Publications

This paper aims to integrate some key constructs in the cognitive neuroscience of cognitive control and executive function by formalising the notion of cognitive (or mental) effort in terms of active inference. To do so, we call upon a task used in neuropsychology to assess impulse inhibition-a Stroop task. In this task, participants must suppress the impulse to read a colour word and instead report the colour of the text of the word. The Stroop task is characteristically effortful, and we unpack a theory of mental effort in which, to perform this task accurately, participants must overcome prior beliefs about how they would normally act. However, our interest here is not in overt action, but in covert (mental) action. Mental actions change our beliefs but have no (direct) effect on the outside world-much like deploying covert attention. This account of effort as mental action lets us generate multimodal (choice, reaction time, and electrophysiological) data of the sort we might expect from a human participant engaging in this task. We analyse how parameters determining cognitive effort influence simulated responses and demonstrate that-when provided only with performance data-these parameters can be recovered, provided they are within a certain range.

Abstract Functional neuroimaging research on anxiety has traditionally focused on brain networks associated with the psychological aspects of anxiety. Here, instead, we target the somatic aspects of anxiety. Motivated by the growing appreciation that top-down cortical processing plays a crucial role in perception and action, we used resting-state functional MRI data from the Human Connectome Project and Dynamic Causal Modeling (DCM) to characterize effective connectivity among hierarchically organized regions in the exteroceptive, interoceptive, and motor cortices. In people with high (fear-related) somatic arousal, top-down effective connectivity was enhanced in all three networks: an observation that corroborates well with the phenomenology of anxiety. The anxiety-associated changes in connectivity were sufficiently reliable to predict whether a new participant has mild or severe somatic anxiety. Interestingly, the increase in top-down connections to sensorimotor cortex were not associated with fear affect scores, thus establishing the (relative) dissociation between somatic and cognitive dimensions of anxiety. Overall, enhanced top-down effective connectivity in sensorimotor cortices emerges as a promising and quantifiable candidate marker of trait somatic anxiety.

Twenty years have passed since the dysconnection hypothesis was first proposed (Friston and Frith, 1995; Weinberger, 1993). In that time, neuroscience has witnessed tremendous advances: we now live in a world of non-invasive neuroanatomy, computational neuroimaging and the Bayesian brain. The genomics era has come and gone. Connectomics and large-scale neuroinformatics initiatives are emerging everywhere. So where is the dysconnection hypothesis now? This article considers how the notion of schizophrenia as a dysconnection syndrome has developed - and how it has been enriched by recent advances in clinical neuroscience. In particular, we examine the dysconnection hypothesis in the context of (i) theoretical neurobiology and computational psychiatry; (ii) the empirical insights afforded by neuroimaging and associated connectomics - and (iii) how bottom-up (molecular biology and genetics) and top-down (systems biology) perspectives are converging on the mechanisms and nature of dysconnections in schizophrenia.

Abstract Why do brains have so many connections? The principles exposed by Andy Clark provide answers to questions like this by appealing to the notion that brains distil causal regularities in the sensorium and embody them in models of their world. For example, connections embody the fact that causes have particular consequences. This commentary considers the imperatives for this form of embodiment.

Abstract The effect of stimulus rate and its interaction with stimulus type on brain activity during reading was investigated using functional magnetic resonance imaging (fMRI). This (i) enabled the segregation of brain regions showing differential responses, (ii) identified the optimum experimental design parameters for maximizing sensitivity, and (iii) allowed us to evaluate further the sources of discrepancy between positron emission tomography (PET) and fMRI signals. The effect of visual word rate has already been investigated in a previous PET study. However, rate effects can be very different in PET and fMRI, as seen in previous studies of auditory word processing. In this work, we attempt to replicate rate-sensitive activations observed with PET using fMRI. Our objective was to characterize the discrepancies in regionally specific rate-sensitive effects between the two imaging modalities. Subjects were presented with words and pseudowords at varying rates while performing a silent reading task. The analysis specifically identified regions showing (i) an effect of stimulus rate on brain activity during reading; (ii) modulation of this effect by word type; and (iii) increased activity during reading relative to rest, but with no dependence on stimulus rate. The results identified similar effects of rate for words and pseudowords (no interactions between rate and word type reached significance). Irrespective of word type, strong positive linear effects of rate (i.e., activity increasing with rate) were detected in visual areas, right superior temporal gyrus, and bilateral precentral gyrus. These findings replicate the results of the previous PET study, confirming that activation in regions associated with visual processing and response generation increases with the number of stimuli. Likewise, we detected rate-independent effects reported in the previous PET study in bilateral anterior middle temporal, inferior frontal, and superior parietal regions. These results differentiate the functionally specific responses in rate-dependent and rate-independent areas. However, for negative effects of rate, fMRI did not replicate the effects seen in PET, suggesting some form of hemodynamic “rectification.” The discussion focuses on differences between evoked rCBF and BOLD signals.

This article concerns the nature of evoked brain responses and the principles underlying their generation. We start with the premise that the sensory brain has evolved to represent or infer the causes of changes in its sensory inputs. The problem of inference is well formulated in statistical terms. The statistical fundaments of inference may therefore afford important constraints on neuronal implementation. By formulating the original ideas of Helmholtz on perception, in terms of modern-day statistical theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. It turns out that the problems of inferring the causes of sensory input (perceptual inference) and learning the relationship between input and cause (perceptual learning) can be resolved using exactly the same principle. Specifically, both inference and learning rest on minimizing the brain's free energy, as defined in statistical physics. Furthermore, inference and learning can proceed in a biologically plausible fashion. Cortical responses can be seen as the brain’s attempt to minimize the free energy induced by a stimulus and thereby encode the most likely cause of that stimulus. Similarly, learning emerges from changes in synaptic efficacy that minimize the free energy, averaged over all stimuli encountered. The underlying scheme rests on empirical Bayes and hierarchical models of how sensory input is caused. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of cortical organization and responses. The aim of this article is to encompass many apparently unrelated anatomical, physiological and psychophysical attributes of the brain within a single theoretical perspective. In terms of cortical architectures, the theoretical treatment predicts that sensory cortex should be arranged hierarchically, that connections should be reciprocal and that forward and backward connections should show a functional asymmetry (forward connections are driving, whereas backward connections are both driving and modulatory). In terms of synaptic physiology, it predicts associative plasticity and, for dynamic models, spike-timing-dependent plasticity. In terms of electrophysiology, it accounts for classical and extra classical receptive field effects and long-latency or endogenous components of evoked cortical responses. It predicts the attenuation of responses encoding prediction error with perceptual learning and explains many phenomena such as repetition suppression, mismatch negativity (MMN) and the P300 in electroencephalography. In psychophysical terms, it accounts for the behavioural correlates of these physiological phenomena, for example, priming and global precedence. The final focus of this article is on perceptual learning as measured with the MMN and the implications for empirical studies of coupling among cortical areas using evoked sensory responses.

Cerebellar computations are necessary for fine behavioural control and may rely on internal models for estimation of behaviourally relevant states. Here, we propose that the central cerebellar function is to estimate how states interact with each other, and to use these estimates to coordinates extra-cerebellar neuronal dynamics underpinning a range of interconnected behaviours. To support this claim, we describe a cerebellar model for state estimation that includes state interactions, and link this model with the neuronal architecture and dynamics observed empirically. This is formalised using the free energy principle, which provides a dual perspective on a system in terms of both the dynamics of its physical-in this case neuronal-states, and the inferential process they entail. As a demonstration of this proposal, we simulate cerebellar-dependent synchronisation of whisking and respiration, which are known to be tightly coupled in rodents, as well as limb and tail coordination during locomotion. In summary, we propose that the ubiquitous involvement of the cerebellum in behaviour arises from its central role in precisely coupling behavioural domains.

This article reviews the disconnection hypothesis of schizophrenia and presents a mechanistic account of how dysfunctional integration among neuronal systems might arise. This neurobiological account is based on the central role played by neuronal plasticity in shaping the connections and the ensuing dynamics that underlie brain function. The particular hypothesis put forward here is that the pathophysiology of schizophrenia is expressed at the level of modulation of associative changes in synaptic efficacy; specifically the modulation of plasticity in those brain systems responsible for emotional learning and memory, in the post-natal period. This modulation is mediated by ascending neurotransmitter systems that: (i) have been implicated in schizophrenia; and (ii) are known to be involved in consolidating synaptic connections during learning. The proposed pathophysiology would translate, in functional terms, into a disruption of the reinforcement of adaptive behaviour that is consistent with the disintegrative aspects of schizophrenic neuropsychology.

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.

The meta-problem of consciousness (Chalmers (this issue)) is the problem of explaining the behaviors and verbal reports that we associate with the so-called ‘hard problem of consciousness’. These may include reports of puzzlement, of the attractiveness of dualism, of explanatory gaps, and the like. We present and defend a solution to the meta-problem. Our solution takes as its starting point the emerging picture of the brain as a hierarchical inference engine. We show why such a device, operating under familiar forms of adaptive pressure, may come to represent some of its mid-level inferences as especially certain. These mid-level states confidently re-code raw sensory stimulation in ways that (they are able to realize) fall short of fully determining how properties and states of affairs are arranged in the distal world. This drives a wedge between experience and the world. Advanced agents then represent these mid-level inferences as irreducibly special, becoming increasingly puzzled as a result.

Primate studies show slow ramping activity in posterior parietal cortex (PPC) neurons during perceptual decision-making. These findings have inspired a rich theoretical literature to account for this activity. These accounts are largely unrelated to Bayesian theories of perception and predictive coding, a related formulation of perceptual inference in the cortical hierarchy. Here, we tested a key prediction of such hierarchical inference, namely that the estimated precision (reliability) of information ascending the cortical hierarchy plays a key role in determining both the speed of decision-making and the rate of increase of PPC activity. Using dynamic causal modelling of magnetoencephalographic (MEG) evoked responses, recorded during a simple perceptual decision-making task, we recover ramping-activity from an anatomically and functionally plausible network of regions, including early visual cortex, the middle temporal area (MT) and PPC. Precision, as reflected by the gain on pyramidal cell activity, was strongly correlated with both the speed of decision making and the slope of PPC ramping activity. Our findings indicate that the dynamics of neuronal activity in the human PPC during perceptual decision-making recapitulate those observed in the macaque, and in so doing we link observations from primate electrophysiology and human choice behaviour. Moreover, the synaptic gain control modulating these dynamics is consistent with predictive coding formulations of evidence accumulation.