Search

Biological agents demonstrate a remarkable proficiency in calibrating appropriate scales of planning and evaluation when interacting with their environments. It follows logically that any decision-making algorithm aspiring to neurobiological plausibility must mirror these attributes, particularly regarding computational expenditure and the intricacy of evaluative processes. However, active inference encounters notable challenges in simulating apt behaviours within complex environments. These stem chiefly from its substantial computational demands and the intricate task of defining the agent's behaviour preference. We address these through a two-fold approach. First, we introduce a planning algorithm by using the Bellman-optimality principle to minimise the planning cost function (i.e., expected free energy). Briefly, we recursively compute the expected free energy of actions in reverse temporal sequence to significantly reduce the computational complexity. Secondly, inspired by the Z-learning algorithm, we propose a novel method to learn time-constrained agent preferences. We face-validate the efficacy of these through grid-world simulations and demonstrate precise model learning and planning, even under uncertainty. These algorithmic advances create new opportunities for various applications—in neuroscience and machine learning. • Introduced the Dynamic Programming Expected Free Energy (DPEFE) for efficient active inference planning. • Developed a new algorithm for learning time-constrained agent behaviour preferences. • Demonstrated, theoretically and via simulations, reduced computational cost by orders of magnitude. [ABSTRACT FROM AUTHOR]

This paper introduces active listening, as a unified framework for synthesising and recognising speech. The notion of active listening inherits from active inference, which considers perception and action under one universal imperative: to maximise the evidence for our (generative) models of the world. First, we describe a generative model of spoken words that simulates (i) how discrete lexical, prosodic, and speaker attributes give rise to continuous acoustic signals; and conversely (ii) how continuous acoustic signals are recognised as words. The 'active' aspect involves (covertly) segmenting spoken sentences and borrows ideas from active vision. It casts speech segmentation as the selection of internal actions, corresponding to the placement of word boundaries. Practically, word boundaries are selected that maximise the evidence for an internal model of how individual words are generated. We establish face validity by simulating speech recognition and showing how the inferred content of a sentence depends on prior beliefs and background noise. Finally, we consider predictive validity by associating neuronal or physiological responses, such as the mismatch negativity and P300, with belief updating under active listening, which is greatest in the absence of accurate prior beliefs about what will be heard next. • Describes a generative model for synthesising and recognising speech. • Considers speech segmentation (placing word boundaries) as an active process. • Treats speech segmentation and lexical inferences as complementary. • Associates neural mismatch responses (e.g., MMN) with belief updating. [ABSTRACT FROM AUTHOR]

• New (hierarchical) generative model for linguistic interactions. • Builds on active inference formulations of dyadic interactions. • We simulate agents that ask and answer questions together. • Theta-gamma coupling emerges from belief updating under this framework. This paper presents a biologically plausible generative model and inference scheme that is capable of simulating communication between synthetic subjects who talk to each other. Building on active inference formulations of dyadic interactions, we simulate linguistic exchange to explore generative models that support dialogues. These models employ high-order interactions among abstract (discrete) states in deep (hierarchical) models. The sequential nature of language processing mandates generative models with a particular factorial structure—necessary to accommodate the rich combinatorics of language. We illustrate linguistic communication by simulating a synthetic subject who can play the 'Twenty Questions' game. In this game, synthetic subjects take the role of the questioner or answerer, using the same generative model. This simulation setup is used to illustrate some key architectural points and demonstrate that many behavioural and neurophysiological correlates of linguistic communication emerge under variational (marginal) message passing, given the right kind of generative model. For example, we show that theta-gamma coupling is an emergent property of belief updating, when listening to another. [ABSTRACT FROM AUTHOR]