Consciousness seminars

Nick Bostrom and Brian Greene examine how increasingly capable artificial intelligence challenges familiar accounts of human creativity, originality and consciousness. They discuss whether machine creativity differs fundamentally from human creativity and what evidence could bear on the possibility of subjective experience in AI systems. The conversation also considers Bostrom’s concerns about systems that recognise when they are being evaluated and change their behaviour, and the resulting difficulty of assessing alignment. It then turns to a hypothetical future in which automation removes much instrumental work: what would give human lives purpose, and would the elimination of practical problems suffice for a worthwhile life? The discussion includes education, human agency and the distinction between pleasurable simulated experience and engagement with reality.

Note the later time in the USA!

The neurobiological mechanisms of arousal and anesthesia remain poorly understood. Recent evidence highlights the key role of interactions between the cerebral cortex and the diffusely projecting matrix thalamic nuclei. Here, we interrogate these processes in a whole-brain corticothalamic neural mass model endowed with targeted and diffusely projecting thalamocortical nuclei inferred from empirical data. This model captures key features seen in propofol anesthesia, including diminished network integration, lowered state diversity, impaired susceptibility to perturbation, and decreased corticocortical coherence. Collectively, these signatures reflect a suppression of information transfer across the cerebral cortex. We recover these signatures of conscious arousal by selectively stimulating the matrix thalamus, recapitulating empirical results in macaque, as well as wake-like information processing states that reflect the thalamic modulation of largescale cortical attractor dynamics. Our results highlight the role of matrix thalamocortical projections in shaping many features of complex cortical dynamics to facilitate the unique communication states supporting conscious awareness.

We are now clearly entering a new age in our relationship with machines. The power of AI natural language processors and image generators has rapidly exceeded the expectations of even those who developed them. Serious questions are now being asked about the extent to which machines could become — or perhaps already are — sentient or conscious. Do AI machines understand the instructions they are given and the answers they provide? In this talk I will consider the prospects for conscious machines, by which I mean machines that have feelings, know about their own existence, and about ours. I will suggest that the recent focus on information processing in models of consciousness, in which the brain is treated as a kind of digital computer, have mislead us about the nature of consciousness and how it is produced in biological systems. Treating the brain as an energy processing system is more likely to yield answers to these fundamental questions and help us understand how and when machines might become minds.

Repetitive spatiotemporal patterns in resting-state brain activities have been widely observed in various species and regions, such as rat and cat visual cortices. Since they resemble the preceding brain activities during tasks, they are assumed to reflect past experiences embedded in neuronal circuits. Moreover, spatiotemporal patterns involving whole-brain activities may also reflect a process that integrates information distributed over the entire brain, such as motor and visual information. Therefore, revealing such patterns may elucidate how the information is integrated to generate consciousness. In this talk, I will introduce our proposed method to estimate repetitive spatiotemporal patterns from resting-state brain activity data and show the spatiotemporal patterns estimated from human resting-state magnetoencephalography (MEG) and electroencephalography (EEG) data. Our analyses suggest that the patterns involved whole-brain propagating activities that reflected a process to integrate the information distributed over frequencies and networks. I will also introduce our current attempt to reveal signal flows and their roles in the spatiotemporal patterns using a big dataset. - Takeda et al., Estimating repetitive spatiotemporal patterns from resting-state brain activity data. NeuroImage (2016); 133:251-65. - Takeda et al., Whole-brain propagating patterns in human resting-state brain activities. NeuroImage (2021); 245:118711.

In popular media, there is often a connection drawn between the advent of awareness in artificial agents and those same agents simultaneously achieving human or superhuman level intelligence. In this talk, I will examine the validity and potential application of this seemingly intuitive link between consciousness and intelligence. I will do so by examining the cognitive abilities associated with three contemporary theories of conscious function: Global Workspace Theory (GWT), Information Generation Theory (IGT), and Attention Schema Theory (AST), and demonstrating that all three theories specifically relate conscious function to some aspect of domain-general intelligence in humans. With this insight, we will turn to the field of Artificial Intelligence (AI) and find that, while still far from demonstrating general intelligence, many state-of-the-art deep learning methods have begun to incorporate key aspects of each of the three functional theories. Given this apparent trend, I will use the motivating example of mental time travel in humans to propose ways in which insights from each of the three theories may be combined into a unified model. I believe that doing so can enable the development of artificial agents which are not only more generally intelligent but are also consistent with multiple current theories of conscious function.

The way we represent and perceive time has crucial implications for studying temporality in conscious experience. Contrasting positions posit that temporal information is separately abstracted out like any other perceptual property, or that time is represented through representations having temporal properties themselves. To add to this debate, we investigated alterations in felt time in conditions where only conscious visual experience is altered while a bistable figure remains physically unchanged. In this talk, I will discuss two studies that we have done in relation to answering this question. In study 1, we investigated whether perceptual switches in fixed intervals altered felt time. In three experiments we showed that a break in visual experience (via a perceptual switch) also leads to a break in felt time. In study 2, we are currently looking at figure-ground perception in ambigous displays. Here, in experiment 1 we show that differences in flicker frequencies on ambigous regions can induce figure-ground segregation. To see if a reverse complementarity exists for felt time, we ask participants to view ambigous regions as figure/ground and show that they have different temporal resolutions for the same region based on whether it is seen as figure or background. Overall, the two studies provide evidence for temporal mirroring and isomorphism in visual experience, arguing for a link between the timing of experience and time perception.

Theories of consciousness are commonly grouped into "first-order" and "higher-order" families. As conventional wisdom has it, many more animals are likely to be conscious if a first-order theory is correct. But two recent developments have put pressure on the first/higher-order distinction. One is the argument (from Shea and Frith) that an effective global workspace mechanism must involve a form of metacognition. The second is Lau's "perceptual reality monitoring" (PRM) theory, a member of the "higher-order" family in which conscious sensory content is not re-represented, only tagged with a temporal index and marked as reliable. I argue that the first/higher-order distinction has become so blurred that it is no longer particularly useful. Moreover, the conventional wisdom about animals should not be trusted. It could be, for example, that the distribution of PRM in the animal kingdom is wider than the distribution of global broadcasting.

We examine consciousness from the perspective of theoretical computer science (TCS), a branch of mathematics concerned with understanding the underlying principles of computation and complexity, including the implications and surprising consequences of resource limitations. We propose a formal TCS model, the Conscious Turing Machine (CTM). The CTM is influenced by Alan Turing's simple yet powerful model of computation, the Turing machine (TM), and by the global workspace theory (GWT) of consciousness originated by cognitive neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene, Jean-Pierre Changeux, George Mashour, and others. However, the CTM is not a standard Turing Machine. It’s not the input-output map that gives the CTM its feeling of consciousness, but what’s under the hood. Nor is the CTM a standard GW model. In addition to its architecture, what gives the CTM its feeling of consciousness is its predictive dynamics (cycles of prediction, feedback and learning), its internal multi-modal language Brainish, and certain special Long Term Memory (LTM) processors, including its Inner Speech and Model of the World processors. Phenomena generally associated with consciousness, such as blindsight, inattentional blindness, change blindness, dream creation, and free will, are considered. Explanations derived from the model draw confirmation from consistencies at a high level, well above the level of neurons, with the cognitive neuroscience literature. Reference. L. Blum and M. Blum, "A theory of consciousness from a theoretical computer science perspective: Insights from the Conscious Turing Machine," PNAS, vol. 119, no. 21, 24 May 2022. https://www.pnas.org/doi/epdf/10.1073/pnas.2115934119

Scientific studies of consciousness can be roughly categorized into two directions: (1) How/where does consciousness emerge? (the mechanism of consciousness) and (2) Why is there consciousness? (the function of consciousness). I will summarize my past research on the quest for consciousness in these two directions.

Flow is defined as an altered state of consciousness with excessive attention and enormous sense of pleasure, when engaged in a challenging task, first postulated by a psychologist, the late M. Csikszentmihayli. The main focus of this talk will be “Team Flow,” but there were two lines of previous studies in our laboratory as its background. First is inter-body and inter-brain coordination/synchrony between individuals. Considering various rhythmic echoing/synchronization phenomena in animal behavior, it could be regarded as the biological, sub-symbolic and implicit origin of social interactions. The second line of precursor research is on the state of Solo Flow in game playing. We employed attenuation of AEP (Auditory Evoked Potential) to task-irrelevant sound probes as an objective-neural indicator of such a Flow status, and found that; 1) Mutual link between the ACC & the TP is critical, and 2) overall, top-down influence is enhanced while bottom-up causality is attenuated. Having these as the background, I will present our latest study of Team Flow in game playing. We found that; 3) the neural correlates of Team Flow is distinctively different from those of Solo Flow nor of non-flow social, 4) the left medial temporal cortex seems to form an integrative node for Team Flow, receiving input related to Solo Flow state from the right PFC and input related to social state from the right IFC, and 5) Intra-brain (dis)similarity of brain activity well predicts (dis)similarity of skills/cognition as well as affinity for inter-brain coherence.

Can we learn without conscious awareness? Numerous evidences in the research of implicit learning have indicated that people can learn the statistical structure of the stimuli but seemingly without any awareness of its underlying rules. However, it remains unclear what types of knowledge can be learned in implicit learning, what is the relationship between conscious and unconscious knowledge, and what are the neural substrates for the acquisition of conscious and unconscious knowledge. In this talk, I will discuss with you about these ongoing questions.

Measuring the degree of consciousness an organism possesses has remained a longstanding challenge in Neuroscience. In part, this is due to the difficulty of finding the appropriate mathematical tools for describing such a subjective phenomenon. Current methods relate the level of consciousness to the complexity of neural activity, i.e., using the information contained in a stream of recorded signals they can tell whether the subject might be awake, asleep, or anaesthetised. Usually, the signals stemming from a complex system are correlated in time; the behaviour of the future depends on the patterns in the neural activity of the past. However these past-future relationships remain either hidden to, or not taken into account in the current measures of consciousness. These past-future correlations are likely to contain more information and thus can reveal a richer understanding about the behaviour of complex systems like a brain. Our work employs the "epsilon-machines” framework to account for the time correlations in neural recordings. In a nutshell, epsilon-machines reveal how much of the past neural activity is needed in order to accurately predict how the activity in the future will behave, and this is summarised in a single number called "statistical complexity". If a lot of past neural activity is required to predict the future behaviour, then can we say that the brain was more “awake" at the time of recording? Furthermore, if we read the recordings in reverse, does the difference between forward and reverse-time statistical complexity allow us to quantify the level of time asymmetry in the brain? Neuroscience predicts that there should be a degree of time asymmetry in the brain. However, this has never been measured. To test this, we used neural recordings measured from the brains of fruit flies and inferred the epsilon-machines. We found that the nature of the past and future correlations of neural activity in the brain, drastically changes depending on whether the fly was awake or anaesthetised. Not only does our study find that wakeful and anaesthetised fly brains are distinguished by how statistically complex they are, but that the amount of correlations in wakeful fly brains was much more sensitive to whether the neural recordings were read forward vs. backwards in time, compared to anaesthetised brains. In other words, wakeful fly brains were more complex, and time asymmetric than anaesthetised ones.

In this talk I will present an account of how an agent designed or evolved to be intelligent may come to enjoy subjective experiences. First, the agent is stipulated to be capable of (meta)representing subjective ‘qualitative’ sensory information, in the sense that it can easily assess how exactly similar a sensory signal is to all other possible sensory signals. This information is subjective in the sense that it concerns how the different stimuli can be distinguished by the agent itself, rather than how physically similar they are. For this to happen, sensory coding needs to satisfy sparsity and smoothness constraints, which are known to facilitate metacognition and generalization. Second, this qualitative information can under some specific circumstances acquire an ‘assertoric force’. This happens when a certain self-monitoring mechanism decides that the qualitative information reliably tracks the current state of the world, and informs a general symbolic reasoning system of this fact. I will argue that the having of subjective conscious experiences amounts to nothing more than having qualitative sensory information acquiring an assertoric status within one’s belief system. When this happens, the perceptual content presents itself as reflecting the state of the world right now, in ways that seem undeniably rational to the agent. At the same time, without effort, the agent also knows what the perceptual content is like, in terms of how subjectively similar it is to all other possible precepts. I will discuss the computational benefits of this architecture, for which consciousness might have arisen as a byproduct.

In the first part of this talk, I will briefly present the unfolding argument by Doerig et al. (2019) and the various replies in the philosophical and neuroscientific literature. In the second part of the talk, I will explore the ramifications that this debate has for the science of consciousness and its philosophy, with particular focus on these questions: (i) which type of explanation should a theory of consciousness provide? (ii) what is the evidential basis for theories of consciousness?

I will build from Niccolo's discussion of the Blockhead argument to argue that having an FeedForward Network (FN) responding like an recurrent network (RN) in a consciousness experiment is not enough to convince us the two are the same with regards to the posession of mental states and conscious experience. I will then argue that a robust functional equivalence between FFN and RN is akso not supported by the mathematical work on the Universal Approximator theorem, and is also unlikely to hold, as a conjecture, given data in cognitive neuroscience; I will argue that an equivalence of RN and FFN may only apply to static functions between input/output layers and not to the temporal patterns or to the network's reactions to structural perturbations. Finally, I review data indicating that consciousness has functional characteristics, such as a flexible control of behavior, and that cognitive/brain dynamics reveal interacting top-down and bottom-up processes, which are necessary for the mediation of such control processes.