The cybernetic Bayesian brain: from interoceptive inference to sensorimotor contingencies

Is there a single principle by which neural operations can account for perception, cognition, action, and even consciousness? A strong candidate is now taking shape in the form of “predictive processing”. On this theory, brains engage in predictive inference on the causes of sensory inputs by continuous minimization of prediction errors or informational “free energy”. Predictive processing can account, supposedly, not only for perception, but also for action and for the essential contribution of the body and environment in structuring sensorimotor interactions. In this paper I draw together some recent developments within predictive processing that involve predictive modelling of internal physiological states (interoceptive inference), and integration with “enactive” and “embodied” approaches to cognitive science (predictive perception of sensorimotor contingencies). The upshot is a development of predictive processing that originates, not in Helmholtzian perception-as-inference, but rather in 20th-century cybernetic principles that emphasized homeostasis and predictive control. This way of thinking leads to (i) a new view of emotion as active interoceptive inference; (ii) a common predictive framework linking experiences of body ownership, emotion, and exteroceptive perception; (iii) distinct interpretations of active inference as involving disruptive and disambiguatory—not just confirmatory—actions to test perceptual hypotheses; (iv) a neurocognitive operationalization of the “mastery of sensorimotor contingencies” (where sensorimotor contingencies reflect the rules governing sensory changes produced by various actions); and (v) an account of the sense of subjective reality of perceptual contents (“perceptual presence”) in terms of the extent to which predictive models encode potential sensorimotor relations (this being “counterfactual richness”). This is rich and varied territory, and surveying its landmarks emphasizes the need for experimental tests of its key contributions

Pragmatism and the pragmatic turn in cognitive science

This chapter examines the pragmatist approach to cognition and experience and provides some of the conceptual background to the “pragmatic turn” currently underway in cognitive science. Classical pragmatists wrote extensively on cognition from a naturalistic perspective, and many of their views are compatible with contemporary pragmatist approaches such as enactivist, extended, and embodied-Bayesian approaches to cognition. Three principles of a pragmatic approach to cognition frame the discussion: First, thinking is structured by the interaction of an organism with its environment. Second, cognition develops via exploratory inference, which remains a core cognitive ability throughout the life cycle. Finally, inquiry/problem solving begins with genuinely irritating doubts that arise in a situation and is carried out by exploratory inference

Causal connectivity of evolved neural networks during behavior

To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics

Drifting perceptual patterns suggest prediction errors fusion rather than hypothesis selection: replicating the rubber-hand illusion on a robot

Humans can experience fake body parts as theirs just by simple visuo-tactile synchronous stimulation. This body-illusion is accompanied by a drift in the perception of the real limb towards the fake limb, suggesting an update of body estimation resulting from stimulation. This work compares body limb drifting patterns of human participants, in a rubber hand illusion experiment, with the end-effector estimation displacement of a multisensory robotic arm enabled with predictive processing perception. Results show similar drifting patterns in both human and robot experiments, and they also suggest that the perceptual drift is due to prediction error fusion, rather than hypothesis selection. We present body inference through prediction error minimization as one single process that unites predictive coding and causal inference and that it is responsible for the effects in perception when we are subjected to intermodal sensory perturbations.Comment: Proceedings of the 2018 IEEE International Conference on Development and Learning and Epigenetic Robotic

Action and behavior: a free-energy formulation

We have previously tried to explain perceptual inference and learning under a free-energy principle that pursues Helmholtz’s agenda to understand the brain in terms of energy minimization. It is fairly easy to show that making inferences about the causes of sensory data can be cast as the minimization of a free-energy bound on the likelihood of sensory inputs, given an internal model of how they were caused. In this article, we consider what would happen if the data themselves were sampled to minimize this bound. It transpires that the ensuing active sampling or inference is mandated by ergodic arguments based on the very existence of adaptive agents. Furthermore, it accounts for many aspects of motor behavior; from retinal stabilization to goal-seeking. In particular, it suggests that motor control can be understood as fulfilling prior expectations about proprioceptive sensations. This formulation can explain why adaptive behavior emerges in biological agents and suggests a simple alternative to optimal control theory. We illustrate these points using simulations of oculomotor control and then apply to same principles to cued and goal-directed movements. In short, the free-energy formulation may provide an alternative perspective on the motor control that places it in an intimate relationship with perception

Plasticity and dystonia: a hypothesis shrouded in variability.

Studying plasticity mechanisms with Professor John Rothwell was a shared highlight of our careers. In this article, we discuss non-invasive brain stimulation techniques which aim to induce and quantify plasticity, the mechanisms and nature of their inherent variability and use such observations to review the idea that excessive and abnormal plasticity is a pathophysiological substrate of dystonia. We have tried to define the tone of our review by a couple of Professor John Rothwell's many inspiring characteristics; his endless curiosity to refine knowledge and disease models by scientific exploration and his wise yet humble readiness to revise scientific doctrines when the evidence is supportive. We conclude that high variability of response to non-invasive brain stimulation plasticity protocols significantly clouds the interpretation of historical findings in dystonia research. There is an opportunity to wipe the slate clean of assumptions and armed with an informative literature in health, re-evaluate whether excessive plasticity has a causal role in the pathophysiology of dystonia

Comprensión de textos como una situación de solución de problemas

La investigación en la comprensión de textos ha dado detalles de cómo las características del texto y los procesos cognitivos interactúan con el fin de consituir la comprensión y generar significado. Sin embargo, no existe un vínculo explícito entre los procesos cognitivos desplegados durante la comprensión de textos y su lugar en la cognición de orden superior, como en la resolución de problemas. El propósito de este trabajo es proponer un modelo cognitivo en el que la comprensión de textos se hace similar a una resolución de problemas y la situación que se basa en la investigación actual sobre los procesos cognitivos conocidos como la generación de la inferencia, la memoria y las simulaciones. La característica clave del modelo es que incluye explícitamente la formulación de las preguntas como un componente que aumenta la potencia de representación. Otras características del modelo se especifican y sus extensiones a la investigación básica y en la comprensión de textos y de orden superior los procesos cognitivos se describen aplican.Research in text comprehension has provided details as to how text features and cognitive processes interact in order to build comprehension and generate meaning. However, there is no explicit link between the cognitive processes deployed during text comprehension and their place in higher-order cognition, as in problem solving. The purpose of this paper is to propose a cognitive model in which text comprehension is made analogous to a problem solving situation and that relies on current research on well-known cognitive processes such as inference generation, memory, and simulations. The key characteristic of the model is that it explicitly includes the formulation of questions as a component that boosts representational power. Other characteristics of the model are specified and its extensions to basic and applied research in text comprehension and higher-order cognitive processes are outlined.Fil: Marmolejo Ramos, Fernando. University of Adelaide; AustraliaFil: Yomha Cevasco, Jazmin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Time, action and psychosis: using subjective time to investigate the effects of ketamine on sense of agency

Sense of agency refers to the experience of initiating and controlling actions in order to influence events in the outside world. A disturbed sense of agency is found in certain psychiatric and neurological disorders, most notably schizophrenia. Sense of agency is associated with a subjective compression of time: actions and their outcomes are perceived as bound together in time. This is known as ‘intentional binding’ and, in healthy adults, depends partly on advance prediction of action outcomes. Notably, this predictive contribution is disrupted in patients with schizophrenia. In the present study we aimed to characterise the psychotomimetic effect of ketamine, a drug model for psychosis, on the predictive contribution to intentional binding. It was shown that ketamine produced a disruption that closely resembled previous data from patients in the early, prodromal, stage of schizophrenic illness. These results are discussed in terms of established models of delusion formation in schizophrenia. The link between time and agency, more generally, is also considered

Towards a Multi-Subject Analysis of Neural Connectivity

Directed acyclic graphs (DAGs) and associated probability models are widely used to model neural connectivity and communication channels. In many experiments, data are collected from multiple subjects whose connectivities may differ but are likely to share many features. In such circumstances it is natural to leverage similarity between subjects to improve statistical efficiency. The first exact algorithm for estimation of multiple related DAGs was recently proposed by Oates et al. 2014; in this letter we present examples and discuss implications of the methodology as applied to the analysis of fMRI data from a multi-subject experiment. Elicitation of tuning parameters requires care and we illustrate how this may proceed retrospectively based on technical replicate data. In addition to joint learning of subject-specific connectivity, we allow for heterogeneous collections of subjects and simultaneously estimate relationships between the subjects themselves. This letter aims to highlight the potential for exact estimation in the multi-subject setting.Comment: to appear in Neural Computation 27:1-2