Biological systems-like ourselves-are constantly faced with uncertainty. Despite noisy sensory data, and volatile environments, creatures appear to actively maintain their integrity. To account for this remarkable ability to make optimal decisions in the face of a capricious world, we propose a generative model that represents the beliefs an agent might possess about their own uncertainty. By simulating a noisy and volatile environment, we demonstrate how uncertainty influences optimal epistemic (visual) foraging. In our simulations, saccades were deployed less frequently to regions with a lower sensory precision, while a greater volatility led to a shorter inhibition of return. These simulations illustrate a principled explanation for some cardinal aspects of visual foraging-and allow us to propose a correspondence between the representation of uncertainty and ascending neuromodulatory systems, complementing that suggested by Yu & Dayan (Yu & Dayan 2005 Neuron46, 681-692. (doi:10.1016/j.neuron.2005.04.026)).
This paper describes a Bayesian method for three-dimensional registration of brain images. A finite element approach is used to obtain a maximum a posteriori estimate of the deformation field at every voxel of a template volume. The priors used by the MAP estimate penalize unlikely deformations and enforce a continuous one-to-one mapping. The deformations are assumed to have some form of symmetry, in that priors describing the probability distribution of the deformations should be identical to those for the inverses (i.e., warping brain A to brain B should not be different probablistically from warping B to A). A gradient descent algorithm is presented for estimating the optimum deformations.
One of the central claims of the Self-model Theory of Subjectivity is that the experience of being someone – even in a minimal form – arises through a transparent phenomenal self-model, which itself can in principle be reduced to brain processes. Here, we consider whether it is possible to distinguish between phenomenally transparent and opaque states in terms of active inference. We propose a relationship of phenomenal opacity to expected uncertainty or precision; i.e., the capacity for introspective attention and implicit mental action. Thus we associate introspective attention with the deployment of 'precision' that may render the perceptual evidence (for action) opaque, while treating transparency as a necessary aspect of beliefs about action, i.e., 'what I am' doing. We conclude by proposing how we may have to nuance our conception of minimal phenomenal selfhood and agency in light of this active inference conception of transparency-opacity.
Abstract The ability to adapt to a changing environment underwrites sentient behaviour e.g., wearing a raincoat when walking in the rain but removing it when indoors. In such instances, agents act to satisfy some preferred mode of behaviour that leads to predictable states necessary for survival, i.e., states that are characteristic of that agent. In this chapter, we describe how active inference agents, equipped with preference learning, can exhibit these distinct behavioural modes – influenced by environment dynamics – to aptly trade-off between preference satisfaction and exploration. We validate this in a modified OpenAI Gym FrozenLake environment (without any extrinsic signal) with and without volatility under a fixed model of the environment. In a static (i.e., without volatility) environment, preference-learning agents accumulate confident (Bayesian) beliefs about their behaviour and act to satisfy them. In contrast, volatile dynamics led to preference uncertainty and exploratory behaviour. This demonstrates that active inference agents, equipped with preference learning, have the appropriate machinery to (i) engage in adaptive behaviour under appropriate levels of volatility, and (ii) learn context-dependent subjective preferences.
Background Previous studies have suggested that the default mode network (DMN) plays a central role in the physiopathology of major depressive disorder (MDD). However, the effect of antidepressant treatment on functional connectivity within the DMN has yet to be established. Considering the very high rates of relapse in recovered subjects, we hypothesized that abnormalities in DMN functional connectivity would persist in recovered MDD subjects. Methods Resting state functional magnetic resonance imaging images were collected from 24 MDD patients and 29 healthy control subjects. After 12 weeks of antidepressant treatment, 18 recovered MDD subjects were scanned again. Group independent component analysis was performed to decompose the resting state images into spatially independent components. Default mode subnetworks were identified using a template based on previous studies. Group differences in the ensuing subnetworks were tested using two-sample t tests. Results Two spatially independent default mode subnetworks were detected in all subjects: the anterior subnetwork and the posterior subnetwork. Both subnetworks showed increased functional connectivity in pretreatment MDD subjects, relative to control subjects. Differences in the posterior subnetwork were normalized after antidepressant treatment, while abnormal functional connectivity persisted within the anterior subnetwork. Conclusions Our findings suggest a dissociation of the DMN into subnetworks, where persistent abnormal functional connectivity within the anterior subnetwork in recovered MDD subjects may constitute a biomarker of asymptomatic depression and potential for relapse.
This technical note applies active inference to the problem of learning goal-directed behaviour from scratch, namely, de novo learning. By de novo learning, we mean discovering, directly from observations, the structure and parameters of a discrete generative model for sequential policy optimisation. Concretely, our procedure grows and then reduces a model until it discovers a pullback attractor over (generalised) states; this attracting set supplies paths of least action among goal states while avoiding costly states. The implicit efficiency rests upon reframing the learning problem through the lens of the free energy principle, under which it is sufficient to learn a generative model whose dynamics feature such an attracting set. For context, we briefly relate this perspective to value-based formulations (e.g., Bellman optimality) and then apply the active inference formulation to a small arcade game to illustrate de novo structure learning and ensuing agency.
This article considers the evolution of brain architectures for predictive processing. We argue that brain mechanisms for predictive perception and action are not late evolutionary additions of advanced creatures like us. Rather, they emerged gradually from simpler predictive loops (e.g. autonomic and motor reflexes) that were a legacy from our earlier evolutionary ancestors—and were key to solving their fundamental problems of adaptive regulation. We characterize simpler-to-more-complex brains formally, in terms of generative models that include predictive loops of increasing hierarchical breadth and depth. These may start from a simple homeostatic motif and be elaborated during evolution in four main ways: these include the multimodal expansion of predictive control into an allostatic loop; its duplication to form multiple sensorimotor loops that expand an animal's behavioural repertoire; and the gradual endowment of generative models with hierarchical depth (to deal with aspects of the world that unfold at different spatial scales) and temporal depth (to select plans in a future-oriented manner). In turn, these elaborations underwrite the solution to biological regulation problems faced by increasingly sophisticated animals. Our proposal aligns neuroscientific theorising—about predictive processing—with evolutionary and comparative data on brain architectures in different animal species. This article is part of the theme issue ‘Systems neuroscience through the lens of evolutionary theory’.
This paper presents a method for the coregistration and partitioning (i.e., tissue segmentation) of brain images that have been acquired in different modalities. The basic idea is that instead of matching two images directly, one performs intermediate within-modality registrations to two template images that are already in register. One can use a least-squares minimization to determine the affine transformations that map between the templates and the images. By incorporating suitable constraints, a rigid body transformation which directly maps between the images can be extracted from these more general affine transformations. A further refinement capitalizes on the implicit normalization of both images into a standard space. This facilitates segmentation or partitioning of both original images into homologous tissue classifications. Once partitioned, the partitions can be jointly matched, further increasing the accuracy of the coregistration. In short, these techniques reduce the between-modality problem to a series of simpler within-modality problems. These methods are relatively robust, address a number of problems in image transformations, and require no manual intervention.
