Getting Warmer: Predictive Processing and the Nature of Emotion

Abstract

Predictive processing accounts of neural function view the brain as a kind of prediction machine that forms models of its environment in order to anticipate the upcoming stream of sensory stimulation. These models are then continuously updated in light of incoming error signals. Predictive processing has offered a powerful new perspective on cognition, action, and perception. In this chapter we apply the insights from predictive processing to the study of emotions. The upshot is a picture of emotion as inseparable from perception and cognition, and a key feature of the embodied mind. Predictive Processing and Emotion -The Story So FarEmotion and cognition are typically thought of in contrast to one another, sitting on opposite sides of a divide between passion and reason, the hot and the cold.But what does our best theory of the brain and central nervous system (CNS) tell us about the nature of emotion?According to an increasingly popular framework in computational neuroscience, the brain is a hierarchically arranged prediction machine (Clark (2013a)). Contrary to once-popular feedforward approaches, the brain does not simply take inputs from the outside world, process them, and pass them deeper and deeper into the processing economy. Instead, whenever information from the world impacts on your sensory surfaces, it is already, even at the earliest stages, greeted by a downward-flowing prediction on the part of your nervous system. This prediction comes from your brain's best model of what is going on in the world, and this model is constantly being updated by the mistakes it makes, by the so-called 'prediction error signal', which it constantly tries to keep to a minimum (Lee and Mumford

Losing Ourselves: Active Inference, Depersonalization, and Meditation

“…This is perceptual inference since the model has to accommodate the input. Building on our recent work ( Miller and Clark, 2017 ; Wilkinson et al, 2019 ), we, in contrast, view emotion, and affect more generally, as involving active inference, too. In terms of ACM, it is a central part of allostasis.…”

On the Non-uniqueness Problem in Integrated Information Theory

“…As demonstration of these problems inherent with each of the four existing solutions, we reanalyze our corpus enforcing each criterion in turn (Figure 7) 3 . As expected, the KO17 and Moon solution yield Φ = 0 for several systems that are integrated (e.g., that cannot be tensor factorized without changing the underlying dynamics).…”

“…Integrated Information Theory (IIT) is a leading contender among theories of consciousness due to its provision of a scalar mathematical measure, Φ, posited to predict the overall level of consciousness in virtually any dynamical system. In comparison to other contemporary theories of consciousness, such as Global Neuronal Workspace Theory [1] or Predictive Processing [2][3][4][5][6], IIT is set apart by its mathematical rigor. The concrete, mathematical formulation of the theory is at least partially responsible for its widespread adoption over the past decade 1 .…”

Epistemic Emotions: The Case of Wonder

“…Cognitive science is now proving that our brains try to overcome uncertainty building patterns of predictability (Clark 2016). What is relevant for my argument is that emotions seem to be built from predictions (Wilkinson et al 2019) and that it is the surprise brought by the unexpected what obliges the brain to continuously remake the patterns for replying to the prediction-errors. The unexpected leads to prediction errors and these errors are what first provoke the predictions revision and, finally, to improve future performance.…”