scholarly journals “Project for a Spatiotemporal Neuroscience” – Brain and Psyche Share Their Topography and Dynamic

2021 ◽  
Vol 12 ◽  
Author(s):  
Georg Northoff ◽  
Andrea Scalabrini
Keyword(s):  
Neural Activity ◽  
Predictive Coding ◽  
Social Functions ◽  
Scientific Psychology ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Intimate Link ◽  
Spatial Topography ◽  
The Brain ◽  
Practical Implications

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.”

scholarly journals Strengthened beliefs under psychedelics (SEBUS)? A Commentary on “REBUS and the Anarchic Brain: Toward a Unified Model of the Brain Action of Psychedelics”

2020 ◽  
Author(s):  
Adam Safron
Keyword(s):  
Free Energy ◽  
Conscious Experience ◽  
Predictive Coding ◽  
Alternative Account ◽  
Levels Of Abstraction ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Free Energy Landscapes ◽  
The Mind ◽  
The Brain

In introducing a model of “relaxed beliefs under psychedelics” (REBUS), Carhart-Harris and Friston (2019) have presented a compelling account of the effects of psychedelics on brain and mind. This model is contextualized within the Free Energy Principle (Friston et al., 2006; Friston, 2010), which may represent the first unified paradigm in the mind and life sciences. By this view, mental systems adaptively regulate their actions and interactions with the world via predictive models, whose dynamics are governed by a singular objective of minimizing prediction-error, or “free energy.” According to REBUS, psychedelics flatten the depth of free energy landscapes, or the differential attracting forces associated with various (Bayesian) beliefs, so promoting flexibility in inference and learning. Here, I would like to propose an alternative account of the effects of psychedelics that is in many ways compatible with REBUS, albeit with some important differences. Based on considerations of the distributions of 5-HT2a receptors within cortical laminae and canonical microcircuits for predictive coding, I propose that 5-HT2a agonism may also involve a strengthening of beliefs, particularly at intermediate levels of abstraction associated with conscious experience (Safron, 2020).

scholarly journals A tale of two densities: active inference is enactive inference

2019 ◽  
Vol 28 (4) ◽  
pp. 225-239 ◽  
Author(s):  
Maxwell JD Ramstead ◽  
Michael D Kirchhoff ◽  
Karl J Friston
Keyword(s):  
Predictive Coding ◽  
Generative Models ◽  
Predictive Processing ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
The One ◽  
And Control ◽  
The Brain ◽  
Selection Of

The aim of this article is to clarify how best to interpret some of the central constructs that underwrite the free-energy principle (FEP) – and its corollary, active inference – in theoretical neuroscience and biology: namely, the role that generative models and variational densities play in this theory. We argue that these constructs have been systematically misrepresented in the literature, because of the conflation between the FEP and active inference, on the one hand, and distinct (albeit closely related) Bayesian formulations, centred on the brain – variously known as predictive processing, predictive coding or the prediction error minimisation framework. More specifically, we examine two contrasting interpretations of these models: a structural representationalist interpretation and an enactive interpretation. We argue that the structural representationalist interpretation of generative and recognition models does not do justice to the role that these constructs play in active inference under the FEP. We propose an enactive interpretation of active inference – what might be called enactive inference. In active inference under the FEP, the generative and recognition models are best cast as realising inference and control – the self-organising, belief-guided selection of action policies – and do not have the properties ascribed by structural representationalists.

Is Our Brain an Open or Closed System? Prediction Model of Brain and World–Brain Relation

2018 ◽  
pp. 55-76
Author(s):  
Georg Northoff
Keyword(s):  
Prediction Model ◽  
Closed System ◽  
Neural Activity ◽  
Resting State ◽  
Clinical Symptoms ◽  
Predictive Coding ◽  
Human Cognition ◽  
The World ◽  
World Brain ◽  
The Brain

Some recent philosophical discussions consider whether the brain is best understood as an open or closed system. This issue has major epistemic consequences akin to the scepticism engendered by the famous Cartesian demon. Specifically, one and the same empirical theory of brain function, predictive coding, entailing a prediction model of brain, have been associated with contradictory views of the brain as either open (Clark, 2012, 2013) or closed (Hohwy, 2013, 2014). Based on recent empirical evidence, the present paper argues that contrary to appearances, these views of the brain are compatible with one another. I suggest that there are two main forms of neural activity in the brain, one of which can be characterized as open, and the other as closed. Stimulus-induced activity, because it relies on predictive coding is indeed closed to the world, which entails that in certain respects, the brain is an inferentially secluded and self-evidencing system. In contrast, the brain’s resting state or spontaneous activity is best taken as open because it is a world-evidencing system that allows for the brain’s neural activity to align with the statistically-based spatiotemporal structure of objects and events in the world. This model requires an important caveat, however. Due to its statistically-based nature, the resting state’s alignment to the world comes in degrees. In extreme cases, the degree of alignment can be extremely low, resulting in a resting state that is barely if at all aligned to the world. This is for instance the case in schizophrenia. Clinical symptoms such as delusions and hallucinations in schizophrenics are indicative of the fundamental delicateness of the alignment between the brain’s resting-state and the world’s phenomena. Nevertheless, I argue that so long as we are dealing with a well-functioning brain, the more dire epistemic implications of predictive coding can be forestalled. That the brain is in part a self-evidencing system does not yield any generalizable reason to worry that human cognition is out of step with the real world. Instead, the brain is aligned to the world accounting for “world-brain relation” that mitigates sceptistic worries.

scholarly journals ‘Through others we become ourselves’: The dialectics of predictive coding and active inference

2019 ◽  
Author(s):  
Dimitris Bolis ◽  
Leonhard Schilbach
Keyword(s):  
Free Energy ◽  
Social Interaction ◽  
Predictive Coding ◽  
Real Life ◽  
Other Minds ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Multiscale Dynamics ◽  
Cultural Processes

Thinking Through Other Minds (TTOM) creatively situates the free energy principle within real-life cultural processes, thereby enriching both sociocultural theories and Bayesian accounts of cognition. Here, shifting the attention from thinking to becoming, we suggest complementing such an account by focusing on the empirical, computational and conceptual investigation of the multiscale dynamics of social interaction.

scholarly journals Combining Active Inference and Hierarchical Predictive Coding: A Tutorial Introduction and Case Study

2019 ◽  
Author(s):  
Beren Millidge
Keyword(s):  
Free Energy ◽  
Reinforcement Learning ◽  
Predictive Coding ◽  
Action Selection ◽  
Proof Of Concept ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Perceptual Model

This paper combines the active inference formulation of action (Friston, 2009) with hierarchical predictive coding models (Friston, 2003) to provide a proof-of-concept implementation of an active inference agent able to solve a common reinforcement learning baseline -- the cart-pole environment in OpenAI gym. It demonstrates empirically that predictive coding and active inference approaches can be successfully scaled up to tasks more challenging than the mountain car (Friston 2009, 2012). We show that hierarchical predictive coding models can be learned from scratch during the task, and can successfully drive action selection via active inference. To our knowledge, it is the first implemented active inference agent to combine active inference with a hierarchical predictive coding perceptual model. We also provide a tutorial walk-through of the free-energy principle, hierarchical predictive coding, and active inference, including an in-depth derivation of our agent.

scholarly journals From Learning to Consciousness: An Example Using Expected Float Entropy Minimisation

Entropy ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 60
Author(s):  
Jonathan Mason
Keyword(s):  
Information Theory ◽  
Free Energy ◽  
Learning Processes ◽  
Brain State ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Association Learning ◽  
The Relationship ◽  
The Brain ◽  
Integrated Information Theory

Over recent decades several mathematical theories of consciousness have been put forward including Karl Friston’s Free Energy Principle and Giulio Tononi’s Integrated Information Theory. In this article we further investigate theory based on Expected Float Entropy (EFE) minimisation which has been around since 2012. EFE involves a version of Shannon Entropy parameterised by relationships. It turns out that, for systems with bias due to learning, certain choices for the relationship parameters are isolated since giving much lower EFE values than others and, hence, the system defines relationships. It is proposed that, in the context of all these relationships, a brain state acquires meaning in the form of the relational content of the associated experience. EFE minimisation is itself an association learning process and its effectiveness as such is tested in this article. The theory and results are consistent with the proposition of there being a close connection between association learning processes and the emergence of consciousness. Such a theory may explain how the brain defines the content of consciousness up to relationship isomorphism.

scholarly journals Predictive coding under the free-energy principle

2009 ◽  
Vol 364 (1521) ◽  
pp. 1211-1221 ◽  
Author(s):  
Karl Friston ◽  
Stefan Kiebel
Keyword(s):  
Free Energy ◽  
Causal Structure ◽  
Predictive Coding ◽  
Dynamical Structure ◽  
Energy Principle ◽  
Inference Problem ◽  
Model Inversion ◽  
Sensory Data ◽  
Energy Bound ◽  
The Brain

This paper considers prediction and perceptual categorization as an inference problem that is solved by the brain. We assume that the brain models the world as a hierarchy or cascade of dynamical systems that encode causal structure in the sensorium. Perception is equated with the optimization or inversion of these internal models, to explain sensory data. Given a model of how sensory data are generated, we can invoke a generic approach to model inversion, based on a free energy bound on the model's evidence. The ensuing free-energy formulation furnishes equations that prescribe the process of recognition, i.e. the dynamics of neuronal activity that represent the causes of sensory input. Here, we focus on a very general model, whose hierarchical and dynamical structure enables simulated brains to recognize and predict trajectories or sequences of sensory states. We first review hierarchical dynamical models and their inversion. We then show that the brain has the necessary infrastructure to implement this inversion and illustrate this point using synthetic birds that can recognize and categorize birdsongs.

scholarly journals “Through others we become ourselves”: The dialectics of predictive coding and active inference

2020 ◽  
Vol 43 ◽  
Author(s):  
Dimitris Bolis ◽  
Leonhard Schilbach
Keyword(s):  
Free Energy ◽  
Social Interaction ◽  
Predictive Coding ◽  
Real Life ◽  
Other Minds ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Multiscale Dynamics ◽  
Cultural Processes

Abstract Thinking through other minds creatively situates the free-energy principle within real-life cultural processes, thereby enriching both sociocultural theories and Bayesian accounts of cognition. Here, shifting the attention from thinking-through to becoming-with, we suggest complementing such an account by focusing on the empirical, computational, and conceptual investigation of the multiscale dynamics of social interaction.

scholarly journals Active inference models do not contradict folk psychology

2021 ◽  
Author(s):  
Ryan Smith ◽  
maxwell ramstead ◽  
Alex Kiefer
Keyword(s):  
Decision Making ◽  
Folk Psychology ◽  
Predictive Coding ◽  
Decision Processes ◽  
Unified Theory ◽  
Active Inference ◽  
Energy Principle ◽  
Inference Models ◽  
Free Energy Principle ◽  
Dark Room

Active inference offers a unified theory of perception, learning, and decision-making at computational and neural levels of description. In this article, we address the worry that active inference may be in tension with folk psychology because it does not include terms for desires (or other conative constructs) at the mathematical level of description. To resolve this concern, we first provide a brief review of the historical progression from predictive coding to active inference, enabling us to distinguish between active inference formulations of motor control (which need not have desires under folk psychology) and active inference formulations of decision processes (which do have desires within folk psychology). We then show that, despite a superficial tension when viewed at the mathematical level, the active inference formalism contains terms that are readily identifiable as encoding both the objects of desire and the strength of desire at the psychological level. We demonstrate this with simple simulations of an active inference agent motivated to leave a dark room for different reasons. Despite their consistency, we further show how active inference may increase the granularity of folk-psychological descriptions by highlighting distinctions between drives to seek information vs. reward – and how it may also offer more precise, quantitative folk-psychological predictions. Finally, we consider how the implicitly conative components of active inference may have partial analogues (i.e., “as if” desires) in other systems describable by the broader free energy principle to which it conforms.

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