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Limanowski J, Adams RA, Kilner J, Parr T. The Many Roles of Precision in Action. ENTROPY (BASEL, SWITZERLAND) 2024; 26:790. [PMID: 39330123 PMCID: PMC11431491 DOI: 10.3390/e26090790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2024] [Revised: 09/05/2024] [Accepted: 09/07/2024] [Indexed: 09/28/2024]
Abstract
Active inference describes (Bayes-optimal) behaviour as being motivated by the minimisation of surprise of one's sensory observations, through the optimisation of a generative model (of the hidden causes of one's sensory data) in the brain. One of active inference's key appeals is its conceptualisation of precision as biasing neuronal communication and, thus, inference within generative models. The importance of precision in perceptual inference is evident-many studies have demonstrated the importance of ensuring precision estimates are correct for normal (healthy) sensation and perception. Here, we highlight the many roles precision plays in action, i.e., the key processes that rely on adequate estimates of precision, from decision making and action planning to the initiation and control of muscle movement itself. Thereby, we focus on the recent development of hierarchical, "mixed" models-generative models spanning multiple levels of discrete and continuous inference. These kinds of models open up new perspectives on the unified description of hierarchical computation, and its implementation, in action. Here, we highlight how these models reflect the many roles of precision in action-from planning to execution-and the associated pathologies if precision estimation goes wrong. We also discuss the potential biological implementation of the associated message passing, focusing on the role of neuromodulatory systems in mediating different kinds of precision.
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Affiliation(s)
- Jakub Limanowski
- Institute of Psychology, University of Greifswald, 17487 Greifswald, Germany
| | - Rick A. Adams
- Institute of Cognitive Neuroscience, University College London, London WC1N 3AZ, UK; (R.A.A.); (J.K.)
- Centre for Medical Image Computing, University College London, London WC1N 6LJ, UK
| | - James Kilner
- Institute of Cognitive Neuroscience, University College London, London WC1N 3AZ, UK; (R.A.A.); (J.K.)
| | - Thomas Parr
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 4AL, UK;
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2
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Quirmbach F, Limanowski J. Visuomotor prediction during action planning in the human frontoparietal cortex and cerebellum. Cereb Cortex 2024; 34:bhae382. [PMID: 39325000 DOI: 10.1093/cercor/bhae382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 08/28/2024] [Accepted: 09/04/2024] [Indexed: 09/27/2024] Open
Abstract
The concept of forward models in the brain, classically applied to describing on-line motor control, can in principle be extended to action planning, i.e. assuming forward sensory predictions are issued during the mere preparation of movements. To test this idea, we combined a delayed movement task with a virtual reality based manipulation of visuomotor congruence during functional magnetic resonance imaging. Participants executed simple hand movements after a delay. During the delay, two aspects of the upcoming movement could be cued: the movement type and the visuomotor mapping (i.e. congruence of executed hand movements and visual movement feedback by a glove-controlled virtual hand). Frontoparietal areas showed increased delay period activity when preparing pre-specified movements (cued > uncued). The cerebellum showed increased activity during the preparation for incongruent > congruent visuomotor mappings. The left anterior intraparietal sulcus showed an interaction effect, responding most strongly when a pre-specified (cued) movement was prepared under expected visuomotor incongruence. These results suggest that motor planning entails a forward prediction of visual body movement feedback, which can be adjusted in anticipation of nonstandard visuomotor mappings, and which is likely computed by the cerebellum and integrated with state estimates for (planned) control in the anterior intraparietal sulcus.
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Affiliation(s)
- Felix Quirmbach
- Faculty of Psychology, Technical University of Dresden, Helmholtzstraße 10, 01069 Dresden, Germany
- Center for Tactile Internet with Human-in-the-Loop, Technical University of Dresden, Georg-Schumann-Str. 9, 01187 Dresden, Germany
| | - Jakub Limanowski
- Center for Tactile Internet with Human-in-the-Loop, Technical University of Dresden, Georg-Schumann-Str. 9, 01187 Dresden, Germany
- Institute of Psychology, University of Greifswald, Franz-Mehring-Straße 47, 17489 Greifswald, Germany
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3
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Heins C, Millidge B, Da Costa L, Mann RP, Friston KJ, Couzin ID. Collective behavior from surprise minimization. Proc Natl Acad Sci U S A 2024; 121:e2320239121. [PMID: 38630721 PMCID: PMC11046639 DOI: 10.1073/pnas.2320239121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 03/08/2024] [Indexed: 04/19/2024] Open
Abstract
Collective motion is ubiquitous in nature; groups of animals, such as fish, birds, and ungulates appear to move as a whole, exhibiting a rich behavioral repertoire that ranges from directed movement to milling to disordered swarming. Typically, such macroscopic patterns arise from decentralized, local interactions among constituent components (e.g., individual fish in a school). Preeminent models of this process describe individuals as self-propelled particles, subject to self-generated motion and "social forces" such as short-range repulsion and long-range attraction or alignment. However, organisms are not particles; they are probabilistic decision-makers. Here, we introduce an approach to modeling collective behavior based on active inference. This cognitive framework casts behavior as the consequence of a single imperative: to minimize surprise. We demonstrate that many empirically observed collective phenomena, including cohesion, milling, and directed motion, emerge naturally when considering behavior as driven by active Bayesian inference-without explicitly building behavioral rules or goals into individual agents. Furthermore, we show that active inference can recover and generalize the classical notion of social forces as agents attempt to suppress prediction errors that conflict with their expectations. By exploring the parameter space of the belief-based model, we reveal nontrivial relationships between the individual beliefs and group properties like polarization and the tendency to visit different collective states. We also explore how individual beliefs about uncertainty determine collective decision-making accuracy. Finally, we show how agents can update their generative model over time, resulting in groups that are collectively more sensitive to external fluctuations and encode information more robustly.
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Affiliation(s)
- Conor Heins
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, KonstanzD-78457, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, KonstanzD-78457, Germany
- Department of Biology, University of Konstanz, KonstanzD-78457, Germany
- VERSES Research Lab, Los Angeles, CA90016
| | - Beren Millidge
- Medical Research Council Brain Networks Dynamics Unit, University of Oxford, OxfordOX1 3TH, United Kingdom
| | - Lancelot Da Costa
- VERSES Research Lab, Los Angeles, CA90016
- Department of Mathematics, Imperial College London, LondonSW7 2AZ, United Kingdom
- Wellcome Centre for Human Neuroimaging, University College London, LondonWC1N 3AR, United Kingdom
| | - Richard P. Mann
- Department of Statistics, School of Mathematics, University of Leeds, LeedsLS2 9JT, United Kingdom
| | - Karl J. Friston
- VERSES Research Lab, Los Angeles, CA90016
- Wellcome Centre for Human Neuroimaging, University College London, LondonWC1N 3AR, United Kingdom
| | - Iain D. Couzin
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, KonstanzD-78457, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, KonstanzD-78457, Germany
- Department of Biology, University of Konstanz, KonstanzD-78457, Germany
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4
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Tarrano C, Galléa C, Delorme C, McGovern EM, Atkinson-Clement C, Barnham IJ, Brochard V, Thobois S, Tranchant C, Grabli D, Degos B, Corvol JC, Pedespan JM, Krystkowiak P, Houeto JL, Degardin A, Defebvre L, Valabrègue R, Beranger B, Apartis E, Vidailhet M, Roze E, Worbe Y. Association of abnormal explicit sense of agency with cerebellar impairment in myoclonus-dystonia. Brain Commun 2024; 6:fcae105. [PMID: 38601915 PMCID: PMC11004927 DOI: 10.1093/braincomms/fcae105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 02/20/2024] [Accepted: 03/26/2024] [Indexed: 04/12/2024] Open
Abstract
Non-motor aspects in dystonia are now well recognized. The sense of agency, which refers to the experience of controlling one's own actions, has been scarcely studied in dystonia, even though its disturbances can contribute to movement disorders. Among various brain structures, the cerebral cortex, the cerebellum, and the basal ganglia are involved in shaping the sense of agency. In myoclonus dystonia, resulting from a dysfunction of the motor network, an altered sense of agency may contribute to the clinical phenotype of the condition. In this study, we compared the explicit and implicit sense of agency in patients with myoclonus dystonia caused by a pathogenic variant of SGCE (DYT-SGCE) and control participants. We utilized behavioural tasks to assess the sense of agency and performed neuroimaging analyses, including structural, resting-state functional connectivity, and dynamic causal modelling, to explore the relevant brain regions involved in the sense of agency. Additionally, we examined the relationship between behavioural performance, symptom severity, and neuroimaging findings. We compared 19 patients with DYT-SGCE and 24 healthy volunteers. Our findings revealed that patients with myoclonus-dystonia exhibited a specific impairment in explicit sense of agency, particularly when implicit motor learning was involved. However, their implicit sense of agency remained intact. These patients also displayed grey-matter abnormalities in the motor cerebellum, as well as increased functional connectivity between the cerebellum and pre-supplementary motor area. Dynamic causal modelling analysis further identified reduced inhibitory effects of the cerebellum on the pre-supplementary motor area, decreased excitatory effects of the pre-supplementary motor area on the cerebellum, and increased self-inhibition within the pre-supplementary motor area. Importantly, both cerebellar grey-matter alterations and functional connectivity abnormalities between the cerebellum and pre-supplementary motor area were found to correlate with explicit sense of agency impairment. Increased self-inhibition within the pre-supplementary motor area was associated with less severe myoclonus symptoms. These findings highlight the disruption of higher-level cognitive processes in patients with myoclonus-dystonia, further expanding the spectrum of neurological and psychiatric dysfunction already identified in this disorder.
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Affiliation(s)
- Clément Tarrano
- CNRS UMR 7225, Sorbonne Université, Paris Brain Institute—Institut du Cerveau et de la Moelle épinière, Inserm U1127, Paris 75013, France
- Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Department of Neurology, Clinical Investigation Center for Neurosciences, Paris 75013, France
| | - Cécile Galléa
- CNRS UMR 7225, Sorbonne Université, Paris Brain Institute—Institut du Cerveau et de la Moelle épinière, Inserm U1127, Paris 75013, France
- Department of Research Neuroimaging, Centre de NeuroImagerie de Recherche (CENIR), Sorbonne Université, Paris 75013, France
| | - Cécile Delorme
- CNRS UMR 7225, Sorbonne Université, Paris Brain Institute—Institut du Cerveau et de la Moelle épinière, Inserm U1127, Paris 75013, France
- Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Department of Neurology, Clinical Investigation Center for Neurosciences, Paris 75013, France
| | - Eavan M McGovern
- Department of Neurology, Beaumont Hospital, Dublin 9, D09 VY21, Ireland
- School of Medicine, Royal College of Surgeons in Ireland, Dublin 2, D02 YN77, Ireland
| | - Cyril Atkinson-Clement
- CNRS UMR 7225, Sorbonne Université, Paris Brain Institute—Institut du Cerveau et de la Moelle épinière, Inserm U1127, Paris 75013, France
- School of Medicine, University of Nottingham, Nottingham NG7 2RD, UK
| | | | - Vanessa Brochard
- Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Department of Neurology, Clinical Investigation Center for Neurosciences, Paris 75013, France
| | - Stéphane Thobois
- Department of Neurology, Hospices Civils de Lyon, Lyon 69000, France
| | - Christine Tranchant
- Département de Neurologie, Hôpitaux Universitaires de Strasbourg, Hôpital de Hautepierre, Strasbourg 67098, France
- INSERM-U964/CNRS-UMR7104, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Université de Strasbourg, Illkirch 67404, France
- Fédération de Médecine Translationnelle de Strasbourg (FMTS), Université de Strasbourg, Strasbourg 67000, France
| | - David Grabli
- CNRS UMR 7225, Sorbonne Université, Paris Brain Institute—Institut du Cerveau et de la Moelle épinière, Inserm U1127, Paris 75013, France
- Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Department of Neurology, Clinical Investigation Center for Neurosciences, Paris 75013, France
| | - Bertrand Degos
- Department of Neurology, Assistance Publique-Hôpitaux de Paris, Avicenne Hospital, Sorbonne Paris Nord, Bobigny 93000, France
| | - Jean Christophe Corvol
- CNRS UMR 7225, Sorbonne Université, Paris Brain Institute—Institut du Cerveau et de la Moelle épinière, Inserm U1127, Paris 75013, France
- Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Department of Neurology, Clinical Investigation Center for Neurosciences, Paris 75013, France
| | - Jean-Michel Pedespan
- Department of Neuropediatry, Universitary Hospital of Pellegrin, Bordeaux 33076, France
| | - Pierre Krystkowiak
- Department of Neurology, Abu Dhabi Stem Cells Centre, Abu Dhabi, United Arab Emirates
| | - Jean-Luc Houeto
- Department of Neurology CHU Limoges, Inserm U1094, IRD U270, Univ. Limoges, EpiMaCT—Epidemiology of chronic diseases in tropical zone, Institute of Epidemiology and Tropical Neurology, OmegaHealth, Limoges 87000, France
| | - Adrian Degardin
- Department of Neurology, Tourcoing Hospital, Tourcoing 59599, France
| | - Luc Defebvre
- Department of Neurology, University of Lille, Lille 59000, France
- Department of Neurology, Lille Centre of Excellence for Neurodegenerative Diseases » (LiCEND), Lille F-59000, France
| | - Romain Valabrègue
- CNRS UMR 7225, Sorbonne Université, Paris Brain Institute—Institut du Cerveau et de la Moelle épinière, Inserm U1127, Paris 75013, France
- Department of Research Neuroimaging, Centre de NeuroImagerie de Recherche (CENIR), Sorbonne Université, Paris 75013, France
| | - Benoit Beranger
- CNRS UMR 7225, Sorbonne Université, Paris Brain Institute—Institut du Cerveau et de la Moelle épinière, Inserm U1127, Paris 75013, France
- Department of Research Neuroimaging, Centre de NeuroImagerie de Recherche (CENIR), Sorbonne Université, Paris 75013, France
| | - Emmanuelle Apartis
- CNRS UMR 7225, Sorbonne Université, Paris Brain Institute—Institut du Cerveau et de la Moelle épinière, Inserm U1127, Paris 75013, France
- Department of Neurophysiology, Saint-Antoine Hospital, Paris 75012, France
| | - Marie Vidailhet
- CNRS UMR 7225, Sorbonne Université, Paris Brain Institute—Institut du Cerveau et de la Moelle épinière, Inserm U1127, Paris 75013, France
- Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Department of Neurology, Clinical Investigation Center for Neurosciences, Paris 75013, France
| | - Emmanuel Roze
- CNRS UMR 7225, Sorbonne Université, Paris Brain Institute—Institut du Cerveau et de la Moelle épinière, Inserm U1127, Paris 75013, France
- Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Department of Neurology, Clinical Investigation Center for Neurosciences, Paris 75013, France
| | - Yulia Worbe
- CNRS UMR 7225, Sorbonne Université, Paris Brain Institute—Institut du Cerveau et de la Moelle épinière, Inserm U1127, Paris 75013, France
- Department of Neurophysiology, Saint-Antoine Hospital, Paris 75012, France
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5
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Pezzulo G, Parr T, Friston K. Active inference as a theory of sentient behavior. Biol Psychol 2024; 186:108741. [PMID: 38182015 DOI: 10.1016/j.biopsycho.2023.108741] [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: 07/18/2023] [Revised: 12/05/2023] [Accepted: 12/29/2023] [Indexed: 01/07/2024]
Abstract
This review paper offers an overview of the history and future of active inference-a unifying perspective on action and perception. Active inference is based upon the idea that sentient behavior depends upon our brains' implicit use of internal models to predict, infer, and direct action. Our focus is upon the conceptual roots and development of this theory of (basic) sentience and does not follow a rigid chronological narrative. We trace the evolution from Helmholtzian ideas on unconscious inference, through to a contemporary understanding of action and perception. In doing so, we touch upon related perspectives, the neural underpinnings of active inference, and the opportunities for future development. Key steps in this development include the formulation of predictive coding models and related theories of neuronal message passing, the use of sequential models for planning and policy optimization, and the importance of hierarchical (temporally) deep internal (i.e., generative or world) models. Active inference has been used to account for aspects of anatomy and neurophysiology, to offer theories of psychopathology in terms of aberrant precision control, and to unify extant psychological theories. We anticipate further development in all these areas and note the exciting early work applying active inference beyond neuroscience. This suggests a future not just in biology, but in robotics, machine learning, and artificial intelligence.
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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, UK
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK; VERSES AI Research Lab, Los Angeles, CA 90016, USA
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6
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Priorelli M, Pezzulo G, Stoianov IP. Deep kinematic inference affords efficient and scalable control of bodily movements. Proc Natl Acad Sci U S A 2023; 120:e2309058120. [PMID: 38085784 PMCID: PMC10743426 DOI: 10.1073/pnas.2309058120] [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: 05/31/2023] [Accepted: 10/24/2023] [Indexed: 12/18/2023] Open
Abstract
Performing goal-directed movements requires mapping goals from extrinsic (workspace-relative) to intrinsic (body-relative) coordinates and then to motor signals. Mainstream approaches based on optimal control realize the mappings by minimizing cost functions, which is computationally demanding. Instead, active inference uses generative models to produce sensory predictions, which allows a cheaper inversion to the motor signals. However, devising generative models to control complex kinematic chains like the human body is challenging. We introduce an active inference architecture that affords a simple but effective mapping from extrinsic to intrinsic coordinates via inference and easily scales up to drive complex kinematic chains. Rich goals can be specified in both intrinsic and extrinsic coordinates using attractive or repulsive forces. The proposed model reproduces sophisticated bodily movements and paves the way for computationally efficient and biologically plausible control of actuated systems.
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Affiliation(s)
- Matteo Priorelli
- National Research Council, Institute of Cognitive Sciences and Technologies, Padova35137, Italy
| | - Giovanni Pezzulo
- National Research Council, Institute of Cognitive Sciences and Technologies, Rome00185, Italy
| | - Ivilin Peev Stoianov
- National Research Council, Institute of Cognitive Sciences and Technologies, Padova35137, Italy
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7
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Proietti R, Pezzulo G, Tessari A. An active inference model of hierarchical action understanding, learning and imitation. Phys Life Rev 2023; 46:92-118. [PMID: 37354642 DOI: 10.1016/j.plrev.2023.05.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 05/31/2023] [Indexed: 06/26/2023]
Abstract
We advance a novel active inference model of the cognitive processing that underlies the acquisition of a hierarchical action repertoire and its use for observation, understanding and imitation. We illustrate the model in four simulations of a tennis learner who observes a teacher performing tennis shots, forms hierarchical representations of the observed actions, and imitates them. Our simulations show that the agent's oculomotor activity implements an active information sampling strategy that permits inferring the kinematic aspects of the observed movement, which lie at the lowest level of the action hierarchy. In turn, this low-level kinematic inference supports higher-level inferences about deeper aspects of the observed actions: proximal goals and intentions. Finally, the inferred action representations can steer imitative responses, but interfere with the execution of different actions. Our simulations show that hierarchical active inference provides a unified account of action observation, understanding, learning and imitation and helps explain the neurobiological underpinnings of visuomotor cognition, including the multiple routes for action understanding in the dorsal and ventral streams and mirror mechanisms.
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Affiliation(s)
| | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.
| | - Alessia Tessari
- Department of Psychology, University of Bologna, Italy; Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy
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8
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Arthur T, Vine S, Buckingham G, Brosnan M, Wilson M, Harris D. Testing predictive coding theories of autism spectrum disorder using models of active inference. PLoS Comput Biol 2023; 19:e1011473. [PMID: 37695796 PMCID: PMC10529610 DOI: 10.1371/journal.pcbi.1011473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 09/27/2023] [Accepted: 08/28/2023] [Indexed: 09/13/2023] Open
Abstract
Several competing neuro-computational theories of autism have emerged from predictive coding models of the brain. To disentangle their subtly different predictions about the nature of atypicalities in autistic perception, we performed computational modelling of two sensorimotor tasks: the predictive use of manual gripping forces during object lifting and anticipatory eye movements during a naturalistic interception task. In contrast to some accounts, we found no evidence of chronic atypicalities in the use of priors or weighting of sensory information during object lifting. Differences in prior beliefs, rates of belief updating, and the precision weighting of prediction errors were, however, observed for anticipatory eye movements. Most notably, we observed autism-related difficulties in flexibly adapting learning rates in response to environmental change (i.e., volatility). These findings suggest that atypical encoding of precision and context-sensitive adjustments provide a better explanation of autistic perception than generic attenuation of priors or persistently high precision prediction errors. Our results did not, however, support previous suggestions that autistic people perceive their environment to be persistently volatile.
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Affiliation(s)
- Tom Arthur
- School of Public Health and Sport Sciences, Medical School, University of Exeter, Exeter, United Kingdom
- Centre for Applied Autism Research, Department of Psychology, University of Bath, Bath, United Kingdom
| | - Sam Vine
- School of Public Health and Sport Sciences, Medical School, University of Exeter, Exeter, United Kingdom
| | - Gavin Buckingham
- School of Public Health and Sport Sciences, Medical School, University of Exeter, Exeter, United Kingdom
| | - Mark Brosnan
- Centre for Applied Autism Research, Department of Psychology, University of Bath, Bath, United Kingdom
| | - Mark Wilson
- School of Public Health and Sport Sciences, Medical School, University of Exeter, Exeter, United Kingdom
| | - David Harris
- School of Public Health and Sport Sciences, Medical School, University of Exeter, Exeter, United Kingdom
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9
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Moura N, Vidal M, Aguilera AM, Vilas-Boas JP, Serra S, Leman M. Knee flexion of saxophone players anticipates tonal context of music. NPJ SCIENCE OF LEARNING 2023; 8:22. [PMID: 37369691 PMCID: PMC10300100 DOI: 10.1038/s41539-023-00172-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 06/14/2023] [Indexed: 06/29/2023]
Abstract
Music performance requires high levels of motor control. Professional musicians use body movements not only to accomplish and help technical efficiency, but to shape expressive interpretation. Here, we recorded motion and audio data of twenty participants performing four musical fragments varying in the degree of technical difficulty to analyze how knee flexion is employed by expert saxophone players. Using a computational model of the auditory periphery, we extracted emergent acoustical properties of sound to inference critical cognitive patterns of music processing and relate them to motion data. Results showed that knee flexion is causally linked to tone expectations and correlated to rhythmical density, suggesting that this gesture is associated with expressive and facilitative purposes. Furthermore, when instructed to play immobile, participants tended to microflex (>1 Hz) more frequently compared to when playing expressively, possibly indicating a natural urge to move to the music. These results underline the robustness of body movement in musical performance, providing valuable insights for the understanding of communicative processes, and development of motor learning cues.
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Affiliation(s)
- Nádia Moura
- Research Centre for Science and Technology of the Arts, School of Arts, Universidade Católica Portuguesa, Rua de Diogo Botelho 1327, 4169-005, Porto, Portugal.
| | - Marc Vidal
- Institute for Psychoacoustics and Electronic Music, Ghent University, Miriam Makebaplein 1, 9000, Ghent, Belgium.
- Department of Statistics and Institute of Mathematics, Universidad de Granada, Campus de Fuentenueva, 18071, Granada, Spain.
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103, Leipzig, Germany.
| | - Ana M Aguilera
- Department of Statistics and Institute of Mathematics, Universidad de Granada, Campus de Fuentenueva, 18071, Granada, Spain
| | - João Paulo Vilas-Boas
- Centre of Research, Education, Innovation and Intervention in Sport (CIFI2D), Porto Biomechanics Laboratory (LABIOMEP-UP), Faculty of Sport, University of Porto, 4099-002, Porto, Portugal
| | - Sofia Serra
- Research Centre for Science and Technology of the Arts, School of Arts, Universidade Católica Portuguesa, Rua de Diogo Botelho 1327, 4169-005, Porto, Portugal
| | - Marc Leman
- Institute for Psychoacoustics and Electronic Music, Ghent University, Miriam Makebaplein 1, 9000, Ghent, Belgium.
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10
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Parr T, Holmes E, Friston KJ, Pezzulo G. Cognitive effort and active inference. Neuropsychologia 2023; 184:108562. [PMID: 37080424 PMCID: PMC10636588 DOI: 10.1016/j.neuropsychologia.2023.108562] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 04/03/2023] [Accepted: 04/11/2023] [Indexed: 04/22/2023]
Abstract
This paper aims to integrate some key constructs in the cognitive neuroscience of cognitive control and executive function by formalising the notion of cognitive (or mental) effort in terms of active inference. To do so, we call upon a task used in neuropsychology to assess impulse inhibition-a Stroop task. In this task, participants must suppress the impulse to read a colour word and instead report the colour of the text of the word. The Stroop task is characteristically effortful, and we unpack a theory of mental effort in which, to perform this task accurately, participants must overcome prior beliefs about how they would normally act. However, our interest here is not in overt action, but in covert (mental) action. Mental actions change our beliefs but have no (direct) effect on the outside world-much like deploying covert attention. This account of effort as mental action lets us generate multimodal (choice, reaction time, and electrophysiological) data of the sort we might expect from a human participant engaging in this task. We analyse how parameters determining cognitive effort influence simulated responses and demonstrate that-when provided only with performance data-these parameters can be recovered, provided they are within a certain range.
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Affiliation(s)
- Thomas Parr
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, UK.
| | - Emma Holmes
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, UK
| | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
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11
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From fear of falling to choking under pressure: A predictive processing perspective of disrupted motor control under anxiety. Neurosci Biobehav Rev 2023; 148:105115. [PMID: 36906243 DOI: 10.1016/j.neubiorev.2023.105115] [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: 10/03/2022] [Revised: 02/27/2023] [Accepted: 02/28/2023] [Indexed: 03/11/2023]
Abstract
Under the Predictive Processing Framework, perception is guided by internal models that map the probabilistic relationship between sensory states and their causes. Predictive processing has contributed to a new understanding of both emotional states and motor control but is yet to be fully applied to their interaction during the breakdown of motor movements under heightened anxiety or threat. We bring together literature on anxiety and motor control to propose that predictive processing provides a unifying principle for understanding motor breakdowns as a disruption to the neuromodulatory control mechanisms that regulate the interactions of top-down predictions and bottom-up sensory signals. We illustrate this account using examples from disrupted balance and gait in populations who are anxious/fearful of falling, as well as 'choking' in elite sport. This approach can explain both rigid and inflexible movement strategies, as well as highly variable and imprecise action and conscious movement processing, and may also unite the apparently opposing self-focus and distraction approaches to choking. We generate predictions to guide future work and propose practical recommendations.
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12
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Sokołowska B. Impact of Virtual Reality Cognitive and Motor Exercises on Brain Health. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4150. [PMID: 36901160 PMCID: PMC10002333 DOI: 10.3390/ijerph20054150] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
Innovative technologies of the 21st century have an extremely significant impact on all activities of modern humans. Among them, virtual reality (VR) offers great opportunities for scientific research and public health. The results of research to date both demonstrate the beneficial effects of using virtual worlds, and indicate undesirable effects on bodily functions. This review presents interesting recent findings related to training/exercise in virtual environments and its impact on cognitive and motor functions. It also highlights the importance of VR as an effective tool for assessing and diagnosing these functions both in research and modern medical practice. The findings point to the enormous future potential of these rapidly developing innovative technologies. Of particular importance are applications of virtual reality in basic and clinical neuroscience.
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Affiliation(s)
- Beata Sokołowska
- Bioinformatics Laboratory, Mossakowski Medical Research Institute, Polish Academy of Sciences, 02-106 Warsaw, Poland
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13
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Bruno V, Castellani N, Garbarini F, Christensen MS. Moving without sensory feedback: online TMS over the dorsal premotor cortex impairs motor performance during ischemic nerve block. Cereb Cortex 2023; 33:2315-2327. [PMID: 35641143 DOI: 10.1093/cercor/bhac210] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 05/05/2022] [Accepted: 05/06/2022] [Indexed: 11/14/2022] Open
Abstract
The study investigates the role of dorsal premotor cortex (PMd) in generating predicted sensory consequences of movements, i.e. corollary discharges. In 2 different sessions, we disrupted PMd and parietal hand's multisensory integration site (control area) with transcranial magnetic stimulation (TMS) during a finger-sequence-tapping motor task. In this TMS sham-controlled design, the task was performed with normal sensory feedback and during upper-limb ischemic nerve block (INB), in a time-window where participants moved without somatosensation. Errors and movement timing (objective measures) and ratings about movement perception (subjective measures) were collected. We found that INB overall worsens objective and subjective measures, but crucially in the PMd session, the absence of somatosensation together with TMS disruption induced more errors, less synchronized movements, and increased subjective difficulty ratings as compared with the parietal control session (despite a carryover effect between real and sham stimulation to be addressed in future studies). Contrarily, after parietal area interference session, when sensory information is already missing due to INB, motor performance was not aggravated. Altogether these findings suggest that the loss of actual (through INB) and predicted (through PMd disruption) somatosensory feedback degraded motor performance and perception, highlighting the crucial role of PMd in generating corollary discharge.
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Affiliation(s)
- Valentina Bruno
- Manibus Lab, Department of Psychology, University of Turin, Via Verdi 10, 10124 Turin, Italy
| | - Nicolò Castellani
- Manibus Lab, Department of Psychology, University of Turin, Via Verdi 10, 10124 Turin, Italy.,Molecular Mind Lab, IMT School for Advanced Studies, Piazza S. Ponziano, 6, 55100 Lucca, Italy
| | - Francesca Garbarini
- Manibus Lab, Department of Psychology, University of Turin, Via Verdi 10, 10124 Turin, Italy
| | - Mark Schram Christensen
- Christensen Lab, Department of Neuroscience, University of Copenhagen, Panum Institute 33-3, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
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14
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Maselli A, Lanillos P, Pezzulo G. Active inference unifies intentional and conflict-resolution imperatives of motor control. PLoS Comput Biol 2022; 18:e1010095. [PMID: 35714105 PMCID: PMC9205531 DOI: 10.1371/journal.pcbi.1010095] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 04/11/2022] [Indexed: 11/19/2022] Open
Abstract
The field of motor control has long focused on the achievement of external goals through action (e.g., reaching and grasping objects). However, recent studies in conditions of multisensory conflict, such as when a subject experiences the rubber hand illusion or embodies an avatar in virtual reality, reveal the presence of unconscious movements that are not goal-directed, but rather aim at resolving multisensory conflicts; for example, by aligning the position of a person’s arm with that of an embodied avatar. This second, conflict-resolution imperative of movement control did not emerge in classical studies of motor adaptation and online corrections, which did not allow movements to reduce the conflicts; and has been largely ignored so far in formal theories. Here, we propose a model of movement control grounded in the theory of active inference that integrates intentional and conflict-resolution imperatives. We present three simulations showing that the active inference model is able to characterize movements guided by the intention to achieve an external goal, by the necessity to resolve multisensory conflict, or both. Furthermore, our simulations reveal a fundamental difference between the (active) inference underlying intentional and conflict-resolution imperatives by showing that it is driven by two different (model and sensory) kinds of prediction errors. Finally, our simulations show that when movement is only guided by conflict resolution, the model incorrectly infers that is velocity is zero, as if it was not moving. This result suggests a novel speculative explanation for the fact that people are unaware of their subtle compensatory movements to avoid multisensory conflict. Furthermore, it can potentially help shed light on deficits of motor awareness that arise in psychopathological conditions.
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Affiliation(s)
- Antonella Maselli
- Institute of Cognitive Sciences and Technology, National Research Council (CNR), Rome, Italy
| | - Pablo Lanillos
- Donders Institute for Brain, Cognition and Behaviour, Artificial Intelligence Department, Radboud University, Nijmegen, The Netherlands
| | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technology, National Research Council (CNR), Rome, Italy
- * E-mail:
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15
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Da Costa L, Friston K, Heins C, Pavliotis GA. Bayesian mechanics for stationary processes. Proc Math Phys Eng Sci 2022; 477:20210518. [PMID: 35153603 PMCID: PMC8652275 DOI: 10.1098/rspa.2021.0518] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/27/2021] [Indexed: 01/02/2023] Open
Abstract
This paper develops a Bayesian mechanics for adaptive systems. Firstly, we model the interface between a system and its environment with a Markov blanket. This affords conditions under which states internal to the blanket encode information about external states. Second, we introduce dynamics and represent adaptive systems as Markov blankets at steady state. This allows us to identify a wide class of systems whose internal states appear to infer external states, consistent with variational inference in Bayesian statistics and theoretical neuroscience. Finally, we partition the blanket into sensory and active states. It follows that active states can be seen as performing active inference and well-known forms of stochastic control (such as PID control), which are prominent formulations of adaptive behaviour in theoretical biology and engineering.
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Affiliation(s)
- Lancelot Da Costa
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK.,Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - Conor Heins
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz D-78457, Germany.,Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz D-78457, Germany.,Department of Biology, University of Konstanz, Konstanz D-78457, Germany
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16
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Safron A, Klimaj V, Hipólito I. On the Importance of Being Flexible: Dynamic Brain Networks and Their Potential Functional Significances. Front Syst Neurosci 2022; 15:688424. [PMID: 35126062 PMCID: PMC8814434 DOI: 10.3389/fnsys.2021.688424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 11/10/2021] [Indexed: 12/14/2022] Open
Abstract
In this theoretical review, we begin by discussing brains and minds from a dynamical systems perspective, and then go on to describe methods for characterizing the flexibility of dynamic networks. We discuss how varying degrees and kinds of flexibility may be adaptive (or maladaptive) in different contexts, specifically focusing on measures related to either more disjoint or cohesive dynamics. While disjointed flexibility may be useful for assessing neural entropy, cohesive flexibility may potentially serve as a proxy for self-organized criticality as a fundamental property enabling adaptive behavior in complex systems. Particular attention is given to recent studies in which flexibility methods have been used to investigate neurological and cognitive maturation, as well as the breakdown of conscious processing under varying levels of anesthesia. We further discuss how these findings and methods might be contextualized within the Free Energy Principle with respect to the fundamentals of brain organization and biological functioning more generally, and describe potential methodological advances from this paradigm. Finally, with relevance to computational psychiatry, we propose a research program for obtaining a better understanding of ways that dynamic networks may relate to different forms of psychological flexibility, which may be the single most important factor for ensuring human flourishing.
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Affiliation(s)
- Adam Safron
- Center for Psychedelic and Consciousness Research, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Kinsey Institute, Indiana University, Bloomington, IN, United States
- Cognitive Science Program, Indiana University, Bloomington, IN, United States
| | - Victoria Klimaj
- Cognitive Science Program, Indiana University, Bloomington, IN, United States
- Complex Networks and Systems, Informatics Department, Indiana University, Bloomington, IN, United States
| | - Inês Hipólito
- Department of Philosophy, Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
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17
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Palaniyappan L, Venkatasubramanian G. The Bayesian brain and cooperative communication in schizophrenia. J Psychiatry Neurosci 2022; 47:E48-E54. [PMID: 35135834 PMCID: PMC8834248 DOI: 10.1503/jpn.210231] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
- Lena Palaniyappan
- From the Department of Psychiatry, Schulich School of Medicine & Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robart Research Institute & Lawson Health Research Institute, London, Ont., Canada (Palaniyappan); and the InSTAR Program, Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bangalore, India (Venkatasubramanian)
| | - Ganesan Venkatasubramanian
- From the Department of Psychiatry, Schulich School of Medicine & Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robart Research Institute & Lawson Health Research Institute, London, Ont., Canada (Palaniyappan); and the InSTAR Program, Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bangalore, India (Venkatasubramanian)
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18
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Kiepe F, Kraus N, Hesselmann G. Sensory Attenuation in the Auditory Modality as a Window Into Predictive Processing. Front Hum Neurosci 2021; 15:704668. [PMID: 34803629 PMCID: PMC8602204 DOI: 10.3389/fnhum.2021.704668] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 10/14/2021] [Indexed: 11/23/2022] Open
Abstract
Self-generated auditory input is perceived less loudly than the same sounds generated externally. The existence of this phenomenon, called Sensory Attenuation (SA), has been studied for decades and is often explained by motor-based forward models. Recent developments in the research of SA, however, challenge these models. We review the current state of knowledge regarding theoretical implications about the significance of Sensory Attenuation and its role in human behavior and functioning. Focusing on behavioral and electrophysiological results in the auditory domain, we provide an overview of the characteristics and limitations of existing SA paradigms and highlight the problem of isolating SA from other predictive mechanisms. Finally, we explore different hypotheses attempting to explain heterogeneous empirical findings, and the impact of the Predictive Coding Framework in this research area.
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Affiliation(s)
- Fabian Kiepe
- Psychologische Hochschule Berlin (PHB), Berlin Psychological University, Berlin, Germany
| | - Nils Kraus
- Psychologische Hochschule Berlin (PHB), Berlin Psychological University, Berlin, Germany
| | - Guido Hesselmann
- Psychologische Hochschule Berlin (PHB), Berlin Psychological University, Berlin, Germany
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19
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Parr T, Pezzulo G. Understanding, Explanation, and Active Inference. Front Syst Neurosci 2021; 15:772641. [PMID: 34803619 PMCID: PMC8602880 DOI: 10.3389/fnsys.2021.772641] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 10/15/2021] [Indexed: 11/13/2022] Open
Abstract
While machine learning techniques have been transformative in solving a range of problems, an important challenge is to understand why they arrive at the decisions they output. Some have argued that this necessitates augmenting machine intelligence with understanding such that, when queried, a machine is able to explain its behaviour (i.e., explainable AI). In this article, we address the issue of machine understanding from the perspective of active inference. This paradigm enables decision making based upon a model of how data are generated. The generative model contains those variables required to explain sensory data, and its inversion may be seen as an attempt to explain the causes of these data. Here we are interested in explanations of one's own actions. This implies a deep generative model that includes a model of the world, used to infer policies, and a higher-level model that attempts to predict which policies will be selected based upon a space of hypothetical (i.e., counterfactual) explanations-and which can subsequently be used to provide (retrospective) explanations about the policies pursued. We illustrate the construct validity of this notion of understanding in relation to human understanding by highlighting the similarities in computational architecture and the consequences of its dysfunction.
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Affiliation(s)
- Thomas Parr
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
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20
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Parr T, Da Costa L, Heins C, Ramstead MJD, Friston KJ. Memory and Markov Blankets. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1105. [PMID: 34573730 PMCID: PMC8469145 DOI: 10.3390/e23091105] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 08/20/2021] [Accepted: 08/22/2021] [Indexed: 12/11/2022]
Abstract
In theoretical biology, we are often interested in random dynamical systems-like the brain-that appear to model their environments. This can be formalized by appealing to the existence of a (possibly non-equilibrium) steady state, whose density preserves a conditional independence between a biological entity and its surroundings. From this perspective, the conditioning set, or Markov blanket, induces a form of vicarious synchrony between creature and world-as if one were modelling the other. However, this results in an apparent paradox. If all conditional dependencies between a system and its surroundings depend upon the blanket, how do we account for the mnemonic capacity of living systems? It might appear that any shared dependence upon past blanket states violates the independence condition, as the variables on either side of the blanket now share information not available from the current blanket state. This paper aims to resolve this paradox, and to demonstrate that conditional independence does not preclude memory. Our argument rests upon drawing a distinction between the dependencies implied by a steady state density, and the density dynamics of the system conditioned upon its configuration at a previous time. The interesting question then becomes: What determines the length of time required for a stochastic system to 'forget' its initial conditions? We explore this question for an example system, whose steady state density possesses a Markov blanket, through simple numerical analyses. We conclude with a discussion of the relevance for memory in cognitive systems like us.
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Affiliation(s)
- Thomas Parr
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK; (L.D.C.); (M.J.D.R.); (K.J.F.)
| | - Lancelot Da Costa
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK; (L.D.C.); (M.J.D.R.); (K.J.F.)
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK
| | - Conor Heins
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, D-78457 Konstanz, Germany;
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, D-78457 Konstanz, Germany
- Department of Biology, University of Konstanz, D-78457 Konstanz, Germany
- Nested Minds Network, London EC4A 3TW, UK
| | - Maxwell James D. Ramstead
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK; (L.D.C.); (M.J.D.R.); (K.J.F.)
- Nested Minds Network, London EC4A 3TW, UK
- Spatial Web Foundation, Los Angeles, CA 90016, USA
- Division of Social and Transcultural Psychiatry, Department of Psychiatry, McGill University, Montreal, QC H3A 1A1, Canada
| | - Karl J. Friston
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK; (L.D.C.); (M.J.D.R.); (K.J.F.)
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21
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Safron A. The Radically Embodied Conscious Cybernetic Bayesian Brain: From Free Energy to Free Will and Back Again. ENTROPY (BASEL, SWITZERLAND) 2021; 23:783. [PMID: 34202965 PMCID: PMC8234656 DOI: 10.3390/e23060783] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 05/12/2021] [Accepted: 05/27/2021] [Indexed: 11/24/2022]
Abstract
Drawing from both enactivist and cognitivist perspectives on mind, I propose that explaining teleological phenomena may require reappraising both "Cartesian theaters" and mental homunculi in terms of embodied self-models (ESMs), understood as body maps with agentic properties, functioning as predictive-memory systems and cybernetic controllers. Quasi-homuncular ESMs are suggested to constitute a major organizing principle for neural architectures due to their initial and ongoing significance for solutions to inference problems in cognitive (and affective) development. Embodied experiences provide foundational lessons in learning curriculums in which agents explore increasingly challenging problem spaces, so answering an unresolved question in Bayesian cognitive science: what are biologically plausible mechanisms for equipping learners with sufficiently powerful inductive biases to adequately constrain inference spaces? Drawing on models from neurophysiology, psychology, and developmental robotics, I describe how embodiment provides fundamental sources of empirical priors (as reliably learnable posterior expectations). If ESMs play this kind of foundational role in cognitive development, then bidirectional linkages will be found between all sensory modalities and frontal-parietal control hierarchies, so infusing all senses with somatic-motoric properties, thereby structuring all perception by relevant affordances, so solving frame problems for embodied agents. Drawing upon the Free Energy Principle and Active Inference framework, I describe a particular mechanism for intentional action selection via consciously imagined (and explicitly represented) goal realization, where contrasts between desired and present states influence ongoing policy selection via predictive coding mechanisms and backward-chained imaginings (as self-realizing predictions). This embodied developmental legacy suggests a mechanism by which imaginings can be intentionally shaped by (internalized) partially-expressed motor acts, so providing means of agentic control for attention, working memory, imagination, and behavior. I further describe the nature(s) of mental causation and self-control, and also provide an account of readiness potentials in Libet paradigms wherein conscious intentions shape causal streams leading to enaction. Finally, I provide neurophenomenological handlings of prototypical qualia including pleasure, pain, and desire in terms of self-annihilating free energy gradients via quasi-synesthetic interoceptive active inference. In brief, this manuscript is intended to illustrate how radically embodied minds may create foundations for intelligence (as capacity for learning and inference), consciousness (as somatically-grounded self-world modeling), and will (as deployment of predictive models for enacting valued goals).
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Affiliation(s)
- Adam Safron
- Center for Psychedelic and Consciousness Research, Johns Hopkins University School of Medicine, Baltimore, MD 21218, USA;
- Kinsey Institute, Indiana University, Bloomington, IN 47405, USA
- Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA
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22
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Parr T. Message Passing and Metabolism. ENTROPY (BASEL, SWITZERLAND) 2021; 23:606. [PMID: 34068913 PMCID: PMC8156486 DOI: 10.3390/e23050606] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/09/2021] [Accepted: 05/10/2021] [Indexed: 11/16/2022]
Abstract
Active inference is an increasingly prominent paradigm in theoretical biology. It frames the dynamics of living systems as if they were solving an inference problem. This rests upon their flow towards some (non-equilibrium) steady state-or equivalently, their maximisation of the Bayesian model evidence for an implicit probabilistic model. For many models, these self-evidencing dynamics manifest as messages passed among elements of a system. Such messages resemble synaptic communication at a neuronal network level but could also apply to other network structures. This paper attempts to apply the same formulation to biochemical networks. The chemical computation that occurs in regulation of metabolism relies upon sparse interactions between coupled reactions, where enzymes induce conditional dependencies between reactants. We will see that these reactions may be viewed as the movement of probability mass between alternative categorical states. When framed in this way, the master equations describing such systems can be reformulated in terms of their steady-state distribution. This distribution plays the role of a generative model, affording an inferential interpretation of the underlying biochemistry. Finally, we see that-in analogy with computational neurology and psychiatry-metabolic disorders may be characterized as false inference under aberrant prior beliefs.
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Affiliation(s)
- Thomas Parr
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
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23
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Parr T, Sajid N, Da Costa L, Mirza MB, Friston KJ. Generative Models for Active Vision. Front Neurorobot 2021; 15:651432. [PMID: 33927605 PMCID: PMC8076554 DOI: 10.3389/fnbot.2021.651432] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 03/15/2021] [Indexed: 11/13/2022] Open
Abstract
The active visual system comprises the visual cortices, cerebral attention networks, and oculomotor system. While fascinating in its own right, it is also an important model for sensorimotor networks in general. A prominent approach to studying this system is active inference-which assumes the brain makes use of an internal (generative) model to predict proprioceptive and visual input. This approach treats action as ensuring sensations conform to predictions (i.e., by moving the eyes) and posits that visual percepts are the consequence of updating predictions to conform to sensations. Under active inference, the challenge is to identify the form of the generative model that makes these predictions-and thus directs behavior. In this paper, we provide an overview of the generative models that the brain must employ to engage in active vision. This means specifying the processes that explain retinal cell activity and proprioceptive information from oculomotor muscle fibers. In addition to the mechanics of the eyes and retina, these processes include our choices about where to move our eyes. These decisions rest upon beliefs about salient locations, or the potential for information gain and belief-updating. A key theme of this paper is the relationship between "looking" and "seeing" under the brain's implicit generative model of the visual world.
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Affiliation(s)
- Thomas Parr
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, London, United Kingdom
| | - Noor Sajid
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, London, United Kingdom
| | - Lancelot Da Costa
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, London, United Kingdom
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - M. Berk Mirza
- Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Karl J. Friston
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, London, United Kingdom
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