The dark room problem in predictive processing and active inference, a legacy of cognitivism?

The free energy principle describes cognitive functions such as perception, action, learning and attention in terms of surprisal minimisation. Under simplifying assumptions, agents are depicted as systems minimising a weighted sum of prediction errors encoding the mismatch between incoming sensations and an agent’s predictions about such sensations. The “dark room” is defined as a state that an agent would occupy should it only look to minimise this sum of prediction errors. This (paradoxical) state emerges as the contrast between the attempts to describe the richness of human and animal behaviour in terms of surprisal minimisation and the trivial solution of a dark room, where the complete lack of sensory stimuli would provide the easiest way to minimise prediction errors, i.e., to be in a perfectly predictable state of darkness with no incoming stimuli. Using a process theory derived from the free energy principle, active inference, we investigate with an agent-based model the meaning of the dark room problem and discuss some of its implications for natural and artificial systems. In this set up, we propose that the presence of this paradox is primarily due to the long-standing belief that agents should encode accurate world models, typical of traditional (computational) theories of cognition

An active inference implementation of phototaxis

Active inference is emerging as a possible unifying theory ofperception and action in cognitive and computational neuro-science. On this theory, perception is a process of inferringthe causes of sensory data by minimising the error betweenactual sensations and those predicted by an innergenerative(probabilistic) model. Action on the other hand is drawn as aprocess that modifies the world such that the consequent sen-sory input meets expectations encoded in the same internalmodel. These two processes, inferring properties of the worldand inferring actions needed to meet expectations, close thesensory/motor loop and suggest a deep symmetry betweenaction and perception. In this work we present a simpleagent-based model inspired by this new theory that offers in-sights on some of its central ideas. Previous implementationsof active inference have typically examined a “perception-oriented” view of this theory, assuming that agents are en-dowed with a detailed generative model of their surround-ing environment. In contrast, we present an “action-oriented”solution showing how adaptive behaviour can emerge evenwhen agents operate with a simple model which bears littleresemblance to their environment. We examine how variousparameters of this formulation allow phototaxis and presentan example of a different, “pathological” behaviour

The modularity of action and perception revisited using control theory and active inference

The assumption that action and perception can be investigated independently is entrenched in theories, models and experimental approaches across the brain and mind sciences. In cognitive science, this has been a central point of contention between computationalist and 4Es (enactive, embodied, extended and embedded) theories of cognition, with the former embracing the “classical sandwich”, modular, architecture of the mind and the latter actively denying this separation can be made. In this work we suggest that the modular independence of action and perception strongly resonates with the separation principle of control theory and furthermore that this principle provides formal criteria within which to evaluate the implications of the modularity of action and perception. We will also see that real-time feedback with the environment, often considered necessary for the definition of 4Es ideas, is not however a sufficient condition to avoid the “classical sandwich”. Finally, we argue that an emerging framework in the cognitive and brain sciences, active inference, extends ideas derived from control theory to the study of biological systems while disposing of the separation principle, describing non-modular models of behaviour strongly aligned with 4Es theories of cognition

Nonmodular architectures of cognitive systems based on active inference

In psychology and neuroscience it is common to describe cognitive systems as input/output devices where perceptual and motor functions are implemented in a purely feedforward, open-loop fashion. On this view, perception and action are often seen as encapsulated modules with limited interaction between them. While embodied and enactive approaches to cognitive science have challenged the idealisation of the brain as an input/output device, we argue that even the more recent attempts to model systems using closed-loop architectures still heavily rely on a strong separation between motor and perceptual functions. Previously, we have suggested that the mainstream notion of modularity strongly resonates with the separation principle of control theory. In this work we present a minimal model of a sensorimotor loop implementing an architecture based on the separation principle. We link this to popular formulations of perception and action in the cognitive sciences, and show its limitations when, for instance, external forces are not modelled by an agent. These forces can be seen as variables that an agent cannot directly control, i.e., a perturbation from the environment or an interference caused by other agents. As an alternative approach inspired by embodied cognitive science, we then propose a nonmodular architecture based on active inference. We demonstrate the robustness of this architecture to unknown external inputs and show that the mechanism with which this is achieved in linear models is equivalent to integral control

A Bayesian perspective on classical control

The connections between optimal control and Bayesian inference have long been recognised, with the field of stochastic (optimal) control combining these frameworks for the solution of partially observable control problems. In particular, for the linear case with quadratic functions and Gaussian noise, stochastic control has shown remarkable results in different fields, including robotics, reinforcement learning and neuroscience, especially thanks to the established duality of estimation and control processes. Following this idea we recently introduced a formulation of PID control, one of the most popular methods from classical control, based on active inference, a theory with roots in variational Bayesian methods, and applications in the biological and neural sciences. In this work, we highlight the advantages of our previous formulation and introduce new and more general ways to tackle some existing problems in current controller design procedures. In particular, we consider 1) a gradient-based tuning rule for the parameters (or gains) of a PID controller, 2) an implementation of multiple degrees of freedom for independent responses to different types of signals (e.g., two-degree-of-freedom PID), and 3) a novel time-domain formalisation of the performance-robustness trade-off in terms of tunable constraints (i.e., priors in a Bayesian model) of a single cost functional, variational free energy.Comment: 8 pages, Accepted at IJCNN 202

Brazilian guideline for the treatment of patients with opioids dependence syndrome

There is a relatively low prevalence of opioid use in Brazil, particularly involving the non-medical use of codeine and opiate-containing syrups. However, opioid dependence syndrome shows a significant total impact on mortality and morbidity. Over the past 20 years, scientific progress has changed our understanding of the nature of opioid addiction and its various possible treatments. Addiction is a chronic illness treatable if the treatment is well-delivered and tailored to the needs of the particular patient. There is indeed an array of treatments that can effectively reduce drug use, help manage drug cravings, prevent relapses and restore people to productive social functioning. The treatment of drug addiction will be part of long-term, medical, psychological, and social perspectives. This guideline aims at providing guidance to psychiatrists and other mental health professionals who care for patients with Opioid Dependence Syndrome. It comments on the somatic and psychosocial treatment that is used for such patients, and reviews scientific evidences and their strength. Also, the essential historical, epidemiological and neurobiological aspects of Opioid Dependence are reviewed.Existe uma prevalência relativamente baixa do uso de ópioides no Brasil, em particular envolvendo o uso não médico da codeína e de xaropes que contêm opióides. No entanto, a síndrome de dependência apresenta um significativo impacto total na mortalidade e morbidade. Nos últimos 20 anos, o avanço científico tem modificado nosso entendimento sobre a natureza da adição aos opióides e os variados tratamentos possíveis. A adição é uma doença crônica tratável se o tratamento for realizado e adaptado tendo em vista as necessidades do paciente específico. Há, de um fato, um conjunto de tratamentos que podem efetivamente reduzir o uso da droga, ajudar a gerenciar a fissura pela droga, prevenir recaídas e recuperar as pessoas para o funcionamento social produtivo. O tratamento da dependência de drogas será parte de perspectivas de longo prazo do ponto de vista médico, psicológico e social. Esta diretriz almeja fornecer um guia para os psiquiatras e outros profissionais de saúde que tratam de pacientes com Síndrome de Dependência de Opióides. Ela tece comentários sobre o tratamento somático e psicossocial que é utilizado nesses pacientes e revisa as evidências científicas e seu poder. Da mesma forma, os aspectos históricos, epidemiológicos e neurobiológicos da dependência de opióides são revisados.Universidade de São Paulo Hospital de Clínicas Instituto de PsiquiatriaFaculdade de Medicina do ABCJohns Hopkins University School of MedicineAssociação Brasileira de Psiquiatria Departamento de Dependência QuímicaHospital Albert EinsteinUniversidade Federal de São Paulo (UNIFESP)UNIADUNIFESPSciEL

PID control as a process of active inference with linear generative models

In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. In particular, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to offer a unified understanding of life and cognition within a general mathematical framework derived from information and control theory, and statistical mechanics. However, we argue that if the active inference proposal is to be taken as a general process theory for biological systems, it is necessary to understand how it relates to existing control theoretical approaches routinely used to study and explain biological systems. For example, recently, PID control has been shown to be implemented in simple molecular systems and is becoming a popular mechanistic explanation of behaviours such as chemotaxis in bacteria and amoebae, and robust adaptation in biochemical networks. In this work, we will show how PID controllers can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation when using approximate linear generative models of the world. This more general interpretation provides also a new perspective on traditional problems of PID controllers such as parameter tuning as well as the need to balance performances and robustness conditions of a controller. Specifically, we then show how these problems can be understood in terms of the optimisation of the precisions (inverse variances) modulating different prediction errors in the free energy functional