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The Emperor Is Naked: Replies to commentaries on the target article. Behav Brain Sci 2022; 45:e219. [PMID: 36172792 DOI: 10.1017/s0140525x22000656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The 35 commentaries cover a wide range of topics and take many different stances on the issues explored by the target article. We have organised our response to the commentaries around three central questions: Are Friston blankets just Pearl blankets? What ontological and metaphysical commitments are implied by the use of Friston blankets? What kind of explanatory work are Friston blankets capable of? We conclude our reply with a short critical reflection on the indiscriminate use of both Markov blankets and the free energy principle.
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52
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Eckert AL, Pabst K, Endres DM. A Bayesian model for chronic pain. FRONTIERS IN PAIN RESEARCH 2022; 3:966034. [PMID: 36303889 PMCID: PMC9595216 DOI: 10.3389/fpain.2022.966034] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/25/2022] [Indexed: 11/13/2022] Open
Abstract
The perceiving mind constructs our coherent and embodied experience of the world from noisy, ambiguous and multi-modal sensory information. In this paper, we adopt the perspective that the experience of pain may similarly be the result of a probabilistic, inferential process. Prior beliefs about pain, learned from past experiences, are combined with incoming sensory information in a Bayesian manner to give rise to pain perception. Chronic pain emerges when prior beliefs and likelihoods are biased towards inferring pain from a wide range of sensory data that would otherwise be perceived as harmless. We present a computational model of interoceptive inference and pain experience. It is based on a Bayesian graphical network which comprises a hidden layer, representing the inferred pain state; and an observable layer, representing current sensory information. Within the hidden layer, pain states are inferred from a combination of priors p(pain), transition probabilities between hidden states p(paint+1∣paint) and likelihoods of certain observations p(sensation∣pain). Using variational inference and free-energy minimization, the model is able to learn from observations over time. By systematically manipulating parameter settings, we demonstrate that the model is capable of reproducing key features of both healthy- and chronic pain experience. Drawing on mathematical concepts, we finally simulate treatment resistant chronic pain and discuss mathematically informed treatment options.
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53
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Allen M, Levy A, Parr T, Friston KJ. In the Body’s Eye: The computational anatomy of interoceptive inference. PLoS Comput Biol 2022; 18:e1010490. [PMID: 36099315 PMCID: PMC9506608 DOI: 10.1371/journal.pcbi.1010490] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 09/23/2022] [Accepted: 08/13/2022] [Indexed: 11/24/2022] Open
Abstract
A growing body of evidence highlights the intricate linkage of exteroceptive perception to the rhythmic activity of the visceral body. In parallel, interoceptive inference theories of affective perception and self-consciousness are on the rise in cognitive science. However, thus far no formal theory has emerged to integrate these twin domains; instead, most extant work is conceptual in nature. Here, we introduce a formal model of cardiac active inference, which explains how ascending cardiac signals entrain exteroceptive sensory perception and uncertainty. Through simulated psychophysics, we reproduce the defensive startle reflex and commonly reported effects linking the cardiac cycle to affective behaviour. We further show that simulated ‘interoceptive lesions’ blunt affective expectations, induce psychosomatic hallucinations, and exacerbate biases in perceptual uncertainty. Through synthetic heart-rate variability analyses, we illustrate how the balance of arousal-priors and visceral prediction errors produces idiosyncratic patterns of physiological reactivity. Our model thus offers a roadmap for computationally phenotyping disordered brain-body interaction. Understanding interactions between the brain and the body has become a topic of increased interest in computational neuroscience and psychiatry. A particular question here concerns how visceral, homeostatic rhythms such as the heart beat influence sensory, affective, and cognitive processing. To better understand these and other oscillatory brain-body interactions, we here introduce a novel computational model of interoceptive inference in which a synthetic agent’s perceptual beliefs are coupled to the rhythm of the heart. Our model both helps to explain emerging empirical data indicating that perceptual inference depends upon beat-to-beat heart rhythms, and can be used to better quantify intra- and inter-individual differences in heart-brain coupling. Using proof-of-principle simulations, we demonstrate how future empirical works could utilize our model to better understand and stratify disorders of interoception and brain-body interaction.
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Affiliation(s)
- Micah Allen
- Centre of Functionally Integrative Neuroscience, Aarhus University Hospital, Denmark
- Cambridge Psychiatry, Cambridge University, Cambridge, United Kingdom
- * E-mail:
| | - Andrew Levy
- 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 J. Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
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54
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Fiorio M, Braga M, Marotta A, Villa-Sánchez B, Edwards MJ, Tinazzi M, Barbiani D. Functional neurological disorder and placebo and nocebo effects: shared mechanisms. Nat Rev Neurol 2022; 18:624-635. [PMID: 36075980 DOI: 10.1038/s41582-022-00711-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/04/2022] [Indexed: 11/09/2022]
Abstract
Functional neurological disorder (FND) is characterized by neurological symptoms that cannot be explained by a structural neurological cause. Among the different aetiological models that have been proposed for FND, of note is the Bayesian predictive coding model, which posits that perception relies on top-down cortical predictions (priors) to infer the source of incoming sensory information. This model can also apply to non-pathological experiences, such as placebo and nocebo effects, wherein sensory information is shaped by prior expectations and learning. To date, most studies of the relationship between placebo and nocebo effects and FND have focused on the use of placebos for diagnosis and treatment of FND. Here, we propose that this relationship might go beyond diagnosis and therapy. We develop a framework in which shared cognitive, personality and neuroanatomical factors justify the consideration of a deeper link between FND and placebo and nocebo effects. This new perspective might offer guidance for clarification of the pathogenesis of FND and for the identification of potential biomarkers and therapeutic targets.
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Affiliation(s)
- Mirta Fiorio
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy.
| | - Miriam Braga
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Angela Marotta
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | - Mark J Edwards
- Institute of Molecular and Clinical Sciences, St George's University of London, London, UK
| | - Michele Tinazzi
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Diletta Barbiani
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
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55
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Champion T, Grześ M, Bowman H. Branching Time Active Inference with Bayesian Filtering. Neural Comput 2022; 34:2132-2144. [PMID: 36027722 DOI: 10.1162/neco_a_01529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 05/26/2022] [Indexed: 11/04/2022]
Abstract
Branching time active inference is a framework proposing to look at planning as a form of Bayesian model expansion. Its root can be found in active inference, a neuroscientific framework widely used for brain modeling, as well as in Monte Carlo tree search, a method broadly applied in the reinforcement learning literature. Up to now, the inference of the latent variables was carried out by taking advantage of the flexibility offered by variational message passing, an iterative process that can be understood as sending messages along the edges of a factor graph. In this letter, we harness the efficiency of an alternative method for inference, Bayesian filtering, which does not require the iteration of the update equations until convergence of the variational free energy. Instead, this scheme alternates between two phases: integration of evidence and prediction of future states. Both phases can be performed efficiently, and this provides a forty times speedup over the state of the art.
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Affiliation(s)
| | - Marek Grześ
- University of Kent, School of Computing, Canterbury CT2 7NZ, U.K.
| | - Howard Bowman
- University of Birmingham, School of Psychology, Birmingham B15 2TT, U.K.,University of Kent, School of Computing, Canterbury CT2 7NZ, U.K.
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56
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Deane G. Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence. ARTIFICIAL LIFE 2022; 28:289-309. [PMID: 35881678 DOI: 10.1162/artl_a_00368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
What role do affective feelings (feelings/emotions/moods) play in adaptive behaviour? What are the implications of this for understanding and developing artificial general intelligence? Leading theoretical models of brain function are beginning to shed light on these questions. While artificial agents have excelled within narrowly circumscribed and specialised domains, domain-general intelligence has remained an elusive goal in artificial intelligence research. By contrast, humans and nonhuman animals are characterised by a capacity for flexible behaviour and general intelligence. In this article I argue that computational models of mental phenomena in predictive processing theories of the brain are starting to reveal the mechanisms underpinning domain-general intelligence in biological agents, and can inform the understanding and development of artificial general intelligence. I focus particularly on approaches to computational phenomenology in the active inference framework. Specifically, I argue that computational mechanisms of affective feelings in active inference-affective self-modelling-are revealing of how biological agents are able to achieve flexible behavioural repertoires and general intelligence. I argue that (i) affective self-modelling functions to "tune" organisms to the most tractable goals in the environmental context; and (ii) affective and agentic self-modelling is central to the capacity to perform mental actions in goal-directed imagination and creative cognition. I use this account as a basis to argue that general intelligence of the level and kind found in biological agents will likely require machines to be implemented with analogues of affective self-modelling.
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Affiliation(s)
- George Deane
- University of Edinburgh, School of Philosophy, Psychology, and Language Sciences.
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57
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Branching time active inference: Empirical study and complexity class analysis. Neural Netw 2022; 152:450-466. [DOI: 10.1016/j.neunet.2022.05.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 03/26/2022] [Accepted: 05/10/2022] [Indexed: 12/25/2022]
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58
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Tietz S, Wagner-Skacel J, Angel HF, Ratzenhofer M, Fellendorf FT, Fleischmann E, Körner C, Reininghaus EZ, Seitz RJ, Dalkner N. Believing processes during the COVID-19 pandemic in individuals with bipolar disorder: An exploratory study. World J Psychiatry 2022; 12:929-943. [PMID: 36051599 PMCID: PMC9331453 DOI: 10.5498/wjp.v12.i7.929] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 03/27/2022] [Accepted: 06/27/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Believing or “credition” refers to psychological processes that integrate the cognitions and emotions that influence our behavior. In the credition model by Angel and Seitz, four parameters are postulated: proposition, certainty, emotion and mightiness. It is assumed that believing processes are influenced by both the individual as well as socio-cultural factors and external circumstances. External or environmental circumstances can include threatening situations such as the ongoing pandemic. It has been hypothesized that believing processes related to the pandemic differ between individuals with bipolar disorder (BD) and healthy controls (HC).
AIM To investigate credition in individuals with BD during the coronavirus disease 2019 (COVID-19) pandemic.
METHODS Psychiatrically stable individuals with BD (n = 52) and age- and sex matched HC (n = 52) participated in an online survey during the first lockdown of the COVID-19 pandemic. The survey took place between April 9th and June 4th, 2020, in Austria. Participants completed the Brief Symptom Inventory-18, the Beck Depression Inventory-II, the Altman Self-Rating Mania Scale, the Pittsburgh Sleep Quality Index and a dedicated Believing Questionnaire assessing four parameters of credition (proposition, certainty, emotion and mightiness). The MAXQDA software was used to analyze the qualitative data. Statistical analyses included analyses of variance, a multivariate analysis of variance and a multivariate analysis of co-variance.
RESULTS Individuals with BD reported significantly more negative propositions [F (1,102) = 8.89, P = 0.004, η2p = 0.08] and negative emotions [Welch´s F (1,82.46) = 18.23, P < 0.001, η2p = 0.18], while HC showed significantly more positive propositions [F (1,102) = 7.78, P = 0.006, η2p = 0.07] and emotions [F (1,102) = 14.31, P < 0.001, η2p = 0.12]. In addition, individuals with BD showed a higher incongruence between their propositions and their emotions [F (1,102) = 9.42, P = 0.003, η2p = 0.08] and showed strong correlations between the parameters of the Believing Questionnaire and their psychiatric symptoms (r = 0.51-0.77, all P < 0.001). Positive as well as negative emotions and propositions were associated with scores measuring symptoms of depression, anxiety and sleep quality.
CONCLUSION Believing parameters were associated with psychiatric symptoms in BD during the pandemic. Findings broaden knowledge about the susceptibility of believing processes for ambient challenges in individuals with BD.
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Affiliation(s)
- Sophie Tietz
- Institute of Psychology, University of Graz, Graz 8010, Austria
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz 8036, Austria
| | - Jolana Wagner-Skacel
- Department of Medical Psychology and Psychotherapy, Medical University of Graz, Graz 8036, Austria
| | - Hans-Ferdinand Angel
- Department of Catechetics and Religious Education, University of Graz, Graz 8010, Austria
| | - Michaela Ratzenhofer
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz 8036, Austria
| | - Frederike T Fellendorf
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz 8036, Austria
| | - Eva Fleischmann
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz 8036, Austria
| | - Christof Körner
- Institute of Psychology, University of Graz, Graz 8010, Austria
| | - Eva Z Reininghaus
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz 8036, Austria
| | - Rüdiger J Seitz
- Department of Neurology, Centre of Neurology and Neuropsychiatry Heinrich-Heine-University Düsseldorf, Medical Faculty, Düsseldorf D-40629, Germany
| | - Nina Dalkner
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz 8036, Austria
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59
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Le Bec B, Troncoso XG, Desbois C, Passarelli Y, Baudot P, Monier C, Pananceau M, Frégnac Y. Horizontal connectivity in V1: Prediction of coherence in contour and motion integration. PLoS One 2022; 17:e0268351. [PMID: 35802625 PMCID: PMC9269411 DOI: 10.1371/journal.pone.0268351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 04/26/2022] [Indexed: 11/30/2022] Open
Abstract
This study demonstrates the functional importance of the Surround context relayed laterally in V1 by the horizontal connectivity, in controlling the latency and the gain of the cortical response to the feedforward visual drive. We report here four main findings: 1) a centripetal apparent motion sequence results in a shortening of the spiking latency of V1 cells, when the orientation of the local inducer and the global motion axis are both co-aligned with the RF orientation preference; 2) this contextual effects grows with visual flow speed, peaking at 150–250°/s when it matches the propagation speed of horizontal connectivity (0.15–0.25 mm/ms); 3) For this speed range, the axial sensitivity of V1 cells is tilted by 90° to become co-aligned with the orientation preference axis; 4) the strength of modulation by the surround context correlates with the spatiotemporal coherence of the apparent motion flow. Our results suggest an internally-generated binding process, linking local (orientation /position) and global (motion/direction) features as early as V1. This long-range diffusion process constitutes a plausible substrate in V1 of the human psychophysical bias in speed estimation for collinear motion. Since it is demonstrated in the anesthetized cat, this novel form of contextual control of the cortical gain and phase is a built-in property in V1, whose expression does not require behavioral attention and top-down control from higher cortical areas. We propose that horizontal connectivity participates in the propagation of an internal “prediction” wave, shaped by visual experience, which links contour co-alignment and global axial motion at an apparent speed in the range of saccade-like eye movements.
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Affiliation(s)
- Benoit Le Bec
- NeuroPSI-UNIC, Paris-Saclay Institute of Neuroscience, CNRS, Paris-Saclay University, Gif-sur-Yvette, France
| | - Xoana G. Troncoso
- NeuroPSI-UNIC, Paris-Saclay Institute of Neuroscience, CNRS, Paris-Saclay University, Gif-sur-Yvette, France
| | - Christophe Desbois
- NeuroPSI-UNIC, Paris-Saclay Institute of Neuroscience, CNRS, Paris-Saclay University, Gif-sur-Yvette, France
- Ecole Nationale Vétérinaire d’Alfort, Maisons-Alfort, France
| | - Yannick Passarelli
- NeuroPSI-UNIC, Paris-Saclay Institute of Neuroscience, CNRS, Paris-Saclay University, Gif-sur-Yvette, France
| | - Pierre Baudot
- NeuroPSI-UNIC, Paris-Saclay Institute of Neuroscience, CNRS, Paris-Saclay University, Gif-sur-Yvette, France
| | - Cyril Monier
- NeuroPSI-UNIC, Paris-Saclay Institute of Neuroscience, CNRS, Paris-Saclay University, Gif-sur-Yvette, France
| | - Marc Pananceau
- NeuroPSI-UNIC, Paris-Saclay Institute of Neuroscience, CNRS, Paris-Saclay University, Gif-sur-Yvette, France
| | - Yves Frégnac
- NeuroPSI-UNIC, Paris-Saclay Institute of Neuroscience, CNRS, Paris-Saclay University, Gif-sur-Yvette, France
- * E-mail:
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60
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Bouttier V, Duttagupta S, Denève S, Jardri R. Circular inference predicts nonuniform overactivation and dysconnectivity in brain-wide connectomes. Schizophr Res 2022; 245:59-67. [PMID: 33618940 DOI: 10.1016/j.schres.2020.12.045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 12/22/2020] [Accepted: 12/26/2020] [Indexed: 12/17/2022]
Abstract
Schizophrenia is a severe mental disorder whose neural basis remains difficult to ascertain. Among the available pathophysiological theories, recent work has pointed towards subtle perturbations in the excitation-inhibition (E/I) balance within different neural circuits. Computational approaches have suggested interesting mechanisms that can account for both E/I imbalances and psychotic symptoms. Based on hierarchical neural networks propagating information through a message-passing algorithm, it was hypothesized that changes in the E/I ratio could cause a "circular belief propagation" in which bottom-up and top-down information reverberate. This circular inference (CI) was proposed to account for the clinical features of schizophrenia. Under this assumption, this paper examined the impact of CI on network dynamics in light of brain imaging findings related to psychosis. Using brain-inspired graphical models, we show that CI causes overconfidence and overactivation most specifically at the level of connector hubs (e.g., nodes with many connections allowing integration across networks). By also measuring functional connectivity in these graphs, we provide evidence that CI is able to predict specific changes in modularity known to be associated with schizophrenia. Altogether, these findings suggest that the CI framework may facilitate behavioral and neural research on the multifaceted nature of psychosis.
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Affiliation(s)
- Vincent Bouttier
- Univ Lille, INSERM U1172, CHU Lille, Lille Neurosciences & Cognition Centre (LiNC), Plasticity & SubjectivitY team, 59037 Lille, France; Group for Neural Theory, Laboratoire de Neurosciences Cognitives et Computationnelles (LNC(2)), Ecole Normale Supérieure, INSERM U960, PSL University, 75005 Paris, France.
| | - Suhrit Duttagupta
- Group for Neural Theory, Laboratoire de Neurosciences Cognitives et Computationnelles (LNC(2)), Ecole Normale Supérieure, INSERM U960, PSL University, 75005 Paris, France
| | - Sophie Denève
- Group for Neural Theory, Laboratoire de Neurosciences Cognitives et Computationnelles (LNC(2)), Ecole Normale Supérieure, INSERM U960, PSL University, 75005 Paris, France
| | - Renaud Jardri
- Univ Lille, INSERM U1172, CHU Lille, Lille Neurosciences & Cognition Centre (LiNC), Plasticity & SubjectivitY team, 59037 Lille, France; Group for Neural Theory, Laboratoire de Neurosciences Cognitives et Computationnelles (LNC(2)), Ecole Normale Supérieure, INSERM U960, PSL University, 75005 Paris, France.
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61
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Smith R, Taylor S, Stewart JL, Guinjoan SM, Ironside M, Kirlic N, Ekhtiari H, White EJ, Zheng H, Kuplicki R, Paulus MP. Slower Learning Rates from Negative Outcomes in Substance Use Disorder over a 1-Year Period and Their Potential Predictive Utility. COMPUTATIONAL PSYCHIATRY (CAMBRIDGE, MASS.) 2022; 6:117-141. [PMID: 38774781 PMCID: PMC11104312 DOI: 10.5334/cpsy.85] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 04/27/2022] [Indexed: 11/20/2022]
Abstract
Computational modelling is a promising approach to parse dysfunctional cognitive processes in substance use disorders (SUDs), but it is unclear how much these processes change during the recovery period. We assessed 1-year follow-up data on a sample of treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 83) that were previously assessed at baseline within a prior computational modelling study. Relative to healthy controls (HCs; N = 48), these participants were found at baseline to show altered learning rates and less precise action selection while completing an explore-exploit decision-making task. Here we replicated these analyses when these individuals returned and re-performed the task 1 year later to assess the stability of baseline differences. We also examined whether baseline modelling measures could predict symptoms at follow-up. Bayesian and frequentist analyses indicated that: (a) group differences in learning rates were stable over time (posterior probability = 1); and (b) intra-class correlations (ICCs) between model parameters at baseline and follow-up were significant and ranged from small to moderate (.25 ≤ ICCs ≤ .54). Exploratory analyses also suggested that learning rates and/or information-seeking values at baseline were associated with substance use severity at 1-year follow-up in stimulant and opioid users (.36 ≤ rs ≤ .43). These findings suggest that learning dysfunctions are moderately stable during recovery and could correspond to trait-like vulnerability factors. In addition, computational measures at baseline had some predictive value for changes in substance use severity over time and could be clinically informative.
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Affiliation(s)
- Ryan Smith
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Samuel Taylor
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Jennifer L. Stewart
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Department of Community Medicine, University of Tulsa, Tulsa, OK USA
| | | | | | - Namik Kirlic
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | | | - Evan J. White
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Haixia Zheng
- Laureate Institute for Brain Research, Tulsa, OK, USA
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62
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Seitz RJ. Believing and Beliefs-Neurophysiological Underpinnings. Front Behav Neurosci 2022; 16:880504. [PMID: 35517575 PMCID: PMC9063518 DOI: 10.3389/fnbeh.2022.880504] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 03/23/2022] [Indexed: 11/25/2022] Open
Affiliation(s)
- Rüdiger J Seitz
- Department of Neurology, Centre of Neurology and Neuropsychiatry, LVR-Klinikum Düsseldorf, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
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63
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WSN-driven Posture Recognition and Correction towards Basketball Exercise. INTERNATIONAL JOURNAL OF INFORMATION SYSTEM MODELING AND DESIGN 2022. [DOI: 10.4018/ijismd.300777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In order to enhance the daily training for basketball sport, this paper establishes a human posture estimation framework by using monocular camera and wireless sensor network. First, the daily basketball training images are collected by monocular camera and transmitted through wireless sensor network. Second, the collected images are processed by an observation and reasoning model which is based on component and graph reasoning. The basketball player’s posture is depicted by the rotation invariant features of edge field. The extracted features are used to learn a boosting classifier as the observation model. The experimental results show that the posture recognition rate can achieve more than 88% for basketball player’s action.
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64
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Champion T, Da Costa L, Bowman H, Grześ M. Branching Time Active Inference: The theory and its generality. Neural Netw 2022; 151:295-316. [PMID: 35468491 DOI: 10.1016/j.neunet.2022.03.036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 03/25/2022] [Accepted: 03/28/2022] [Indexed: 12/01/2022]
Abstract
Over the last 10 to 15 years, active inference has helped to explain various brain mechanisms from habit formation to dopaminergic discharge and even modelling curiosity. However, the current implementations suffer from an exponential (space and time) complexity class when computing the prior over all the possible policies up to the time-horizon. Fountas et al. (2020) used Monte Carlo tree search to address this problem, leading to impressive results in two different tasks. In this paper, we present an alternative framework that aims to unify tree search and active inference by casting planning as a structure learning problem. Two tree search algorithms are then presented. The first propagates the expected free energy forward in time (i.e., towards the leaves), while the second propagates it backward (i.e., towards the root). Then, we demonstrate that forward and backward propagations are related to active inference and sophisticated inference, respectively, thereby clarifying the differences between those two planning strategies.
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Affiliation(s)
- Théophile Champion
- University of Kent, School of Computing, Canterbury CT2 7NZ, United Kingdom.
| | - Lancelot Da Costa
- Imperial College London, Department of Mathematics, London SW7 2AZ, United Kingdom; Wellcome Centre for Human Neuroimaging, University College London, London, WC1N 3AR, United Kingdom.
| | - Howard Bowman
- University of Birmingham, School of Psychology, Birmingham B15 2TT, United Kingdom; University of Kent, School of Computing, Canterbury CT2 7NZ, United Kingdom.
| | - Marek Grześ
- University of Kent, School of Computing, Canterbury CT2 7NZ, United Kingdom.
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65
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Smith R, Friston KJ, Whyte CJ. A step-by-step tutorial on active inference and its application to empirical data. JOURNAL OF MATHEMATICAL PSYCHOLOGY 2022; 107:102632. [PMID: 35340847 PMCID: PMC8956124 DOI: 10.1016/j.jmp.2021.102632] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
The active inference framework, and in particular its recent formulation as a partially observable Markov decision process (POMDP), has gained increasing popularity in recent years as a useful approach for modeling neurocognitive processes. This framework is highly general and flexible in its ability to be customized to model any cognitive process, as well as simulate predicted neuronal responses based on its accompanying neural process theory. It also affords both simulation experiments for proof of principle and behavioral modeling for empirical studies. However, there are limited resources that explain how to build and run these models in practice, which limits their widespread use. Most introductions assume a technical background in programming, mathematics, and machine learning. In this paper we offer a step-by-step tutorial on how to build POMDPs, run simulations using standard MATLAB routines, and fit these models to empirical data. We assume a minimal background in programming and mathematics, thoroughly explain all equations, and provide exemplar scripts that can be customized for both theoretical and empirical studies. Our goal is to provide the reader with the requisite background knowledge and practical tools to apply active inference to their own research. We also provide optional technical sections and multiple appendices, which offer the interested reader additional technical details. This tutorial should provide the reader with all the tools necessary to use these models and to follow emerging advances in active inference research.
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Affiliation(s)
- Ryan Smith
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Karl J. Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, WC1N 3AR, UK
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66
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Chan A, Northoff G, Karasik R, Ouyang J, Williams K. Flights and Perchings of the BrainMind: A Temporospatial Approach to Psychotherapy. Front Psychol 2022; 13:828035. [PMID: 35444594 PMCID: PMC9014955 DOI: 10.3389/fpsyg.2022.828035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 02/21/2022] [Indexed: 11/13/2022] Open
Abstract
This article introduces a process-oriented approach for improving present moment conceptualization in psychotherapy that is in alignment with neuroscience: the Temporospatial movements of mind (TSMM) model. We elaborate on seven temporal movements that describe the moment-to-moment morphogenesis of emotional feelings and thoughts from inception to maturity. Temporal refers to the passage of time through which feelings and thoughts develop, and electromagnetic activity, that among other responsibilities, bind information across time. Spatial dynamics extend from an undifferentiated to three dimensional experiences of emotional and cognitive processes. Neurophysiologically, spatial refers to structures within the brain and their varying interactions with one another. This article culminates in the development of an atheoretical temporospatial grid that may help clinicians conceptualize where patients are in their cognitive and emotional development to further guide technique.
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Affiliation(s)
- Aldrich Chan
- Graduate School of Education and Psychology, Pepperdine University, Malibu, CA, United States
- Center for Neuropsychology and Consciousness, Miami, FL, United States
- *Correspondence: Aldrich Chan,
| | - Georg Northoff
- Faculty of Medicine, Centre for Neural Dynamics, The Royal’s Institute of Mental Health Research, Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
- Mental Health Centre, Zhejiang University School of Medicine, Hangzhou, China
- Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China
| | - Ryan Karasik
- Graduate School of Education and Psychology, Pepperdine University, Malibu, CA, United States
- Center for Neuropsychology and Consciousness, Miami, FL, United States
| | - Jason Ouyang
- Graduate School of Education and Psychology, Pepperdine University, Malibu, CA, United States
- Center for Neuropsychology and Consciousness, Miami, FL, United States
| | - Kathryn Williams
- Graduate School of Education and Psychology, Pepperdine University, Malibu, CA, United States
- Center for Neuropsychology and Consciousness, Miami, FL, United States
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Ramstead MJD, Seth AK, Hesp C, Sandved-Smith L, Mago J, Lifshitz M, Pagnoni G, Smith R, Dumas G, Lutz A, Friston K, Constant A. From Generative Models to Generative Passages: A Computational Approach to (Neuro) Phenomenology. REVIEW OF PHILOSOPHY AND PSYCHOLOGY 2022; 13:829-857. [PMID: 35317021 PMCID: PMC8932094 DOI: 10.1007/s13164-021-00604-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 10/28/2021] [Indexed: 12/16/2022]
Abstract
This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. Our approach can be described as computational phenomenology because it applies methods originally developed in computational modelling to provide a formal model of the descriptions of lived experience in the phenomenological tradition of philosophy (e.g., the work of Edmund Husserl, Maurice Merleau-Ponty, etc.). The first section presents a brief review of the overall project to naturalize phenomenology. The second section presents and evaluates philosophical objections to that project and situates our version of computational phenomenology with respect to these projects. The third section reviews the generative modelling framework. The final section presents our approach in detail. We conclude by discussing how our approach differs from previous attempts to use generative modelling to help understand consciousness. In summary, we describe a version of computational phenomenology which uses generative modelling to construct a computational model of the inferential or interpretive processes that best explain this or that kind of lived experience.
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Affiliation(s)
- Maxwell J. D. Ramstead
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- VERSES Research Lab and Spatial Web Foundation, Los Angeles, California USA
| | - Anil K. Seth
- School of Engineering and Informatics, University of Sussex, Brighton, BN1 9QJ UK
- Canadian Institute for Advanced Research (CIFAR), Program on Brain, Mind, and Consciousness, Toronto, Ontario, M5G 1M1 Canada
| | - Casper Hesp
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Department of Psychology, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands
- Amsterdam Brain and Cognition Centre, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands
- Institute for Advanced Study, University of Amsterdam, Oude Turfmarkt 147, 1012 GC Amsterdam, Netherlands
| | - Lars Sandved-Smith
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Lyon Neuroscience Research Centre, INSERM U1028, CNRS UMR5292, Lyon 1 University, Lyon, France
| | - Jonas Mago
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Integrated Program in Neuroscience, Department of Neuroscience, McGill University, Montreal, Canada
- Division of Social and Transcultural Psychiatry, McGill University, Montreal, Canada
| | - Michael Lifshitz
- Division of Social and Transcultural Psychiatry, McGill University, Montreal, Canada
- Lady Davis Institute for Medical Research, Montreal Jewish General Hospital, Montreal, Canada
| | - Giuseppe Pagnoni
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy
| | - Ryan Smith
- Laureate Institute for Brain Research, Tulsa, Oklahoma USA
| | - Guillaume Dumas
- CHU Sainte-Justine Research Center, Department of Psychiatry, University of Montreal, Montreal, Canada
- Mila – Quebec Artificial Intelligence Institute, University of Montreal, Montreal, Canada
| | - Antoine Lutz
- Lyon Neuroscience Research Centre, INSERM U1028, CNRS UMR5292, Lyon 1 University, Lyon, France
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- VERSES Research Lab and Spatial Web Foundation, Los Angeles, California USA
| | - Axel Constant
- Charles Perkins Centre, The University of Sydney, Sydney, Australia
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68
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Da Costa L, Lanillos P, Sajid N, Friston K, Khan S. How Active Inference Could Help Revolutionise Robotics. ENTROPY (BASEL, SWITZERLAND) 2022; 24:361. [PMID: 35327872 PMCID: PMC8946999 DOI: 10.3390/e24030361] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/24/2022] [Accepted: 02/28/2022] [Indexed: 02/05/2023]
Abstract
Recent advances in neuroscience have characterised brain function using mathematical formalisms and first principles that may be usefully applied elsewhere. In this paper, we explain how active inference-a well-known description of sentient behaviour from neuroscience-can be exploited in robotics. In short, active inference leverages the processes thought to underwrite human behaviour to build effective autonomous systems. These systems show state-of-the-art performance in several robotics settings; we highlight these and explain how this framework may be used to advance robotics.
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Affiliation(s)
- Lancelot Da Costa
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK; (N.S.); (K.F.)
| | - Pablo Lanillos
- Department of Artificial Intelligence, Donders Institute for Brain, Cognition and Behavior, Radboud University, 6525 XZ Nijmegen, The Netherlands;
| | - Noor Sajid
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK; (N.S.); (K.F.)
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK; (N.S.); (K.F.)
| | - Shujhat Khan
- Milton Keynes Hospital, Oxford Deanery, Milton Keynes MK6 5LD, UK;
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Amunts K, DeFelipe J, Pennartz C, Destexhe A, Migliore M, Ryvlin P, Furber S, Knoll A, Bitsch L, Bjaalie JG, Ioannidis Y, Lippert T, Sanchez-Vives MV, Goebel R, Jirsa V. Linking Brain Structure, Activity, and Cognitive Function through Computation. eNeuro 2022; 9:ENEURO.0316-21.2022. [PMID: 35217544 PMCID: PMC8925650 DOI: 10.1523/eneuro.0316-21.2022] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 01/11/2022] [Accepted: 01/17/2022] [Indexed: 01/19/2023] Open
Abstract
Understanding the human brain is a "Grand Challenge" for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generative multiscale models, which enable the investigation of causation across scales and are guided by principles and theories of brain function, are instrumental for linking brain structure and function. An example of a resource enabling such an integrated approach to neuroscientific discovery is the BigBrain, which spatially anchors tissue models and data across different scales and ensures that multiscale models are supported by the data, making the bridge to both basic neuroscience and medicine. Research at the intersection of neuroscience, computing and robotics has the potential to advance neuro-inspired technologies by taking advantage of a growing body of insights into perception, plasticity and learning. To render data, tools and methods, theories, basic principles and concepts interoperable, the Human Brain Project (HBP) has launched EBRAINS, a digital neuroscience research infrastructure, which brings together a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating insights and perspectives for societal benefits.
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Affiliation(s)
- Katrin Amunts
- Institute of Neurosciences and Medicine (INM-1), Research Centre Jülich, Jülich 52425, Germany
- C. & O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid 28223, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Madrid 28002, Spain
| | - Cyriel Pennartz
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, 1098 XH, The Netherlands
| | - Alain Destexhe
- Centre National de la Recherche Scientifique, Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Gif sur Yvette 91400, France
| | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo 90146, Italy
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, Lausanne CH-1011, Switzerland
| | - Steve Furber
- Department of Computer Science, The University of Manchester, Manchester M13 9PL, United Kingdom
| | - Alois Knoll
- Department of Informatics, Technical University of Munich, Garching 385748, Germany
| | - Lise Bitsch
- The Danish Board of Technology Foundation, Copenhagen, 2650 Hvidovre, Denmark
| | - Jan G Bjaalie
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Yannis Ioannidis
- ATHENA Research & Innovation Center, Athena 12125, Greece
- Department of Informatics & Telecom, Nat'l and Kapodistrian University of Athens, 157 84 Athens, Greece
| | - Thomas Lippert
- Institute for Advanced Simulation (IAS), Jülich Supercomputing Centre (JSC), Research Centre Jülich, Jülich 52425, Germany
| | - Maria V Sanchez-Vives
- ICREA and Systems Neuroscience, Institute of Biomedical Investigations August Pi i Sunyer, Barcelona 08036, Spain
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht 6229 EV, The Netherlands
| | - Viktor Jirsa
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
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70
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Mazzaglia P, Verbelen T, Çatal O, Dhoedt B. The Free Energy Principle for Perception and Action: A Deep Learning Perspective. ENTROPY (BASEL, SWITZERLAND) 2022; 24:301. [PMID: 35205595 PMCID: PMC8871280 DOI: 10.3390/e24020301] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 02/14/2022] [Accepted: 02/15/2022] [Indexed: 02/05/2023]
Abstract
The free energy principle, and its corollary active inference, constitute a bio-inspired theory that assumes biological agents act to remain in a restricted set of preferred states of the world, i.e., they minimize their free energy. Under this principle, biological agents learn a generative model of the world and plan actions in the future that will maintain the agent in an homeostatic state that satisfies its preferences. This framework lends itself to being realized in silico, as it comprehends important aspects that make it computationally affordable, such as variational inference and amortized planning. In this work, we investigate the tool of deep learning to design and realize artificial agents based on active inference, presenting a deep-learning oriented presentation of the free energy principle, surveying works that are relevant in both machine learning and active inference areas, and discussing the design choices that are involved in the implementation process. This manuscript probes newer perspectives for the active inference framework, grounding its theoretical aspects into more pragmatic affairs, offering a practical guide to active inference newcomers and a starting point for deep learning practitioners that would like to investigate implementations of the free energy principle.
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Affiliation(s)
- Pietro Mazzaglia
- IDLab, Ghent University, 9052 Gent, Belgium; (T.V.); (O.Ç.); (B.D.)
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71
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Da Costa L, Friston K, Heins C, Pavliotis GA. Bayesian mechanics for stationary processes. Proc Math Phys Eng Sci 2022; 477:20210518. [PMID: 35153603 PMCID: PMC8652275 DOI: 10.1098/rspa.2021.0518] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/27/2021] [Indexed: 01/02/2023] Open
Abstract
This paper develops a Bayesian mechanics for adaptive systems. Firstly, we model the interface between a system and its environment with a Markov blanket. This affords conditions under which states internal to the blanket encode information about external states. Second, we introduce dynamics and represent adaptive systems as Markov blankets at steady state. This allows us to identify a wide class of systems whose internal states appear to infer external states, consistent with variational inference in Bayesian statistics and theoretical neuroscience. Finally, we partition the blanket into sensory and active states. It follows that active states can be seen as performing active inference and well-known forms of stochastic control (such as PID control), which are prominent formulations of adaptive behaviour in theoretical biology and engineering.
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Affiliation(s)
- Lancelot Da Costa
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK.,Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - Conor Heins
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz D-78457, Germany.,Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz D-78457, Germany.,Department of Biology, University of Konstanz, Konstanz D-78457, Germany
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72
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Pezzulo G, Parr T, Friston K. The evolution of brain architectures for predictive coding and active inference. Philos Trans R Soc Lond B Biol Sci 2022; 377:20200531. [PMID: 34957844 PMCID: PMC8710884 DOI: 10.1098/rstb.2020.0531] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 09/08/2021] [Indexed: 01/13/2023] Open
Abstract
This article considers the evolution of brain architectures for predictive processing. We argue that brain mechanisms for predictive perception and action are not late evolutionary additions of advanced creatures like us. Rather, they emerged gradually from simpler predictive loops (e.g. autonomic and motor reflexes) that were a legacy from our earlier evolutionary ancestors-and were key to solving their fundamental problems of adaptive regulation. We characterize simpler-to-more-complex brains formally, in terms of generative models that include predictive loops of increasing hierarchical breadth and depth. These may start from a simple homeostatic motif and be elaborated during evolution in four main ways: these include the multimodal expansion of predictive control into an allostatic loop; its duplication to form multiple sensorimotor loops that expand an animal's behavioural repertoire; and the gradual endowment of generative models with hierarchical depth (to deal with aspects of the world that unfold at different spatial scales) and temporal depth (to select plans in a future-oriented manner). In turn, these elaborations underwrite the solution to biological regulation problems faced by increasingly sophisticated animals. Our proposal aligns neuroscientific theorising-about predictive processing-with evolutionary and comparative data on brain architectures in different animal species. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.
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Affiliation(s)
- Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Via S. Martino della Battaglia, 44, 00185 Rome, Italy
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK
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73
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Neacsu V, Convertino L, Friston KJ. Synthetic Spatial Foraging With Active Inference in a Geocaching Task. Front Neurosci 2022; 16:802396. [PMID: 35210988 PMCID: PMC8861269 DOI: 10.3389/fnins.2022.802396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 01/14/2022] [Indexed: 11/13/2022] Open
Abstract
Humans are highly proficient in learning about the environments in which they operate. They form flexible spatial representations of their surroundings that can be leveraged with ease during spatial foraging and navigation. To capture these abilities, we present a deep Active Inference model of goal-directed behavior, and the accompanying belief updating. Active Inference rests upon optimizing Bayesian beliefs to maximize model evidence or marginal likelihood. Bayesian beliefs are probability distributions over the causes of observable outcomes. These causes include an agent's actions, which enables one to treat planning as inference. We use simulations of a geocaching task to elucidate the belief updating-that underwrites spatial foraging-and the associated behavioral and neurophysiological responses. In a geocaching task, the aim is to find hidden objects in the environment using spatial coordinates. Here, synthetic agents learn about the environment via inference and learning (e.g., learning about the likelihoods of outcomes given latent states) to reach a target location, and then forage locally to discover the hidden object that offers clues for the next location.
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Affiliation(s)
- Victorita Neacsu
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | - Laura Convertino
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
- School of Life and Medical Sciences, Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Karl J. Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
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74
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Safron A, Klimaj V, Hipólito I. On the Importance of Being Flexible: Dynamic Brain Networks and Their Potential Functional Significances. Front Syst Neurosci 2022; 15:688424. [PMID: 35126062 PMCID: PMC8814434 DOI: 10.3389/fnsys.2021.688424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 11/10/2021] [Indexed: 12/14/2022] Open
Abstract
In this theoretical review, we begin by discussing brains and minds from a dynamical systems perspective, and then go on to describe methods for characterizing the flexibility of dynamic networks. We discuss how varying degrees and kinds of flexibility may be adaptive (or maladaptive) in different contexts, specifically focusing on measures related to either more disjoint or cohesive dynamics. While disjointed flexibility may be useful for assessing neural entropy, cohesive flexibility may potentially serve as a proxy for self-organized criticality as a fundamental property enabling adaptive behavior in complex systems. Particular attention is given to recent studies in which flexibility methods have been used to investigate neurological and cognitive maturation, as well as the breakdown of conscious processing under varying levels of anesthesia. We further discuss how these findings and methods might be contextualized within the Free Energy Principle with respect to the fundamentals of brain organization and biological functioning more generally, and describe potential methodological advances from this paradigm. Finally, with relevance to computational psychiatry, we propose a research program for obtaining a better understanding of ways that dynamic networks may relate to different forms of psychological flexibility, which may be the single most important factor for ensuring human flourishing.
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Affiliation(s)
- Adam Safron
- Center for Psychedelic and Consciousness Research, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Kinsey Institute, Indiana University, Bloomington, IN, United States
- Cognitive Science Program, Indiana University, Bloomington, IN, United States
| | - Victoria Klimaj
- Cognitive Science Program, Indiana University, Bloomington, IN, United States
- Complex Networks and Systems, Informatics Department, Indiana University, Bloomington, IN, United States
| | - Inês Hipólito
- Department of Philosophy, Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
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75
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Braun Janzen T, Koshimori Y, Richard NM, Thaut MH. Rhythm and Music-Based Interventions in Motor Rehabilitation: Current Evidence and Future Perspectives. Front Hum Neurosci 2022; 15:789467. [PMID: 35111007 PMCID: PMC8801707 DOI: 10.3389/fnhum.2021.789467] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 12/27/2021] [Indexed: 12/17/2022] Open
Abstract
Research in basic and clinical neuroscience of music conducted over the past decades has begun to uncover music’s high potential as a tool for rehabilitation. Advances in our understanding of how music engages parallel brain networks underpinning sensory and motor processes, arousal, reward, and affective regulation, have laid a sound neuroscientific foundation for the development of theory-driven music interventions that have been systematically tested in clinical settings. Of particular significance in the context of motor rehabilitation is the notion that musical rhythms can entrain movement patterns in patients with movement-related disorders, serving as a continuous time reference that can help regulate movement timing and pace. To date, a significant number of clinical and experimental studies have tested the application of rhythm- and music-based interventions to improve motor functions following central nervous injury and/or degeneration. The goal of this review is to appraise the current state of knowledge on the effectiveness of music and rhythm to modulate movement spatiotemporal patterns and restore motor function. By organizing and providing a critical appraisal of a large body of research, we hope to provide a revised framework for future research on the effectiveness of rhythm- and music-based interventions to restore and (re)train motor function.
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Affiliation(s)
- Thenille Braun Janzen
- Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Yuko Koshimori
- Music and Health Science Research Collaboratory, Faculty of Music, University of Toronto, Toronto, ON, Canada
- Brain Health Imaging Centre, CAMH, Toronto, ON, Canada
| | - Nicole M. Richard
- Music and Health Science Research Collaboratory, Faculty of Music, University of Toronto, Toronto, ON, Canada
- Faculty of Music, Belmont University, Nashville, TN, United States
| | - Michael H. Thaut
- Music and Health Science Research Collaboratory, Faculty of Music, University of Toronto, Toronto, ON, Canada
- Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada
- *Correspondence: Michael H. Thaut,
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76
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Isomura T, Shimazaki H, Friston KJ. Canonical neural networks perform active inference. Commun Biol 2022; 5:55. [PMID: 35031656 PMCID: PMC8760273 DOI: 10.1038/s42003-021-02994-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 12/21/2021] [Indexed: 12/03/2022] Open
Abstract
This work considers a class of canonical neural networks comprising rate coding models, wherein neural activity and plasticity minimise a common cost function-and plasticity is modulated with a certain delay. We show that such neural networks implicitly perform active inference and learning to minimise the risk associated with future outcomes. Mathematical analyses demonstrate that this biological optimisation can be cast as maximisation of model evidence, or equivalently minimisation of variational free energy, under the well-known form of a partially observed Markov decision process model. This equivalence indicates that the delayed modulation of Hebbian plasticity-accompanied with adaptation of firing thresholds-is a sufficient neuronal substrate to attain Bayes optimal inference and control. We corroborated this proposition using numerical analyses of maze tasks. This theory offers a universal characterisation of canonical neural networks in terms of Bayesian belief updating and provides insight into the neuronal mechanisms underlying planning and adaptive behavioural control.
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Affiliation(s)
- Takuya Isomura
- Brain Intelligence Theory Unit, RIKEN Center for Brain Science, Wako, Saitama, 351-0198, Japan.
| | - Hideaki Shimazaki
- Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN), Hokkaido University, Sapporo, Hokkaido, 060-0812, Japan
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3AR, UK
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77
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Luczak A, Kubo Y. Predictive Neuronal Adaptation as a Basis for Consciousness. Front Syst Neurosci 2022; 15:767461. [PMID: 35087383 PMCID: PMC8789243 DOI: 10.3389/fnsys.2021.767461] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 11/29/2021] [Indexed: 01/07/2023] Open
Abstract
Being able to correctly predict the future and to adjust own actions accordingly can offer a great survival advantage. In fact, this could be the main reason why brains evolved. Consciousness, the most mysterious feature of brain activity, also seems to be related to predicting the future and detecting surprise: a mismatch between actual and predicted situation. Similarly at a single neuron level, predicting future activity and adapting synaptic inputs accordingly was shown to be the best strategy to maximize the metabolic energy for a neuron. Following on these ideas, here we examined if surprise minimization by single neurons could be a basis for consciousness. First, we showed in simulations that as a neural network learns a new task, then the surprise within neurons (defined as the difference between actual and expected activity) changes similarly to the consciousness of skills in humans. Moreover, implementing adaptation of neuronal activity to minimize surprise at fast time scales (tens of milliseconds) resulted in improved network performance. This improvement is likely because adapting activity based on the internal predictive model allows each neuron to make a more "educated" response to stimuli. Based on those results, we propose that the neuronal predictive adaptation to minimize surprise could be a basic building block of conscious processing. Such adaptation allows neurons to exchange information about own predictions and thus to build more complex predictive models. To be precise, we provide an equation to quantify consciousness as the amount of surprise minus the size of the adaptation error. Since neuronal adaptation can be studied experimentally, this can allow testing directly our hypothesis. Specifically, we postulate that any substance affecting neuronal adaptation will also affect consciousness. Interestingly, our predictive adaptation hypothesis is consistent with multiple ideas presented previously in diverse theories of consciousness, such as global workspace theory, integrated information, attention schema theory, and predictive processing framework. In summary, we present a theoretical, computational, and experimental support for the hypothesis that neuronal adaptation is a possible biological mechanism of conscious processing, and we discuss how this could provide a step toward a unified theory of consciousness.
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Affiliation(s)
- Artur Luczak
- Canadian Center for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, Canada
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78
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Tschantz A, Barca L, Maisto D, Buckley CL, Seth AK, Pezzulo G. Simulating homeostatic, allostatic and goal-directed forms of interoceptive control using active inference. Biol Psychol 2022; 169:108266. [DOI: 10.1016/j.biopsycho.2022.108266] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Revised: 01/06/2022] [Accepted: 01/14/2022] [Indexed: 12/28/2022]
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79
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Fleischmann E, Fellendorf F, Schönthaler EM, Lenger M, Hiendl L, Bonkat N, Wagner-Skacel J, Bengesser S, Angel HF, Seitz RJ, Reininghaus EZ, Dalkner N. Believing processes around COVID-19 vaccination: An exploratory study investigating workers in the health sector. Front Psychiatry 2022; 13:993323. [PMID: 36213920 PMCID: PMC9532762 DOI: 10.3389/fpsyt.2022.993323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 08/30/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND The processes underlying believing have been labeled "creditions", which are important brain functions between emotion and cognition. Creditions are influenced by both internal and external factors, one of which is the coronavirus disease 2019 (COVID-19) pandemic and the vaccination against the disease. METHODS To investigate believing processes shortly before the implementation of a mandatory vaccination in Austria, both vaccinated and unvaccinated workers in the health sector (WHS) were surveyed in December 2021/January 2022. In total, 1,062 vaccinated and 97 unvaccinated WHS (920 females) completed the online survey. Beliefs were assessed using the parameters of the credition model (narrative, certainty, emotion, and mightiness) with regard to (1) the COVID-19 pandemic in general, and (2) the vaccination. Type of emotion and narrative were divided into positive, negative, and indifferent. Moreover, the congruence between emotion and narrative was calculated. RESULTS The vaccination rate of the sample was 91.6%, with a significantly higher percentage of men being in the group of vaccinated (21.1%) as compared to unvaccinated individuals (12.4%). Pertaining beliefs about the COVID-19 vaccination, unvaccinated WHS reported more negative and less positive emotions as well as content of narrative than vaccinated WHS. In addition, they showed higher levels of certainty as well as mightiness while believing and felt less sufficiently informed about governmental and workplace-related COVID-19 measures. The groups did not differ in the type of emotion or content of narrative in their beliefs about the pandemic in general. CONCLUSION In conclusion, unvaccinated WHS had more negative and less positive emotions and thoughts than vaccinated WHS in their beliefs about the COVID-19 vaccination and their motivations for not having received it. They were more certain about their beliefs and felt stronger negative emotions in their beliefs compared to vaccinated individuals. Providing unvaccinated WHS with adequate information might be helpful in reducing their mental burden.
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Affiliation(s)
- Eva Fleischmann
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria
| | - Frederike Fellendorf
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria
| | - Elena M Schönthaler
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria
| | - Melanie Lenger
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria
| | - Lena Hiendl
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria
| | - Nina Bonkat
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria
| | - Jolana Wagner-Skacel
- Department of Medical Psychology and Psychotherapy, Medical University of Graz, Graz, Austria
| | - Susanne Bengesser
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria
| | - Hans-Ferdinand Angel
- Department of Catechetics and Religious Education, Karl-Franzens-University of Graz, Graz, Austria
| | - Rüdiger J Seitz
- Department of Neurology, Medical Faculty, Centre of Neurology and Neuropsychiatry, LVR-Klinikum Düsseldorf, Heinrich-Heine-University of Düsseldorf, Düsseldorf, Germany
| | - Eva Z Reininghaus
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria
| | - Nina Dalkner
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria
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80
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Isomura T. Active inference leads to Bayesian neurophysiology. Neurosci Res 2021; 175:38-45. [PMID: 34968557 DOI: 10.1016/j.neures.2021.12.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 12/13/2021] [Accepted: 12/14/2021] [Indexed: 01/20/2023]
Abstract
The neuronal substrates that implement the free-energy principle and ensuing active inference at the neuron and synapse level have not been fully elucidated. This Review considers possible neuronal substrates underlying the principle. First, the foundations of the free-energy principle are introduced, and then its ability to empirically explain various brain functions and psychological and biological phenomena in terms of Bayesian inference is described. Mathematically, the dynamics of neural activity and plasticity that minimise a cost function can be cast as performing Bayesian inference that minimises variational free energy. This equivalence licenses the adoption of the free-energy principle as a universal characterisation of neural networks. Further, the neural network structure itself represents a generative model under which an agent operates. A virtue of this perspective is that it enables the formal association of neural network properties with prior beliefs that regulate inference and learning. The possible neuronal substrates that implement prior beliefs and how to empirically examine the theory are discussed. This perspective renders brain activity explainable, leading to a deeper understanding of the neuronal mechanisms underlying basic psychology and psychiatric disorders in terms of an implicit generative model.
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Affiliation(s)
- Takuya Isomura
- Brain Intelligence Theory Unit, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan.
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81
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Duch W. Memetics and neural models of conspiracy theories. PATTERNS 2021; 2:100353. [PMID: 34820645 PMCID: PMC8600249 DOI: 10.1016/j.patter.2021.100353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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82
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Lavazza A. 'Consciousnessoids': clues and insights from human cerebral organoids for the study of consciousness. Neurosci Conscious 2021; 7:niab029. [PMID: 34729213 PMCID: PMC8557395 DOI: 10.1093/nc/niab029] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 08/02/2021] [Accepted: 08/17/2021] [Indexed: 12/21/2022] Open
Abstract
Human cerebral organoids (HCOs) are an in vitro three-dimensional model of early neural development, aimed at modelling and understanding brain development and neurological disorders. In just a few years, there has been a rapid and considerable progress in the attempt to create a brain model capable of showcasing the structure and functions of the human brain. There are still strong limitations to address, including the absence of vascularization that makes it difficult to feed the central layers of organoids. Nevertheless, some important features of the nervous system have recently been observed: HCOs manifest electrical activity, are sensitive to light stimulation and are able to connect to a spinal cord by sending impulses that make a muscle contract. Recent data show that cortical organoid network development at 10 months resembles some preterm babies' electroencephalography (EEG) patterns. In the light of the fast pace of research in this field, one might consider the hypothesis that HCOs might become a living laboratory for studying the emergence of consciousness and investigating its mechanisms and neural correlates. HCOs could be also a benchmark for different neuroscientific theories of consciousness. In this paper, I propose some potential lines of research and offer some clues and insights so as to use HCOs in trying to unveil some puzzles concerning our conscious states. Finally, I consider some relevant ethical issues regarding this specific experimentation on HCOs and conclude that some of them could require strict regulation in this field.
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Affiliation(s)
- Andrea Lavazza
- Centro Universitario Internazionale, Via Garbasso, 42, Arezzo 52100, Italy
- University of Pavia, Department of Brain and Behavioural Sciences, Piazza Botta, 11, Pavia 27100, Italy
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83
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Holmes E, Parr T, Griffiths TD, Friston KJ. Active inference, selective attention, and the cocktail party problem. Neurosci Biobehav Rev 2021; 131:1288-1304. [PMID: 34687699 DOI: 10.1016/j.neubiorev.2021.09.038] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 08/27/2021] [Accepted: 09/17/2021] [Indexed: 11/25/2022]
Abstract
In this paper, we introduce a new generative model for an active inference account of preparatory and selective attention, in the context of a classic 'cocktail party' paradigm. In this setup, pairs of words are presented simultaneously to the left and right ears and an instructive spatial cue directs attention to the left or right. We use this generative model to test competing hypotheses about the way that human listeners direct preparatory and selective attention. We show that assigning low precision to words at attended-relative to unattended-locations can explain why a listener reports words from a competing sentence. Under this model, temporal changes in sensory precision were not needed to account for faster reaction times with longer cue-target intervals, but were necessary to explain ramping effects on event-related potentials (ERPs)-resembling the contingent negative variation (CNV)-during the preparatory interval. These simulations reveal that different processes are likely to underlie the improvement in reaction times and the ramping of ERPs that are associated with spatial cueing.
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Affiliation(s)
- Emma Holmes
- Department of Speech Hearing and Phonetic Sciences, UCL, London, WC1N 1PF, UK; Wellcome Centre for Human Neuroimaging, UCL, London, WC1N 3AR, UK.
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, UCL, London, WC1N 3AR, UK
| | - Timothy D Griffiths
- Wellcome Centre for Human Neuroimaging, UCL, London, WC1N 3AR, UK; Biosciences Institute, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, UCL, London, WC1N 3AR, UK
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84
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Arthur T, Harris DJ. Predictive eye movements are adjusted in a Bayes-optimal fashion in response to unexpectedly changing environmental probabilities. Cortex 2021; 145:212-225. [PMID: 34749190 DOI: 10.1016/j.cortex.2021.09.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 08/18/2021] [Accepted: 09/27/2021] [Indexed: 11/30/2022]
Abstract
This study examined the application of active inference to dynamic visuomotor control. Active inference proposes that actions are dynamically planned according to uncertainty about sensory information, prior expectations, and the environment, with motor adjustments serving to minimise future prediction errors. We investigated whether predictive gaze behaviours are indeed adjusted in this Bayes-optimal fashion during a virtual racquetball task. In this task, participants intercepted bouncing balls with varying levels of elasticity, under conditions of higher or lower environmental volatility. Participants' gaze patterns differed between stable and volatile conditions in a manner consistent with generative models of Bayes-optimal behaviour. Partially observable Markov models also revealed an increased rate of associative learning in response to unpredictable shifts in environmental probabilities, although there was no overall effect of volatility on this parameter. Findings extend active inference frameworks into complex and unconstrained visuomotor tasks and present important implications for a neurocomputational understanding of the visual guidance of action.
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Affiliation(s)
- Tom Arthur
- School of Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, Exeter, EX1 2LU, UK; Centre for Applied Autism Research, Department of Psychology, University of Bath, Bath, BA2 7AY, UK
| | - David J Harris
- School of Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, Exeter, EX1 2LU, UK.
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85
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Northoff G, Scalabrini A. "Project for a Spatiotemporal Neuroscience" - Brain and Psyche Share Their Topography and Dynamic. Front Psychol 2021; 12:717402. [PMID: 34721166 PMCID: PMC8552334 DOI: 10.3389/fpsyg.2021.717402] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 09/13/2021] [Indexed: 12/27/2022] Open
Abstract
What kind of neuroscience does psychoanalysis require? At his time, Freud in his "Project for a Scientific Psychology" searched for a model of the brain that could relate to incorporate the psyche's topography and dynamic. Current neuropsychoanalysis builds on specific functions as investigated in Affective and Cognitive (and Social) Neuroscience including embodied approaches. The brain's various functions are often converged with prediction as operationalized in predictive coding (PC) and free energy principle (FEP) which, recently, have been conceived as core for a "New Project for Scientific Psychology." We propose to search for a yet more comprehensive and holistic neuroscience that focuses primarily on its topography and dynamic analogous to Freud's model of the psyche. This leads us to what we describe as "Spatiotemporal Neuroscience" that focuses on the spatial topography and temporal dynamic of the brain's neural activity including how they shape affective, cognitive, and social functions including PC and FEP (first part). That is illustrated by the temporally and spatially nested neural hierarchy of the self in the brain's neural activity (second and third part). This sets the ground for developing our proposed "Project for a Spatiotemporal Neuroscience," which complements and extends both Freud's and Solms' projects (fourth part) and also carries major practical implications as it lays the ground for a novel form of neuroscientifically informed psychotherapy, namely, "Spatiotemporal Psychotherapy." In conclusion, "Spatiotemporal Neuroscience" provides an intimate link of brain and psyche by showing topography and dynamic as their shared features, that is, "common currency."
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Affiliation(s)
- Georg Northoff
- Faculty of Medicine, Centre for Neural Dynamics, The Royal’s Institute of Mental Health Research, Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
- Mental Health Centre, Zhejiang University School of Medicine, Hangzhou, China
- Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China
| | - Andrea Scalabrini
- Department of Psychological, Health and Territorial Sciences (DiSPuTer), D’Annunzio University of Chieti-Pescara, Chieti, Italy
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86
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Rorot W. Bayesian theories of consciousness: a review in search for a minimal unifying model. Neurosci Conscious 2021; 2021:niab038. [PMID: 34650816 PMCID: PMC8512254 DOI: 10.1093/nc/niab038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 09/10/2021] [Accepted: 09/22/2021] [Indexed: 11/30/2022] Open
Abstract
The goal of the paper is to review existing work on consciousness within the frameworks of Predictive Processing, Active Inference, and Free Energy Principle. The emphasis is put on the role played by the precision and complexity of the internal generative model. In the light of those proposals, these two properties appear to be the minimal necessary components for the emergence of conscious experience-a Minimal Unifying Model of consciousness.
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Affiliation(s)
- Wiktor Rorot
- Faculty of Philosophy and Faculty of Psychology, University of Warsaw, ul. Krakowskie Przedmieście 3, 00-927, Stawki 5/7, Warsaw 00-183, Poland
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87
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Schoeller F, Miller M, Salomon R, Friston KJ. Trust as Extended Control: Human-Machine Interactions as Active Inference. Front Syst Neurosci 2021; 15:669810. [PMID: 34720895 PMCID: PMC8548360 DOI: 10.3389/fnsys.2021.669810] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 08/16/2021] [Indexed: 11/13/2022] Open
Abstract
In order to interact seamlessly with robots, users must infer the causes of a robot's behavior-and be confident about that inference (and its predictions). Hence, trust is a necessary condition for human-robot collaboration (HRC). However, and despite its crucial role, it is still largely unknown how trust emerges, develops, and supports human relationship to technological systems. In the following paper we review the literature on trust, human-robot interaction, HRC, and human interaction at large. Early models of trust suggest that it is a trade-off between benevolence and competence; while studies of human to human interaction emphasize the role of shared behavior and mutual knowledge in the gradual building of trust. We go on to introduce a model of trust as an agent' best explanation for reliable sensory exchange with an extended motor plant or partner. This model is based on the cognitive neuroscience of active inference and suggests that, in the context of HRC, trust can be casted in terms of virtual control over an artificial agent. Interactive feedback is a necessary condition to the extension of the trustor's perception-action cycle. This model has important implications for understanding human-robot interaction and collaboration-as it allows the traditional determinants of human trust, such as the benevolence and competence attributed to the trustee, to be defined in terms of hierarchical active inference, while vulnerability can be described in terms of information exchange and empowerment. Furthermore, this model emphasizes the role of user feedback during HRC and suggests that boredom and surprise may be used in personalized interactions as markers for under and over-reliance on the system. The description of trust as a sense of virtual control offers a crucial step toward grounding human factors in cognitive neuroscience and improving the design of human-centered technology. Furthermore, we examine the role of shared behavior in the genesis of trust, especially in the context of dyadic collaboration, suggesting important consequences for the acceptability and design of human-robot collaborative systems.
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Affiliation(s)
- Felix Schoeller
- Massachusetts Institute of Technology, Cambridge, MA, United States
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
| | - Mark Miller
- Center for Human Nature, Artificial Intelligence and Neuroscience, Hokkaido University, Sapporo, Japan
- Department of Informatics, University of Sussex, Brighton, United Kingdom
| | - Roy Salomon
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
| | - Karl J. Friston
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
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88
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Friston K, Moran RJ, Nagai Y, Taniguchi T, Gomi H, Tenenbaum J. World model learning and inference. Neural Netw 2021; 144:573-590. [PMID: 34634605 DOI: 10.1016/j.neunet.2021.09.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 07/28/2021] [Accepted: 09/09/2021] [Indexed: 11/19/2022]
Abstract
Understanding information processing in the brain-and creating general-purpose artificial intelligence-are long-standing aspirations of scientists and engineers worldwide. The distinctive features of human intelligence are high-level cognition and control in various interactions with the world including the self, which are not defined in advance and are vary over time. The challenge of building human-like intelligent machines, as well as progress in brain science and behavioural analyses, robotics, and their associated theoretical formalisations, speaks to the importance of the world-model learning and inference. In this article, after briefly surveying the history and challenges of internal model learning and probabilistic learning, we introduce the free energy principle, which provides a useful framework within which to consider neuronal computation and probabilistic world models. Next, we showcase examples of human behaviour and cognition explained under that principle. We then describe symbol emergence in the context of probabilistic modelling, as a topic at the frontiers of cognitive robotics. Lastly, we review recent progress in creating human-like intelligence by using novel probabilistic programming languages. The striking consensus that emerges from these studies is that probabilistic descriptions of learning and inference are powerful and effective ways to create human-like artificial intelligent machines and to understand intelligence in the context of how humans interact with their world.
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Affiliation(s)
- Karl Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London (UCL), WC1N 3BG, UK.
| | - Rosalyn J Moran
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, UK.
| | - Yukie Nagai
- International Research Center for Neurointelligence (IRCN), The University of Tokyo, Tokyo, Japan.
| | - Tadahiro Taniguchi
- College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan.
| | - Hiroaki Gomi
- NTT Communication Science Labs., Nippon Telegraph and Telephone, Kanawaga, Japan.
| | - Josh Tenenbaum
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA; The Center for Brains, Minds and Machines, MIT, Cambridge, MA, USA.
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89
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Mannella F, Maggiore F, Baltieri M, Pezzulo G. Active inference through whiskers. Neural Netw 2021; 144:428-437. [PMID: 34563752 DOI: 10.1016/j.neunet.2021.08.037] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 08/29/2021] [Accepted: 08/31/2021] [Indexed: 10/20/2022]
Abstract
Rodents use whisking to probe actively their environment and to locate objects in space, hence providing a paradigmatic biological example of active sensing. Numerous studies show that the control of whisking has anticipatory aspects. For example, rodents target their whisker protraction to the distance at which they expect objects, rather than just reacting fast to contacts with unexpected objects. Here we characterize the anticipatory control of whisking in rodents as an active inference process. In this perspective, the rodent is endowed with a prior belief that it will touch something at the end of the whisker protraction, and it continuously modulates its whisking amplitude to minimize (proprioceptive and somatosensory) prediction errors arising from an unexpected whisker-object contact, or from a lack of an expected contact. We will use the model to qualitatively reproduce key empirical findings about the ways rodents modulate their whisker amplitude during exploration and the scanning of (expected or unexpected) objects. Furthermore, we will discuss how the components of active inference model can in principle map to the neurobiological circuits of rodent whisking.
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Affiliation(s)
- Francesco Mannella
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.
| | - Federico Maggiore
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.
| | - Manuel Baltieri
- Laboratory for Neural Computation and Adaptation, RIKEN Centre for Brain Science, Wako-shi, Japan.
| | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.
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90
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Smith R, Mayeli A, Taylor S, Al Zoubi O, Naegele J, Khalsa SS. Gut inference: A computational modelling approach. Biol Psychol 2021; 164:108152. [PMID: 34311031 PMCID: PMC8429276 DOI: 10.1016/j.biopsycho.2021.108152] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 07/20/2021] [Accepted: 07/22/2021] [Indexed: 12/22/2022]
Abstract
Neurocomputational theories have hypothesized that Bayesian inference underlies interoception, which has become a topic of recent experimental work in heartbeat perception. To extend this approach beyond cardiac interoception, we describe the application of a Bayesian computational model to a recently developed gastrointestinal interoception task completed by 40 healthy individuals undergoing simultaneous electroencephalogram (EEG) and peripheral physiological recording. We first present results that support the validity of this modelling approach. Second, we provide a test of, and confirmatory evidence supporting, the neural process theory associated with a particular Bayesian framework (active inference) that predicts specific relationships between computational parameters and event-related potentials in EEG. We also offer some exploratory evidence suggesting that computational parameters may influence the regulation of peripheral physiological states. We conclude that this computational approach offers promise as a tool for studying individual differences in gastrointestinal interoception.
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Affiliation(s)
- Ryan Smith
- Laureate Institute for Brain Research, Tulsa, OK, United States.
| | - Ahmad Mayeli
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Samuel Taylor
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Obada Al Zoubi
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Jessyca Naegele
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Sahib S Khalsa
- Laureate Institute for Brain Research, Tulsa, OK, United States; Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, United States.
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91
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Sandved-Smith L, Hesp C, Mattout J, Friston K, Lutz A, Ramstead MJD. Towards a computational phenomenology of mental action: modelling meta-awareness and attentional control with deep parametric active inference. Neurosci Conscious 2021; 2021:niab018. [PMID: 34457352 PMCID: PMC8396119 DOI: 10.1093/nc/niab018] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 06/23/2021] [Accepted: 07/14/2021] [Indexed: 11/29/2022] Open
Abstract
Meta-awareness refers to the capacity to explicitly notice the current content of consciousness and has been identified as a key component for the successful control of cognitive states, such as the deliberate direction of attention. This paper proposes a formal model of meta-awareness and attentional control using hierarchical active inference. To do so, we cast mental action as policy selection over higher-level cognitive states and add a further hierarchical level to model meta-awareness states that modulate the expected confidence (precision) in the mapping between observations and hidden cognitive states. We simulate the example of mind-wandering and its regulation during a task involving sustained selective attention on a perceptual object. This provides a computational case study for an inferential architecture that is apt to enable the emergence of these central components of human phenomenology, namely, the ability to access and control cognitive states. We propose that this approach can be generalized to other cognitive states, and hence, this paper provides the first steps towards the development of a computational phenomenology of mental action and more broadly of our ability to monitor and control our own cognitive states. Future steps of this work will focus on fitting the model with qualitative, behavioural, and neural data.
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Affiliation(s)
- Lars Sandved-Smith
- Lyon Neuroscience Research Centre, INSERM U1028, CNRS UMR5292, Lyon 1 University, 95 Bd Pinel, Lyon 69500, France
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3BG, UK
| | - Casper Hesp
- Department of Developmental Psychology, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, Netherlands
- Amsterdam Brain and Cognition Centre, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, Netherlands
- Institute for Advanced Study, University of Amsterdam, Oude Turfmarkt 147, Amsterdam 1012 GC, Netherlands
| | - Jérémie Mattout
- Lyon Neuroscience Research Centre, INSERM U1028, CNRS UMR5292, Lyon 1 University, 95 Bd Pinel, Lyon 69500, France
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3BG, UK
| | - Antoine Lutz
- Lyon Neuroscience Research Centre, INSERM U1028, CNRS UMR5292, Lyon 1 University, 95 Bd Pinel, Lyon 69500, France
| | - Maxwell J D Ramstead
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3BG, UK
- Division of Social and Transcultural Psychiatry, Department of Psychiatry, McGill University, Montreal, 1033 Pine Ave W, QC H3A 1A1, Canada
- Culture, Mind, and Brain Program, McGill University, Montreal, 1033 Pine Ave W, QC H3A 1A1, Canada
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92
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Abstract
Active inference offers a first principle account of sentient behavior, from which special and important cases-for example, reinforcement learning, active learning, Bayes optimal inference, Bayes optimal design-can be derived. Active inference finesses the exploitation-exploration dilemma in relation to prior preferences by placing information gain on the same footing as reward or value. In brief, active inference replaces value functions with functionals of (Bayesian) beliefs, in the form of an expected (variational) free energy. In this letter, we consider a sophisticated kind of active inference using a recursive form of expected free energy. Sophistication describes the degree to which an agent has beliefs about beliefs. We consider agents with beliefs about the counterfactual consequences of action for states of affairs and beliefs about those latent states. In other words, we move from simply considering beliefs about "what would happen if I did that" to "what I would believe about what would happen if I did that." The recursive form of the free energy functional effectively implements a deep tree search over actions and outcomes in the future. Crucially, this search is over sequences of belief states as opposed to states per se. We illustrate the competence of this scheme using numerical simulations of deep decision problems.
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Affiliation(s)
- Karl Friston
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London WC1N 3AR, U.K.
| | - Lancelot Da Costa
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London WC1N 3AR, U.K., and Department of Mathematics, Imperial College London, U.K.
| | - Danijar Hafner
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada, and Google Research, Brain Team, Toronto, ON MSH 153, Canada
| | - Casper Hesp
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London WC1N 3AR, U.K., and Amsterdam Brain and Cognition Center, University of Amsterdam, Amsterdam 1001 NK, The Netherlands
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London WC1N 3AR, U.K.
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93
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Friston KJ, Da Costa L, Parr T. Some Interesting Observations on the Free Energy Principle. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1076. [PMID: 34441216 PMCID: PMC8391698 DOI: 10.3390/e23081076] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 07/26/2021] [Accepted: 08/10/2021] [Indexed: 01/28/2023]
Abstract
Biehl et al. (2021) present some interesting observations on an early formulation of the free energy principle. We use these observations to scaffold a discussion of the technical arguments that underwrite the free energy principle. This discussion focuses on solenoidal coupling between various (subsets of) states in sparsely coupled systems that possess a Markov blanket-and the distinction between exact and approximate Bayesian inference, implied by the ensuing Bayesian mechanics.
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Affiliation(s)
- Karl J. Friston
- The Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK; (K.J.F.); (L.D.C.)
| | - Lancelot Da Costa
- The Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK; (K.J.F.); (L.D.C.)
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK
| | - Thomas Parr
- The Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK; (K.J.F.); (L.D.C.)
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94
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Molapour T, Hagan CC, Silston B, Wu H, Ramstead M, Friston K, Mobbs D. Seven computations of the social brain. Soc Cogn Affect Neurosci 2021; 16:745-760. [PMID: 33629102 PMCID: PMC8343565 DOI: 10.1093/scan/nsab024] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Revised: 12/01/2020] [Accepted: 02/24/2021] [Indexed: 02/07/2023] Open
Abstract
The social environment presents the human brain with the most complex information processing demands. The computations that the brain must perform occur in parallel, combine social and nonsocial cues, produce verbal and nonverbal signals and involve multiple cognitive systems, including memory, attention, emotion and learning. This occurs dynamically and at timescales ranging from milliseconds to years. Here, we propose that during social interactions, seven core operations interact to underwrite coherent social functioning; these operations accumulate evidence efficiently-from multiple modalities-when inferring what to do next. We deconstruct the social brain and outline the key components entailed for successful human-social interaction. These include (i) social perception; (ii) social inferences, such as mentalizing; (iii) social learning; (iv) social signaling through verbal and nonverbal cues; (v) social drives (e.g. how to increase one's status); (vi) determining the social identity of agents, including oneself and (vii) minimizing uncertainty within the current social context by integrating sensory signals and inferences. We argue that while it is important to examine these distinct aspects of social inference, to understand the true nature of the human social brain, we must also explain how the brain integrates information from the social world.
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Affiliation(s)
- Tanaz Molapour
- Department of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA
| | - Cindy C Hagan
- Department of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA
| | - Brian Silston
- Department of Psychology, Columbia University, New York, NY 10027, USA
| | - Haiyan Wu
- Department of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA
- CAS Key Laboratory of Behavioral Science, Department of Psychology, University of Chinese Academy of Sciences, Beijing, 10010, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 10010 China
| | - Maxwell Ramstead
- Division of Social and Transcultural Psychiatry, Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A2, Canada
- Culture, Mind, and Brain Program, McGill University, Montreal, Quebec H3A 1A2, Canada
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Dean Mobbs
- Department of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA
- Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA 91125, USA
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95
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Parr T, Limanowski J, Rawji V, Friston K. The computational neurology of movement under active inference. Brain 2021; 144:1799-1818. [PMID: 33704439 PMCID: PMC8320263 DOI: 10.1093/brain/awab085] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 11/08/2020] [Accepted: 12/20/2020] [Indexed: 12/31/2022] Open
Abstract
We propose a computational neurology of movement based on the convergence of theoretical neurobiology and clinical neurology. A significant development in the former is the idea that we can frame brain function as a process of (active) inference, in which the nervous system makes predictions about its sensory data. These predictions depend upon an implicit predictive (generative) model used by the brain. This means neural dynamics can be framed as generating actions to ensure sensations are consistent with these predictions-and adjusting predictions when they are not. We illustrate the significance of this formulation for clinical neurology by simulating a clinical examination of the motor system using an upper limb coordination task. Specifically, we show how tendon reflexes emerge naturally under the right kind of generative model. Through simulated perturbations, pertaining to prior probabilities of this model's variables, we illustrate the emergence of hyperreflexia and pendular reflexes, reminiscent of neurological lesions in the corticospinal tract and cerebellum. We then turn to the computational lesions causing hypokinesia and deficits of coordination. This in silico lesion-deficit analysis provides an opportunity to revisit classic neurological dichotomies (e.g. pyramidal versus extrapyramidal systems) from the perspective of modern approaches to theoretical neurobiology-and our understanding of the neurocomputational architecture of movement control based on first principles.
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Affiliation(s)
- Thomas Parr
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Jakub Limanowski
- Faculty of Psychology and Center for Tactile Internet with Human-in-the-Loop, Technische Universität Dresden, Dresden, Germany
| | - Vishal Rawji
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
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96
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Albarracin M, Constant A, Friston KJ, Ramstead MJD. A Variational Approach to Scripts. Front Psychol 2021; 12:585493. [PMID: 34354621 PMCID: PMC8329037 DOI: 10.3389/fpsyg.2021.585493] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 06/28/2021] [Indexed: 01/14/2023] Open
Abstract
This paper proposes a formal reconstruction of the script construct by leveraging the active inference framework, a behavioral modeling framework that casts action, perception, emotions, and attention as processes of (Bayesian or variational) inference. We propose a first principles account of the script construct that integrates its different uses in the behavioral and social sciences. We begin by reviewing the recent literature that uses the script construct. We then examine the main mathematical and computational features of active inference. Finally, we leverage the resources of active inference to offer a formal model of scripts. Our integrative model accounts for the dual nature of scripts (as internal, psychological schema used by agents to make sense of event types and as constitutive behavioral categories that make up the social order) and also for the stronger and weaker conceptions of the construct (which do and do not relate to explicit action sequences, respectively).
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Affiliation(s)
- Mahault Albarracin
- Département d’informatique Cognitive, Université du Québec à Montréal, Montreal, QC, Canada
| | - Axel Constant
- Division of Social Transcultural Psychiatry, McGill University, Montreal, QC, Canada
| | - Karl J. Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
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97
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Champion T, Grześ M, Bowman H. Realizing Active Inference in Variational Message Passing: The Outcome-Blind Certainty Seeker. Neural Comput 2021; 33:2762-2826. [PMID: 34280302 DOI: 10.1162/neco_a_01422] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 04/20/2021] [Indexed: 11/04/2022]
Abstract
Active inference is a state-of-the-art framework in neuroscience that offers a unified theory of brain function. It is also proposed as a framework for planning in AI. Unfortunately, the complex mathematics required to create new models can impede application of active inference in neuroscience and AI research. This letter addresses this problem by providing a complete mathematical treatment of the active inference framework in discrete time and state spaces and the derivation of the update equations for any new model. We leverage the theoretical connection between active inference and variational message passing as described by John Winn and Christopher M. Bishop in 2005. Since variational message passing is a well-defined methodology for deriving Bayesian belief update equations, this letter opens the door to advanced generative models for active inference. We show that using a fully factorized variational distribution simplifies the expected free energy, which furnishes priors over policies so that agents seek unambiguous states. Finally, we consider future extensions that support deep tree searches for sequential policy optimization based on structure learning and belief propagation.
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Affiliation(s)
| | - Marek Grześ
- University of Kent, School of Computing, Canterbury CT2 7NZ, U.K.
| | - Howard Bowman
- University of Birmingham, School of Psychology, Birmingham B15 2TT, U.K., and University of Kent, School of Computing, Canterbury CT2 7NZ, U.K.
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98
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van de Laar T, Wymeersch H, Şenöz İ, Özçelikkale A. Chance-Constrained Active Inference. Neural Comput 2021; 33:2710-2735. [PMID: 34280254 DOI: 10.1162/neco_a_01427] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 05/03/2021] [Indexed: 11/04/2022]
Abstract
Active inference (ActInf) is an emerging theory that explains perception and action in biological agents in terms of minimizing a free energy bound on Bayesian surprise. Goal-directed behavior is elicited by introducing prior beliefs on the underlying generative model. In contrast to prior beliefs, which constrain all realizations of a random variable, we propose an alternative approach through chance constraints, which allow for a (typically small) probability of constraint violation, and demonstrate how such constraints can be used as intrinsic drivers for goal-directed behavior in ActInf. We illustrate how chance-constrained ActInf weights all imposed (prior) constraints on the generative model, allowing, for example, for a trade-off between robust control and empirical chance constraint violation. Second, we interpret the proposed solution within a message passing framework. Interestingly, the message passing interpretation is not only relevant to the context of ActInf, but also provides a general-purpose approach that can account for chance constraints on graphical models. The chance constraint message updates can then be readily combined with other prederived message update rules without the need for custom derivations. The proposed chance-constrained message passing framework thus accelerates the search for workable models in general and can be used to complement message-passing formulations on generative neural models.
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Affiliation(s)
- Thijs van de Laar
- Eindhoven University of Technology, 5612 AP, Eindhoven, The Netherlands
| | - Henk Wymeersch
- Chalmers University of Technology, 41296, Gothenburg, Sweden
| | - İsmail Şenöz
- Eindhoven University of Technology, 5612 AP, Eindhoven, The Netherlands
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99
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Parr T, Rikhye RV, Halassa MM, Friston KJ. Prefrontal Computation as Active Inference. Cereb Cortex 2021; 30:682-695. [PMID: 31298270 PMCID: PMC7444741 DOI: 10.1093/cercor/bhz118] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 05/01/2019] [Accepted: 05/11/2019] [Indexed: 12/22/2022] Open
Abstract
The prefrontal cortex is vital for a range of cognitive processes, including working memory, attention, and decision-making. Notably, its absence impairs the performance of tasks requiring the maintenance of information through a delay period. In this paper, we formulate a rodent task—which requires maintenance of delay-period activity—as a Markov decision process and treat optimal task performance as an (active) inference problem. We simulate the behavior of a Bayes optimal mouse presented with 1 of 2 cues that instructs the selection of concurrent visual and auditory targets on a trial-by-trial basis. Formulating inference as message passing, we reproduce features of neuronal coupling within and between prefrontal regions engaged by this task. We focus on the micro-circuitry that underwrites delay-period activity and relate it to functional specialization within the prefrontal cortex in primates. Finally, we simulate the electrophysiological correlates of inference and demonstrate the consequences of lesions to each part of our in silico prefrontal cortex. In brief, this formulation suggests that recurrent excitatory connections—which support persistent neuronal activity—encode beliefs about transition probabilities over time. We argue that attentional modulation can be understood as the contextualization of sensory input by these persistent beliefs.
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Affiliation(s)
- Thomas Parr
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, UK
| | - Rajeev Vijay Rikhye
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Michael M Halassa
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Stanley Center for Psychiatric Genetics, Broad Institute, Cambridge, MA 02139, USA
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, UK
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100
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Smith R, Kirlic N, Stewart JL, Touthang J, Kuplicki R, McDermott TJ, Taylor S, Khalsa SS, Paulus MP, Aupperle RL. Long-term stability of computational parameters during approach-avoidance conflict in a transdiagnostic psychiatric patient sample. Sci Rep 2021; 11:11783. [PMID: 34083701 PMCID: PMC8175390 DOI: 10.1038/s41598-021-91308-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 05/17/2021] [Indexed: 11/16/2022] Open
Abstract
Maladaptive behavior during approach-avoidance conflict (AAC) is common to multiple psychiatric disorders. Using computational modeling, we previously reported that individuals with depression, anxiety, and substance use disorders (DEP/ANX; SUDs) exhibited differences in decision uncertainty and sensitivity to negative outcomes versus reward (emotional conflict) relative to healthy controls (HCs). However, it remains unknown whether these computational parameters and group differences are stable over time. We analyzed 1-year follow-up data from a subset of the same participants (N = 325) to assess parameter stability and relationships to other clinical and task measures. We assessed group differences in the entire sample as well as a subset matched for age and IQ across HCs (N = 48), SUDs (N = 29), and DEP/ANX (N = 121). We also assessed 2-3 week reliability in a separate sample of 30 HCs. Emotional conflict and decision uncertainty parameters showed moderate 1-year intra-class correlations (.52 and .46, respectively) and moderate to excellent correlations over the shorter period (.84 and .54, respectively). Similar to previous baseline findings, parameters correlated with multiple response time measures (ps < .001) and self-reported anxiety (r = .30, p < .001) and decision difficulty (r = .44, p < .001). Linear mixed effects analyses revealed that patients remained higher in decision uncertainty (SUDs, p = .009) and lower in emotional conflict (SUDs, p = .004, DEP/ANX, p = .02) relative to HCs. This computational modelling approach may therefore offer relatively stable markers of transdiagnostic psychopathology.
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Affiliation(s)
- Ryan Smith
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA.
| | - Namik Kirlic
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Jennifer L Stewart
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - James Touthang
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Rayus Kuplicki
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Timothy J McDermott
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Samuel Taylor
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Sahib S Khalsa
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Martin P Paulus
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Robin L Aupperle
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
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