Publications

Neural Correlates of Hand–Object Congruency Effects during Action Planning

Abstract Selecting hand actions to manipulate an object is affected both by perceptual factors and by action goals. Affordances may contribute to “stimulus–response” congruency effects driven by habitual actions to an object. In previous studies, we have demonstrated an influence of the congruency between hand and object orientations on response times when reaching to turn an object, such as a cup. In this study, we investigated how the representation of hand postures triggered by planning to turn a cup was influenced by this congruency effect, in an fMRI scanning environment. Healthy participants were asked to reach and turn a real cup that was placed in front of them either in an upright orientation or upside–down. They were instructed to use a hand orientation that was either congruent or incongruent with the cup orientation. As expected, the motor responses were faster when the hand and cup orientations were congruent. There was increased activity in a network of brain regions involving object-directed actions during action planning, which included bilateral primary and extrastriate visual, medial, and superior temporal areas, as well as superior parietal, primary motor, and premotor areas in the left hemisphere. Specific activation of the dorsal premotor cortex was associated with hand–object orientation congruency during planning and prior to any action taking place. Activity in that area and its connectivity with the lateral occipito-temporal cortex increased when planning incongruent (goal-directed) actions. The increased activity in premotor areas in trials where the orientation of the hand was incongruent to that of the object suggests a role in eliciting competing representations specified by hand postures in lateral occipito-temporal cortex.

Active inference and cognitive-emotional interactions in the brain

Abstract All organisms must integrate cognition, emotion, and motivation to guide action toward valuable (goal) states, as described by active inference. Within this framework, cognition, emotion, and motivation interact through the (Bayesian) fusion of exteroceptive, proprioceptive, and interoceptive signals, the precision-weighting of prediction errors, and the “affective tuning” of neuronal representations. Crucially, misregulation of these processes may have profound psychopathological consequences.

PsPM-HRA1: Skin conductance responses in fear conditioning with visual CS and electrical US

This dataset includes skin conductance response (SCR) measurements, CS and US information, keypress responses, keypress response times, key correctness and shock ratings for each of 20 healthy unmedicated participants (10 males and 10 females aged 22.2+/-4.0 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. CS is a visual stimulus appearing in the middle of the screen with variation in color. US is an electric shock as a 500 Hz current pulses train (individual pulse width: 0.5ms, varying current amplitudes (0.90+/-0.63 mA) train width:500 ms). SOA between the CS and US is 3.5 s. The ITI is randomly determined on each trial to be 7, 8, 9, 10 or 11 s. (This was correctly stated in Staib et al. (2015) but wrongly described in Bach et al. (2010).)

Effective connectivity during animacy perception – dynamic causal modelling of Human Connectome Project data

Abstract Biological agents are the most complex systems humans have to model and predict. In predictive coding, high-level cortical areas inform sensory cortex about incoming sensory signals, a comparison between the predicted and actual sensory feedback is made and information about unpredicted sensory information is passed forward to higher-level areas. Predictions about animate motion – relative to inanimate motion – should result in prediction error and increase signal passing from lower level sensory area MT+/V5, which is responsive to all motion, to higher-order posterior superior temporal sulcus (pSTS), which is selectively activated by animate motion. We tested this hypothesis by investigating effective connectivity in a large-scale fMRI dataset from the Human Connectome Project. 132 participants viewed animations of triangles that were designed to move in a way that appeared animate (moving intentionally), or inanimate (moving in a mechanical way). We found that forward connectivity from V5 to the pSTS increased and inhibitory self-connection in the pSTS decreased, when viewing intentional motion versus inanimate motion. These prediction errors associated with animate motion may be the cause for increased attention to animate stimuli found in previous studies.

Stress and its sequelae: An active inference account of the etiological pathway from allostatic overload to depression

Survival requires the implementation of adaptive changes that demand energy resources. The efficient regulation of energetic resources thus plays a critical role in enabling systems to adapt to the demands of their internal and external environments. The framework of active inference explains how living organisms can build probabilistic models that enable them to predict, track, and regulate energy expenditure in the short and long run. The aim of the paper is to characterize the physiological changes that accompany stress, and the relationship between these changes and the loss of confidence in a system's predictions about its internal and external milieu-ultimately manifesting as depressive symptomatology. We identify the systems that underwrite goal-directed behavior, and the neuroendocrine and immunological systems, as the hierarchical controller that regulates energy resources. In doing so, we establish an etiological pathway from allostatic overload to depression via active inference.

EEG–fMRI Information Fusion: Biophysics and Data Analysis

Force-related BOLD effects during naturalistic and symbolic effort observation

Bayesian state estimation using generalized coordinates

This paper reviews a simple solution to the continuous-discrete Bayesian nonlinear state estimation problem that has been proposed recently. The key ideas are analytic noise processes, variational Bayes, and the formulation of the problem in terms of generalized coordinates of motion. Some of the algorithms, specifically dynamic expectation maximization and variational filtering, have been shown to outperform existing approaches like extended Kalman filtering and particle filtering. A pedagogical review of the theoretical formulation is presented, with an emphasis on concepts that are not as widely known in the filtering literature. We illustrate the appliction of these concepts using a numerical example.

Active Inference, homeostatic regulation and adaptive behavioural control

We review a theory of homeostatic regulation and adaptive behavioural control within the Active Inference framework. Our aim is to connect two research streams that are usually considered independently; namely, Active Inference and associative learning theories of animal behaviour. The former uses a probabilistic (Bayesian) formulation of perception and action, while the latter calls on multiple (Pavlovian, habitual, goal-directed) processes for homeostatic and behavioural control. We offer a synthesis these classical processes and cast them as successive hierarchical contextualisations of sensorimotor constructs, using the generative models that underpin Active Inference. This dissolves any apparent mechanistic distinction between the optimization processes that mediate classical control or learning. Furthermore, we generalize the scope of Active Inference by emphasizing interoceptive inference and homeostatic regulation. The ensuing homeostatic (or allostatic) perspective provides an intuitive explanation for how priors act as drives or goals to enslave action, and emphasises the embodied nature of inference.

Therapeutic Alliance as Active Inference: The Role of Therapeutic Touch and Biobehavioural Synchrony in Musculoskeletal Care

Touch is recognised as crucial for survival, fostering cooperative communication, accelerating recovery, reducing hospital stays, and promoting overall wellness and the therapeutic alliance. In this hypothesis and theory paper, we present an entwined model that combines touch for alignment and active inference to explain how the brain develops “priors” necessary for the health care provider to engage with the patient effectively. We appeal to active inference to explain the empirically integrative neurophysiological and behavioural mechanisms that underwrite synchronous relationships through touch. Specifically, we offer a formal framework for understanding – and explaining – the role of therapeutic touch and hands-on care in developing a therapeutic alliance and synchrony between health care providers and their patients in musculoskeletal care. We first review the crucial importance of therapeutic touch and its clinical role in facilitating the formation of a solid therapeutic alliance and in regulating allostasis. We then consider how touch is used clinically – to promote cooperative communication, demonstrate empathy, overcome uncertainty, and infer the mental states of others – through the lens of active inference. We conclude that touch plays a crucial role in achieving successful clinical outcomes and adapting previous priors to create intertwined beliefs. The ensuing framework may help healthcare providers in the field of musculoskeletal care to use hands-on care to strengthen the therapeutic alliance, minimise prediction errors (a.k.a., free energy), and thereby promote recovery from physical and psychological impairments.

Segregating the functions of human hippocampus

It is now accepted that hippocampal lesions impair episodic memory. However, the precise functional role of the hippocampus in episodic memory remains elusive. Recent functional imaging data implicate the hippocampus in processing novelty, a finding supported by human in vivo recordings and event-related potential studies. Here we measure hippocampal responses to novelty, using functional MRI (fMRI), during an item-learning paradigm generated from an artificial grammar system. During learning, two distinct types of novelty were periodically introduced: perceptual novelty, pertaining to the physical characteristics of stimuli (in this case visual characteristics), and exemplar novelty, reflecting semantic characteristics of stimuli (in this case grammatical status within a rule system). We demonstrate a left anterior hippocampal response to both types of novelty and adaptation of these responses with stimulus familiarity. By contrast to these novelty effects, we also show bilateral posterior hippocampal responses with increasing exemplar familiarity. These results suggest a functional dissociation within the hippocampus with respect to the relative familiarity of study items. Neural responses in anterior hippocampus index generic novelty, whereas posterior hippocampal responses index familiarity to stimuli that have behavioral relevance (i.e., only exemplar familiarity). These findings add to recent evidence for functional segregation within the human hippocampus during learning.

Dysconnection in Schizophrenia: From Abnormal Synaptic Plasticity to Failures of Self-monitoring

Over the last 2 decades, a large number of neurophysiological and neuroimaging studies of patients with schizophrenia have furnished in vivo evidence for dysconnectivity, ie, abnormal functional integration of brain processes. While the evidence for dysconnectivity in schizophrenia is strong, its etiology, pathophysiological mechanisms, and significance for clinical symptoms are unclear. First, dysconnectivity could result from aberrant wiring of connections during development, from aberrant synaptic plasticity, or from both. Second, it is not clear how schizophrenic symptoms can be understood mechanistically as a consequence of dysconnectivity. Third, if dysconnectivity is the primary pathophysiology, and not just an epiphenomenon, then it should provide a mechanistic explanation for known empirical facts about schizophrenia. This article addresses these 3 issues in the framework of the dysconnection hypothesis. This theory postulates that the core pathology in schizophrenia resides in aberrant N-methyl-D-aspartate receptor (NMDAR)-mediated synaptic plasticity due to abnormal regulation of NMDARs by neuromodulatory transmitters like dopamine, serotonin, or acetylcholine. We argue that this neurobiological mechanism can explain failures of self-monitoring, leading to a mechanistic explanation for first-rank symptoms as pathognomonic features of schizophrenia, and may provide a basis for future diagnostic classifications with physiologically defined patient subgroups. Finally, we test the explanatory power of our theory against a list of empirical facts about schizophrenia.

Embodied inference and spatial cognition

How much about our interactions with—and experience of—our world can be deduced from basic principles? This paper reviews recent attempts to understand the self-organised behaviour of embodied agents, like ourselves, as satisfying basic imperatives for sustained exchanges with the environment. In brief, one simple driving force appears to explain many aspects of perception, action and the perception of action. This driving force is the minimisation of surprise or prediction error, which—in the context of perception—corresponds to Bayes-optimal predictive coding (that suppresses exteroceptive prediction errors) and—in the context of action—reduces to classical motor reflexes (that suppress proprioceptive prediction errors). In what follows, we look at some of the phenomena that emerge from this single principle, such as the perceptual encoding of spatial trajectories that can both generate movement (of self) and recognise the movements (of others). These emergent behaviours rest upon prior beliefs about itinerant (wandering) states of the world—but where do these beliefs come from? In this paper, we focus on the nature of prior beliefs and how they underwrite the active sampling of a spatially extended sensorium. Put simply, to avoid surprising states of the world, it is necessary to minimise uncertainty about those states. When this minimisation is implemented via prior beliefs—about how we sample the world—the resulting behaviour is remarkably reminiscent of searches seen in foraging or visual searches with saccadic eye movements.

Bayesian Model Selection for Group Studies

Transient phase‐locking and dynamic correlations: Are they the same thing?

This work represents an attempt to bring together two important themes in neuronal dynamics. The first is the characterization of dynamic correlations in multiunit recordings of spike activity using joint-peri-stimulus time histograms (J-PSTHs) [Aertsen and Preissl, 1991: Non Linear Dynamics and Neural Networks]. The second is transient phase-locking at high (gamma) frequencies, either in terms of spiking in separable spike trains [e.g., Eckhorn et al., 1988: Biol Cybern 60:121–130, Gray and Singer, 1989 Proc Natl Acad Sci USA 86:1698–1702], or using continuous electrical or biomagnetic signals [e.g., Desmedt and Tomberg, 1994 Neurosci Lett 168:126–129]. In this paper we suggest that transient phase-locking is necessary for frequency-specific, dynamic event-related correlations. This point is demonstrated using the gamma-frequency (36 Hz) component of neuromagnetic signals measured in the prefrontal and partial regions of a subject during self-paced movements. A J-PSTH analysis revealed dynamic changes in prefronto-parietal correlations in relation to movement onset. These frequency-specific dynamic correlations were associated with changes in the degree of phase-locking, of the sort reported by Desmedt and Tomberg [1994 Neurosci Lett 168:126–129]. Hum. Brain Mapping 5:48–57, 1997. © 1997 Wiley-Liss, Inc.

Computational Psychiatry and the Bayesian Brain

Abstract Computational psychiatry is a burgeoning field that uses formal models of brain function to characterize the mechanisms of psychopathology, with the hope that these mechanistic views can generate objective and predictive targets for treatment and intervention. This chapter introduces relevant key concepts and rationale relevant for computational psychiatry. It focuses on the Bayesian brain framework as an example to demonstrate how computational theories and models can account for false beliefs observed across many mental disorders. This framework is also compared with other formal theories of the brain. Specifically, the chapter reviews findings on delusions in schizophrenia as an example of false sensory inference; it also uses false interoceptive and interpersonal inference in autism to demonstrate the impact of faulty inference during neurodevelopment. The chapter concludes by summarizing the status quo of the field and noting future directions of research.

Approaches to the cortical analysis of auditory objects

Neural Mechanisms of Belief Inference during Cooperative Games

Humans have the arguably unique ability to understand the mental representations of others. For success in both competitive and cooperative interactions, however, this ability must be extended to include representations of others' belief about our intentions, their model about our belief about their intentions, and so on. We developed a “stag hunt” game in which human subjects interacted with a computerized agent using different degrees of sophistication (recursive inferences) and applied an ecologically valid computational model of dynamic belief inference. We show that rostral medial prefrontal (paracingulate) cortex, a brain region consistently identified in psychological tasks requiring mentalizing, has a specific role in encoding the uncertainty of inference about the other's strategy. In contrast, dorsolateral prefrontal cortex encodes the depth of recursion of the strategy being used, an index of executive sophistication. These findings reveal putative computational representations within prefrontal cortex regions, supporting the maintenance of cooperation in complex social decision making.