Scene Construction, Visual Foraging, and Active Inference

This paper describes an active inference scheme for visual searches and the perceptual synthesis entailed by scene construction. Active inference assumes that perception and action minimize variational free energy, where actions are selected to minimize the free energy expected in the future. This assumption generalizes risk-sensitive control and expected utility theory to include epistemic value; namely, the value (or salience) of information inherent in resolving uncertainty about the causes of ambiguous cues or outcomes. Here, we apply active inference to saccadic searches of a visual scene. We consider the (difficult) problem of categorizing a scene, based on the spatial relationship among visual objects where, crucially, visual cues are sampled myopically through a sequence of saccadic eye movements. This means that evidence for competing hypotheses about the scene has to be accumulated sequentially, calling upon both prediction (planning) and postdiction (memory). Our aim is to highlight some simple but fundamental aspects of the requisite functional anatomy; namely, the link between approximate Bayesian inference under mean field assumptions and functional segregation in the visual cortex. This link rests upon the (neurobiologically plausible) process theory that accompanies the normative formulation of active inference for Markov decision processes. In future work, we hope to use this scheme to model empirical saccadic searches and identify the prior beliefs that underwrite intersubject variability in the way people forage for information in visual scenes (e.g., in schizophrenia). Copyright © 2016 Mirza, Adams, Mathys, Friston

Active Inference, Novelty and Neglect

In this chapter, we provide an overview of the principles of active inference. We illustrate how different forms of short-term memory are expressed formally (mathematically) through appealing to beliefs about the causes of our sensations and about the actions we pursue. This is used to motivate an approach to active vision that depends upon inferences about the causes of 'what I have seen' and learning about 'what I would see if I were to look there'. The former could manifest as persistent 'delay-period' activity - of the sort associated with working memory, while the latter is better suited to changes in synaptic efficacy - of the sort that underlies short-term learning and adaptation. We review formulations of these ideas in terms of active inference, their role in directing visual exploration and the consequences - for active vision - of their failures. To illustrate the latter, we draw upon some of our recent work on the computational anatomy of visual neglect

Computational Modelling of Information Gathering

This thesis describes computational modelling of information gathering behaviour under active inference – a framework for describing Bayes optimal behaviour. Under active inference perception, attention and action all serve for same purpose: minimising variational free energy. Variational free energy is an upper bound on surprise and minimising it maximises an agent’s evidence for its survival. An agent achieves this by acquiring information (resolving uncertainty) about the hidden states of the world and uses the acquired information to act on the outcomes it prefers. In this work I placed special emphasis on the resolution of uncertainty about the states of the world. I first created a visual search task called scene construction task. In this task one needs to accumulate evidence for competing hypotheses (different visual scenes) through sequential sampling of a visual scene and categorising it once there is sufficient evidence. I showed that a computational agent attends to the most salient (epistemically valuable) locations in this task. In the next, this task was performed by healthy humans. Healthy people’s exploration strategies provided evidence for uncertainty driven exploration. I also showed how different exploratory behaviours can be characterised using canonical correlation analysis. In the next study I showed how exploration of a visual scene under different instructions could be explained by appealing to the computational mechanisms that may correspond to attention. This entailed manipulating the precision of task irrelevant cues and their hidden causes as a function of instructions. In the final work, I was interested in characterising impulsive behaviour using a patch leaving paradigm. By varying the parameters of the MDP model, I showed that there could be at least three distinct causes of impulsive behaviour, namely a lower depth of planning, a lower capacity to maintain and process information, and an increased perceived value of immediate rewards

All Thinking is 'Wishful' Thinking

Motivation to engage in any epistemic behavior can be decomposed into two basic types that emerge in various guises across different disciplines and areas of study. The first basic dimension refers to a desire to approach versus avoid nonspecific certainty, which has epistemic value. It describes a need for an unambiguous, precise answer to a question, regardless of that answer’s specific content. Second basic dimension refers to a desire to approach versus avoid specific certainty, which has instrumental value. It concerns a need for the specific content of one’s beliefs and prior preferences. Together, they explain diverse epistemic behaviors, such as seeking, avoiding, and biasing new information and revising and updating, versus protecting, one’s beliefs, when confronted with new evidence. The relative strength of these motivational components determines the form of (Bayes optimal) epistemic behavior that follows

Active inference and the anatomy of oculomotion

Given that eye movement control can be framed as an inferential process, how are the requisite forces generated to produce anticipated or desired fixation? Starting from a generative model based on simple Newtonian equations of motion, we derive a variational solution to this problem and illustrate the plausibility of its implementation in the oculomotor brainstem. We show, through simulation, that the Bayesian filtering equations that implement ‘planning as inference’ can generate both saccadic and smooth pursuit eye movements. Crucially, the associated message passing maps well onto the known connectivity and neuroanatomy of the brainstem – and the changes in these messages over time are strikingly similar to single unit recordings of neurons in the corresponding nuclei. Furthermore, we show that simulated lesions to axonal pathways reproduce eye movement patterns of neurological patients with damage to these tracts

You only look as much as you have to : using the free energy principle for active vision

Active vision considers the problem of choosing the optimal next viewpoint from which an autonomous agent can observe its environment. In this paper, we propose to use the active inference paradigm as a natural solution to this problem, and evaluate this on a realistic scenario with a robot manipulator. We tackle this problem using a generative model that was learned unsupervised purely from pixel-based observations. We show that our agent exhibits information-seeking behavior, choosing viewpoints of regions it has not yet observed. We also show that goal-seeking behavior emerges when the agent has to reach a target goal, and it does so more efficiently than a systematic grid search

Reframing PTSD for computational psychiatry with the active inference framework

Introduction: Recent advances in research on stress and, respectively, on disorders of perception, learning, and behaviour speak to a promising synthesis of current insights from (i) neurobiology, cognitive neuroscience and psychology of stress and post-traumatic stress disorder (PTSD), and (ii) computational psychiatry approaches to pathophysiology (e.g. of schizophrenia and autism). Methods: Specifically, we apply this synthesis to PTSD. The framework of active inference offers an embodied and embedded lens through which to understand neuronal mechanisms, structures, and processes of cognitive function and dysfunction. In turn, this offers an explanatory model of how healthy mental functioning can go awry due to psychopathological conditions that impair inference about our environment and our bodies. In this context, auditory phenomena - known to be especially relevant to studies of PTSD and schizophrenia - and traditional models of auditory function can be viewed from an evolutionary perspective based on active inference. Results: We assess and contextualise a range of evidence on audition, stress, psychosis, and PTSD, and bring some existing partial models of PTSD into multilevel alignment. Conclusions: The novel perspective on PTSD we present aims to serve as a basis for new experimental designs and therapeutic interventions that integrate fundamentally biological, cognitive, behavioural, and environmental factors