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Yurchenko SB. Panpsychism and dualism in the science of consciousness. Neurosci Biobehav Rev 2024; 165:105845. [PMID: 39106941 DOI: 10.1016/j.neubiorev.2024.105845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 07/26/2024] [Accepted: 08/02/2024] [Indexed: 08/09/2024]
Abstract
A resurgence of panpsychism and dualism is a matter of ongoing debate in modern neuroscience. Although metaphysically hostile, panpsychism and dualism both persist in the science of consciousness because the former is proposed as a straightforward answer to the problem of integrating consciousness into the fabric of physical reality, whereas the latter proposes a simple solution to the problem of free will by endowing consciousness with causal power as a prerequisite for moral responsibility. I take the Integrated Information Theory (IIT) as a paradigmatic exemplar of a theory of consciousness (ToC) that makes its commitments to panpsychism and dualism within a unified framework. These features are not, however, unique for IIT. Many ToCs are implicitly prone to some degree of panpsychism whenever they strive to propose a universal definition of consciousness, associated with one or another known phenomenon. Yet, those ToCs that can be characterized as strongly emergent are at risk of being dualist. A remedy against both covert dualism and uncomfortable corollaries of panpsychism can be found in the evolutionary theory of life, called here "bioprotopsychism" and generalized in terms of autopoiesis and the free energy principle. Bioprotopsychism provides a biologically inspired basis for a minimalist approach to consciousness via the triad "chemotaxis-efference copy mechanism-counterfactual active inference" by associating the stream of weakly emergent conscious states with an amount of information (best guesses) of the brain, engaged in unconscious predictive processing.
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Affiliation(s)
- Sergey B Yurchenko
- Brain and Consciousness Independent Research Center, Andijan 710132, Uzbekistan.
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2
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Parvizi-Wayne D, Sandved-Smith L, Pitliya RJ, Limanowski J, Tufft MRA, Friston KJ. Forgetting ourselves in flow: an active inference account of flow states and how we experience ourselves within them. Front Psychol 2024; 15:1354719. [PMID: 38887627 PMCID: PMC11182004 DOI: 10.3389/fpsyg.2024.1354719] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 04/26/2024] [Indexed: 06/20/2024] Open
Abstract
Flow has been described as a state of optimal performance, experienced universally across a broad range of domains: from art to athletics, gaming to writing. However, its phenomenal characteristics can, at first glance, be puzzling. Firstly, individuals in flow supposedly report a loss of self-awareness, even though they perform in a manner which seems to evince their agency and skill. Secondly, flow states are felt to be effortless, despite the prerequisite complexity of the tasks that engender them. In this paper, we unpick these features of flow, as well as others, through the active inference framework, which posits that action and perception are forms of active Bayesian inference directed at sustained self-organisation; i.e., the minimisation of variational free energy. We propose that the phenomenology of flow is rooted in the deployment of high precision weight over (i) the expected sensory consequences of action and (ii) beliefs about how action will sequentially unfold. This computational mechanism thus draws the embodied cognitive system to minimise the ensuing (i.e., expected) free energy through the exploitation of the pragmatic affordances at hand. Furthermore, given the challenging dynamics the flow-inducing situation presents, attention must be wholly focussed on the unfolding task whilst counterfactual planning is restricted, leading to the attested loss of the sense of self-as-object. This involves the inhibition of both the sense of self as a temporally extended object and higher-order, meta-cognitive forms of self-conceptualisation. Nevertheless, we stress that self-awareness is not entirely lost in flow. Rather, it is pre-reflective and bodily. Our approach to bodily-action-centred phenomenology can be applied to similar facets of seemingly agentive experience beyond canonical flow states, providing insights into the mechanisms of so-called selfless experiences, embodied expertise and wellbeing.
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Affiliation(s)
- Darius Parvizi-Wayne
- Department of Experimental Psychology, University College London, London, United Kingdom
| | - Lars Sandved-Smith
- Monash Centre for Consciousness and Contemplative Studies, Monash University, Clayton, VIC, Australia
| | - Riddhi J. Pitliya
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
- VERSES AI Research Lab, Los Angeles, CA, United States
| | - Jakub Limanowski
- Institute of Psychology, University of Greifswald, Greifswald, Germany
| | - Miles R. A. Tufft
- Department of Experimental Psychology, University College London, London, United Kingdom
| | - Karl J. Friston
- VERSES AI Research Lab, Los Angeles, CA, United States
- Queen Square Institute of Neurology, University College London, London, United Kingdom
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Albarracin M, Bouchard-Joly G, Sheikhbahaee Z, Miller M, Pitliya RJ, Poirier P. Feeling our place in the world: an active inference account of self-esteem. Neurosci Conscious 2024; 2024:niae007. [PMID: 38562605 PMCID: PMC10984624 DOI: 10.1093/nc/niae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 12/21/2023] [Accepted: 02/20/2024] [Indexed: 04/04/2024] Open
Abstract
Self-esteem, the evaluation of one's own worth or value, is a critical aspect of psychological well-being and mental health. In this paper, we propose an active inference account of self-esteem, casting it as a sociometer or an inferential capacity to interpret one's standing within a social group. This approach allows us to explore the interaction between an individual's self-perception and the expectations of their social environment.When there is a mismatch between these perceptions and expectations, the individual needs to adjust their actions or update their self-perception to better align with their current experiences. We also consider this hypothesis in relation with recent research on affective inference, suggesting that self-esteem enables the individual to track and respond to this discrepancy through affective states such as anxiety or positive affect. By acting as an inferential sociometer, self-esteem allows individuals to navigate and adapt to their social environment, ultimately impacting their psychological well-being and mental health.
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Affiliation(s)
- Mahault Albarracin
- Département d'Informatique, Université du Québec à Montréal, 405 Rue Sainte-Catherine Est, Montreal H2L 2C4, QC, Canada
| | - Gabriel Bouchard-Joly
- Département d'Informatique, Université du Québec à Montréal, 405 Rue Sainte-Catherine Est, Montreal H2L 2C4, QC, Canada
| | - Zahra Sheikhbahaee
- CHU Sainte-Justine Research Center, University of Montreal, 5757, Av. Decelles bureau 500, Montreal, QC H3S 2C3, Canada
| | - Mark Miller
- Monash Centre for Consciousness and Contemplative Studies, Monash University, Melboume, Australia
- Psychology Department, University of Toronto, Toronto, ON M5S 3G3, Canada
| | - Riddhi J Pitliya
- Department of Experimental Psychology Oxford University, Oxford OX1 2JD, United Kingdom
| | - Pierre Poirier
- Département d'Informatique, Université du Québec à Montréal, 405 Rue Sainte-Catherine Est, Montreal H2L 2C4, QC, Canada
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Pezzulo G, Parr T, Cisek P, Clark A, Friston K. Generating meaning: active inference and the scope and limits of passive AI. Trends Cogn Sci 2024; 28:97-112. [PMID: 37973519 DOI: 10.1016/j.tics.2023.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 10/03/2023] [Accepted: 10/05/2023] [Indexed: 11/19/2023]
Abstract
Prominent accounts of sentient behavior depict brains as generative models of organismic interaction with the world, evincing intriguing similarities with current advances in generative artificial intelligence (AI). However, because they contend with the control of purposive, life-sustaining sensorimotor interactions, the generative models of living organisms are inextricably anchored to the body and world. Unlike the passive models learned by generative AI systems, they must capture and control the sensory consequences of action. This allows embodied agents to intervene upon their worlds in ways that constantly put their best models to the test, thus providing a solid bedrock that is - we argue - essential to the development of genuine understanding. We review the resulting implications and consider future directions for generative AI.
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Affiliation(s)
- Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.
| | - Thomas Parr
- Nuffield Department of Clinical Neurosciences, University of Oxford
| | - Paul Cisek
- Department of Neuroscience, University of Montréal, Montréal, Québec, Canada
| | - Andy Clark
- Department of Philosophy, University of Sussex, Brighton, UK; Department of Informatics, University of Sussex, Brighton, UK; Department of Philosophy, Macquarie University, Sydney, New South Wales, Australia
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK; VERSES AI Research Lab, Los Angeles, CA, USA
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Sheikhbahaee Z, Safron A, Hesp C, Dumas G. From physics to sentience: Deciphering the semantics of the free-energy principle and evaluating its claims: Comment on "Path integrals, particular kinds, and strange things" by Karl Friston et al. Phys Life Rev 2023; 47:276-278. [PMID: 37992466 DOI: 10.1016/j.plrev.2023.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 11/13/2023] [Indexed: 11/24/2023]
Affiliation(s)
- Zahra Sheikhbahaee
- CHU Sainte-Justine Research Center, Department of Psychiatry, University of Montreal, Canada
| | - Adam Safron
- Center for Psychedelic & Consciousness Research, John Hopkins University, United States of America
| | - Casper Hesp
- Department for Developmental Psychology, University of Amsterdam, Netherlands
| | - Guillaume Dumas
- CHU Sainte-Justine Research Center, Department of Psychiatry, University of Montreal, Canada; Mila - Quebec AI Institute, Canada.
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Safron A, Hipólito I, Clark A. Editorial: Bio A.I. - from embodied cognition to enactive robotics. Front Neurorobot 2023; 17:1301993. [PMID: 38034837 PMCID: PMC10682788 DOI: 10.3389/fnbot.2023.1301993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 10/03/2023] [Indexed: 12/02/2023] Open
Affiliation(s)
- Adam Safron
- Center for Psychedelic and Consciousness Research, Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Institute for Advanced Consciousness Studies, Santa Monica, CA, United States
- Cognitive Science Program, Indiana University, Bloomington, IN, United States
- Kinsey Institute, Indiana University, Bloomington, IN, United States
| | - Inês Hipólito
- Department of Philosophy, Macquarie University, Sydney, NSW, Australia
| | - Andy Clark
- Department of Philosophy, Macquarie University, Sydney, NSW, Australia
- Department of Philosophy and Department of Informatics, University of Sussex, Brighton, United Kingdom
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Safron A. Integrated world modeling theory expanded: Implications for the future of consciousness. Front Comput Neurosci 2022; 16:642397. [PMID: 36507308 PMCID: PMC9730424 DOI: 10.3389/fncom.2022.642397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 10/24/2022] [Indexed: 11/27/2022] Open
Abstract
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.
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Affiliation(s)
- Adam Safron
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Center for Psychedelic and Consciousness Research, Baltimore, MD, United States
- Cognitive Science Program, Indiana University, Bloomington, IN, United States
- Institute for Advanced Consciousness Studies (IACS), Santa Monica, CA, United States
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Safron A, Çatal O, Verbelen T. Generalized Simultaneous Localization and Mapping (G-SLAM) as unification framework for natural and artificial intelligences: towards reverse engineering the hippocampal/entorhinal system and principles of high-level cognition. Front Syst Neurosci 2022; 16:787659. [PMID: 36246500 PMCID: PMC9563348 DOI: 10.3389/fnsys.2022.787659] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 09/02/2022] [Indexed: 11/24/2022] Open
Abstract
Simultaneous localization and mapping (SLAM) represents a fundamental problem for autonomous embodied systems, for which the hippocampal/entorhinal system (H/E-S) has been optimized over the course of evolution. We have developed a biologically-inspired SLAM architecture based on latent variable generative modeling within the Free Energy Principle and Active Inference (FEP-AI) framework, which affords flexible navigation and planning in mobile robots. We have primarily focused on attempting to reverse engineer H/E-S "design" properties, but here we consider ways in which SLAM principles from robotics may help us better understand nervous systems and emergent minds. After reviewing LatentSLAM and notable features of this control architecture, we consider how the H/E-S may realize these functional properties not only for physical navigation, but also with respect to high-level cognition understood as generalized simultaneous localization and mapping (G-SLAM). We focus on loop-closure, graph-relaxation, and node duplication as particularly impactful architectural features, suggesting these computational phenomena may contribute to understanding cognitive insight (as proto-causal-inference), accommodation (as integration into existing schemas), and assimilation (as category formation). All these operations can similarly be describable in terms of structure/category learning on multiple levels of abstraction. However, here we adopt an ecological rationality perspective, framing H/E-S functions as orchestrating SLAM processes within both concrete and abstract hypothesis spaces. In this navigation/search process, adaptive cognitive equilibration between assimilation and accommodation involves balancing tradeoffs between exploration and exploitation; this dynamic equilibrium may be near optimally realized in FEP-AI, wherein control systems governed by expected free energy objective functions naturally balance model simplicity and accuracy. With respect to structure learning, such a balance would involve constructing models and categories that are neither too inclusive nor exclusive. We propose these (generalized) SLAM phenomena may represent some of the most impactful sources of variation in cognition both within and between individuals, suggesting that modulators of H/E-S functioning may potentially illuminate their adaptive significances as fundamental cybernetic control parameters. Finally, we discuss how understanding H/E-S contributions to G-SLAM may provide a unifying framework for high-level cognition and its potential realization in artificial intelligences.
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Affiliation(s)
- Adam Safron
- Center for Psychedelic and Consciousness Research, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Cognitive Science Program, Indiana University, Bloomington, IN, United States
- Institute for Advanced Consciousness Studies, Santa Monica, CA, United States
| | - Ozan Çatal
- IDLab, Department of Information Technology, Ghent University—imec, Ghent, Belgium
| | - Tim Verbelen
- IDLab, Department of Information Technology, Ghent University—imec, Ghent, Belgium
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Majumdar G, Yazin F, Banerjee A, Roy D. Emotion dynamics as hierarchical Bayesian inference in time. Cereb Cortex 2022; 33:3750-3772. [PMID: 36030379 DOI: 10.1093/cercor/bhac305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/06/2022] [Accepted: 07/07/2022] [Indexed: 11/13/2022] Open
Abstract
What fundamental property of our environment would be most valuable and optimal in characterizing the emotional dynamics we experience in daily life? Empirical work has shown that an accurate estimation of uncertainty is necessary for our optimal perception, learning, and decision-making. However, the role of this uncertainty in governing our affective dynamics remains unexplored. Using Bayesian encoding, decoding and computational modeling, on a large-scale neuroimaging and behavioral data on a passive movie-watching task, we showed that emotions naturally arise due to ongoing uncertainty estimations about future outcomes in a hierarchical neural architecture. Several prefrontal subregions hierarchically encoded a lower-dimensional signal that highly correlated with the evolving uncertainty. Crucially, the lateral orbitofrontal cortex (lOFC) tracked the temporal fluctuations of this uncertainty and was predictive of the participants' predisposition to anxiety. Furthermore, we observed a distinct functional double-dissociation within OFC with increased connectivity between medial OFC and DMN, while with that of lOFC and FPN in response to the evolving affect. Finally, we uncovered a temporally predictive code updating an individual's beliefs spontaneously with fluctuating outcome uncertainty in the lOFC. A biologically relevant and computationally crucial parameter in the theories of brain function, we propose uncertainty to be central to the definition of complex emotions.
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Affiliation(s)
- Gargi Majumdar
- Cognitive Brain Dynamics Lab, National Brain Research Centre, NH 8, Manesar, Gurgaon, Haryana 122052, India
| | - Fahd Yazin
- Cognitive Brain Dynamics Lab, National Brain Research Centre, NH 8, Manesar, Gurgaon, Haryana 122052, India
| | - Arpan Banerjee
- Cognitive Brain Dynamics Lab, National Brain Research Centre, NH 8, Manesar, Gurgaon, Haryana 122052, India
| | - Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Centre, NH 8, Manesar, Gurgaon, Haryana 122052, India.,Centre for Brain Science and Applications, School of AIDE, IIT Jodhpur, NH 62, Surpura Bypass Rd, Karwar, Rajasthan 342030, India
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Badcock PB, Ramstead MJD, Sheikhbahaee Z, Constant A. Applying the Free Energy Principle to Complex Adaptive Systems. ENTROPY (BASEL, SWITZERLAND) 2022; 24:689. [PMID: 35626572 PMCID: PMC9141822 DOI: 10.3390/e24050689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 05/11/2022] [Indexed: 11/30/2022]
Abstract
The free energy principle (FEP) is a formulation of the adaptive, belief-driven behaviour of self-organizing systems that gained prominence in the early 2000s as a unified model of the brain [...].
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Affiliation(s)
- Paul B. Badcock
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC 3010, Australia
- Orygen, Parkville, VIC 3052, Australia
| | - Maxwell J. D. Ramstead
- VERSES Research Lab and the Spatial Web Foundation, Los Angeles, CA 90016, USA;
- Wellcome Centre for Human Neuroimaging, University College London, London WC1E 6BT, UK
| | - Zahra Sheikhbahaee
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
| | - Axel Constant
- Charles Perkins Centre, The University of Sydney, John Hopkins Drive, Camperdown, NSW 2006, Australia;
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11
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Van de Maele T, Verbelen T, Çatal O, Dhoedt B. Embodied Object Representation Learning and Recognition. Front Neurorobot 2022; 16:840658. [PMID: 35496899 PMCID: PMC9049856 DOI: 10.3389/fnbot.2022.840658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/23/2022] [Indexed: 11/24/2022] Open
Abstract
Scene understanding and decomposition is a crucial challenge for intelligent systems, whether it is for object manipulation, navigation, or any other task. Although current machine and deep learning approaches for object detection and classification obtain high accuracy, they typically do not leverage interaction with the world and are limited to a set of objects seen during training. Humans on the other hand learn to recognize and classify different objects by actively engaging with them on first encounter. Moreover, recent theories in neuroscience suggest that cortical columns in the neocortex play an important role in this process, by building predictive models about objects in their reference frame. In this article, we present an enactive embodied agent that implements such a generative model for object interaction. For each object category, our system instantiates a deep neural network, called Cortical Column Network (CCN), that represents the object in its own reference frame by learning a generative model that predicts the expected transform in pixel space, given an action. The model parameters are optimized through the active inference paradigm, i.e., the minimization of variational free energy. When provided with a visual observation, an ensemble of CCNs each vote on their belief of observing that specific object category, yielding a potential object classification. In case the likelihood on the selected category is too low, the object is detected as an unknown category, and the agent has the ability to instantiate a novel CCN for this category. We validate our system in an simulated environment, where it needs to learn to discern multiple objects from the YCB dataset. We show that classification accuracy improves as an embodied agent can gather more evidence, and that it is able to learn about novel, previously unseen objects. Finally, we show that an agent driven through active inference can choose their actions to reach a preferred observation.
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Affiliation(s)
- Toon Van de Maele
- IDLab, Department of Information Technology, Ghent University - imec, Ghent, Belgium
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Goekoop R, de Kleijn R. Permutation Entropy as a Universal Disorder Criterion: How Disorders at Different Scale Levels Are Manifestations of the Same Underlying Principle. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1701. [PMID: 34946007 PMCID: PMC8700347 DOI: 10.3390/e23121701] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 12/10/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022]
Abstract
What do bacteria, cells, organs, people, and social communities have in common? At first sight, perhaps not much. They involve totally different agents and scale levels of observation. On second thought, however, perhaps they share everything. A growing body of literature suggests that living systems at different scale levels of observation follow the same architectural principles and process information in similar ways. Moreover, such systems appear to respond in similar ways to rising levels of stress, especially when stress levels approach near-lethal levels. To explain such communalities, we argue that all organisms (including humans) can be modeled as hierarchical Bayesian controls systems that are governed by the same biophysical principles. Such systems show generic changes when taxed beyond their ability to correct for environmental disturbances. Without exception, stressed organisms show rising levels of 'disorder' (randomness, unpredictability) in internal message passing and overt behavior. We argue that such changes can be explained by a collapse of allostatic (high-level integrative) control, which normally synchronizes activity of the various components of a living system to produce order. The selective overload and cascading failure of highly connected (hub) nodes flattens hierarchical control, producing maladaptive behavior. Thus, we present a theory according to which organic concepts such as stress, a loss of control, disorder, disease, and death can be operationalized in biophysical terms that apply to all scale levels of organization. Given the presumed universality of this mechanism, 'losing control' appears to involve the same process anywhere, whether involving bacteria succumbing to an antibiotic agent, people suffering from physical or mental disorders, or social systems slipping into warfare. On a practical note, measures of disorder may serve as early warning signs of system failure even when catastrophic failure is still some distance away.
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Affiliation(s)
- Rutger Goekoop
- Parnassia Group, PsyQ Parnassia Academy, Department of Anxiety Disorders, Early Detection and Intervention Team (EDIT), Lijnbaan 4, 2512 VA Den Haag, The Netherlands
| | - Roy de Kleijn
- Cognitive Psychology Unit, Institute of Psychology & Leiden Institute for Brain and Cognition, Leiden University, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands;
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Ciaunica A, Safron A, Delafield-Butt J. Back to square one: the bodily roots of conscious experiences in early life. Neurosci Conscious 2021; 2021:niab037. [PMID: 38633139 PMCID: PMC11021924 DOI: 10.1093/nc/niab037] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 09/16/2021] [Accepted: 11/15/2021] [Indexed: 04/19/2024] Open
Abstract
Most theoretical and empirical discussions about the nature of consciousness are typically couched in a way that endorses a tacit adult-centric and vision-based perspective. This paper defends the idea that consciousness science may be put on a fruitful track for its next phase by examining the nature of subjective experiences through a bottom-up developmental lens. We draw attention to the intrinsic link between consciousness, experiences and experiencing subjects, which are first and foremost embodied and situated organisms essentially concerned with self-preservation within a precarious environment. Our paper suggests that in order to understand what consciousness 'is', one should first tackle the fundamental question: how do embodied experiences 'arise' from square one? We then highlight one key yet overlooked aspect of human consciousness studies, namely that the earliest and closest environment of an embodied experiencing subject is the body of another human experiencing subject. We present evidence speaking in favour of fairly sophisticated forms of early sensorimotor integration of bodily signals and self-generated actions already being established in utero. We conclude that these primitive and fundamentally relational and co-embodied roots of our early experiences may have a crucial impact on the way human beings consciously experience the self, body and the world across their lifespan.
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Affiliation(s)
- Anna Ciaunica
- Centre for Philosophy of Science (CFCUL), University of Lisbon, Lisbon 1749-016, Portugal
- Institute of Cognitive Neuroscience, University College London, London WC1N 3AR, UK
| | - Adam Safron
- Kinsey Institute, Indiana University, Lindley Hall, 150 S Woodlawn Ave, Bloomington, IN 47405, USA
- Cognitive Science Program, 1001 E. 10th St. Indiana University, Bloomington, IN 47405, USA
| | - Jonathan Delafield-Butt
- Laboratory for Innovation in Autism, University of Strathclyde, Glasgow G1 1QE, UK
- Faculty of Humanities and Social Sciences, University of Strathclyde, Glasgow G4 0LT, UK
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