon could provide valuable insight into dreams' generative and functional mechanisms, the factors that promote sleep continuity, and the processes that underlie conscious awareness. Moreover, harnessing sensory stimuli for dream engineering could benefit individuals suffering from dream-related alterations. This PRISMA-compliant systematic review assessed the current evidence concerning the influence of sensory stimulation on sleep mentation. We included 51 publications, of which 21 focused on auditory stimulation, ten on somatosensory stimulation, eight on olfactory stimulation, four on visual stimulation, two on vestibular stimulation, and one on multimodal stimulation. Furthermore, nine references explored conditioned associative stimulation: six focused on targeted memory reactivation protocols and three on targeted lucid reactivation protocols. The reported frequency of stimulus-dependent dream changes across studies ranged from 0 to ∼80%, likely reflecting a considerable heterogeneity of definitions and methodological approaches. Our findings highlight a lack of comprehensive understanding of the mechanisms, functions, and neurophysiological correlates of stimulus-dependent dream changes. We suggest that a paradigm shift is required for meaningful progress in this field.

An implementation of integrated information theory in resting-state fMRI

Integrated Information Theory was developed to explain and quantify consciousness, arguing that conscious systems consist of elements that are integrated through their causal properties. This study presents an implementation of Integrated Information Theory 3.0, the latest version of this framework, to functional MRI data. Data were acquired from 17 healthy subjects who underwent sedation with propofol, a short-acting anaesthetic. Using the PyPhi software package, we systematically analyze how Φ^max, a measure of integrated information, is modulated by the sedative in different resting-state networks. We compare Φ^max to other proposed measures of conscious level, including the previous version of integrated information, Granger causality, and correlation-based functional connectivity. Our results indicate that Φ^max presents a variety of sedative-induced behaviours for different networks. Notably, changes to Φ^max closely reflect changes to subjects' conscious level in the frontoparietal and dorsal attention networks, which are responsible for higher-order cognitive functions. In conclusion, our findings present important insight into different measures of conscious level that will be useful in future implementations to functional MRI and other forms of neuroimaging.

Complex of global functional network as the core of consciousness

Finding the neural basis of consciousness is challenging, and the distribution location of the core of consciousness remains inconclusive. Integrated information theory (IIT) argues that the posterior part of the brain is the hot zone of consciousness, especially phenological consciousness. The IIT has proposed a "main complex", a set of elements determined such that the information loss in a hierarchical partition approach is the largest among those of any other supersets and subsets, as the core of consciousness in a dynamic system. This approach may be applicable not only to phenomenal but also to access-consciousness. This study estimated the main complex of brain dynamics using functional magnetic resonance imaging in Human Connectome Project (HCP) and sleep datasets. The complex analyses revealed the common networks across various tasks and rest-state in HCP, composed of executive control, salience, and dorsal/ventral attention networks. The set of networks of the main complex was maintained during sleep. However, compared with the wakefulness stage, the amount of information of these networks and the default mode network, was reduced for the hypnagogic stage. The global interconnected structure composed of major functional networks can comprise the core of consciousness.

Obsessive-compulsive symptoms and dissociative experiences: Suggested underlying mechanisms and implications for science and practice

A strong and specific link between obsessive-compulsive disorder or symptoms (OCD/S) and a tendency for dissociative experiences (e.g., depersonalization-derealization, absorption and imaginative involvement) cannot be explained by trauma and is poorly understood. The present theoretical formulation proposes five different models conceptualizing the relationship. According to Model 1, dissociative experiences result from OCD/S through inward-focused attention and repetition. According to Model 2, dissociative absorption causally brings about both OCD/S and associated cognitive risk factors, such as thought-action fusion, partly through impoverished sense of agency. The remaining models highlight common underlying causal mechanisms: temporo-parietal abnormalities impairing embodiment and sensory integration (Model 3); sleep alterations causing sleepiness and dreamlike thought or mixed sleep-wake states (Model 4); and a hyperactive, intrusive imagery system with a tendency for pictorial thinking (Model 5). The latter model relates to Maladaptive Daydreaming, a suggested dissociative syndrome with strong ties to the obsessive-compulsive spectrum. These five models point to potential directions for future research, as these theoretical accounts may aid the two fields in interacting with each other, to the benefit of both. Finally, several dissociation-informed paths for further developing clinical intervention in OCD are identified.

Pedunculopontine-induced cortical decoupling as the neurophysiological locus of dissociation

Mounting evidence suggests an association between aberrant sleep phenomena and dissociative experiences. However, no wake-sleep boundary theory provides a compelling explanation of dissociation or specifies its physiological substrates. We present a theoretical account of dissociation that integrates theories and empirical results from multiple lines of research concerning the domain of dissociation and the regulation of rapid eye movement (REM) sleep. This theory posits that individual differences in the circuitry governing the REM sleep promoting Pedunculopontine Nucleus and Laterodorsal Tegmental Nucleus determine the degree of similarity in the cortical connectivity profiles of wakefulness and REM sleep. We propose that a latent trait characterized by elevated dissociative experiences emerges from the decoupling of frontal executive regions due to a REM sleep-like aminergic/cholinergic balance. The Pedunculopontine-Induced Cortical Decoupling Account of Dissociation (PICDAD) suggests multiple fruitful lines of inquiry and provides novel insights. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Emergency Braking Evoked Brain Activities during Distracted Driving

Electroencephalogram (EEG) was used to analyze the mechanisms and differences in brain neural activity of drivers in visual, auditory, and cognitive distracted vs. normal driving emergency braking conditions. A pedestrian intrusion emergency braking stimulus module and three distraction subtasks were designed in a simulated experiment, and 30 subjects participated in the study. The common activated brain regions during emergency braking in different distracted driving states included the inferior temporal gyrus, associated with visual information processing and attention; the left dorsolateral superior frontal gyrus, related to cognitive decision-making; and the postcentral gyrus, supplementary motor area, and paracentral lobule associated with motor control and coordination. When performing emergency braking under different driving distraction states, the brain regions were activated in accordance with the need to process the specific distraction task. Furthermore, the extent and degree of activation of cognitive function-related prefrontal regions increased accordingly with the increasing task complexity. All distractions caused a lag in emergency braking reaction time, with 107.22, 67.15, and 126.38 ms for visual, auditory, and cognitive distractions, respectively. Auditory distraction had the least effect and cognitive distraction the greatest effect on the lag.

Integrated world modeling theory expanded: Implications for the future of consciousness

Integrated world modeling theory (IWMT) is a synthetic theory of consciousness that uses the free energy principle and active inference (FEP-AI) framework to combine insights from integrated information theory (IIT) and global neuronal workspace theory (GNWT). Here, I first review philosophical principles and neural systems contributing to IWMT's integrative perspective. I then go on to describe predictive processing models of brains and their connections to machine learning architectures, with particular emphasis on autoencoders (perceptual and active inference), turbo-codes (establishment of shared latent spaces for multi-modal integration and inferential synergy), and graph neural networks (spatial and somatic modeling and control). Future directions for IIT and GNWT are considered by exploring ways in which modules and workspaces may be evaluated as both complexes of integrated information and arenas for iterated Bayesian model selection. Based on these considerations, I suggest novel ways in which integrated information might be estimated using concepts from probabilistic graphical models, flow networks, and game theory. Mechanistic and computational principles are also considered with respect to the ongoing debate between IIT and GNWT regarding the physical substrates of different kinds of conscious and unconscious phenomena. I further explore how these ideas might relate to the "Bayesian blur problem," or how it is that a seemingly discrete experience can be generated from probabilistic modeling, with some consideration of analogies from quantum mechanics as potentially revealing different varieties of inferential dynamics. I go on to describe potential means of addressing critiques of causal structure theories based on network unfolding, and the seeming absurdity of conscious expander graphs (without cybernetic symbol grounding). Finally, I discuss future directions for work centered on attentional selection and the evolutionary origins of consciousness as facilitated "unlimited associative learning." While not quite solving the Hard problem, this article expands on IWMT as a unifying model of consciousness and the potential future evolution of minds.

Escaping from the IIT Munchausen method: Re-establishing the scientific method in the study of consciousness

Integrated information theory (IIT) is an example of "ironic science" and obstructs the scientific study of consciousness. By confusing the ontological status of a method to quantify network complexity with that of a theory of consciousness, IIT has to square the circle and spirals toward its panpsychism conclusion. I analyze the consequences of this fallacy and suggest how the study of consciousness can be brought back into the realm of rational, empirical science.

Applications of brain imaging methods in driving behaviour research

Applications of neuroimaging methods have substantially contributed to the scientific understanding of human factors during driving by providing a deeper insight into the neuro-cognitive aspects of driver brain. This has been achieved by conducting simulated (and occasionally, field) driving experiments while collecting driver brain signals of various types. Here, this sector of studies is comprehensively reviewed at both macro and micro scales. At the macro scale, bibliometric aspects of these studies are analysed. At the micro scale, different themes of neuroimaging driving behaviour research are identified and the findings within each theme are synthesised. The surveyed literature has reported on applications of four major brain imaging methods. These include Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS) and Magnetoencephalography (MEG), with the first two being the most common methods in this domain. While collecting driver fMRI signal has been particularly instrumental in studying neural correlates of intoxicated driving (e.g. alcohol or cannabis) or distracted driving, the EEG method has been predominantly utilised in relation to the efforts aiming at development of automatic fatigue/drowsiness detection systems, a topic to which the literature on neuro-ergonomics of driving particularly has shown a spike of interest within the last few years. The survey also reveals that topics such as driver brain activity in semi-automated settings or neural activity of drivers with brain injuries or chronic neurological conditions have by contrast been investigated to a very limited extent. Potential topics in driving behaviour research are identified that could benefit from the adoption of neuroimaging methods in future studies. In terms of practicality, while fMRI and MEG experiments have proven rather invasive and technologically challenging for adoption in driving behaviour research, EEG and fNIRS applications have been more diverse. They have even been tested beyond simulated driving settings, in field driving experiments. Advantages and limitations of each of these four neuroimaging methods in the context of driving behaviour experiments are outlined in the paper.

Singularity and consciousness: A neuropsychological contribution

In common sense experience based on introspection, consciousness is singular. There is only one ‘me’ and that is the one that is conscious. This means that ‘singularity’ is a defining aspect of ‘consciousness’. However, the three main theories of consciousness, Integrated Information, Global Workspace and Recurrent Processing theory, are generally not very clear on this issue. These theories have traditionally relied heavily on neuropsychological observations and have interpreted various disorders, such as anosognosia, neglect and split‐brain as impairments in conscious awareness without any reference to ‘the singularity’. In this review, we will re‐examine the theoretical implications of these impairments in conscious awareness and propose a new way how to conceptualize consciousness of singularity. We will argue that the subjective feeling of singularity can coexist with several disunified conscious experiences. Singularity awareness may only come into existence due to environmental response constraints. That is, perceptual, language, memory, attentional and motor processes may largely proceed unintegrated in parallel, whereas a sense of unity only arises when organisms need to respond coherently constrained by the affordances of the environment. Next, we examine from this perspective psychiatric disorders and psycho‐active drugs. Finally, we present a first attempt to test this hypothesis with a resting state imaging experiment in a split‐brain patient. The results suggest that there is substantial coherence of activation across the two hemispheres. These data show that a complete lesioning of the corpus callosum does not, in general, alter the resting state networks of the brain. Thus, we propose that we have separate systems in the brain that generate distributed conscious. The sense of singularity, the experience of a ‘Me‐ness’, emerges in the interaction between the world and response‐planning systems, and this leads to coherent activation in the different functional networks across the cortex.

A general spectral decomposition of causal influences applied to integrated information

BACKGROUND

Quantifying interactions among many neurons is fundamental to understanding system-level phenomena such as attention, learning and even conscious experience. Causal influences in the brain, quantified as integrated information, are thought to support subjective conscious experience. Recent empirical work has shown that the spectral decomposition of causal influences, for example using Granger causality, can reveal frequency-specific influences that are not observed in the time domain. However, a spectral decomposition of integrated information has not been put forward, limiting its adoption for analyzing neural data.

NEW METHOD

We present a general and flexible framework for deriving the spectral decomposition of causal influences in autoregressive processes.

RESULTS

We use the framework to derive a spectral decomposition of integrated information. We show that other well-known measures, including Granger causality, can be derived using the same framework. Using simulations, we demonstrate a complex interplay between the spectral decomposition of integrated information and other measures that is not observed in the time domain.

COMPARISON WITH EXISTING METHODS

This paper provides a spectral decomposition of integrated information for the first time. Although a spectral decomposition of Granger causality has been derived, that approach is only applicable to uni-directional causal influences, not multi-directional causal influences as required for integrated information.

CONCLUSIONS

Our novel framework can be used to derive the spectral decomposition of uni- and multi-directional measures of causal influences. We use this framework to derive a spectral decomposition of integrated information, paving the way for better understanding how frequency-specific causal influences in the brain relate to cognition.

Electrophysiological correlates of spatial processing during multitasking

Multitasking is ubiquitous in everyday life. It can have a detrimental effect on several cognitive abilities including spatial processing in both brain-damaged and healthy participants. The present study investigated, in healthy adults, the electrophysiological mechanisms associated with correct detection vs. misdetection of peripheral visual target(s) while processing concurrent visual or auditory stimuli. Correct responses were coupled with increased N1 amplitude under visual (i.e., intra-modal) load but not under auditory (i.e., cross-modal) load. Under visual load, error responses were associated to opposite patterns on N1/N2 components for unilateral and bilateral stimuli. In particular, errors were marked by significantly reduced N1 and N2 amplitude for the left and right visual field, respectively, whereas higher N1 amplitude was found for errors to bilateral targets. This suggests that early negative components represent the biological marker of target awareness under visual load, whereby correct target detection is grounded on a threshold criterion. These results provide an electrophysiological correlate for the allocation of capacity-limited cognitive resources during the concurrent processing of multiple and heterogeneous visual stimuli.

Neural Correlates of Simulated Driving While Performing a Secondary Task: A Review

Distracted driving consists in performing a secondary task while driving, such as cell-phone conversation. Given the limited resources of the attentional system, engaging in a secondary task while driving increases the risk to have car accidents. The secondary task engagement while driving can depend on or be affected by different factors, including driver's individual characteristics, necessities, environmental conditions, and so forth. In the present work, the neuroimaging studies that investigated the brain areas involved in simulated driving during the execution of a secondary task (visual and overall auditory tasks) were reviewed in light of driving settings. In general, although there are also differences in decrease and increase brain activations across studies, due to the varieties of paradigms used (simulators, secondary tasks and neuroimaging techniques), the dual-task condition (simulated driving plus secondary task), as compared to the simulated driving-alone condition, was generally found to yield a significant shift in activations from occipital to fronto-parietal brain regions. These findings show that when a secondary task is added during driving the neural system redirects attentional resources away from visual processing, increasing the possibility of incorrect, dangerous or risky behavioral responses. The shift of the attentional resources can occur even if driving behavior is not explicitly affected. Limits of the neuroimaging studies reviewed and future research directions, including the need to explore the role of personality factors in the modulation of the neural programs while engaging distracted driving, are briefly discussed.

Consciousness: The last 50 years (and the next)

The mind and brain sciences began with consciousness as a central concern. But for much of the 20th century, ideological and methodological concerns relegated its empirical study to the margins. Since the 1990s, studying consciousness has regained a legitimacy and momentum befitting its status as the primary feature of our mental lives. Nowadays, consciousness science encompasses a rich interdisciplinary mixture drawing together philosophical, theoretical, computational, experimental, and clinical perspectives, with neuroscience its central discipline. Researchers have learned a great deal about the neural mechanisms underlying global states of consciousness, distinctions between conscious and unconscious perception, and self-consciousness. Further progress will depend on specifying closer explanatory mappings between (first-person subjective) phenomenological descriptions and (third-person objective) descriptions of (embodied and embedded) neuronal mechanisms. Such progress will help reframe our understanding of our place in nature and accelerate clinical approaches to a wide range of psychiatric and neurological disorders.

Integrated information and dimensionality in continuous attractor dynamics

There has been increasing interest in the integrated information theory (IIT) of consciousness, which hypothesizes that consciousness is integrated information within neuronal dynamics. However, the current formulation of IIT poses both practical and theoretical problems when empirically testing the theory by computing integrated information from neuronal signals. For example, measuring integrated information requires observing all the elements in a considered system at the same time, but this is practically very difficult. Here, we propose that some aspects of these problems are resolved by considering the topological dimensionality of shared attractor dynamics as an indicator of integrated information in continuous attractor dynamics. In this formulation, the effects of unobserved nodes on the attractor dynamics can be reconstructed using a technique called delay embedding, which allows us to identify the dimensionality of an embedded attractor from partial observations. We propose that the topological dimensionality represents a critical property of integrated information, as it is invariant to general coordinate transformations. We illustrate this new framework with simple examples and discuss how it fits with recent findings based on neural recordings from awake and anesthetized animals. This topological approach extends the existing notions of IIT to continuous dynamical systems and offers a much-needed framework for testing the theory with experimental data by substantially relaxing the conditions required for evaluating integrated information in real neural systems.

20 Years of ASSC: are we ready for its coming of age?

This is a subjective summary of the recent meeting of the Association for the Scientific Study of Consciousness (ASSC) in Buenos Aires (2016), with some highlights, as well as reflections on the state of the field in general. I argue that we are likely at a critical point where the field is in the process of transforming itself, and the ASSC meeting is accordingly becoming the premier venue to update each other on the latest exciting findings, rigorous methods, and novel ideas. I also discuss the rapidly changing roles of authoritative opinion and theoretical ideas based largely on speculation, whether we still need them, and where may be the best venues for disseminating them.