Lifeboat Foundation

This paper provides evidence that a theoretical computer science (TCS) perspective can add to our understanding of consciousness by providing a simple framework for employing tools from computational complexity theory and machine learning. Just as the Turing machine is a simple model to define and explore computation, the Conscious Turing Machine (CTM) is a simple model to define and explore consciousness (and related concepts). The CTM is not a model of the brain or cognition, nor is it intended to be, but a simple substrate-independent computational model of (the admittedly complex concept of) consciousness. This paper is intended to introduce this approach, show its possibilities, and stimulate research in consciousness from a TCS perspective.

A strategy for investigating consciousness that has proven very productive has focused on comparing brain processes that are accompanied by consciousness with processes that are not. But comparatively little attention has been given to a related strategy that promises to be even more fertile. This strategy exploits the fact that as individuals develop, new classes of brain processes can transition from operating ‘in the dark’ to becoming conscious. It has been suggested that these transitions occur when a new class of brain processes becomes object to a new, emergent, higher-level subject. Similar transitions are likely to have occurred during evolution. An evolutionary/developmental research strategy sets out to identify the nature of the transitions in brain processes that shift them from operating in the dark to ‘lighting up’. The paper begins the application of this strategy by extrapolating the sequence of transitions back towards its origin. The goal is to reconstruct a minimally-complex, subject-object subsystem that would be capable of giving rise to consciousness and providing adaptive benefits. By focusing on reconstructing a subsystem that is simple and understandable, this approach avoids the homunculus fallacy. The reconstruction suggests that the emergence of such a minimally-complex subsystem was driven by its capacity to coordinate body-environment interactions in real time e.g. hand-eye coordination. Conscious processing emerged initially because of its central role in organising real-time sensorimotor coordination. The paper goes on to identify and examine a number of subsequent major transitions in consciousness, including the emergence of capacities for conscious mental modelling. Each transition is driven by its potential to solve adaptive challenges that cannot be overcome at lower levels. The paper argues that mental modelling arose out of a pre-existing capacity to use simulations of motor actions to anticipate the consequences of the actions. As the capacity developed, elements of the simulations could be changed, and the consequences of these changes could be ‘thought through’ consciously. This enabled alternative motor responses to be evaluated. The paper goes on to predict significant new major transitions in consciousness.

Consciousness defines our existence. It is, in a sense, all we really have, all we really are, The nature of consciousness has been pondered in many ways, in many cultures, for many years. But we still can’t quite fathom it.

Continue reading “Consciousness is the collapse of the wave function” »

This article argues that consciousness has a logically sound, explanatory framework, different from typical accounts that suffer from hidden mysticism. The article has three main parts. The first describes background principles concerning information processing in the brain, from which one can deduce a general, rational framework for explaining consciousness. The second part describes a specific theory that embodies those background principles, the Attention Schema Theory. In the past several years, a growing body of experimental evidence—behavioral evidence, brain imaging evidence, and computational modeling—has addressed aspects of the theory. The final part discusses the evolution of consciousness. By emphasizing the specific role of consciousness in cognition and behavior, the present approach leads to a proposed account of how consciousness may have evolved over millions of years, from fish to humans. The goal of this article is to present a comprehensive, overarching framework in which we can understand scientifically what consciousness is and what key adaptive roles it plays in brain function.

Abstract

As a sensate infrastructure, the body conveys information to and from the brain to complete a perceptual concordance with consciousness. This system of reciprocal communication both positions consciousness in spacetime, and allows that consciousness is dependent upon the body to roam. Through movement we comprehend. The corporeal occupation of spacetime permits human consciousness access to the phenomena of its physical environment, whereby it uses language (utterance) to both construct and describe this existence. This mediated transmission evolved into story and narrative in an attempt to apprehend, control and more importantly convey what is perceived. It is precisely the components of space and time, critical elements to our own existence that play such a paramount role in our ability to generate meaning and narrative comprehension. As our dimensional understanding has evolved and extended, so too has our understanding that space and time are crucial components of narrative. With the emergence of auxiliary narrative spaces, this movement of consciousness affords opportunities to create new narrative imperatives. In the theoretical realm of physics, the tesseract makes it possible to overcome the restraints of time. The tesseract is a gravitational wormhole that represents the physical compression of space that circumvents time in order to move from one location in spacetime to another. The index, as part of the body, but also the mechanism for applying a collapsed signification, requires both utterance (mediation) and event (temporal-frame) in order to create cognitive meaning. The indexical functions as a linguistic tesseract that collapses language creating a bridge over the semantic divide between utterance and meaning. This paper places the function and potential of the tesseract within the paradigm of cognitive narratology through the argument that compression is the mechanism for narrative construction of story, autopoiesis, and the locality of self.

A new deep learning algorithm is able to quantify arousal and awareness in humans at the same time.

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Summary: A new deep learning algorithm is able to quantify arousal and awareness in humans at the same time.

Continue reading “Quantifying Human Consciousness With the Help of AI” »

The classical neurophysiological approach for calculating PCI, power spectral density, and spectral exponent relies on many epochs to improve the reliability of statistical estimates of these indices²¹. However, these methods are only suitable for investigating the averaged brain states and they can only clarify general neurophysiological aspects. Machine learning (ML) allows decoding and identifying specific brain states and discriminating them from unrelated brain signals, even in a single trial in real-time²². This can potentially transform statistical results at the group level into individual predictions⁹. A deep neural network, which is a popular approach in ML, has been employed to classify or predict brain states using EEG data²³. Particularly, a convolutional neural network (CNN) is the most extensively used technique in deep learning and has proven to be effective in the classification of EEG data²⁴. However, a CNN has the drawback that it cannot provide information on why it made a particular prediction²⁵. Recently, layer-wise relevance propagation (LRP) has successfully demonstrated why classifiers such as CNNs have made a specific decision²⁶. Specifically, the relevance score resulting from the LRP indicates the contribution of each input variable to the classification or prediction decision. Thus, a high score in a particular area of an input variable implies that the classifier has made the classification or prediction using this feature. For example, neurophysiological data suggest that the left motor region is activated during right-hand motor imagery²⁷. The LRP indicates that the neural network classifies EEG data as right-hand motor imagery because of the activity of the left motor region²⁸. Therefore, the relevance score was higher in the left motor region than in other regions. Thus, it is possible to interpret the neurophysiological phenomena underlying the decisions of CNNs using LRP.

In this work, we develop a metric, called the explainable consciousness indicator (ECI), to simultaneously quantify the two components of consciousness—arousal and awareness—using CNN. The processed time-series EEG data were used as an input of the CNN. Unlike PCI, which relies on source modeling and permutation-based statistical analysis, ECI used event-related potentials at the sensor level for spatiotemporal dynamics and ML approaches. For a generalized model, we used the leave-one-participant-out (LOPO) approach for transfer learning, which is a type of ML that transfers information to a new participant not included in the training phase^24,27. The proposed indicator is a 2D value consisting of indicators of arousal (ECI^aro) and awareness (ECI^awa). First, we used TMS–EEG data collected from healthy participants during NREM sleep with no subjective experience, REM sleep with subjective experience, and healthy wakefulness to consider each component of consciousness (i.e., low/high arousal and low/high awareness) with the aim to analyze correlations between the proposed ECI and the three states, namely NREM, REM, and wakefulness. Next, we measured ECI using TMS–EEG data collected under general anesthesia with ketamine, propofol, and xenon, again with the aim to measure correlation with these three anesthetics. Before anesthesia, TMS–EEG data were also recorded during healthy wakefulness. Upon awakening, healthy participants reported conscious experience during ketamine-induced anesthesia and no conscious experience during propofol-and xenon-induced anesthesia. Finally, TMS–EEG data were collected from patients with disorders of consciousness (DoC), which includes patients diagnosed as UWS and MCS patients. We hypothesized that our proposed ECI can clearly distinguish between the two components of consciousness under physiological, pharmacological, and pathological conditions.

To verify the proposed indicator, we next compared ECI^awa with PCI, which is a reliable index for consciousness. Then, we applied ECI to additional resting-state EEG data acquired in the anesthetized participants and patients with DoC. We hypothesize that if CNN can learn characteristics related to consciousness, it could calculate ECI accurately even without TMS in the proposed framework. In terms of clinical applicability, it is important to use the classifier from the previous LOPO training of the old data to classify the new data (without additional training). Therefore, we computed ECI in patients with DoC using a hold-out approach²⁹, where training data and evaluation data are arbitrarily divided, instead of cross-validation. Finally, we investigated why the classifier generated these decisions using LRP to interpret ECI³⁰.

Are quantum events required for consciousness in a very special sense, far beyond the general sense that quantum events are part of all physical systems? What would it take for quantum events, on such a micro-scale, to be relevant for brain function, which operates at the much higher level of neurons and brain circuits? What would it mean?

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Continue reading “Quantum Physics of Consciousness” »

A controversial theory suggesting that quantum effects in the brain could explain consciousness may hold more weight than scientists originally thought.