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von Werder D, Regnath F, Schäfer D, Jörres R, Lehnen N, Glasauer S. Post-COVID breathlessness: a mathematical model of respiratory processing in the brain. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-023-01739-y. [PMID: 38502207 DOI: 10.1007/s00406-023-01739-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 12/11/2023] [Indexed: 03/21/2024]
Abstract
Breathlessness is among the most common post-COVID symptoms. In a considerable number of patients, severe breathlessness cannot be explained by peripheral organ impairment. Recent concepts have described how such persistent breathlessness could arise from dysfunctional processing of respiratory information in the brain. In this paper, we present a first quantitative and testable mathematical model of how processing of respiratory-related signals could lead to breathlessness perception. The model is based on recent theories that the brain holds an adaptive and dynamic internal representation of a respiratory state that is based on previous experiences and comprises gas exchange between environment, lung and tissue cells. Perceived breathlessness reflects the brain's estimate of this respiratory state signaling a potentially hazardous disequilibrium in gas exchange. The internal respiratory state evolves from the respiratory state of the last breath, is updated by a sensory measurement of CO2 concentration, and is dependent on the current activity context. To evaluate our model and thus test the assumed mechanism, we used data from an ongoing rebreathing experiment investigating breathlessness in patients with post-COVID without peripheral organ dysfunction (N = 5) and healthy control participants without complaints after COVID-19 (N = 5). Although the observed breathlessness patterns varied extensively between individual participants in the rebreathing experiment, our model shows good performance in replicating these individual, heterogeneous time courses. The model assumes the same underlying processes in the central nervous system in all individuals, i.e., also between patients and healthy control participants, and we hypothesize that differences in breathlessness are explained by different weighting and thus influence of these processes on the final percept. Our model could thus be applied in future studies to provide insight into where in the processing cascade of respiratory signals a deficit is located that leads to (post-COVID) breathlessness. A potential clinical application could be, e.g., the monitoring of effects of pulmonary rehabilitation on respiratory processing in the brain to improve the therapeutic strategies.
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Affiliation(s)
- Dina von Werder
- Institute of Medical Technology, Brandenburg University of Technology Cottbus-Senftenberg, Lipezker Strasse 47, 03048, Cottbus, Germany.
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, Munich, Germany.
- Klinikum rechts der Isar, Department of Psychosomatic Medicine and Psychotherapy, Technical University Munich, Munich, Germany.
| | - Franziska Regnath
- Klinikum rechts der Isar, Department of Psychosomatic Medicine and Psychotherapy, Technical University Munich, Munich, Germany
- TUM Graduate School, Faculty of Sport and Health Sciences, Technical University Munich, Munich, Germany
| | - Daniel Schäfer
- Klinikum rechts der Isar, Department of Psychosomatic Medicine and Psychotherapy, Technical University Munich, Munich, Germany
| | - Rudolf Jörres
- Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU, Munich, Germany
- Comprehensive Pneumology Center Munich (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Nadine Lehnen
- Institute of Medical Technology, Brandenburg University of Technology Cottbus-Senftenberg, Lipezker Strasse 47, 03048, Cottbus, Germany
- Klinikum rechts der Isar, Department of Psychosomatic Medicine and Psychotherapy, Technical University Munich, Munich, Germany
| | - Stefan Glasauer
- Institute of Medical Technology, Brandenburg University of Technology Cottbus-Senftenberg, Lipezker Strasse 47, 03048, Cottbus, Germany
- Faculty of Health Sciences Brandenburg, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany
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Stubbs G, Friston K. The police hunch: the Bayesian brain, active inference, and the free energy principle in action. Front Psychol 2024; 15:1368265. [PMID: 38510309 PMCID: PMC10951090 DOI: 10.3389/fpsyg.2024.1368265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 02/22/2024] [Indexed: 03/22/2024] Open
Abstract
In the realm of law enforcement, the "police hunch" has long been a mysterious but crucial aspect of decision-making. Drawing on the developing framework of Active Inference from cognitive science, this theoretical article examines the genesis, mechanics, and implications of the police hunch. It argues that hunches - often vital in high-stakes situations - should not be described as mere intuitions, but as intricate products of our mind's generative models. These models, shaped by observations of the social world and assimilated and enacted through active inference, seek to reduce surprise and make hunches an indispensable tool for officers, in exactly the same way that hypotheses are indispensable for scientists. However, the predictive validity of hunches is influenced by a range of factors, including experience and bias, thus warranting critical examination of their reliability. This article not only explores the formation of police hunches but also provides practical insights for officers and researchers on how to harness the power of active inference to fully understand policing decisions and subsequently explore new avenues for future research.
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Affiliation(s)
| | - Karl Friston
- Institute of Neurology, University College London, London, United Kingdom
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Schilling A, Sedley W, Gerum R, Metzner C, Tziridis K, Maier A, Schulze H, Zeng FG, Friston KJ, Krauss P. Predictive coding and stochastic resonance as fundamental principles of auditory phantom perception. Brain 2023; 146:4809-4825. [PMID: 37503725 PMCID: PMC10690027 DOI: 10.1093/brain/awad255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 06/27/2023] [Accepted: 07/15/2023] [Indexed: 07/29/2023] Open
Abstract
Mechanistic insight is achieved only when experiments are employed to test formal or computational models. Furthermore, in analogy to lesion studies, phantom perception may serve as a vehicle to understand the fundamental processing principles underlying healthy auditory perception. With a special focus on tinnitus-as the prime example of auditory phantom perception-we review recent work at the intersection of artificial intelligence, psychology and neuroscience. In particular, we discuss why everyone with tinnitus suffers from (at least hidden) hearing loss, but not everyone with hearing loss suffers from tinnitus. We argue that intrinsic neural noise is generated and amplified along the auditory pathway as a compensatory mechanism to restore normal hearing based on adaptive stochastic resonance. The neural noise increase can then be misinterpreted as auditory input and perceived as tinnitus. This mechanism can be formalized in the Bayesian brain framework, where the percept (posterior) assimilates a prior prediction (brain's expectations) and likelihood (bottom-up neural signal). A higher mean and lower variance (i.e. enhanced precision) of the likelihood shifts the posterior, evincing a misinterpretation of sensory evidence, which may be further confounded by plastic changes in the brain that underwrite prior predictions. Hence, two fundamental processing principles provide the most explanatory power for the emergence of auditory phantom perceptions: predictive coding as a top-down and adaptive stochastic resonance as a complementary bottom-up mechanism. We conclude that both principles also play a crucial role in healthy auditory perception. Finally, in the context of neuroscience-inspired artificial intelligence, both processing principles may serve to improve contemporary machine learning techniques.
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Affiliation(s)
- Achim Schilling
- Neuroscience Lab, University Hospital Erlangen, 91054 Erlangen, Germany
- Cognitive Computational Neuroscience Group, University Erlangen-Nürnberg, 91058 Erlangen, Germany
| | - William Sedley
- Translational and Clinical Research Institute, Newcastle University Medical School, Newcastle upon Tyne NE2 4HH, UK
| | - Richard Gerum
- Cognitive Computational Neuroscience Group, University Erlangen-Nürnberg, 91058 Erlangen, Germany
- Department of Physics and Astronomy and Center for Vision Research, York University, Toronto, ON M3J 1P3, Canada
| | - Claus Metzner
- Neuroscience Lab, University Hospital Erlangen, 91054 Erlangen, Germany
| | | | - Andreas Maier
- Pattern Recognition Lab, University Erlangen-Nürnberg, 91058 Erlangen, Germany
| | - Holger Schulze
- Neuroscience Lab, University Hospital Erlangen, 91054 Erlangen, Germany
| | - Fan-Gang Zeng
- Center for Hearing Research, Departments of Anatomy and Neurobiology, Biomedical Engineering, Cognitive Sciences, Otolaryngology–Head and Neck Surgery, University of California Irvine, Irvine, CA 92697, USA
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Patrick Krauss
- Neuroscience Lab, University Hospital Erlangen, 91054 Erlangen, Germany
- Cognitive Computational Neuroscience Group, University Erlangen-Nürnberg, 91058 Erlangen, Germany
- Pattern Recognition Lab, University Erlangen-Nürnberg, 91058 Erlangen, Germany
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Bottemanne H, Berkovitch L, Gauld C, Balcerac A, Schmidt L, Mouchabac S, Fossati P. Storm on predictive brain: A neurocomputational account of ketamine antidepressant effect. Neurosci Biobehav Rev 2023; 154:105410. [PMID: 37793581 DOI: 10.1016/j.neubiorev.2023.105410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 08/24/2023] [Accepted: 09/26/2023] [Indexed: 10/06/2023]
Abstract
For the past decade, ketamine, an N-methyl-D-aspartate receptor (NMDAr) antagonist, has been considered a promising treatment for major depressive disorder (MDD). Unlike the delayed effect of monoaminergic treatment, ketamine may produce fast-acting antidepressant effects hours after a single administration at subanesthetic dose. Along with these antidepressant effects, it may also induce transient dissociative (disturbing of the sense of self and reality) symptoms during acute administration which resolve within hours. To understand ketamine's rapid-acting antidepressant effect, several biological hypotheses have been explored, but despite these promising avenues, there is a lack of model to understand the timeframe of antidepressant and dissociative effects of ketamine. In this article, we propose a neurocomputational account of ketamine's antidepressant and dissociative effects based on the Predictive Processing (PP) theory, a framework for cognitive and sensory processing. PP theory suggests that the brain produces top-down predictions to process incoming sensory signals, and generates bottom-up prediction errors (PEs) which are then used to update predictions. This iterative dynamic neural process would relies on N-methyl-D-aspartate (NMDAr) and α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic receptors (AMPAr), two major component of the glutamatergic signaling. Furthermore, it has been suggested that MDD is characterized by over-rigid predictions which cannot be updated by the PEs, leading to miscalibration of hierarchical inference and self-reinforcing negative feedback loops. Based on former empirical studies using behavioral paradigms, neurophysiological recordings, and computational modeling, we suggest that ketamine impairs top-down predictions by blocking NMDA receptors, and enhances presynaptic glutamate release and PEs, producing transient dissociative symptoms and fast-acting antidepressant effect in hours following acute administration. Moreover, we present data showing that ketamine may enhance a delayed neural plasticity pathways through AMPAr potentiation, triggering a prolonged antidepressant effect up to seven days for unique administration. Taken together, the two sides of antidepressant effects with distinct timeframe could constitute the keystone of antidepressant properties of ketamine. These PP disturbances may also participate to a ketamine-induced time window of mental flexibility, which can be used to improve the psychotherapeutic process. Finally, these proposals could be used as a theoretical framework for future research into fast-acting antidepressants, and combination with existing antidepressant and psychotherapy.
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Affiliation(s)
- Hugo Bottemanne
- Paris Brain Institute - Institut du Cerveau (ICM), UMR 7225 / UMRS 1127, Sorbonne University / CNRS / INSERM, Paris, France; Sorbonne University, Department of Philosophy, Science Norm Democracy Research Unit, UMR, 8011, Paris, France; Sorbonne University, Department of Psychiatry, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France.
| | - Lucie Berkovitch
- Saclay CEA Centre, Neurospin, Gif-Sur-Yvette Cedex, France; Department of Psychiatry, GHU Paris Psychiatrie et Neurosciences, Service Hospitalo-Universitaire, Paris, France
| | - Christophe Gauld
- Department of Child Psychiatry, CHU de Lyon, F-69000 Lyon, France; Institut des Sciences Cognitives Marc Jeannerod, UMR 5229 CNRS & Université Claude Bernard Lyon 1, F-69000 Lyon, France
| | - Alexander Balcerac
- Paris Brain Institute - Institut du Cerveau (ICM), UMR 7225 / UMRS 1127, Sorbonne University / CNRS / INSERM, Paris, France; Sorbonne University, Department of Neurology, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France
| | - Liane Schmidt
- Paris Brain Institute - Institut du Cerveau (ICM), UMR 7225 / UMRS 1127, Sorbonne University / CNRS / INSERM, Paris, France
| | - Stephane Mouchabac
- Paris Brain Institute - Institut du Cerveau (ICM), UMR 7225 / UMRS 1127, Sorbonne University / CNRS / INSERM, Paris, France; Sorbonne University, Department of Psychiatry, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France
| | - Philippe Fossati
- Paris Brain Institute - Institut du Cerveau (ICM), UMR 7225 / UMRS 1127, Sorbonne University / CNRS / INSERM, Paris, France; Sorbonne University, Department of Philosophy, Science Norm Democracy Research Unit, UMR, 8011, Paris, France
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Pagnini F, Barbiani D, Cavalera C, Volpato E, Grosso F, Minazzi GA, Vailati Riboni F, Graziano F, Di Tella S, Manzoni GM, Silveri MC, Riva G, Phillips D. Placebo and Nocebo Effects as Bayesian-Brain Phenomena: The Overlooked Role of Likelihood and Attention. Perspect Psychol Sci 2023; 18:1217-1229. [PMID: 36656800 DOI: 10.1177/17456916221141383] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The Bayesian-brain framework applied to placebo responses and other mind-body interactions suggests that the effects on the body result from the interaction between priors, such as expectations and learning, and likelihood, such as somatosensorial information. Significant research in this area focuses on the role of the priors, but the relevance of the likelihood has been surprisingly overlooked. One way of manipulating the relevance of the likelihood is by paying attention to sensorial information. We suggest that attention can influence both precision and position (i.e., the relative distance from the priors) of the likelihood by focusing on specific components of the somatosensorial information. Two forms of attention seem particularly relevant in this framework: mindful attention and selective attention. Attention has the potential to be considered a "major player" in placebo/nocebo research, together with expectations and learning. In terms of application, relying on attentional strategies as "amplifiers" or "silencers" of sensorial information could lead to an active involvement of individuals in shaping their care process and health. In this contribution, we discuss the theoretical implications of these intuitions with the aim to provide a comprehensive framework that includes Bayesian brain, placebo/nocebo effects, and the role of attention in mind-body interactions.
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Affiliation(s)
| | - Diletta Barbiani
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona
| | - Cesare Cavalera
- Department of Psychology, Università Cattolica del Sacro Cuore
| | - Eleonora Volpato
- Department of Psychology, Università Cattolica del Sacro Cuore
- IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy
| | | | | | | | - Francesca Graziano
- Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center, University of Milano-Bicocca
- School of Medicine and Surgery, University of Milano
| | - Sonia Di Tella
- Department of Psychology, Università Cattolica del Sacro Cuore
| | | | | | - Giuseppe Riva
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano IRCCS
- Humane Technology Lab., Catholic University of Milan
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Santos D, Agostinho M, Treister R, Canaipa R. Correlations between within-subject variability of pain intensity reports and rubber hand illusion proprioceptive drift. Neurosci Lett 2023; 810:137319. [PMID: 37276916 DOI: 10.1016/j.neulet.2023.137319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 05/29/2023] [Accepted: 05/31/2023] [Indexed: 06/07/2023]
Abstract
INTRODUCTION Consistent with the Bayesian brain hypothesis, the within-subject variability of pain intensity reports as captured with the Focused Analgesia Selection Test (FAST) might be a surrogate measure of the certainty in ascending noxious signals. The outcomes of a non-pain-related task, the rubber hand illusion, were hypothesized to reflect the same construct. This study aimed to explore whether within-subject differences in variability of pain intensity reports and the outcomes of the rubber hand illusion might be related. METHODS Nonclinical participants underwent the classic rubber hand illusion under synchronous (experimental) and asynchronous (control) conditions. Two outcomes were assessed: proprioceptive drift and feeling of ownership. Thereafter, participants underwent the FAST to assess the within-subject variability of pain reports in response to heat stimuli. Intraclass correlation (ICC) and the correlation coefficient (R2) were the main outcomes. Spearman's correlations were used to assess associations between the outcomes of the 2 tasks. RESULTS Thirty-six volunteers completed the study. Both FAST outcomes-ICC (Spearman's r = 0.355, p = 0.033) and R2 (Spearman's r = 0.349, p = 0.037)-were positively correlated with proprioceptive drift in the synchronous but not asynchronous conditions (p > 0.05). The subjective feeling of ownership and FAST outcomes did not correlate (p > 0.05). CONCLUSIONS The associations between the 2 tasks' outcomes imply that both tasks at least partly assess similar constructs. Current knowledge suggests that this construct represents the person's certainty in perceiving ascending sensory signals, or, in Bayesian terminology, the certainty of the likelihood.
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Affiliation(s)
- Duarte Santos
- Institute of Health Sciences, Universidade Católica Portuguesa, Lisbon, Portugal
| | - Mariana Agostinho
- CIIS, Centre for Interdisciplinary Health Research, Institute of Health Sciences, Universidade Católica Portuguesa, Lisbon, Portugal; The Cheryl Spencer Department of Nursing, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel
| | - Roi Treister
- The Cheryl Spencer Department of Nursing, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel
| | - Rita Canaipa
- CIIS, Centre for Interdisciplinary Health Research, Institute of Health Sciences, Universidade Católica Portuguesa, Lisbon, Portugal.
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Visalli A, Capizzi M, Ambrosini E, Kopp B, Vallesi A. P3-like signatures of temporal predictions: a computational EEG study. Exp Brain Res 2023:10.1007/s00221-023-06656-z. [PMID: 37354350 DOI: 10.1007/s00221-023-06656-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 06/18/2023] [Indexed: 06/26/2023]
Abstract
Many cognitive processes, ranging from perception to action, depend on the ability to predict the timing of forthcoming events. Yet, how the brain uses predictive models in the temporal domain is still an unsolved question. In previous work, we began to explore the neural correlates of temporal predictions by using a computational approach in which an ideal Bayesian observer learned the temporal probabilities of target onsets in a simple reaction time task. Because the task was specifically designed to disambiguate updating of predictive models and surprise, changes in temporal probabilities were explicitly cued. However, in the real world, we are usually incidentally exposed to changes in the statistics of the environment. Here, we thus aimed to further investigate the electroencephalographic (EEG) correlates of Bayesian belief updating and surprise associated with incidental learning of temporal probabilities. In line with our previous EEG study, results showed distinct P3-like modulations for updating and surprise. While surprise was indexed by an early fronto-central P3-like modulation, updating was associated with a later and more posterior P3 modulation. Moreover, updating was associated with a P2-like potential at centro-parietal electrodes, likely capturing integration processes between prior beliefs and likelihood of the observed event. These findings support previous evidence of trial-by-trial variability of P3 amplitudes as an index of dissociable inferential processes. Coupled with our previous findings, the present study strongly bolsters the view of the P3 as a key brain signature of temporal Bayesian inference. Data and scripts are shared on OSF: osf.io/sdy8j/.
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Affiliation(s)
- Antonino Visalli
- Department of Neuroscience, University of Padova, 35121, Padua, Italy.
- Padova Neuroscience Center, University of Padova, Padua, Italy.
- IRCCS San Camillo Hospital, 30126, Venice, Italy.
| | - M Capizzi
- Brain and Behavior Research Center (CIMCYC), Department of Experimental Psychology, University of Granada, Granada, Spain
| | - E Ambrosini
- Department of Neuroscience, University of Padova, 35121, Padua, Italy
- Padova Neuroscience Center, University of Padova, Padua, Italy
- Department of General Psychology, University of Padova, Padua, Italy
| | - B Kopp
- Department of Neurology, Hannover Medical School, 30625, Hannover, Germany
| | - Antonino Vallesi
- Department of Neuroscience, University of Padova, 35121, Padua, Italy.
- Padova Neuroscience Center, University of Padova, Padua, Italy.
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Kim CS. Free energy and inference in living systems. Interface Focus 2023; 13:20220041. [PMID: 37065269 PMCID: PMC10102732 DOI: 10.1098/rsfs.2022.0041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 01/18/2023] [Indexed: 04/18/2023] Open
Abstract
Organisms are non-equilibrium, stationary systems self-organized via spontaneous symmetry breaking and undergoing metabolic cycles with broken detailed balance in the environment. The thermodynamic free-energy (FE) principle describes an organism's homeostasis as the regulation of biochemical work constrained by the physical FE cost. By contrast, recent research in neuroscience and theoretical biology explains a higher organism's homeostasis and allostasis as Bayesian inference facilitated by the informational FE. As an integrated approach to living systems, this study presents an FE minimization theory overarching the essential features of both the thermodynamic and neuroscientific FE principles. Our results reveal that the perception and action of animals result from active inference entailed by FE minimization in the brain, and the brain operates as a Schrödinger's machine conducting the neural mechanics of minimizing sensory uncertainty. A parsimonious model suggests that the Bayesian brain develops the optimal trajectories in neural manifolds and induces a dynamic bifurcation between neural attractors in the process of active inference.
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Affiliation(s)
- Chang Sub Kim
- Department of Physics, Chonnam National University, Gwangju 61186, Republic of Korea
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Bottemanne H, Joly L. [Mother brain: Bayesian theory of maternal interoception during pregnancy and postpartum]. Encephale 2023; 49:185-195. [PMID: 36243551 DOI: 10.1016/j.encep.2022.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 05/28/2022] [Accepted: 05/30/2022] [Indexed: 11/05/2022]
Abstract
The perinatal period, including pregnancy and postpartum, causes major morphological, endocrinal, and thermal transitions in women. As the fetus grows, abdominal muscle fibers stretch, internal organs such as the bladder or colon move, and the uterine anatomy changes. Many of these changes involve interoception, the perception of internal body signals such as muscle and visceral sensations. Despite the importance of these interoceptive signals, few studies have explored perinatal interoception. We propose an innovative theory of maternal interoception based on recent findings in neuroscience. We show that interoceptive signals processing during pregnancy is crucial for understanding perinatal phenomenology and psychopathology, such as maternal perception of fetal movements, maternal-infant bonding, denial of pregnancy, phantom fetal movements after childbirth, pseudocyesis or even puerperal delusion. Knowing the importance of these interoceptive mechanisms, clinicians in obstetrics, gynecology and mental health should be particularly vigilant to maternal interoception during the perinatal period.
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Affiliation(s)
- Hugo Bottemanne
- Department of Psychiatry, Sorbonne University, Pitié-Salpêtrière Hospital, DMU Neuroscience, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France; Paris Brain Institute - Institut du Cerveau (ICM), Department of Neuroscience, UMR 7225/UMRS 1127, Sorbonne University/CNRS/INSERM, Paris, France; Sorbonne University, Department of Philosophy, SND Research Unit, UMR 8011, CNRS, Paris, France.
| | - Lucie Joly
- Department of Psychiatry, Sorbonne University, Pitié-Salpêtrière Hospital, DMU Neuroscience, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France; Paris Brain Institute - Institut du Cerveau (ICM), Department of Neuroscience, UMR 7225/UMRS 1127, Sorbonne University/CNRS/INSERM, Paris, France
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10
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Soda T, Ahmadi A, Tani J, Honda M, Hanakawa T, Yamashita Y. Simulating developmental diversity: Impact of neural stochasticity on atypical flexibility and hierarchy. Front Psychiatry 2023; 14:1080668. [PMID: 37009124 PMCID: PMC10050443 DOI: 10.3389/fpsyt.2023.1080668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 02/21/2023] [Indexed: 03/17/2023] Open
Abstract
Introduction Investigating the pathological mechanisms of developmental disorders is a challenge because the symptoms are a result of complex and dynamic factors such as neural networks, cognitive behavior, environment, and developmental learning. Recently, computational methods have started to provide a unified framework for understanding developmental disorders, enabling us to describe the interactions among those multiple factors underlying symptoms. However, this approach is still limited because most studies to date have focused on cross-sectional task performance and lacked the perspectives of developmental learning. Here, we proposed a new research method for understanding the mechanisms of the acquisition and its failures in hierarchical Bayesian representations using a state-of-the-art computational model, referred to as in silico neurodevelopment framework for atypical representation learning. Methods Simple simulation experiments were conducted using the proposed framework to examine whether manipulating the neural stochasticity and noise levels in external environments during the learning process can lead to the altered acquisition of hierarchical Bayesian representation and reduced flexibility. Results Networks with normal neural stochasticity acquired hierarchical representations that reflected the underlying probabilistic structures in the environment, including higher-order representation, and exhibited good behavioral and cognitive flexibility. When the neural stochasticity was high during learning, top-down generation using higher-order representation became atypical, although the flexibility did not differ from that of the normal stochasticity settings. However, when the neural stochasticity was low in the learning process, the networks demonstrated reduced flexibility and altered hierarchical representation. Notably, this altered acquisition of higher-order representation and flexibility was ameliorated by increasing the level of noises in external stimuli. Discussion These results demonstrated that the proposed method assists in modeling developmental disorders by bridging between multiple factors, such as the inherent characteristics of neural dynamics, acquisitions of hierarchical representation, flexible behavior, and external environment.
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Affiliation(s)
- Takafumi Soda
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
- Department of NCNP Brain Physiology and Pathology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | | | - Jun Tani
- Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Manabu Honda
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Takashi Hanakawa
- Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yuichi Yamashita
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
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Reina A, Bose T, Srivastava V, Marshall JAR. Asynchrony rescues statistically optimal group decisions from information cascades through emergent leaders. R Soc Open Sci 2023; 10:230175. [PMID: 36938538 PMCID: PMC10014242 DOI: 10.1098/rsos.230175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
It is usually assumed that information cascades are most likely to occur when an early but incorrect opinion spreads through the group. Here, we analyse models of confidence-sharing in groups and reveal the opposite result: simple but plausible models of naive-Bayesian decision-making exhibit information cascades when group decisions are synchronous; however, when group decisions are asynchronous, the early decisions reached by Bayesian decision-makers tend to be correct and dominate the group consensus dynamics. Thus early decisions actually rescue the group from making errors, rather than contribute to it. We explore the likely realism of our assumed decision-making rule with reference to the evolution of mechanisms for aggregating social information, and known psychological and neuroscientific mechanisms.
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Affiliation(s)
- Andreagiovanni Reina
- Institute for Interdisciplinary Studies on Artificial Intelligence (IRIDIA), Université Libre de Bruxelles, Brussels 1050, Belgium
- Department of Computer Science, University of Sheffield, Sheffield, S1 4DP, UK
| | - Thomas Bose
- Department of Computer Science, University of Sheffield, Sheffield, S1 4DP, UK
| | - Vaibhav Srivastava
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824-1226, USA
| | - James A. R. Marshall
- Department of Computer Science, University of Sheffield, Sheffield, S1 4DP, UK
- Opteran Technologies Limited, Sheffield, UK
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12
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Angeletos Chrysaitis N, Seriès P. 10 years of Bayesian theories of autism: A comprehensive review. Neurosci Biobehav Rev 2023; 145:105022. [PMID: 36581168 DOI: 10.1016/j.neubiorev.2022.105022] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 12/24/2022] [Indexed: 12/27/2022]
Abstract
Ten years ago, Pellicano and Burr published one of the most influential articles in the study of autism spectrum disorders, linking them to aberrant Bayesian inference processes in the brain. In particular, they proposed that autistic individuals are less influenced by their brains' prior beliefs about the environment. In this systematic review, we investigate if this theory is supported by the experimental evidence. To that end, we collect all studies which included comparisons across diagnostic groups or autistic traits and categorise them based on the investigated priors. Our results are highly mixed, with a slight majority of studies finding no difference in the integration of Bayesian priors. We find that priors developed during the experiments exhibited reduced influences more frequently than priors acquired previously, with various studies providing evidence for learning differences between participant groups. Finally, we focus on the methodological and computational aspects of the included studies, showing low statistical power and often inconsistent approaches. Based on our findings, we propose guidelines for future research.
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Affiliation(s)
- Nikitas Angeletos Chrysaitis
- Institute for Adaptive and Neural Computation, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, United Kingdom.
| | - Peggy Seriès
- Institute for Adaptive and Neural Computation, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, United Kingdom.
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13
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Abstract
Ketamine is an N-methyl-d-aspartate antagonist which is increasingly being researched and used as a treatment for depression. In low doses, it can cause a transitory modification in consciousness which was classically labelled as 'dissociation'. However, ketamine is also commonly classified as an atypical psychedelic and it has been recently reported that ego dissolution experiences during ketamine administration are associated with greater antidepressant response. Neuroimaging studies have highlighted several similarities between the effects of ketamine and those of serotonergic psychedelics in the brain; however, no unified account has been proposed for ketamine's multi-level effects - from molecular to network and psychological levels. Here, we propose that the fast, albeit transient, antidepressant effects observed after ketamine infusions are mainly driven by its acute modulation of reward circuits and sub-acute increase in neuroplasticity, while its dissociative and psychedelic properties are driven by dose- and context-dependent disruption of large-scale functional networks. Computationally, as nodes of the salience network (SN) represent high-level priors about the body ('minimal' self) and nodes of the default-mode network (DMN) represent the highest-level priors about narrative self-experience ('biographical' self), we propose that transitory SN desegregation and disintegration accounts for ketamine's 'dissociative' state, while transitory DMN desegregation and disintegration accounts for ketamine's 'psychedelic' state. In psychedelic-assisted psychotherapy, a relaxation of the highest-level beliefs with psychotherapeutic support may allow a revision of pathological self-representation models, for which neuroplasticity plays a permissive role. Our account provides a multi-level rationale for using the psychedelic properties of ketamine to increase its long-term benefits.
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Affiliation(s)
| | | | - Pedro Castro-Rodrigues
- Centro Hospitalar Psiquiátrico de Lisboa, Lisbon, Portugal,NOVA Medical School, NMS, Universidade Nova de Lisboa, Lisbon, Portugal,Pedro Castro-Rodrigues, Centro Hospitalar Psiquiátrico de Lisboa, Avenida do Brasil, 53, Lisbon, 1749-002, Portugal.
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14
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Ahn MH, Alsabbagh N, Lee HJ, Kim HJ, Jung MH, Hong SK. Neurobiological Signatures of Auditory False Perception and Phantom Perception as a Consequence of Sensory Prediction Errors. Biology (Basel) 2022; 11:biology11101501. [PMID: 36290405 PMCID: PMC9598671 DOI: 10.3390/biology11101501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/07/2022] [Accepted: 10/09/2022] [Indexed: 11/06/2022]
Abstract
In this study, we hypothesized that top-down sensory prediction error due to peripheral hearing loss might influence sensorimotor integration using the efference copy (EC) signals as functional connections between auditory and motor brain areas. Using neurophysiological methods, we demonstrated that the auditory responses to self-generated sound were not suppressed in a group of patients with tinnitus accompanied by significant hearing impairment and in a schizophrenia group. However, the response was attenuated in a group with tinnitus accompanied by mild hearing impairment, similar to a healthy control group. The bias of attentional networks to self-generated sound was also observed in the subjects with tinnitus with significant hearing impairment compared to those with mild hearing impairment and healthy subjects, but it did not reach the notable disintegration found in those in the schizophrenia group. Even though the present study had significant constraints in that we did not include hearing loss subjects without tinnitus, these results might suggest that auditory deafferentation (hearing loss) may influence sensorimotor integration process using EC signals. However, the impaired sensorimotor integration in subjects with tinnitus with significant hearing impairment may have resulted from aberrant auditory signals due to sensory loss, not fundamental deficits in the reafference system, as the auditory attention network to self-generated sound is relatively well preserved in these subjects.
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Affiliation(s)
- Min-Hee Ahn
- Laboratory of Brain & Cognitive Sciences for Convergence Medicine, Hallym University College of Medicine, Anyang 14068, Korea
| | - Nour Alsabbagh
- Laboratory of Brain & Cognitive Sciences for Convergence Medicine, Hallym University College of Medicine, Anyang 14068, Korea
| | - Hyo-Jeong Lee
- Laboratory of Brain & Cognitive Sciences for Convergence Medicine, Hallym University College of Medicine, Anyang 14068, Korea
- Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University College of Medicine, Anyang 14068, Korea
| | - Hyung-Jong Kim
- Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University College of Medicine, Anyang 14068, Korea
| | - Myung-Hun Jung
- Department of Psychiatry, Hallym University College of Medicine, Anyang 14068, Korea
- Correspondence: (M.-H.J.); (S.-K.H.); Tel.: +82-31-380-3849 (S.-K.H.)
| | - Sung-Kwang Hong
- Laboratory of Brain & Cognitive Sciences for Convergence Medicine, Hallym University College of Medicine, Anyang 14068, Korea
- Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University College of Medicine, Anyang 14068, Korea
- Correspondence: (M.-H.J.); (S.-K.H.); Tel.: +82-31-380-3849 (S.-K.H.)
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15
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Herzog P, Kube T, Fassbinder E. How childhood maltreatment alters perception and cognition - the predictive processing account of borderline personality disorder. Psychol Med 2022; 52:2899-2916. [PMID: 35979924 PMCID: PMC9693729 DOI: 10.1017/s0033291722002458] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 06/24/2022] [Accepted: 07/18/2022] [Indexed: 01/05/2023]
Abstract
Borderline personality disorder (BPD) is a severe mental disorder, comprised of heterogeneous psychological and neurobiological pathologies. Here, we propose a predictive processing (PP) account of BPD to integrate these seemingly unrelated pathologies. In particular, we argue that the experience of childhood maltreatment, which is highly prevalent in BPD, leaves a developmental legacy with two facets: first, a coarse-grained, alexithymic model of self and others - leading to a rigidity and inflexibility concerning beliefs about self and others. Second, this developmental legacy leads to a loss of confidence or precision afforded beliefs about the consequences of social behavior. This results in an over reliance on sensory evidence and social feedback, with concomitant lability, impulsivity and hypersensitivity. In terms of PP, people with BPD show a distorted belief updating in response to new information with two opposing manifestations: rapid changes in beliefs and a lack of belief updating despite disconfirmatory evidence. This account of distorted information processing has the potential to explain both the instability (of affect, self-image, and interpersonal relationships) and the rigidity (of beliefs about self and others) which is typical of BPD. At the neurobiological level, we propose that enhanced levels of dopamine are associated with the increased integration of negative social feedback, and we also discuss the hypothesis of an impaired inhibitory control of the prefrontal cortex in the processing of negative social information. Our account may provide a new understanding not only of the clinical aspects of BPD, but also a unifying theory of the corresponding neurobiological pathologies. We conclude by outlining some directions for future research on the behavioral, neurobiological, and computational underpinnings of this model, and point to some clinical implications of it.
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Affiliation(s)
- Philipp Herzog
- Department of Psychiatry and Psychotherapy, University of Lübeck, Ratzeburger Allee 160, D-23562 Lübeck, Germany
- Department of Psychiatry and Psychotherapy, Christian-Albrechts-University of Kiel, Niemannsweg 147, D-24105 Kiel, Germany
- Department of Psychology, University of Koblenz-Landau, Ostbahnstr. 10, 76829 Landau, Germany
| | - Tobias Kube
- Department of Psychology, University of Koblenz-Landau, Ostbahnstr. 10, 76829 Landau, Germany
| | - Eva Fassbinder
- Department of Psychiatry and Psychotherapy, Christian-Albrechts-University of Kiel, Niemannsweg 147, D-24105 Kiel, Germany
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16
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Bottemanne H, Barberousse A, Fossati P. [Multidimensional and computational theory of mood]. Encephale 2022; 48:682-699. [PMID: 35987716 DOI: 10.1016/j.encep.2022.02.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 01/31/2022] [Accepted: 02/04/2022] [Indexed: 12/22/2022]
Abstract
What is mood? Despite its crucial place in psychiatric nosography and cognitive science, it is still difficult to delimit its conceptual ground. The distinction between emotion and mood is ambiguous: mood is often presented as an affective state that is more prolonged and less intense than emotion, or as an affective polarity distinguishing high and low mood swinging around a baseline. However, these definitions do not match the clinical reality of mood disorders such as unipolar depression and bipolar disorder, and do not allow us to understand the effect of mood on behaviour, perception and cognition. In this paper, we propose a multidimensional and computational theory of mood inspired by contemporary hypotheses in theoretical neuroscience and philosophy of emotion. After suggesting an operational distinction between emotion and mood, we show how a succession of emotions can cumulatively generate congruent mood over time, making mood an emerging state from emotion. We then present how mood determines mental and behavioral states when interacting with the environment, constituting a dispositional state of emotion, perception, belief, and action. Using this theoretical framework, we propose a computational representation of the emerging and dispositional dimensions of mood by formalizing mood as a layer of third-order Bayesian beliefs encoding the precision of emotion, and regulated by prediction errors associated with interoceptive predictions. Finally, we show how this theoretical framework sheds light on the processes involved in mood disorders, the emergence of mood congruent beliefs, or the mechanisms of antidepressant treatments in clinical psychiatry.
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Affiliation(s)
- Hugo Bottemanne
- Paris Brain Institute - Institut du Cerveau (ICM), Sorbonne University/CNRS/Inserm, Paris, France; Department of philosophy, Sciences Normes Démocratie research unit, Sorbonne university/CNRS, Paris, France; Department of psychiatry, DMU Neuroscience, Pitié-Salpêtrière hospital, Sorbonne university/Assistance publique-Hôpitaux de Paris (AP-HP), Paris, France.
| | - Anouk Barberousse
- Department of philosophy, Sciences Normes Démocratie research unit, Sorbonne university/CNRS, Paris, France
| | - Philippe Fossati
- Paris Brain Institute - Institut du Cerveau (ICM), Sorbonne University/CNRS/Inserm, Paris, France; Department of psychiatry, DMU Neuroscience, Pitié-Salpêtrière hospital, Sorbonne university/Assistance publique-Hôpitaux de Paris (AP-HP), Paris, France
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17
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Trapp S, Guitart-Masip M, Schröger E. A link between age, affect, and predictions? Eur J Ageing 2022; 19:945-952. [PMID: 36692760 PMCID: PMC9729523 DOI: 10.1007/s10433-022-00710-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/10/2022] [Indexed: 01/26/2023] Open
Abstract
The prevalence of depressive symptoms decreases from late adolescence to middle age adulthood. Furthermore, despite significant losses in motor and cognitive functioning, overall emotional well-being tends to increase with age, and a bias to positive information has been observed multiple times. Several causes have been discussed for this age-related development, such as improvement in emotion regulation, less regret, and higher socioeconomic status. Here, we explore a further explanation. Our minds host mental models that generate predictions about forthcoming events to successfully interact with our physical and social environment. To keep these models faithful, the difference between the predicted and the actual event, that is, the prediction error, is computed. We argue that prediction errors are attenuated in the middle age and older mind, which, in turn, may translate to less negative affect, lower susceptibility to affective disorders, and possibly, to a bias to positive information. Our proposal is primarily linked to perceptual inferences, but may hold as well for higher-level, cognitive, and emotional forms of error processing.
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Affiliation(s)
- Sabrina Trapp
- grid.434949.70000 0001 1408 3925Macromedia University of Applied Sciences, Munich, Germany ,grid.4714.60000 0004 1937 0626Aging Research Center, Karolinska Institutet, 17165 Stockholm, Sweden
| | - Marc Guitart-Masip
- grid.4714.60000 0004 1937 0626Aging Research Center, Karolinska Institutet, 17165 Stockholm, Sweden ,grid.467087.a0000 0004 0442 1056Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm, Sweden ,grid.83440.3b0000000121901201Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, WC1B 5EH UK ,grid.9647.c0000 0004 7669 9786Wilhelm Wundt Institute for Psychology, Leipzig University, Leipzig, Germany
| | - Erich Schröger
- grid.4714.60000 0004 1937 0626Aging Research Center, Karolinska Institutet, 17165 Stockholm, Sweden ,grid.9647.c0000 0004 7669 9786Wilhelm Wundt Institute for Psychology, Leipzig University, Leipzig, Germany
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18
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Putica A, Felmingham KL, Garrido MI, O'Donnell ML, Van Dam NT. A predictive coding account of value-based learning in PTSD: Implications for precision treatments. Neurosci Biobehav Rev 2022; 138:104704. [PMID: 35609683 DOI: 10.1016/j.neubiorev.2022.104704] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 04/05/2022] [Accepted: 05/17/2022] [Indexed: 10/18/2022]
Abstract
While there are a number of recommended first-line interventions for posttraumatic stress disorder (PTSD), treatment efficacy has been less than ideal. Generally, PTSD treatment models explain symptom manifestation via associative learning, treating the individual as a passive organism - acted upon - rather than self as agent. At their core, predictive coding (PC) models introduce the fundamental role of self-conceptualisation and hierarchical processing of one's sensory context in safety learning. This theoretical article outlines how predictive coding models of emotion offer a parsimonious framework to explain PTSD treatment response within a value-based decision-making framework. Our model integrates the predictive coding elements of the perceived: self, world and self-in the world and how they impact upon one or more discrete stages of value-based decision-making: (1) mental representation; (2) emotional valuation; (3) action selection and (4) outcome valuation. We discuss treatment and research implications stemming from our hypotheses.
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Affiliation(s)
- Andrea Putica
- Phoenix Australia Centre for Post-traumatic Mental Health, Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia.
| | - Kim L Felmingham
- Melbourne School of Psychological Sciences, University of Melbourne, Parkville, VIC, Australia
| | - Marta I Garrido
- Melbourne School of Psychological Sciences, University of Melbourne, Parkville, VIC, Australia
| | - Meaghan L O'Donnell
- Phoenix Australia Centre for Post-traumatic Mental Health, Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia
| | - Nicholas T Van Dam
- Melbourne School of Psychological Sciences, University of Melbourne, Parkville, VIC, Australia
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19
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Betka S, Adler D, Similowski T, Blanke O. Breathing control, brain, and bodily self-consciousness: Toward immersive digiceuticals to alleviate respiratory suffering. Biol Psychol 2022; 171:108329. [PMID: 35452780 DOI: 10.1016/j.biopsycho.2022.108329] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 01/19/2023]
Abstract
Breathing is peculiar among autonomic functions through several characteristics. It generates a very rich afferent traffic from an array of structures belonging to the respiratory system to various areas of the brain. It is intimately associated with bodily movements. It bears particular relationships with consciousness as its efferent motor control can be automatic or voluntary. In this review within the scope of "respiratory neurophysiology" or "respiratory neuroscience", we describe the physiological organisation of breathing control. We then review findings linking breathing and bodily self-consciousness through respiratory manipulations using virtual reality (VR). After discussing the currently admitted neurophysiological model for dyspnea, as well as a new Bayesian model applied to breathing control, we propose that visuo-respiratory paradigms -as developed in cognitive neuroscience- will foster insights into some of the basic mechanisms of the human respiratory system and will also lead to the development of immersive VR-based digital health tools (i.e. digiceuticals).
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Affiliation(s)
- Sophie Betka
- Laboratory of Cognitive Neuroscience, Brain Mind Institute and Center for Neuroprosthetics, Faculty of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, (EPFL), Geneva 1202, Switzerland.
| | - Dan Adler
- Division of Lung Diseases, University Hospital and Geneva Medical School, University of Geneva, Switzerland
| | - Thomas Similowski
- Sorbonne Université, INSERM, UMRS1158 Neurophysiologie Respiratoire Expérimentale et Clinique, F-75005 Paris, France; AP-HP, Groupe Hospitalier Universitaire APHP-Sorbonne Université, site Pitié-Salpêtrière, Département R3S (Respiration, Réanimation, Réhabilitation respiratoire, Sommeil), F-75013 Paris, France
| | - Olaf Blanke
- Laboratory of Cognitive Neuroscience, Brain Mind Institute and Center for Neuroprosthetics, Faculty of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, (EPFL), Geneva 1202, Switzerland; Department of Clinical Neurosciences, University Hospital and Geneva Medical School, University of Geneva, Switzerland
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20
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Bottemanne H, Frileux S, Guesdon A, Fossati P. [Belief updating and mood congruence in depressive disorder]. Encephale 2021; 48:188-195. [PMID: 34916079 DOI: 10.1016/j.encep.2021.06.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/27/2021] [Accepted: 06/12/2021] [Indexed: 12/28/2022]
Abstract
Depressive disorder is characterized by a polymorphic symptomatology associating emotional, cognitive and behavioral disturbances. One of the most specific symptoms is negative beliefs, called congruent to mood. Despite the importance of these beliefs in the development, the maintenance, and the recurrence of depressive episodes, little is known about the processes underlying the generation of depressive beliefs. In this paper, we detail the link between belief updating mechanisms and the genesis of depressive beliefs. We show how depression alters information processing, generating cognitive immunization when processing positive information, affective updating bias related to the valence of belief and prediction error, and difficultie to disengage from negative information. We suggest that disruption of belief-updating mechanisms forms the basis of belief-mood congruence in depression.
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Affiliation(s)
- H Bottemanne
- Paris Brain Institute - Institut du Cerveau (ICM), UMR 7225/UMRS 1127, CNRS / INSERM, Sorbonne university, Paris, France; Sorbonne University, Department of Philosophy, SND Research Unit, UMR 8011, CNRS, Paris, France; Department of psychiatry, Pitié-Salpêtrière hospital, DMU Neuroscience, Sorbonne university, Assistance publique-Hôpitaux de Paris (AP-HP), Paris, France.
| | - S Frileux
- Paris Brain Institute - Institut du Cerveau (ICM), UMR 7225/UMRS 1127, CNRS / INSERM, Sorbonne university, Paris, France; Department of psychiatry, Pitié-Salpêtrière hospital, DMU Neuroscience, Sorbonne university, Assistance publique-Hôpitaux de Paris (AP-HP), Paris, France
| | - A Guesdon
- Paris Brain Institute - Institut du Cerveau (ICM), UMR 7225/UMRS 1127, CNRS / INSERM, Sorbonne university, Paris, France; Department of psychiatry, Pitié-Salpêtrière hospital, DMU Neuroscience, Sorbonne university, Assistance publique-Hôpitaux de Paris (AP-HP), Paris, France
| | - P Fossati
- Paris Brain Institute - Institut du Cerveau (ICM), UMR 7225/UMRS 1127, CNRS / INSERM, Sorbonne university, Paris, France; Department of psychiatry, Pitié-Salpêtrière hospital, DMU Neuroscience, Sorbonne university, Assistance publique-Hôpitaux de Paris (AP-HP), Paris, France
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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) 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Da Costa L, Parr T, Sengupta B, Friston K. Neural Dynamics under Active Inference: Plausibility and Efficiency of Information Processing. Entropy (Basel) 2021; 23:454. [PMID: 33921298 PMCID: PMC8069154 DOI: 10.3390/e23040454] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 04/06/2021] [Indexed: 02/07/2023]
Abstract
Active inference is a normative framework for explaining behaviour under the free energy principle-a theory of self-organisation originating in neuroscience. It specifies neuronal dynamics for state-estimation in terms of a descent on (variational) free energy-a measure of the fit between an internal (generative) model and sensory observations. The free energy gradient is a prediction error-plausibly encoded in the average membrane potentials of neuronal populations. Conversely, the expected probability of a state can be expressed in terms of neuronal firing rates. We show that this is consistent with current models of neuronal dynamics and establish face validity by synthesising plausible electrophysiological responses. We then show that these neuronal dynamics approximate natural gradient descent, a well-known optimisation algorithm from information geometry that follows the steepest descent of the objective in information space. We compare the information length of belief updating in both schemes, a measure of the distance travelled in information space that has a direct interpretation in terms of metabolic cost. We show that neural dynamics under active inference are metabolically efficient and suggest that neural representations in biological agents may evolve by approximating steepest descent in information space towards the point of optimal inference.
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Affiliation(s)
- Lancelot Da Costa
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK; (T.P.); (B.S.); (K.F.)
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK; (T.P.); (B.S.); (K.F.)
| | - Biswa Sengupta
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK; (T.P.); (B.S.); (K.F.)
- Core Machine Learning Group, Zebra AI, London WC2H 8TJ, UK
- Department of Bioengineering, Imperial College London, London SW7 2AZ, UK
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK; (T.P.); (B.S.); (K.F.)
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23
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Van de Cruys S, Lemmens L, Sapey-Triomphe LA, Chetverikov A, Noens I, Wagemans J. Structural and contextual priors affect visual search in children with and without autism. Autism Res 2021; 14:1484-1495. [PMID: 33811474 DOI: 10.1002/aur.2511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 03/15/2021] [Accepted: 03/22/2021] [Indexed: 11/07/2022]
Abstract
Bayesian predictive coding theories of autism spectrum disorder propose that impaired acquisition or a broader shape of prior probability distributions lies at the core of the condition. However, we still know very little about how probability distributions are learned and encoded by children, let alone children with autism. Here, we take advantage of a recently developed distribution learning paradigm to characterize how children with and without autism acquire information about probability distributions. Twenty-four autistic and 25-matched neurotypical children searched for an odd-one-out target among a set of distractor lines with orientations sampled from a Gaussian distribution repeated across multiple trials to allow for learning of the parameters (mean and variance) of the distribution. We could measure the width (variance) of the participant's encoded distribution by introducing a target-distractor role-reversal while varying the similarity between target and previous distractor mean. Both groups performed similarly on the visual search task and learned the distractor distribution to a similar extent. However, the variance learned was much broader than the one presented, consistent with less informative priors in children irrespective of autism diagnosis. These findings have important implications for Bayesian accounts of perception throughout development, and Bayesian accounts of autism specifically. LAY SUMMARY: Recent theories about the underlying cognitive mechanisms of autism propose that the way autistic individuals estimate variability or uncertainty in their perceptual environment may differ from how typical individuals do so. Children had to search an oddly tilted line in a set of lines pointing in different directions, and based on their response times we examined how they learned about the variability in a set of objects. We found that autistic children learn variability as well as typical children, but both groups learn with less precision than typical adults do on the same task.
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Affiliation(s)
- Sander Van de Cruys
- Laboratory of Experimental Psychology, KU Leuven, Leuven, Belgium
- Leuven Autism Research (LAuRes), KU Leuven, Leuven, Belgium
| | - Lisa Lemmens
- Laboratory of Experimental Psychology, KU Leuven, Leuven, Belgium
- Leuven Autism Research (LAuRes), KU Leuven, Leuven, Belgium
| | - Laurie-Anne Sapey-Triomphe
- Laboratory of Experimental Psychology, KU Leuven, Leuven, Belgium
- Leuven Autism Research (LAuRes), KU Leuven, Leuven, Belgium
| | - Andrey Chetverikov
- Visual Computation Lab, Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands
| | - Ilse Noens
- Leuven Autism Research (LAuRes), KU Leuven, Leuven, Belgium
- Parenting and Special Education Research Unit, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
| | - Johan Wagemans
- Laboratory of Experimental Psychology, KU Leuven, Leuven, Belgium
- Leuven Autism Research (LAuRes), KU Leuven, Leuven, Belgium
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24
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Bhat A, Irizar H, Thygesen JH, Kuchenbaecker K, Pain O, Adams RA, Zartaloudi E, Harju-Seppänen J, Austin-Zimmerman I, Wang B, Muir R, Summerfelt A, Du XM, Bruce H, O'Donnell P, Srivastava DP, Friston K, Hong LE, Hall MH, Bramon E. Transcriptome-wide association study reveals two genes that influence mismatch negativity. Cell Rep 2021; 34:108868. [PMID: 33730571 PMCID: PMC7972991 DOI: 10.1016/j.celrep.2021.108868] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 12/09/2020] [Accepted: 02/24/2021] [Indexed: 01/22/2023] Open
Abstract
Mismatch negativity (MMN) is a differential electrophysiological response measuring cortical adaptability to unpredictable stimuli. MMN is consistently attenuated in patients with psychosis. However, the genetics of MMN are uncharted, limiting the validation of MMN as a psychosis endophenotype. Here, we perform a transcriptome-wide association study of 728 individuals, which reveals 2 genes (FAM89A and ENGASE) whose expression in cortical tissues is associated with MMN. Enrichment analyses of neurodevelopmental expression signatures show that genes associated with MMN tend to be overexpressed in the frontal cortex during prenatal development but are significantly downregulated in adulthood. Endophenotype ranking value calculations comparing MMN and three other candidate psychosis endophenotypes (lateral ventricular volume and two auditory-verbal learning measures) find MMN to be considerably superior. These results yield promising insights into sensory processing in the cortex and endorse the notion of MMN as a psychosis endophenotype.
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Affiliation(s)
- Anjali Bhat
- Division of Psychiatry, University College London, London, UK; Wellcome Centre for Human Neuroimaging, University College London, London, UK; Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK; Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK.
| | - Haritz Irizar
- Division of Psychiatry, University College London, London, UK
| | | | - Karoline Kuchenbaecker
- Division of Psychiatry, University College London, London, UK; UCL Genetics Institute, University College London, London, UK
| | - Oliver Pain
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Rick A Adams
- Division of Psychiatry, University College London, London, UK; Institute of Cognitive Neuroscience, University College London, London, UK
| | | | - Jasmine Harju-Seppänen
- Division of Psychiatry, University College London, London, UK; Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | | | - Baihan Wang
- Division of Psychiatry, University College London, London, UK
| | - Rebecca Muir
- Division of Psychiatry, University College London, London, UK
| | - Ann Summerfelt
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, Baltimore, MD, USA
| | - Xiaoming Michael Du
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, Baltimore, MD, USA
| | - Heather Bruce
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, Baltimore, MD, USA
| | - Patricio O'Donnell
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, Baltimore, MD, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Takeda Pharmaceuticals, Cambridge, MA, USA
| | - Deepak P Srivastava
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK; Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, Baltimore, MD, USA
| | - Mei-Hua Hall
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Psychosis Neurobiology Laboratory, McLean Hospital, Belmont, MA, USA
| | - Elvira Bramon
- Division of Psychiatry, University College London, London, UK; Institute of Cognitive Neuroscience, University College London, London, UK; Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK; Camden and Islington NHS Foundation Trust, London, UK.
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25
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HUZARD D, RAPPENEAU V, MEIJER OC, TOUMA C, ARANGO-LIEVANO M, GARABEDIAN MJ, JEANNETEAU F. Experience and activity-dependent control of glucocorticoid receptors during the stress response in large-scale brain networks. Stress 2021; 24:130-153. [PMID: 32755268 PMCID: PMC7907260 DOI: 10.1080/10253890.2020.1806226] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
The diversity of actions of the glucocorticoid stress hormones among individuals and within organs, tissues and cells is shaped by age, gender, genetics, metabolism, and the quantity of exposure. However, such factors cannot explain the heterogeneity of responses in the brain within cells of the same lineage, or similar tissue environment, or in the same individual. Here, we argue that the stress response is continuously updated by synchronized neural activity on large-scale brain networks. This occurs at the molecular, cellular and behavioral levels by crosstalk communication between activity-dependent and glucocorticoid signaling pathways, which updates the diversity of responses based on prior experience. Such a Bayesian process determines adaptation to the demands of the body and external world. We propose a framework for understanding how the diversity of glucocorticoid actions throughout brain networks is essential for supporting optimal health, while its disruption may contribute to the pathophysiology of stress-related disorders, such as major depression, and resistance to therapeutic treatments.
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Affiliation(s)
- Damien HUZARD
- Department of Neuroscience and Physiology, University of Montpellier, CNRS, INSERM, Institut de Génomique Fonctionnelle, Montpellier, France
| | - Virginie RAPPENEAU
- Department of Behavioural Biology, University of Osnabrück, Osnabrück, Germany
| | - Onno C. MEIJER
- Division of Endocrinology, Department of Internal Medicine, Leiden University Medical Center, Leiden University, Leiden, the Netherlands
| | - Chadi TOUMA
- Department of Behavioural Biology, University of Osnabrück, Osnabrück, Germany
| | - Margarita ARANGO-LIEVANO
- Department of Neuroscience and Physiology, University of Montpellier, CNRS, INSERM, Institut de Génomique Fonctionnelle, Montpellier, France
| | | | - Freddy JEANNETEAU
- Department of Neuroscience and Physiology, University of Montpellier, CNRS, INSERM, Institut de Génomique Fonctionnelle, Montpellier, France
- Corresponding author:
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26
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Sánchez-Cañizares J. The Free Energy Principle: Good Science and Questionable Philosophy in a Grand Unifying Theory. Entropy (Basel) 2021; 23:238. [PMID: 33669529 PMCID: PMC7922226 DOI: 10.3390/e23020238] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/08/2021] [Accepted: 02/16/2021] [Indexed: 11/16/2022]
Abstract
The Free Energy Principle (FEP) is currently one of the most promising frameworks with which to address a unified explanation of life-related phenomena. With powerful formalism that embeds a small set of assumptions, it purports to deal with complex adaptive dynamics ranging from barely unicellular organisms to complex cultural manifestations. The FEP has received increased attention in disciplines that study life, including some critique regarding its overall explanatory power and its true potential as a grand unifying theory (GUT). Recently, FEP theorists presented a contribution with the main tenets of their framework, together with possible philosophical interpretations, which lean towards so-called Markovian Monism (MM). The present paper assumes some of the abovementioned critiques, rejects the arguments advanced to invalidate the FEP's potential to be a GUT, and overcomes criticism thereof by reviewing FEP theorists' newly minted metaphysical commitment, namely MM. Specifically, it shows that this philosophical interpretation of the FEP argues circularly and only delivers what it initially assumes, i.e., a dual information geometry that allegedly explains epistemic access to the world based on prior dual assumptions. The origin of this circularity can be traced back to a physical description contingent on relative system-environment separation. However, the FEP itself is not committed to MM, and as a scientific theory it delivers more than what it assumes, serving as a heuristic unification principle that provides epistemic advancement for the life sciences.
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Affiliation(s)
- Javier Sánchez-Cañizares
- Mind-Brain Group, Institute for Culture and Society, University of Navarra, 31009 Pamplona, Spain
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27
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Visalli A, Capizzi M, Ambrosini E, Kopp B, Vallesi A. Electroencephalographic correlates of temporal Bayesian belief updating and surprise. Neuroimage 2021; 231:117867. [PMID: 33592246 DOI: 10.1016/j.neuroimage.2021.117867] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 02/02/2021] [Accepted: 02/09/2021] [Indexed: 12/16/2022] Open
Abstract
The brain predicts the timing of forthcoming events to optimize responses to them. Temporal predictions have been formalized in terms of the hazard function, which integrates prior beliefs on the likely timing of stimulus occurrence with information conveyed by the passage of time. However, how the human brain updates prior temporal beliefs is still elusive. Here we investigated electroencephalographic (EEG) signatures associated with Bayes-optimal updating of temporal beliefs. Given that updating usually occurs in response to surprising events, we sought to disentangle EEG correlates of updating from those associated with surprise. Twenty-six participants performed a temporal foreperiod task, which comprised a subset of surprising events not eliciting updating. EEG data were analyzed through a regression-based massive approach in the electrode and source space. Distinct late positive, centro-parietally distributed, event-related potentials (ERPs) were associated with surprise and belief updating in the electrode space. While surprise modulated the commonly observed P3b, updating was associated with a later and more sustained P3b-like waveform deflection. Results from source analyses revealed that neural encoding of surprise comprises neural activity in the cingulo-opercular network (CON) and parietal regions. These data provide evidence that temporal predictions are computed in a Bayesian manner, and that this is reflected in P3 modulations, akin to other cognitive domains. Overall, our study revealed that analyzing P3 modulations provides an important window into the Bayesian brain. Data and scripts are shared on OSF: https://osf.io/ckqa5/.
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Affiliation(s)
- Antonino Visalli
- Department of Neuroscience, University of Padova, 35128 Padova, Italy; Department of General Psychology, University of Padova, 35131 Padova, Italy.
| | | | - Ettore Ambrosini
- Department of General Psychology, University of Padova, 35131 Padova, Italy; Department of Neuroscience & Padova Neuroscience Center, University of Padova, 35131 Padova, Italy
| | - Bruno Kopp
- Department of Neurology, Hannover Medical School, 30625 Hannover, Germany
| | - Antonino Vallesi
- Department of Neuroscience & Padova Neuroscience Center, University of Padova, 35131 Padova, Italy; Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, 30126 Venice, Italy.
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28
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Verdonk C, Trousselard M. Commentary: A Computational Theory of Mindfulness Based Cognitive Therapy from the " Bayesian Brain" Perspective. Front Psychiatry 2021; 12:575150. [PMID: 33603685 PMCID: PMC7884459 DOI: 10.3389/fpsyt.2021.575150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 01/07/2021] [Indexed: 11/14/2022] Open
Affiliation(s)
- Charles Verdonk
- Department Neurosciences and Cognitive Sciences, French Armed Forces Biomedical Research Institute, Brétigny-sur-Orge, France
| | - Marion Trousselard
- Department Neurosciences and Cognitive Sciences, French Armed Forces Biomedical Research Institute, Brétigny-sur-Orge, France.,French Military Health Service Academy, Paris, France.,Lorraine University, APEMAC/EPSAM - EA 4360, Metz, France
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29
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Butz MV, Achimova A, Bilkey D, Knott A. Event-Predictive Cognition: A Root for Conceptual Human Thought. Top Cogn Sci 2020; 13:10-24. [PMID: 33274596 DOI: 10.1111/tops.12522] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 10/11/2020] [Accepted: 10/11/2020] [Indexed: 12/11/2022]
Abstract
Our minds navigate a continuous stream of sensorimotor experiences, selectively compressing them into events. Event-predictive encodings and processing abilities have evolved because they mirror interactions between agents and objects-and the pursuance or avoidance of critical interactions lies at the heart of survival and reproduction. However, it appears that these abilities have evolved not only to pursue live-enhancing events and to avoid threatening events, but also to distinguish food sources, to produce and to use tools, to cooperate, and to communicate. They may have even set the stage for the formation of larger societies and the development of cultural identities. Research on event-predictive cognition investigates how events and conceptualizations thereof are learned, structured, and processed dynamically. It suggests that event-predictive encodings and processes optimally mediate between sensorimotor processes and language. On the one hand, they enable us to perceive and control physical interactions with our world in a highly adaptive, versatile, goal-directed manner. On the other hand, they allow us to coordinate complex social interactions and, in particular, to comprehend and produce language. Event-predictive learning segments sensorimotor experiences into event-predictive encodings. Once first encodings are formed, the mind learns progressively higher order compositional structures, which allow reflecting on the past, reasoning, and planning on multiple levels of abstraction. We conclude that human conceptual thought may be grounded in the principles of event-predictive cognition constituting its root.
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Affiliation(s)
- Martin V Butz
- Neuro-Cognitive Modeling Group, Department of Computer Science, Department of Psychology, Faculty of Science, University of Tübingen
| | - Asya Achimova
- Neuro-Cognitive Modeling Group, Department of Computer Science, Department of Psychology, Faculty of Science, University of Tübingen
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30
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Kearton T, Marini D, Cowley F, Belson S, Keshavarzi H, Mayes B, Lee C. The Influence of Predictability and Controllability on Stress Responses to the Aversive Component of a Virtual Fence. Front Vet Sci 2020; 7:580523. [PMID: 33330702 PMCID: PMC7733987 DOI: 10.3389/fvets.2020.580523] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 10/30/2020] [Indexed: 01/27/2023] Open
Abstract
To ensure animal welfare is not compromised, virtual fencing must be predictable and controllable, and this is achieved through associative learning. To assess the influence of predictability and controllability on physiological and behavioral responses to the aversive component of a virtual fence, two methods of training animals were compared. In the first method, positive punishment training involved sheep learning that after an audio stimulus, an electrical stimulus would follow only when they did not respond by stopping or turning at the virtual fence (predictable controllability). In the second method, classical conditioning was used to associate an audio stimulus with an electrical stimulus on all occasions (predictable uncontrollability). Eighty Merino ewes received one of the following treatments: control (no training and no stimuli in testing); positive punishment training with an audio stimulus in testing (PP); classical conditioning training with only an audio stimulus in testing (CC1); and classical conditioning training with an audio stimulus followed by electrical stimulus in testing (CC2). The stimuli were applied manually with an electronic collar. Training occurred on 4 consecutive days with one session per sheep per day. Sheep were then assessed for stress responses to the cues by measuring plasma cortisol, body temperature and behaviors. Predictable controllability (PP) sheep showed no differences in behavioral and physiological responses compared with the control treatment (P < 0.05). Predictable uncontrollability of receiving the aversive stimulus (CC2) induced a higher cortisol and body temperature response compared to the control but was not different to CC1 and PP treatments. CC2 treatment sheep showed a higher number of turning behaviors (P < 0.001), and more time spent running (P < 0.001) than the control and PP treatment groups, indicating that predictability without controllability was stressful. The behavior results also indicate that predicting the event without receiving it (CC1) was less stressful than predicting the event then receiving it (CC2), suggesting that there is a cost to confirmation of uncontrollability. These results demonstrate that a situation of predictability and controllability such as experienced when an animal successfully learns to avoid the aversive component of a virtual fence, induces a comparatively minimal stress response and does not compromise animal welfare.
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Affiliation(s)
- Tellisa Kearton
- School of Environmental and Rural Science, University of New England, Armidale, NSW, Australia.,Agriculture and Food, Commonwealth Scientific and Industrial Research Organization, Armidale, NSW, Australia
| | - Danila Marini
- School of Environmental and Rural Science, University of New England, Armidale, NSW, Australia.,Agriculture and Food, Commonwealth Scientific and Industrial Research Organization, Armidale, NSW, Australia
| | - Frances Cowley
- School of Environmental and Rural Science, University of New England, Armidale, NSW, Australia
| | - Sue Belson
- Agriculture and Food, Commonwealth Scientific and Industrial Research Organization, Armidale, NSW, Australia
| | - Hamideh Keshavarzi
- Agriculture and Food, Commonwealth Scientific and Industrial Research Organization, Armidale, NSW, Australia
| | - Bonnie Mayes
- School of Environmental and Rural Science, University of New England, Armidale, NSW, Australia.,Agriculture and Food, Commonwealth Scientific and Industrial Research Organization, Armidale, NSW, Australia
| | - Caroline Lee
- School of Environmental and Rural Science, University of New England, Armidale, NSW, Australia.,Agriculture and Food, Commonwealth Scientific and Industrial Research Organization, Armidale, NSW, Australia
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31
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D’Alessandro M, Radev ST, Voss A, Lombardi L. A Bayesian brain model of adaptive behavior: an application to the Wisconsin Card Sorting Task. PeerJ 2020; 8:e10316. [PMID: 33335805 PMCID: PMC7713598 DOI: 10.7717/peerj.10316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 10/16/2020] [Indexed: 12/28/2022] Open
Abstract
Adaptive behavior emerges through a dynamic interaction between cognitive agents and changing environmental demands. The investigation of information processing underlying adaptive behavior relies on controlled experimental settings in which individuals are asked to accomplish demanding tasks whereby a hidden regularity or an abstract rule has to be learned dynamically. Although performance in such tasks is considered as a proxy for measuring high-level cognitive processes, the standard approach consists in summarizing observed response patterns by simple heuristic scoring measures. With this work, we propose and validate a new computational Bayesian model accounting for individual performance in the Wisconsin Card Sorting Test (WCST), a renowned clinical tool to measure set-shifting and deficient inhibitory processes on the basis of environmental feedback. We formalize the interaction between the task's structure, the received feedback, and the agent's behavior by building a model of the information processing mechanisms used to infer the hidden rules of the task environment. Furthermore, we embed the new model within the mathematical framework of the Bayesian Brain Theory (BBT), according to which beliefs about hidden environmental states are dynamically updated following the logic of Bayesian inference. Our computational model maps distinct cognitive processes into separable, neurobiologically plausible, information-theoretic constructs underlying observed response patterns. We assess model identification and expressiveness in accounting for meaningful human performance through extensive simulation studies. We then validate the model on real behavioral data in order to highlight the utility of the proposed model in recovering cognitive dynamics at an individual level. We highlight the potentials of our model in decomposing adaptive behavior in the WCST into several information-theoretic metrics revealing the trial-by-trial unfolding of information processing by focusing on two exemplary individuals whose behavior is examined in depth. Finally, we focus on the theoretical implications of our computational model by discussing the mapping between BBT constructs and functional neuroanatomical correlates of task performance. We further discuss the empirical benefit of recovering the assumed dynamics of information processing for both clinical and research practices, such as neurological assessment and model-based neuroscience.
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Affiliation(s)
- Marco D’Alessandro
- Department of Psychology and Cognitive Science, University of Trento, Rovereto, Italy
| | - Stefan T. Radev
- Institute of Psychology, Heidelberg University, Heidelberg, Germany
| | - Andreas Voss
- Institute of Psychology, Heidelberg University, Heidelberg, Germany
| | - Luigi Lombardi
- Department of Psychology and Cognitive Science, University of Trento, Rovereto, Italy
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32
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Heins RC, Mirza MB, Parr T, Friston K, Kagan I, Pooresmaeili A. Deep Active Inference and Scene Construction. Front Artif Intell 2020; 3:509354. [PMID: 33733195 PMCID: PMC7861336 DOI: 10.3389/frai.2020.509354] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 09/10/2020] [Indexed: 11/17/2022] Open
Abstract
Adaptive agents must act in intrinsically uncertain environments with complex latent structure. Here, we elaborate a model of visual foraging-in a hierarchical context-wherein agents infer a higher-order visual pattern (a "scene") by sequentially sampling ambiguous cues. Inspired by previous models of scene construction-that cast perception and action as consequences of approximate Bayesian inference-we use active inference to simulate decisions of agents categorizing a scene in a hierarchically-structured setting. Under active inference, agents develop probabilistic beliefs about their environment, while actively sampling it to maximize the evidence for their internal generative model. This approximate evidence maximization (i.e., self-evidencing) comprises drives to both maximize rewards and resolve uncertainty about hidden states. This is realized via minimization of a free energy functional of posterior beliefs about both the world as well as the actions used to sample or perturb it, corresponding to perception and action, respectively. We show that active inference, in the context of hierarchical scene construction, gives rise to many empirical evidence accumulation phenomena, such as noise-sensitive reaction times and epistemic saccades. We explain these behaviors in terms of the principled drives that constitute the expected free energy, the key quantity for evaluating policies under active inference. In addition, we report novel behaviors exhibited by these active inference agents that furnish new predictions for research on evidence accumulation and perceptual decision-making. We discuss the implications of this hierarchical active inference scheme for tasks that require planned sequences of information-gathering actions to infer compositional latent structure (such as visual scene construction and sentence comprehension). This work sets the stage for future experiments to investigate active inference in relation to other formulations of evidence accumulation (e.g., drift-diffusion models) in tasks that require planning in uncertain environments with higher-order structure.
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Affiliation(s)
- R. Conor Heins
- Department of Collective Behaviour, Max Planck Institute for Animal Behavior, Konstanz, Germany
- Perception and Cognition Group, European Neuroscience Institute, A Joint Initiative of the University Medical Centre Göttingen and the Max-Planck-Society, Göttingen, Germany
- Leibniz Science Campus “Primate Cognition”, Göttingen, Germany
| | - M. Berk Mirza
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- The National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre (BRC) at South London and Maudsley National Health Service (NHS) Foundation Trust and The Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
| | - Igor Kagan
- Leibniz Science Campus “Primate Cognition”, Göttingen, Germany
- Decision and Awareness Group, Cognitive Neuroscience Laboratory, German Primate Centre (DPZ), Göttingen, Germany
| | - Arezoo Pooresmaeili
- Perception and Cognition Group, European Neuroscience Institute, A Joint Initiative of the University Medical Centre Göttingen and the Max-Planck-Society, Göttingen, Germany
- Leibniz Science Campus “Primate Cognition”, Göttingen, Germany
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33
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Abstract
Computational modeling builds mathematical models of cognitive phenomena to simulate patterns of perception, decision-making, and belief updating. These models mathematically represent the information processing by combining an anterior probability distribution, a likelihood function and a set of parameters and hyperparameters. Their use popularized the conception of a nervous system functioning as a predictive machine, or "bayesian brain". Applied to psychiatry, these models seek to explain how psychiatric dysfunction may emerge mechanistically. Despite the significance of emotions for cognitive phenomena and for psychiatric disorders, few computational models offer mathematical representations of emotion or incorporate emotional factors into their modeling parameters. We present here some computational hypotheses for the modeling of affective parameters, and we suggest that computational psychiatry would benefit from these modeling parameters.
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Affiliation(s)
- H Bottemanne
- Department of psychiatry, Pitié-Salpêtrière Hospital, AP-HP, Paris, France.; Control-Interoception-Attention team, Paris Brain Institute, Sorbonne University, Paris, France..
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34
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Abstract
This paper examines the applicability of predictive coding as an explanatory model for perception. This is carried out from two perspectives. First, the central assumptions of the model are re-examined in light of the neuroscientific evidence for the structure and functioning of key brain areas involved in perception. The inferential processes involved in predictive coding are then investigated in the context of ambiguous stimuli. This showed that while predictive coding may provide an accurate explanation for our perceptual experiences in some cases, there are also several instances where the picture is not as clear cut. Following on from this, particular emphasis is placed on ambiguous art in order to examine the psychological and cognitive implications of predictive coding in affective states. This not only sheds light on the impact of predictive coding for cognition and emotion, but also helps clarify the nature of ambiguous art.
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Affiliation(s)
- Jasper Wolf
- Arts and Sciences Department, UCL, Bloomsbury, London, United Kingdom.
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35
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Abstract
Emerging infectious diseases like Covid-19 cause a major threat to global health. When confronted with new pathogens, individuals generate several beliefs about the epidemic phenomenon. Many studies have shown that individual protective behaviors largely depend on these beliefs. Due to the absence of treatment and vaccine against these emerging pathogens, the relation between these beliefs and these behaviors represents a crucial issue for public health policies. In the premises of the Covid-19 pandemic, several preliminary studies have highlighted a delay in the perception of risk by individuals, which potentially holds back the implementing of the necessary precautionary measures: people underestimated the risks associated with the virus, and therefore also the importance of complying with sanitary guidelines. During the peak of the pandemic, the salience of the threat and of the risk of mortality could then have transformed the way people generate their beliefs. This potentially leads to upheavals in the way they understand the world. Here, we propose to explore the evolution of beliefs and behaviors during the Covid-19 crisis, using the theory of predictive coding and the theory of terror management, two influential frameworks in cognitive science and in social psychology.
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Affiliation(s)
- H Bottemanne
- Control-Interoception-Attention team, Institut du cerveau et de la moelle épinière (ICM), UMR 7225/UMR_S 1127, Sorbonne University/CNRS/INSERM, Paris, France; Department for adult psychiatry, Pitié-Salpêtrière hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France.
| | - O Morlaàs
- Control-Interoception-Attention team, Institut du cerveau et de la moelle épinière (ICM), UMR 7225/UMR_S 1127, Sorbonne University/CNRS/INSERM, Paris, France
| | - L Schmidt
- Control-Interoception-Attention team, Institut du cerveau et de la moelle épinière (ICM), UMR 7225/UMR_S 1127, Sorbonne University/CNRS/INSERM, Paris, France
| | - P Fossati
- Control-Interoception-Attention team, Institut du cerveau et de la moelle épinière (ICM), UMR 7225/UMR_S 1127, Sorbonne University/CNRS/INSERM, Paris, France; Department for adult psychiatry, Pitié-Salpêtrière hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France
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36
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Kube T, Berg M, Kleim B, Herzog P. Rethinking post-traumatic stress disorder - A predictive processing perspective. Neurosci Biobehav Rev 2020; 113:448-460. [PMID: 32315695 DOI: 10.1016/j.neubiorev.2020.04.014] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 04/07/2020] [Accepted: 04/09/2020] [Indexed: 12/15/2022]
Abstract
Predictive processing has become a popular framework in neuroscience and computational psychiatry, where it has provided a new understanding of various mental disorders. Here, we apply the predictive processing account to post-traumatic stress disorder (PTSD). We argue that the experience of a traumatic event in Bayesian terms can be understood as a perceptual hypothesis that is subsequently given a very high a-priori likelihood due to its (life-) threatening significance; thus, this hypothesis is re-selected although it does not fit the actual sensory input. Based on this account, we re-conceptualise the symptom clusters of PTSD through the lens of a predictive processing model. We particularly focus on re-experiencing symptoms as the hallmark symptoms of PTSD, and discuss the occurrence of flashbacks in terms of perceptual and interoceptive inference. This account provides not only a new understanding of the clinical profile of PTSD, but also a unifying framework for the corresponding pathologies at the neurobiological level. Finally, we derive directions for future research and discuss implications for psychological and pharmacological interventions.
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Affiliation(s)
- Tobias Kube
- Harvard Medical School, Program in Placebo Studies, Beth Israel Deaconess Medical Center, Brookline Avenue 330, Boston, MA, 02115, USA; University of Koblenz-Landau, Pain and Psychotherapy Research Lab, Ostbahnstr. 10, 76829 Landau, Germany.
| | - Max Berg
- Philipps-University of Marburg, Department of Psychology, Division of Clinical Psychology and Psychological Treatment Gutenbergstraße 18, D-35032, Marburg, Germany
| | - Birgit Kleim
- University of Zurich, Department of Psychology, Binzmühlestrasse 14, Box 8, CH-8050, Zurich, Switzerland; Psychiatric University Hospital (PUK), Lenggstrasse 31, CH-8032, Zurich, Switzerland
| | - Philipp Herzog
- Philipps-University of Marburg, Department of Psychology, Division of Clinical Psychology and Psychological Treatment Gutenbergstraße 18, D-35032, Marburg, Germany; University of Greifswald, Department of Psychology, Clinical Psychology and Psychotherapy, Franz-Mehring-Straße 47, D-17489, Greifswald, Germany; Department of Psychiatry and Psychotherapy, University of Lübeck, Ratzeburger Allee 160, D-23562, Lübeck, Germany
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37
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Abstract
Predictive Processing theories hold that the mind's core aim is to minimize prediction-error about its experiences. But prediction-error minimization can be 'hacked', by placing oneself in highly predictable environments where nothing happens. Recent philosophical work suggests that this is a surprisingly serious challenge, highlighting the obstacles facing 'theories-of-everything' in psychology.
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Affiliation(s)
- Zekun Sun
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Chaz Firestone
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, USA.
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38
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Manjaly ZM, Iglesias S. A Computational Theory of Mindfulness Based Cognitive Therapy from the " Bayesian Brain" Perspective. Front Psychiatry 2020; 11:404. [PMID: 32499726 PMCID: PMC7243935 DOI: 10.3389/fpsyt.2020.00404] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 04/21/2020] [Indexed: 12/21/2022] Open
Abstract
Mindfulness Based Cognitive Therapy (MBCT) was developed to combine methods from cognitive behavioral therapy and meditative techniques, with the specific goal of preventing relapse in recurrent depression. While supported by empirical evidence from multiple clinical trials, the cognitive mechanisms behind the effectiveness of MBCT are not well understood in computational (information processing) or biological terms. This article introduces a testable theory about the computational mechanisms behind MBCT that is grounded in "Bayesian brain" concepts of perception from cognitive neuroscience, such as predictive coding. These concepts regard the brain as embodying a model of its environment (including the external world and the body) which predicts future sensory inputs and is updated by prediction errors, depending on how precise these error signals are. This article offers a concrete proposal how core concepts of MBCT-(i) the being mode (accepting whatever sensations arise, without judging or changing them), (ii) decentering (experiencing thoughts and percepts simply as events in the mind that arise and pass), and (iii) cognitive reactivity (changes in mood reactivate negative beliefs)-could be understood in terms of perceptual and metacognitive processes that draw on specific computational mechanisms of the "Bayesian brain." Importantly, the proposed theory can be tested experimentally, using a combination of behavioral paradigms, computational modelling, and neuroimaging. The novel theoretical perspective on MBCT described in this paper may offer opportunities for finessing the conceptual and practical aspects of MBCT.
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Affiliation(s)
- Zina-Mary Manjaly
- Department of Neurology, Schulthess Clinic, Zurich, Switzerland.,Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Sandra Iglesias
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
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39
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Han K, Wen H, Shi J, Lu KH, Zhang Y, Fu D, Liu Z. Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex. Neuroimage 2019; 198:125-136. [PMID: 31103784 PMCID: PMC6592726 DOI: 10.1016/j.neuroimage.2019.05.039] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Revised: 04/13/2019] [Accepted: 05/15/2019] [Indexed: 01/21/2023] Open
Abstract
Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to be able to predict and decode cortical responses to natural images or videos. Here, we explored an alternative deep neural network, variational auto-encoder (VAE), as a computational model of the visual cortex. We trained a VAE with a five-layer encoder and a five-layer decoder to learn visual representations from a diverse set of unlabeled images. Using the trained VAE, we predicted and decoded cortical activity observed with functional magnetic resonance imaging (fMRI) from three human subjects passively watching natural videos. Compared to CNN, VAE could predict the video-evoked cortical responses with comparable accuracy in early visual areas, but relatively lower accuracy in higher-order visual areas. The distinction between CNN and VAE in terms of encoding performance was primarily attributed to their different learning objectives, rather than their different model architecture or number of parameters. Despite lower encoding accuracies, VAE offered a more convenient strategy for decoding the fMRI activity to reconstruct the video input, by first converting the fMRI activity to the VAE's latent variables, and then converting the latent variables to the reconstructed video frames through the VAE's decoder. This strategy was more advantageous than alternative decoding methods, e.g. partial least squares regression, for being able to reconstruct both the spatial structure and color of the visual input. Such findings highlight VAE as an unsupervised model for learning visual representation, as well as its potential and limitations for explaining cortical responses and reconstructing naturalistic and diverse visual experiences.
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Affiliation(s)
- Kuan Han
- School of Electrical and Computer Engineering, USA; Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, 47906, USA
| | - Haiguang Wen
- School of Electrical and Computer Engineering, USA; Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, 47906, USA
| | - Junxing Shi
- School of Electrical and Computer Engineering, USA; Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, 47906, USA
| | - Kun-Han Lu
- School of Electrical and Computer Engineering, USA; Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, 47906, USA
| | - Yizhen Zhang
- School of Electrical and Computer Engineering, USA; Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, 47906, USA
| | - Di Fu
- School of Electrical and Computer Engineering, USA; Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, 47906, USA
| | - Zhongming Liu
- Weldon School of Biomedical Engineering, USA; School of Electrical and Computer Engineering, USA; Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, 47906, USA.
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40
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Visalli A, Capizzi M, Ambrosini E, Mazzonetto I, Vallesi A. Bayesian modeling of temporal expectations in the human brain. Neuroimage 2019; 202:116097. [PMID: 31415885 DOI: 10.1016/j.neuroimage.2019.116097] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 07/16/2019] [Accepted: 08/11/2019] [Indexed: 12/13/2022] Open
Abstract
The brain predicts the timing of forthcoming events to optimize processes in response to them. Temporal predictions are driven by both our prior expectations on the likely timing of stimulus occurrence and the information conveyed by the passage of time. Specifically, such predictions can be described in terms of the hazard function, that is, the conditional probability that an event will occur, given it has not yet occurred. Events violating expectations cause surprise and often induce updating of prior expectations. While it is well-known that the brain is able to track the temporal hazard of event occurrence, the question of how prior temporal expectations are updated is still unsettled. Here we combined a Bayesian computational approach with brain imaging to map updating of temporal expectations in the human brain. Moreover, since updating is usually highly correlated with surprise, participants performed a task that allowed partially differentiating between the two processes. Results showed that updating and surprise differently modulated activity in areas belonging to two critical networks for cognitive control, the fronto-parietal (FPN) and the cingulo-opercular network (CON). Overall, these data provide a first computational characterization of the neural correlates associated with updating and surprise related to temporal expectation.
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Affiliation(s)
- Antonino Visalli
- Department of Neuroscience, University of Padova, 35128, Padova, Italy; Department of General Psychology, University of Padova, 35131, Padova, Italy.
| | | | - Ettore Ambrosini
- Department of General Psychology, University of Padova, 35131, Padova, Italy; Department of Neuroscience & Padova Neuroscience Center, University of Padova, 35131, Padova, Italy
| | - Ilaria Mazzonetto
- Department of Neuroscience, University of Padova, 35128, Padova, Italy; Department of Information Engineering, University of Padova, 35131, Padova, Italy
| | - Antonino Vallesi
- Department of Neuroscience & Padova Neuroscience Center, University of Padova, 35131, Padova, Italy; Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, 30126, Venice, Italy
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41
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Smith R, Alkozei A, Killgore WDS. Parameters as Trait Indicators: Exploring a Complementary Neurocomputational Approach to Conceptualizing and Measuring Trait Differences in Emotional Intelligence. Front Psychol 2019; 10:848. [PMID: 31057467 PMCID: PMC6482169 DOI: 10.3389/fpsyg.2019.00848] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 04/01/2019] [Indexed: 12/16/2022] Open
Abstract
Current assessments of trait emotional intelligence (EI) rely on self-report inventories. While this approach has seen considerable success, a complementary approach allowing objective assessment of EI-relevant traits would provide some potential advantages. Among others, one potential advantage is that it would aid in emerging efforts to assess the brain basis of trait EI, where self-reported competency levels do not always match real-world behavior. In this paper, we review recent experimental paradigms in computational cognitive neuroscience (CCN), which allow behavioral estimates of individual differences in range of parameter values within computational models of neurocognitive processes. Based on this review, we illustrate how several of these parameters appear to correspond well to EI-relevant traits (i.e., differences in mood stability, stress vulnerability, self-control, and flexibility, among others). In contrast, although estimated objectively, these parameters do not correspond well to the optimal performance abilities assessed within competing “ability models” of EI. We suggest that adapting this approach from CCN—by treating parameter value estimates as objective trait EI measures—could (1) provide novel research directions, (2) aid in characterizing the neural basis of trait EI, and (3) offer a promising complementary assessment method.
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Affiliation(s)
- Ryan Smith
- Laureate Institute for Brain Research, Tulsa, OK, United States.,Department of Psychiatry, University of Arizona, Tucson, AZ, United States
| | - Anna Alkozei
- Department of Psychiatry, University of Arizona, Tucson, AZ, United States
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42
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Modirshanechi A, Kiani MM, Aghajan H. Trial-by-trial surprise-decoding model for visual and auditory binary oddball tasks. Neuroimage 2019; 196:302-17. [PMID: 30980899 DOI: 10.1016/j.neuroimage.2019.04.028] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 02/26/2019] [Accepted: 04/08/2019] [Indexed: 02/02/2023] Open
Abstract
Having to survive in a continuously changing environment has driven the human brain to actively predict the future state of its surroundings. Oddball tasks are specific types of experiments in which this nature of the human brain is studied. Detailed mathematical models have been constructed to explain the brain's perception in these tasks. These models consider a subject as an ideal observer who abstracts a hypothesis from the previous stimuli, and estimates its hyper-parameters - in order to make the next prediction. The corresponding prediction error is assumed to manifest the subjective surprise of the brain. While the approach of earlier works to this problem has been to suggest an encoding model, we investigated the reverse model: if the stimuli's surprise is assumed as the cause of the observer's surprise, it must be possible to decode the surprise of each stimulus, for every single subject, given only their neural responses, i.e. to tell how unexpected a specific stimulus has been for them. Employing machine learning tools, we developed a surprise decoding model for binary oddball tasks. We constructed our model using the ideal observer proposed by Meyniel et al. in 2016, and applied it to three datasets, one with visual, one with auditory, and one with both visual and auditory stimuli. We demonstrated that our decoding model performs very well for both of the sensory modalities with or without the presence of the subject's motor response.
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43
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Abstract
The free energy principle (FEP) has gained widespread interest and growing acceptance as a new paradigm of brain function, but has had little impact on the theory and practice of psychotherapy. The aim of this paper is to redress this. Brains rely on Bayesian inference during which “bottom-up” sensations are matched with “top-down” predictions. Discrepancies result in “prediction error.” The brain abhors informational “surprise,” which is minimized by (1) action enhancing the statistical likelihood of sensory samples, (2) revising inferences in the light of experience, updating “priors” to reality-aligned “posteriors,” and (3) optimizing the complexity of our generative models of a capricious world. In all three, free energy is converted to bound energy. In psychopathology energy either remains unbound, as in trauma and inhibition of agency, or manifests restricted, anachronistic “top-down” narratives. Psychotherapy fosters client agency, linguistic and practical. Temporary uncoupling bottom-up from top-down automatism and fostering scrutinized simulations sets a number of salutary processes in train. Mentalising enriches Bayesian inference, enabling experience and feeling states to be “metabolized” and assimilated. “Free association” enhances more inclusive sensory sampling, while dream analysis foregrounds salient emotional themes as “attractors.” FEP parallels with psychoanalytic theory are outlined, including Freud’s unpublished project, Bion’s “contact barrier” concept, the Fonagy/Target model of sexuality, Laplanche’s therapist as “enigmatic signifier,” and the role of projective identification. The therapy stimulates patients to become aware of and revise the priors’ they bring to interpersonal experience. In the therapeutic “duet for one,” the energy binding skills and non-partisan stance of the analyst help sufferers face trauma without being overwhelmed by psychic entropy. Overall, the FEP provides a sound theoretical basis for psychotherapy practice, training, and research.
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Affiliation(s)
- Jeremy Holmes
- University College London, Anna Freud National Centre for Children and Families, London, United Kingdom
| | - Tobias Nolte
- Department of Psychology, College of Life and Environmental Sciences, University of Exeter, Exeter, United Kingdom
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44
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Sterzer P, Adams RA, Fletcher P, Frith C, Lawrie SM, Muckli L, Petrovic P, Uhlhaas P, Voss M, Corlett PR. The Predictive Coding Account of Psychosis. Biol Psychiatry 2018; 84:634-643. [PMID: 30007575 PMCID: PMC6169400 DOI: 10.1016/j.biopsych.2018.05.015] [Citation(s) in RCA: 358] [Impact Index Per Article: 59.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 05/14/2018] [Accepted: 05/15/2018] [Indexed: 01/12/2023]
Abstract
Fueled by developments in computational neuroscience, there has been increasing interest in the underlying neurocomputational mechanisms of psychosis. One successful approach involves predictive coding and Bayesian inference. Here, inferences regarding the current state of the world are made by combining prior beliefs with incoming sensory signals. Mismatches between prior beliefs and incoming signals constitute prediction errors that drive new learning. Psychosis has been suggested to result from a decreased precision in the encoding of prior beliefs relative to the sensory data, thereby garnering maladaptive inferences. Here, we review the current evidence for aberrant predictive coding and discuss challenges for this canonical predictive coding account of psychosis. For example, hallucinations and delusions may relate to distinct alterations in predictive coding, despite their common co-occurrence. More broadly, some studies implicate weakened prior beliefs in psychosis, and others find stronger priors. These challenges might be answered with a more nuanced view of predictive coding. Different priors may be specified for different sensory modalities and their integration, and deficits in each modality need not be uniform. Furthermore, hierarchical organization may be critical. Altered processes at lower levels of a hierarchy need not be linearly related to processes at higher levels (and vice versa). Finally, canonical theories do not highlight active inference-the process through which the effects of our actions on our sensations are anticipated and minimized. It is possible that conflicting findings might be reconciled by considering these complexities, portending a framework for psychosis more equipped to deal with its many manifestations.
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Affiliation(s)
- Philipp Sterzer
- Department of Psychiatry, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Rick A Adams
- Division of Psychiatry, University College London, London, United Kingdom
| | - Paul Fletcher
- Department of Psychiatry, Addenbrooke's Hospital, University of Cambridge, Cambridge, United Kingdom; Wellcome-MRC Behavioral and Clinical Neuroscience Institute, Cambridge and Peterborough Foundation Trust, Cambridge, United Kingdom
| | - Chris Frith
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
| | - Stephen M Lawrie
- Center for Clinical and Brain Sciences, Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, United Kingdom
| | - Lars Muckli
- Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Predrag Petrovic
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Peter Uhlhaas
- Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Martin Voss
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig Hospital, Berlin Center for Advanced Neuroimaging, Humboldt University Berlin, Berlin, Germany
| | - Philip R Corlett
- Department of Psychiatry, Yale University, New Haven, Connecticut.
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45
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Prosser A, Friston KJ, Bakker N, Parr T. A Bayesian Account of Psychopathy: A Model of Lacks Remorse and Self-Aggrandizing. Comput Psychiatr 2018; 2:92-140. [PMID: 30381799 PMCID: PMC6184370 DOI: 10.1162/cpsy_a_00016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2017] [Accepted: 04/27/2018] [Indexed: 12/28/2022]
Abstract
This article proposes a formal model that integrates cognitive and psychodynamic psychotherapeutic models of psychopathy to show how two major psychopathic traits called lacks remorse and self-aggrandizing can be understood as a form of abnormal Bayesian inference about the self. This model draws on the predictive coding (i.e., active inference) framework, a neurobiologically plausible explanatory framework for message passing in the brain that is formalized in terms of hierarchical Bayesian inference. In summary, this model proposes that these two cardinal psychopathic traits reflect entrenched maladaptive Bayesian inferences about the self, which defend against the experience of deep-seated, self-related negative emotions, specifically shame and worthlessness. Support for the model in extant research on the neurobiology of psychopathy and quantitative simulations are provided. Finally, we offer a preliminary overview of a novel treatment for psychopathy that rests on our Bayesian formulation.
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Affiliation(s)
- Aaron Prosser
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Canada
| | - Karl J. Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Nathan Bakker
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Thomas Parr
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
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46
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Abstract
A recent article shows that the brain automatically estimates the probabilities of possible future actions before it has even received all the information necessary to decide what to do next.
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47
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Smith R, Alkozei A, Killgore WDS, Lane RD. Nested positive feedback loops in the maintenance of major depression: An integration and extension of previous models. Brain Behav Immun 2018; 67:374-397. [PMID: 28943294 DOI: 10.1016/j.bbi.2017.09.011] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Revised: 09/18/2017] [Accepted: 09/19/2017] [Indexed: 12/15/2022] Open
Abstract
Several theories of Major Depressive Disorder (MDD) have previously been proposed, focusing largely on either a psychological (i.e., cognitive/affective), biological, or neural/computational level of description. These theories appeal to somewhat distinct bodies of work that have each highlighted separate factors as being of considerable potential importance to the maintenance of MDD. Such factors include a range of cognitive/attentional information-processing biases, a range of structural and functional brain abnormalities, and also dysregulation within the autonomic, endocrine, and immune systems. However, to date there have been limited efforts to integrate these complimentary perspectives into a single multi-level framework. Here we review previous work in each of these MDD research domains and illustrate how they can be synthesized into a more comprehensive model of how a depressive episode is maintained. In particular, we emphasize how plausible (but insufficiently studied) interactions between the various MDD-related factors listed above can lead to a series of nested positive feedback loops, which are each capable of maintaining an individual in a depressive episode. We also describe how these different feedback loops could be active to different degrees in different individual cases, potentially accounting for heterogeneity in both depressive symptoms and treatment response. We conclude by discussing how this integrative model might extend understanding of current treatment mechanisms, and also potentially guide the search for markers to inform treatment selection in individual cases.
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Affiliation(s)
- Ryan Smith
- Department of Psychiatry, University of Arizona, Tucson, AZ, USA.
| | - Anna Alkozei
- Department of Psychiatry, University of Arizona, Tucson, AZ, USA
| | | | - Richard D Lane
- Department of Psychiatry, University of Arizona, Tucson, AZ, USA
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48
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Gomez-Ramirez J, Costa T. Boredom begets creativity: A solution to the exploitation-exploration trade-off in predictive coding. Biosystems 2017; 162:168-176. [PMID: 28479110 DOI: 10.1016/j.biosystems.2017.04.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Revised: 04/11/2017] [Accepted: 04/12/2017] [Indexed: 11/30/2022]
Abstract
Here we investigate whether systems that minimize prediction error e.g. predictive coding, can also show creativity, or on the contrary, prediction error minimization unqualifies for the design of systems that respond in creative ways to non-recurrent problems. We argue that there is a key ingredient that has been overlooked by researchers that needs to be incorporated to understand intelligent behavior in biological and technical systems. This ingredient is boredom. We propose a mathematical model based on the Black-Scholes-Merton equation which provides mechanistic insights into the interplay between boredom and prediction pleasure as the key drivers of behavior.
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Affiliation(s)
- Jaime Gomez-Ramirez
- The Hospital for Sick Children, Department of Neuroscience and Mental Health, University of Toronto, Bay St. 686, Toronto, Canada.
| | - Tommaso Costa
- Koelliker Hospital, Department of Psychology, University of Turin, Via Verdi, 10, 10124 Turin, Italy
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49
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Abstract
Visual perception is strongly shaped by expectations, but it is poorly understood how such perceptual expectations are learned in our dynamic sensory environment. Here, we applied a Bayesian framework to investigate whether perceptual expectations are continuously updated from different aspects of ongoing experience. In two experiments, human observers performed an associative learning task in which rapidly changing expectations about the appearance of ambiguous stimuli were induced. We found that perception of ambiguous stimuli was biased by both learned associations and previous perceptual outcomes. Computational modeling revealed that perception was best explained by a model that continuously updated priors from associative learning and perceptual history and combined these priors with the current sensory information in a probabilistic manner. Our findings suggest that the construction of visual perception is a highly dynamic process that incorporates rapidly changing expectations from different sources in a manner consistent with Bayesian learning and inference.
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Affiliation(s)
- Katharina Schmack
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin Berlin, Germany
| | - Veith Weilnhammer
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin Berlin, Germany
| | - Jakob Heinzle
- Translational Neuromodelling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich Zurich, Switzerland
| | - Klaas E Stephan
- Translational Neuromodelling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich Zurich, Switzerland
| | - Philipp Sterzer
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin BerlinBerlin, Germany; Bernstein Center for Computational Neuroscience, Charité - Universitätsmedizin BerlinBerlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu BerlinBerlin, Germany
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Thornton C. Predictive processing simplified: The infotropic machine. Brain Cogn 2017; 112:13-24. [PMID: 27102775 DOI: 10.1016/j.bandc.2016.03.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2015] [Revised: 03/18/2016] [Accepted: 03/21/2016] [Indexed: 11/20/2022]
Abstract
On a traditional view of cognition, we see the agent acquiring stimuli, interpreting these in some way, and producing behavior in response. An increasingly popular alternative is the predictive processing framework. This sees the agent as continually generating predictions about the world, and responding productively to any errors made. Partly because of its heritage in the Bayesian brain theory, predictive processing has generally been seen as an inherently Bayesian process. The 'hierarchical prediction machine' which mediates it is envisaged to be a specifically Bayesian device. But as this paper shows, a specification for this machine can also be derived directly from information theory, using the metric of predictive payoff as an organizing concept. Hierarchical prediction machines can be built along purely information-theoretic lines, without referencing Bayesian theory in any way; this simplifies the account to some degree. The present paper describes what is involved and presents a series of working models. An experiment involving the conversion of a Braitenberg vehicle to use a controller of this type is also described.
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