This paper offers an active inference narrative that considers deeply-held social attitudes and beliefs in relation to the cognitive concept of zones of bounded surprisal (ZBS) proposed by Manrique and Walker (2023). It is argued that narrow ZBS band-widths characterise the discriminatory minds of people who see themselves as an in-group. They tend not to be indignant or outspoken, but, instead, acquiescent and silent, when they witness poor behaviour of those whom otherwise they regard as members of their in-group. Plausibly, when such behaviour harms other people, as, for instance, in racist or gender-based violence, its perpetrators have a very narrow ZBS band-width: one that likely is a characteristic of dehumanising minds. Because such perpetrators see themselves as members of an in-group with entitlement to control aspects of society, they humiliate or abuse out-groups to which they assign others, and have no compunction about violating their dignity or human rights. We briefly consider policies that could lessen the unwelcome social repercussions of the behaviour of people with discriminatory and dehumanising minds.
Why are you reading this abstract? In some sense, your answer will cast the exercise as valuable-but what is value? In what follows, we suggest that value is evidence or, more exactly, log Bayesian evidence. This implies that a sufficient explanation for valuable behavior is the accumulation of evidence for internal models of our world. This contrasts with normative models of optimal control and reinforcement learning, which assume the existence of a value function that explains behavior, where (somewhat tautologically) behavior maximizes value. In this paper, we consider an alternative formulation-active inference-that replaces policies in normative models with prior beliefs about the (future) states agents should occupy. This enables optimal behavior to be cast purely in terms of inference: where agents sample their sensorium to maximize the evidence for their generative model of hidden states in the world, and minimize their uncertainty about those states. Crucially, this formulation resolves the tautology inherent in normative models and allows one to consider how prior beliefs are themselves optimized in a hierarchical setting. We illustrate these points by showing that any optimal policy can be specified with prior beliefs in the context of Bayesian inference. We then show how these prior beliefs are themselves prescribed by an imperative to minimize uncertainty. This formulation explains the saccadic eye movements required to read this text and defines the value of the visual sensations you are soliciting.
In this paper we introduce the idea of explaining responses, in one cortical area, in terms of an interaction between the influence of another area and some experimental (sensory or task-related) parameter. We refer to these effects aspsychophysiological interactionsand relate them to interactions based solely on experimental factors (i.e., psychological interactions), in factorial designs, and interactions among neurophysiological measurements (i.e., physiological interactions). We have framed psychophysiological interactions in terms of functional integration by noting that the degree to which the activity in one area can be predicted, on the basis of activity in another, corresponds to the contribution of the second to the first, where this contribution can be related to effective connectivity. A psychophysiological interaction means that the contribution of one area to another changes significantly with the experimental or psychological context. Alternatively these interactions can be thought of as a contribution-dependent change in regional responses to an experimental or psychological factor. In other words the contribution can be thought of as modulating the responses elicited by a particular stimulus or psychological process. The potential importance of this approach lies in (i) conferring a degree of functional specificity on this aspect of effective connectivity and (ii) providing a model of modulation, where the contribution from a distal area can be considered to modulate responses to the psychological or stimulus-specific factor defining the interaction. Although distinct in neurobiological terms, these are equivalent perspectives on the same underlying interaction. We illustrate these points using a functional magnetic resonance imaging study of attention to visual motion and a position emission tomography study of visual priming. We focus on interactions among extrastriate, inferotemporal, and posterior parietal regions during visual processing, under different attentional and perceptual conditions.
This paper considers goal-directed decision-making in terms of embodied or active inference. We associate bounded rationality with approximate Bayesian inference that optimizes a free energy bound on model evidence. Several constructs such as expected utility, exploration or novelty bonuses, softmax choice rules and optimism bias emerge as natural consequences of free energy minimization. Previous accounts of active inference have focused on predictive coding. In this paper, we consider variational Bayes as a scheme that the brain might use for approximate Bayesian inference. This scheme provides formal constraints on the computational anatomy of inference and action, which appear to be remarkably consistent with neuroanatomy. Active inference contextualizes optimal decision theory within embodied inference, where goals become prior beliefs. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (associated with softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution. Crucially, this sensitivity corresponds to the precision of beliefs about behaviour. The changes in precision during variational updates are remarkably reminiscent of empirical dopaminergic responses-and they may provide a new perspective on the role of dopamine in assimilating reward prediction errors to optimize decision-making.
In the past decade the importance of synchronized dynamics in the brain has emerged from both empirical and theoretical perspectives. Fast dynamic synchronous interactions of an oscillatory or nonoscillatory nature may constitute a form of temporal coding that underlies feature binding and perceptual synthesis. The relationship between synchronization among neuronal populations and the population firing rates addresses two important issues: the distinction between rate coding and synchronization coding models of neuronal interactions and the degree to which empirical measurements of population activity, such as those employed by neuroimaging, are sensitive to changes in synchronization. We examined the relationship between mean population activity and synchronization using biologically plausible simulations. In this article, we focus on continuous stationary dynamics. (In a companion article, Chawla (forth-coming), we address the same issue using stimulus-evoked transients.) By manipulating parameters such as extrinsic input, intrinsic noise, synaptic efficacy, density of extrinsic connections, the voltage-sensitive nature of postsynaptic mechanisms, the number of neurons, and the laminar structure within the populations, we were able to introduce variations in both mean activity and synchronization under a variety of simulated neuronal architectures. Analyses of the simulated spike trains and local field potentials showed that in nearly every domain of the model's parameter space, mean activity and synchronization were tightly coupled. This coupling appears to be mediated by an increase in synchronous gain when effective membrane time constants are lowered by increased activity. These observations show that under the assumptions implicit in our models, rate coding and synchrony coding in neural systems with reciprocal interconnections are two perspectives on the same underlying dynamic. This suggests that in the absence of specific mechanisms decoupling changes in synchronization from firing levels, indexes of brain activity that are based purely on synaptic activity (e.g., functional magnetic resonance imaging) may also be sensitive to changes in synchronous coupling.
This chapter focuses on dynamic coordination and how it is achieved, how it is related to cognitive functions and learning processes, and the role of neural oscillations in different frequency bands for dynamic coordination. It also considers modulation of coordination at the systems level and the relation of the mechanisms underlying coordination of behavior and cognition to neuropsychiatric disorders. In addition, the chapter examines dynamic coordination that arises from self-organization, the reduction in dimensionality associated with dynamic coordination, the theory of Coherent Infomax, Dynamic Pattern Theory, how optimization and synchrony are related to dynamic coordination, neural synchrony during human ontogeny, and how coordination contributes to perceptual and motor development. The chapter concludes with a discussion of the role of different frequency bands in sensory processing, attention and awareness, and motor circuits, together with how the prefrontal cortex modulates dynamic coordination.
Abstract In our everyday lives, we are often required to follow a conversation when background noise is present (“speech-in-noise” perception). Speech-in-noise perception varies widely—and people who are worse at speech-in-noise perception are also worse at fundamental auditory grouping, as assessed by figure-ground tasks. Here, we examined the cortical processes that link difficulties with speech-in-noise perception to difficulties with figure-ground perception using functional magnetic resonance imaging (fMRI). We found strong evidence that the earliest stages of the auditory cortical hierarchy (left core and belt areas) are similarly disinhibited when speech-in-noise and figure-ground tasks are more difficult (i.e., at target-to-masker ratios corresponding to 60% than 90% thresholds)—consistent with increased cortical gain at lower levels of the auditory hierarchy. Overall, our results reveal a common neural substrate for these basic (figure-ground) and naturally relevant (speech-in-noise) tasks—which provides a common computational basis for the link between speech-in-noise perception and fundamental auditory grouping.
In this paper we present a general multivariate approach to the analysis of functional imaging studies. This analysis uses standard multivariate techniques to make statistical inferences about activation effects and to describe the important features of these effects. More specifically, the proposed analysis uses multivariate analysis of covariance (ManCova) with Wilk's lambda to test for specific effects of interest (e.g., differences among activation conditions), and canonical variates analysis (CVA) to characterize differential responses in terms of distributed brain systems. The data are subject to ManCova after transformation using their principal components or eigenimages. After significance of the activation effect has been assessed, underlying changes are described in terms of canonical images. Canonical images are like eigenimages but take explicit account of the effects of error or noise. The generality of this approach is assured by the general linear model used in the ManCova. The design and inferences sought are embodied in the design matrix and can, in principle, accommodate most parametric statistical analyses. This multivariate analysis may provide a statistical approach to PET activation studies that 1) complements univariate approaches like statistical parametric mapping, and 2) may facilitate the extension of existing multivariate techniques, like the scaled subprofile model and eigenimage analysis, to include hypothesis testing and statistical inference.
I greatly enjoyed reading Mark Solms's piece on the conscious id. Mindful of writing this commentary, I noted (in the margins of the target article) points of contact between his formulation and our more formal—if somewhat dryer—treatment using Helmholtzian notions of free energy. It was clear, after a few pages, that I was not going to be able to cover every aspect of the remarkable consilience between the two approaches. Instead, I focus on substantiating Solms's key conclusions from the perspective of hierarchical inference in the Bayesian brain.
