The theme of this special issue of the Journal of Abnormal Psychology is on predictive processing and how it can improve our fundamental understanding of neuropsychiatric disorders. Several articles focus on psychosis and demonstrate how the field of computational psychosis research has evolved and matured in recent years through the application of predictive processing theory. These articles suggest that whereas the computational mechanisms underlying psychosis may be complex, careful empirical and theoretical work-using more sophisticated models-can bridge gaps between previous results that appeared to be at odds while providing more explanatory power. There is a particular focus on processing hierarchies; defining which priors are maladaptive and at what stage of illness they become so; and finding compelling neurobiological correlates of computational processes. These articles provide a blueprint for future empirical work. This work-that is licensed theoretically by predictive processing-may improve our understanding of psychosis and its treatment and open new avenues for biomarker and therapeutic development. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Integrating costs and benefits is crucial for optimal decision-making. Although much is known about decisions that involve outcome-related costs (e.g., delay, risk), many of our choices are attached to actions and require an evaluation of the associated motor costs. Yet how the brain incorporates motor costs into choices remains largely unclear. We used human fMRI during choices involving monetary reward and physical effort to identify brain regions that serve as a choice comparator for effort-reward trade-offs. By independently varying both options' effort and reward levels, we were able to identify the neural signature of a comparator mechanism. A network involving supplementary motor area and the caudal portion of dorsal anterior cingulate cortex encoded the difference in reward (positively) and effort levels (negatively) between chosen and unchosen choice options. We next modeled effort-discounted subjective values using a novel behavioral model. This revealed that the same network of regions involving dorsal anterior cingulate cortex and supplementary motor area encoded the difference between the chosen and unchosen options' subjective values, and that activity was best described using a concave model of effort-discounting. In addition, this signal reflected how precisely value determined participants' choices. By contrast, separate signals in supplementary motor area and ventromedial prefrontal cortex correlated with participants' tendency to avoid effort and seek reward, respectively. This suggests that the critical neural signature of decision-making for choices involving motor costs is found in human cingulate cortex and not ventromedial prefrontal cortex as typically reported for outcome-based choice. Furthermore, distinct frontal circuits seem to drive behavior toward reward maximization and effort minimization.The neural processes that govern the trade-off between expected benefits and motor costs remain largely unknown. This is striking because energetic requirements play an integral role in our day-to-day choices and instrumental behavior, and a diminished willingness to exert effort is a characteristic feature of a range of neurological disorders. We use a new behavioral characterization of how humans trade off reward maximization with effort minimization to examine the neural signatures that underpin such choices, using BOLD MRI neuroimaging data. We find the critical neural signature of decision-making, a signal that reflects the comparison of value between choice options, in human cingulate cortex, whereas two distinct brain circuits drive behavior toward reward maximization or effort minimization.
This study examined the pattern of cerebral blood flow observed in chronic schizophrenic patients while they performed a paced verbal fluency task. Such tasks engage a distributed brain system associated with willed action. Since willed action is impaired in many chronic schizophrenic patients we hypothesised that task performance would be associated with an abnormal pattern of blood flow.Positron emission tomography (PET) was applied to 18 chronic schizophrenic patients stratified into three groups on the basis of verbal fluency performance and current symptoms. Regional cerebral blood flow (rCBF) was measured while the patients performed (a) verbal fluency, (b) word categorisation, and (c) word repetition. Results were compared with six normal controls matched for age, sex and premorbid IQ. Analysis was restricted to six brain regions previously identified in studies of normal volunteers.In five brain areas, including the left dorsolateral prefrontal cortex, the patients showed the same pattern of activation as control subjects. However, in the left superior temporal cortex, all patient groups failed to show the normal decrease in blood flow when verbal fluency was compared with word repetition.These observations suggest that (a) chronic schizophrenic patients can show a normal magnitude of frontal activation when matched for performance with controls, and (b) they fail to show the expected reductions of activity in the superior temporal cortex. This latter result may reflect abnormal functional connectivity between frontal and temporal cortex.
Abstract Cerebellar computations are necessary for fine behavioural control and are thought to rely on internal probabilistic models performing state estimation. We propose that the cerebellum infers how states contextualise (i.e., interact with) each other, and coordinates extra-cerebellar neuronal dynamics underpinning a range of behaviours. To support this claim, we describe a cerebellar model for state estimation that includes states interactions, and link the underlying inference 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 inference process they entail. As a proof of principle, we simulate cerebellar-dependent synchronisation of whisking and respiration, which are known to be tightly coupled in rodents. In summary, we suggest that cerebellar-dependent contextualisation of behaviour can explain its ubiquitous involvement in most aspects of behaviour.
This technical note describes a variational or Bayesian implementation of representational similarity analysis (RSA) and pattern component modelling (PCM). It considers RSA and PCM as Bayesian model comparison procedures that assess the evidence for stimulus or condition-specific patterns of responses distributed over voxels or channels. On this view, one can use standard variational inference procedures to quantify the contributions of particular patterns to the data, by evaluating second-order parameters or hyperparameters. Crucially, this allows one to use parametric empirical Bayes (PEB) to infer which patterns are consistent among subjects. At the between-subject level, one can then assess the evidence for different (combinations of) hypotheses about condition-specific effects using Bayesian model comparison. Alternatively, one can select a single hypothesis that best explains the pattern of responses using Bayesian model selection. This note rehearses the technical aspects of within and between-subject RSA using a worked example, as implemented in the Statistical Parametric Mapping (SPM) software. En route, we highlight the connection between univariate and multivariate analyses of neuroimaging data and the sorts of analyses that are possible using component modelling and representational similarity analysis.
This paper addresses the formation of infant attachment types within the context of active inference: a holistic account of action, perception and learning in the brain. We show how the organised forms of attachment (secure, avoidant and ambivalent) might arise in (Bayesian) infants. Specifically, we show that these distinct forms of attachment emerge from a minimisation of free energy—over interoceptive states relating to internal stress levels—when seeking proximity to caregivers who have a varying impact on these interoceptive states. In line with empirical findings in disrupted patterns of affective communication, we then demonstrate how exteroceptive cues (in the form of caregiver-mediated AMBIANCE affective communication errors, ACE) can result in disorganised forms of attachment in infants of caregivers who consistently increase stress when the infant seeks proximity, but can have an organising (towards ambivalence) effect in infants of inconsistent caregivers. In particular, we differentiate disorganised attachment from avoidance in terms of the high epistemic value of proximity seeking behaviours (resulting from the caregiver’s misleading exteroceptive cues) that preclude the emergence of coherent and organised behavioural policies. Our work, the first to formulate infant attachment in terms of active inference, makes a new testable prediction with regards to the types of affective communication errors that engender ambivalent attachment.
In systems biology, questions concerning the molecular and cellular makeup of an organism are of utmost importance, especially when trying to understand how unreliable components—like genetic circuits, biochemical cascades, and ion channels, among others—enable reliable and adaptive behaviour. The repertoire and speed of biological computations are limited by thermodynamic or metabolic constraints: an example can be found in neurons, where fluctuations in biophysical states limit the information they can encode—with almost 20–60% of the total energy allocated for the brain used for signalling purposes, either via action potentials or by synaptic transmission. Here, we consider the imperatives for neurons to optimise computational and metabolic efficiency, wherein benefits and costs trade-off against each other in the context of self-organised and adaptive behaviour. In particular, we try to link information theoretic (variational) and thermodynamic (Helmholtz) free-energy formulations of neuronal processing and show how they are related in a fundamental way through a complexity minimisation lemma.
Active inference offers a first principle account of sentient behavior, from which special and important cases—for example, reinforcement learning, active learning, Bayes optimal inference, Bayes optimal design—can be derived. Active inference finesses the exploitation-exploration dilemma in relation to prior preferences by placing information gain on the same footing as reward or value. In brief, active inference replaces value functions with functionals of (Bayesian) beliefs, in the form of an expected (variational) free energy. In this letter, we consider a sophisticated kind of active inference using a recursive form of expected free energy. Sophistication describes the degree to which an agent has beliefs about beliefs. We consider agents with beliefs about the counterfactual consequences of action for states of affairs and beliefs about those latent states. In other words, we move from simply considering beliefs about “what would happen if I did that” to “what I would believe about what would happen if I did that.” The recursive form of the free energy functional effectively implements a deep tree search over actions and outcomes in the future. Crucially, this search is over sequences of belief states as opposed to states per se. We illustrate the competence of this scheme using numerical simulations of deep decision problems.
British Journal of Psychiatry (1995), 167, 343-349Since the classic study of Ingvar & Franzen (1974),there have been many investigations of schizophreniaby functional brain imaging (Andreasen eta!, 1992).Most (—‘6001o)have observed a relative reduction infrontal metabolism in patients scanned at rest(hypofrontality). However, many studies (—400lo)found no evidence of hypofrontality and a fewobserved hyperfrontality. There are good reasons whyresting scans of schizophrenic patients should lead tocontradictory results. Mental activity is associatedwith detectable brain activity even when no overtbehaviour occurs (e.g. Stephan et al, 1995).Schizophrenic patients vary widely in their currentmental state, and thus varying patterns of cerebralblood flow are to be expected in unselected groups.Liddle eta! (1992) confirmed that current symptomsare related to regional cerebral blood flow (rCBF).Hypofrontality (particularly in the left dorsolateralprefrontal cortex (DLPFC)) wasfound to be associatedwith psychomotor poverty (poverty of speech,flattening of affect, and motor retardation).However, even though schizophrenic symptomssegregate into clusters, and different patterns ofresting cerebral blood flow can be associated withthese syndromes, it remains likely that a coreabnormality is present in all schizophrenic patients.An important approach to the problem of thevariability in mental states during brain imaging isto control mental state, at the time of scanning, byapplying a psychological challenge. This involvespresenting a task during the scan which elicitsspecificmental processes. This method can not only reducevariability between subjects, but also highlight brainareas of interest. For this purpose, various studieshave used psychological tasks sensitiveto frontal lobedamage. When normal volunteers perform suchtasks, there is an associated increase in frontal bloodflow. However, in schizophrenic patients, whoperform such tasks poorly, this increase is oftenreduced or absent (Weinberger, 1986). Thisobservation is particularly marked in patients withnegative features (Andreasen et a!, 1992). At the
