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No abstract is provided for this article.
In this paper we present an approach to the identification of nonlinear input–state–output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.
Over the last 30 years, representationalist and dynamicist positions in the philosophy of cognitive science have argued over whether neurocognitive processes should be viewed as representational or not. Major scientific and technological developments over the years have furnished both parties with ever more sophisticated conceptual weaponry. In recent years, an enactive generalization of predictive processing – known as active inference – has been proposed as a unifying theory of brain functions. Since then, active inference has fueled both representationalist and dynamicist campaigns. However, we believe that when diving into the formal details of active inference, one should be able to find a solution to the war; if not a peace treaty, surely an armistice of a sort. Based on an analysis of these formal details, this paper shows how both representationalist and dynamicist sensibilities can peacefully coexist within the new territory of active inference.
In this paper, we review the nature of illusions using the free-energy formulation of Bayesian perception. We reiterate the notion that illusory percepts are, in fact, Bayes-optimal and represent the most likely explanation for ambiguous sensory input. This point is illustrated using perhaps the simplest of visual illusions; namely, the Cornsweet effect. By using plausible prior beliefs about the spatial gradients of illuminance and reflectance in visual scenes, we show that the Cornsweet effect emerges as a natural consequence of Bayes-optimal perception. Furthermore, we were able to simulate the appearance of secondary illusory percepts (Mach bands) as a function of stimulus contrast. The contrast-dependent emergence of the Cornsweet effect and subsequent appearance of Mach bands were simulated using a simple but plausible generative model. Because our generative model was inverted using a neurobiologically plausible scheme, we could use the inversion as a simulation of neuronal processing and implicit inference. Finally, we were able to verify the qualitative and quantitative predictions of this Bayes-optimal simulation psychophysically, using stimuli presented briefly to normal subjects at different contrast levels, in the context of a fixed alternative forced choice paradigm.
This paper presents a general approach to the analysis of functional MRI time-series from one or more subjects. The approach is predicated on an extension of the general linear model that allows for correlations between error terms due to physiological noise or correlations that ensue after temporal smoothing. This extension uses the effective degrees of freedom associated with the error term. The effective degrees of freedom are a simple function of the number of scans and the temporal autocorrelation function. A specific form for the latter can be assumed if the data are smoothed, in time, to accentuate hemodynamic responses with a neural basis. This assumption leads to an expedient implementation of a flexible statistical framework. The importance of this small extension is that, in contradistinction to our previous approach, any parametric statistical analysis can be implemented. We demonstrate this point using a multiple regression analysis that tests for effects of interest (activations due to word generation), while taking explicit account of some obvious confounds.
Is self-consciousness necessary for consciousness? The answer is yes. So there you have it – the answer is yes. This was my response to a question I was asked to address in a recent AEON piece (https://aeon.co/essays/consciousness-is-not-a-thing-but-a-process-of-inference). What follows is based upon the notes for that essay, with a special focus on self-organisation, self-evidencing and self-modelling. I will try to substantiate my (polemic) answer from the perspective of a physicist. In brief, the argument goes as follows: if we want to talk about creatures, like ourselves, then we have to identify the characteristic behaviours they must exhibit. This is fairly easy to do by noting that living systems return to a set of attracting states time and time again. Mathematically, this implies the existence of a Lyapunov function that turns out to be model evidence (i.e., self-evidence) in Bayesian statistics or surprise (i.e., self-information) in information theory. This means that all biological processes can be construed as performing some form of inference, from evolution through to conscious processing. If this is the case, at what point do we invoke consciousness? The proposal on offer here is that the mind comes into being when self-evidencing has a temporal thickness or counterfactual depth, which grounds inferences about the consequences of my action. On this view, consciousness is nothing more than inference about my future; namely, the self-evidencing consequences of what I could do.
Abstract I enjoyed reading this compelling account of Conviction Narrative Theory (CNT). As a theoretical neurobiologist, I recognised – and applauded – the tenets of CNT. My commentary asks whether its claims could be installed into a Bayesian mechanics of decision-making, in a way that would enable theoreticians to model, reproduce and predict decision-making.
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Abstract It is a pleasure to comment on Bruineberg et al. – who raise some interesting questions of a philosophical and technical nature. I will try to answer three questions posed by the authors. Are Pearl and Friston blankets different things? Are Markov blankets used in an ontological sense? Is there a privileged Markov blanket?
