Publications

In contrast to the symmetries of translation in space, rotation in space, and translation in time, the known laws of physics are not universally invariant under transformation of scale. However, the action can be invariant under change of scale in the special case of a scale free dynamical system that can be described in terms of a Lagrangian, that itself scales inversely with time. Crucially, this means symmetries under change of scale can exist in dynamical systems under certain constraints. Our contribution lies in the derivation of a generalised scale invariant Lagrangian - in the form of a power series expansion - that satisfies these constraints. This generalised Lagrangian furnishes a normal form for dynamic causal models (i.e., state space models based upon differential equations) that can be used to distinguish scale invariance (scale symmetry) from scale freeness in empirical data. We establish face validity with an analysis of simulated data and then show how scale invariance can be identified - and how the associated conserved quantities can be estimated - in neuronal timeseries.

Research suggests that perception and imagination engage neuronal representations in the same visual areas. However, the underlying mechanisms that differentiate sensory perception from imagination remain unclear. Here, we examine the directed coupling (effective connectivity) between fronto-parietal and visual areas during perception and imagery. We found an increase in bottom-up coupling during perception relative to baseline and an increase in top-down coupling during both perception and imagery, with a much stronger increase during imagery. Modulation of the coupling from frontal to early visual areas was common to both perception and imagery. Furthermore, we show that the experienced vividness during imagery was selectively associated with increases in top-down connectivity to early visual cortex. These results highlight the importance of top-down processing in internally as well as externally driven visual experience.

Existing evidence suggests that reward and attentional networks function in concert and that activation in one system influences the other in a reciprocal fashion; however, the nature of these influences remains poorly understood. We therefore developed a three-component task to assess the interaction effects of reward anticipation and conflict resolution on the behavioral performance and the activation of brain reward and attentional systems. Sixteen healthy adult volunteers aged 21-45 years were scanned with functional magnetic resonance imaging (fMRI) while performing the task. A two-way repeated measures analysis of variance (ANOVA) with cue (reward vs. non-reward) and target (congruent vs. incongruent) as within-subjects factors was used to test for main and interaction effects. Neural responses to anticipation, conflict, and reward outcomes were tested. Behaviorally there were main effects of both reward cue and target congruency on reaction time. Neuroimaging results showed that reward anticipation and expected reward outcomes activated components of the attentional networks, including the inferior parietal and occipital cortices, whereas surprising non-rewards activated the frontoinsular cortex bilaterally and deactivated the ventral striatum. In turn, conflict activated a broad network associated with cognitive control and motor functions. Interaction effects showed decreased activity in the thalamus, anterior cingulated gyrus, and middle frontal gyrus bilaterally when difficult conflict trials (e.g., incongruent targets) were preceded by reward cues; in contrast, the ventral striatum and orbitofrontal cortex showed greater activation during congruent targets preceded by reward cues. These results suggest that reward anticipation is associated with lower activation in attentional networks, possibly due to increased processing efficiency, whereas more difficult, conflict trials are associated with lower activity in regions of the reward system, possibly because such trials are experienced as less rewarding.

Viewing the brain as an organ of approximate Bayesian inference can help us understand how it represents the self. We suggest that inferred representations of the self have a normative function: to predict and optimise the likely outcomes of social interactions. Technically, we cast this predict-and-optimise as maximising the chance of favourable outcomes through active inference. Here the utility of outcomes can be conceptualised as prior beliefs about final states. Actions based on interpersonal representations can therefore be understood as minimising surprise - under the prior belief that one will end up in states with high utility. Interpersonal representations thus serve to render interactions more predictable, while the affective valence of interpersonal inference renders self-perception evaluative. Distortions of self-representation contribute to major psychiatric disorders such as depression, personality disorder and paranoia. The approach we review may therefore operationalise the study of interpersonal representations in pathological states.

The deployment of visuospatial attention and the programming of saccades are governed by the inferred likelihood of events. In the present study, we combined computational modeling of psychophysical data with fMRI to characterize the computational and neural mechanisms underlying this flexible attentional control. Sixteen healthy human subjects performed a modified version of Posner9s location-cueing paradigm in which the percentage of cue validity varied in time and the targets required saccadic responses. Trialwise estimates of the certainty (precision) of the prediction that the target would appear at the cued location were derived from a hierarchical Bayesian model fitted to individual trialwise saccadic response speeds. Trial-specific model parameters then entered analyses of fMRI data as parametric regressors. Moreover, dynamic causal modeling (DCM) was performed to identify the most likely functional architecture of the attentional reorienting network and its modulation by (Bayes-optimal) precision-dependent attention. While the frontal eye fields (FEFs), intraparietal sulcus, and temporoparietal junction (TPJ) of both hemispheres showed higher activity on invalid relative to valid trials, reorienting responses in right FEF, TPJ, and the putamen were significantly modulated by precision-dependent attention. Our DCM results suggested that the precision of predictability underlies the attentional modulation of the coupling of TPJ with FEF and the putamen. Our results shed new light on the computational architecture and neuronal network dynamics underlying the context-sensitive deployment of visuospatial attention. <b>SIGNIFICANCE STATEMENT</b> Spatial attention and its neural correlates in the human brain have been studied extensively with the help of fMRI and cueing paradigms in which the location of targets is pre-cued on a trial-by-trial basis. One aspect that has so far been neglected concerns the question of how the brain forms attentional expectancies when no a priori probability information is available but needs to be inferred from observations. This study elucidates the computational and neural mechanisms under which probabilistic inference governs attentional deployment. Our results show that Bayesian belief updating explains changes in cortical connectivity; in that directional influences from the temporoparietal junction on the frontal eye fields and the putamen were modulated by (Bayes-optimal) updates.

Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill posed problem that commonly arises when modelling real world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of (ie, hypotheses about) network architectures and implicit coupling functions in terms of their Bayesian model evidence. These methods are collectively referred to as dynamical casual modelling (DCM). We focus on a relatively new approach that is proving remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems.

Prominent accounts of sentient behavior depict brains as generative models of organismic interaction with the world, evincing intriguing similarities with current advances in generative artificial intelligence (AI). However, because they contend with the control of purposive, life-sustaining sensorimotor interactions, the generative models of living organisms are inextricably anchored to the body and world. Unlike the passive models learned by generative AI systems, they must capture and control the sensory consequences of action. This allows embodied agents to intervene upon their worlds in ways that constantly put their best models to the test, thus providing a solid bedrock that is – we argue – essential to the development of genuine understanding. We review the resulting implications and consider future directions for generative AI.

The mechanisms underlying interhemispheric integration (IHI) remain poorly understood, particularly for lateralized cognitive processes. To test competing theories of IHI, we constructed and fitted dynamic causal models to functional magnetic resonance data from two visual tasks that operated on identical stimuli but showed opposite hemispheric dominance. Using a systematic Bayesian model selection procedure, we found that, in the ventral visual stream, which was activated by letter judgments, interhemispheric connections mediated asymmetric information transfer from the nonspecialized right to the specialized left hemisphere when the latter did not have direct access to stimulus information. Notably, this form of IHI did not engage all areas activated by the task but was specific for areas in the lingual and fusiform gyri. In the dorsal stream, activated by spatial judgments, it did not matter which hemisphere received the stimulus: interhemispheric coupling increased bidirectionally, reflecting recruitment of the nonspecialized left hemisphere. Again, not all areas activated by the task were involved in this form of IHI; instead, it was restricted to interactions between areas in the superior parietal gyrus. Overall, our results provide direct neurophysiological evidence, in terms of effective connectivity, for the existence of context-dependent mechanisms of IHI that are implemented by specific visual areas during task-driven lateralization.