Active Inference: A Process Theory

Abstract

This is the published version of the paper.This version of the publication may differ from the final published version. behavior prescribed by these dynamics has a degree of face validity, providing a formal explanation for reward seeking, context learning, and epistemic foraging. Technically, the fact that a gradient descent appears to be a valid description of neuronal activity means that variational free energy is a Lyapunov function for neuronal dynamics, which therefore conform to Hamilton's principle of least action. Permanent repository link

The Active Inference Approach to Ecological Perception: General Information Dynamics for Natural and Artificial Embodied Cognition

“…AIF considers a thermodynamically open, embodied, and environmentally embedded agent (see, e.g., Friston, 2009Friston, , 2010Friston et al, 2010Friston et al, , 2015aFriston et al, ,b, 2016Friston et al, , 2017a. In AIF, the adaptive behavior of such a "cybernetic" agent is understood to be regulated by ecologically relevant information, underpinned by a perception/action loop.…”

Model-Based Approaches to Active Perception and Control

“…This problem is resolved in terms of a hierarchical (Bayesian) scheme, which weights the "strength" (or more formally, the precision) of priors at higher hierarchical levels, which play the role of goals (e.g., I want an apple) and of prediction errors coming from lower hierarchical levels: when the former dominates the latter, the architecture triggers a cascade of predictions (including perceptual, proprioceptive and interoceptive predictions about the apple-in-my-hand) that, in turn, guide perceptual processing and (through the minimization of proprioceptive prediction error) enslave action until the apple is really in the agent's hand, or a change of mind occurs. This latter concept nicely extends to planning sequences of actions, by considering predictions about entire behavioural policies (e.g., reaching one of the different places where I can secure an apple or obtain cues about where to find apples) as opposed to considering only the current or the immediate next grasping action [35,[80][81][82][83][84][85][86]. A related body of work emphasizes proactive aspects of brain dynamics as well as interoceptive and bodily processes, such as the mobilization of resources in anticipation of future needs [65,[87][88][89].…”

“…In sum, there are multiple ways to implement Active Inference agents and their generative models. This fact should not be surprising, as the same framework has been used to model autonomous systems at various levels of complexity, from cells that self-organize and show the emergence of life from a primordial soup [101] or of morphogenetic processes [102], to more sophisticated agent models that engage in cognitive [122,123] or social tasks [92]-while also appealing within these cognitive models to different inferred variables, including spatial representations [80], action possibilities within an affordance landscape [16], and internal (belief) states of other agents [96]. It is then possible that a reason for dissatisfaction with the above definition of representation is that it does not account for this diversity, and the possibility that we have different epistemological attitudes towards these diverse systems.…”

From symbols to icons: the return of resemblance in the cognitive neuroscience revolution

“…Predictive processing is by far the most ambitious and influential of such approaches, touted by some as the "first unified theory of the brain" (Huang 2008)-a "paradigm shift" (Friston et al 2017, p. 1) that constitutes "the most complete framework to date for explaining perception, cognition, and action in terms of fundamental theoretical principles and neurocognitive architectures" (Seth 2015, p. 1). Roughly, it states that brains self-organize around a single, overarching imperative: the minimization of long-term prediction error, the mismatch between the sensory data they predict on the basis of their model of the world and the sensory data they receive from that world (Clark 2016;Friston 2010;Hohwy 2013).…”

“…It is only by minimizing the error in their predictions of the incoming signal that brains can maintain the organisms of which they are a part within their optimal states and thus fulfil their homeostatic function (Friston 2010;Friston et al 2017). Crucially, to effectively minimize the error in their predictions of the incoming signal requires that they effectively predict how the environmental causes of such signals are likely to evolve under various kinds of alteration and intervention (Clark 2016, p. 6).…”