We propose the use of multivariate autoregressive (MAR) models of functional magnetic resonance imaging time series to make inferences about functional integration within the human brain. The method is demonstrated with synthetic and real data showing how such models are able to characterize interregional dependence. We extend linear MAR models to accommodate nonlinear interactions to model top-down modulatory processes with bilinear terms. MAR models are time series models and thereby model temporal order within measured brain activity. A further benefit of the MAR approach is that connectivity maps may contain loops, yet exact inference can proceed within a linear framework. Model order selection and parameter estimation are implemented by using Bayesian methods.
Detecting the location of salient sounds in the environment rests on the brain's ability to use differences in sounds arriving at both ears. Functional neuroimaging studies in humans indicate that the left and right auditory hemispaces are coded asymmetrically, with a rightward attentional bias that reflects spatial attention in vision. Neuropsychological observations in patients with spatial neglect have led to the formulation of two competing models: the orientation bias and right-hemisphere dominance models. The orientation bias model posits a symmetrical mapping between one side of the sensorium and the contralateral hemisphere, with mutual inhibition of the ipsilateral hemisphere. The right-hemisphere dominance model introduces a functional asymmetry in the brain's coding of space: the left hemisphere represents the right side, whereas the right hemisphere represents both sides of the sensorium. We used Dynamic Causal Modeling of effective connectivity and Bayesian model comparison to adjudicate between these alternative network architectures, based on human electroencephalographic data acquired during an auditory location oddball paradigm. Our results support a hemispheric asymmetry in a frontoparietal network that conforms to the right-hemisphere dominance model. We show that, within this frontoparietal network, forward connectivity increases selectively in the hemisphere contralateral to the side of sensory stimulation. We interpret this finding in light of hierarchical predictive coding as a selective increase in attentional gain, which is mediated by feedforward connections that carry precision-weighted prediction errors during perceptual inference. This finding supports the disconnection hypothesis of unilateral neglect and has implications for theories of its etiology.
By using positron emission tomography (PET) we examined the biological validity of a network model describing changes in cerebral activity associated with intrinsic and extrinsic word generation. The production of words not specified by an extrinsic stimulus constitutes willed or intrinsic generation. Perceiving a heard word is an example of extrinsic generation. The model incorporates three neuronal systems: a pool that stores word representations in a distributed fashion, an afferent system conveying sensory input to the pool and a modulating system that alters the responsivity of neurons in the pool. Simulations based on the model suggested that intrinsic generation would be associated with low activity in the pool, consequent on reduced modulation, and extrinsic generation with high activity. We measured cerebral activity with PET during intrinsic (verbal fluency) and extrinsic (responding to heard words) word generation and found this pattern of changes in the left superior temporal region. We were able to designate this region the site of the distributed word store and implicate the left dorsolateral prefrontal cortex (DLPFC) as the source of modulation. The relation between the superior temporal gyrus and DLPFC was shown by examining the correlation between the two regions in terms of cerebral activity. We conclude that the left DLPFC is responsible for modulating the responsivity of a neural system in the superior temporal gyrus and is the probable mediator of changes in attentional and intentional states that underlies the intrinsic generation of words.
I greatly enjoyed Seth's compelling synthesis of sensorimotor contingencies and active inference. I would also like to thank Jim Hopkins for sending me the quote (below)—which speaks directly to the embodied nature of perceptual inference that underlies the perspectives reconciled in Seth (this issue). These perspectives include perception as hypothesis testing, affordance, and sensorimotor contingencies. This commentary briefly rehearses the fundaments of active inference and offers a formal basis for Seth's key argument.
The aim of this article is to clarify how best to interpret some of the central constructs that underwrite the free-energy principle (FEP) – and its corollary, active inference – in theoretical neuroscience and biology: namely, the role that generative models and variational densities play in this theory. We argue that these constructs have been systematically misrepresented in the literature, because of the conflation between the FEP and active inference, on the one hand, and distinct (albeit closely related) Bayesian formulations, centred on the brain – variously known as predictive processing, predictive coding or the prediction error minimisation framework. More specifically, we examine two contrasting interpretations of these models: a structural representationalist interpretation and an enactive interpretation. We argue that the structural representationalist interpretation of generative and recognition models does not do justice to the role that these constructs play in active inference under the FEP. We propose an enactive interpretation of active inference – what might be called enactive inference. In active inference under the FEP, the generative and recognition models are best cast as realising inference and control – the self-organising, belief-guided selection of action policies – and do not have the properties ascribed by structural representationalists.
This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants -- what we call ''shared intelligence''. This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one's sensed world -- also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales: i.e., inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent's generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing -- leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first -- and key -- step towards such an ecology.
Abstract This paper introduces an active inference formulation of planning and navigation. It illustrates how the exploitation–exploration dilemma is dissolved by acting to minimise uncertainty (i.e., expected surprise or free energy). We use simulations of a maze problem to illustrate how agents can solve quite complicated problems using context sensitive prior preferences to form subgoals. Our focus is on how epistemic behaviour – driven by novelty and the imperative to reduce uncertainty about the world – contextualises pragmatic or goal-directed behaviour. Using simulations, we illustrate the underlying process theory with synthetic behavioural and electrophysiological responses during exploration of a maze and subsequent navigation to a target location. An interesting phenomenon that emerged from the simulations was a putative distinction between ‘place cells’ – that fire when a subgoal is reached – and ‘path cells’ – that fire until a subgoal is reached.
This paper presents a nonlinear image registration algorithm based on the setting of Large Deformation Diffeomorphic Metric Mapping (LDDMM), but with a more efficient optimisation scheme--both in terms of memory required and the number of iterations required to reach convergence. Rather than perform a variational optimisation on a series of velocity fields, the algorithm is formulated to use a geodesic shooting procedure, so that only an initial velocity is estimated. A Gauss-Newton optimisation strategy is used to achieve faster convergence. The algorithm was evaluated using freely available manually labelled datasets, and found to compare favourably with other inter-subject registration algorithms evaluated using the same data.
The perceptual experience of architecture is enacted by the sensory and motor system. When we act, we change the perceived environment according to a set of expectations that depend on our body and the built environment. The continuous process of collecting sensory information is thus based on bodily affordances. Affordances characterize the fit between the physical structure of the body and capacities for movement in the built environment. Since little has been done regarding the role of architectural design in the emergence of perceptual experience on a neuronal level, this paper offers a first step towards the role of architectural design in perceptual experience. An approach to synthesize concepts from computational neuroscience with architectural phenomenology into a computational neurophenomenology is considered. The outcome is a framework under which studies of architecture and cognitive neuroscience can be cast.
This dataset includes skin conductance response (SCR) measurement, keypress responses and keypress response times to stimuli drawn from the International Affective Picture System for each of 24 healthy unmedicated participants (12 males and 12 females aged 27+/-4.6 years). The experiment used a 2x3 factorial design with the factors picture type (aversive, neutral), and mean ISI (3s, 9s, and 19s).
