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Adaptive behavior is only possible by stopping stereotypical actions to generate new plans according to internal goals. It is response inhibition —the ability to stop actions automatically triggered by exogenous cues— that allows for the flexible interplay between bottom-up, stimulus driven behaviors, and top-down strategies. In addition to response inhibition, cognitive control draws on conflict adaptation, the facilitation of top-down actions following high conflict situations. It is currently unclear whether and how response inhibition and conflict adaptation depend on GABAergic signaling, the main inhibitory neurotransmitter in the human brain. Here, we applied a recently developed computational model (SERIA) to data from two studies (N=150 & 50) of healthy volunteers performing Simon and antisaccade tasks. One of these datasets was acquired under placebo-controlled pharmacological enhancement of GABAergic transmission (lorazepam, an allosteric modulator of the GABA-A receptor). Our model-based results suggest that enhanced GABA-A signaling boosts conflict adaptation but impairs response inhibition. More generally, our computational approach establishes a unified account of response inhibition and conflict adaptation in the Simon and antisaccade tasks and provides a novel tool for quantifying specific aspects of cognitive control and their modulation by pharmacology or disease.Author SummaryOur capacity to prepare for situations that afford conflicting responses (conflict adaptation) and to stop our immediate impulses in these scenarios (response inhibition) are the hallmark of cognitive control. As these abilities require both the stopping or slowing of response tendencies, a natural question is whether they are mediated by inhibitory neurotransmission in the brain. Here, we combined computational modeling with two experiments to investigate how conflict adaptation and response inhibition interact with each other (experiment 1) and how these are modulated by lorazepam (experiment 2), a positive modulator of the GABA-A receptor, one of the main inhibitory receptors in the human brain. Using our computational model to disentangle conflict adaptation and response inhibition, our results indicate that while lorazepam impaired response inhibition, it improved conflict adaptation. Thus, our results suggests that conflict adaptation is mediated by GABA-A neurotransmission.

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops “computational assays” for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use.In this paper, we present theTranslationalAlgorithms forPsychiatry-AdvancingScience (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

In generative modeling of neuroimaging data, such as dynamic causal modeling (DCM), one typically considers several alternative models, either to determine the most plausible explanation for observed data (Bayesian model selection) or to account for model uncertainty (Bayesian model averaging). Both procedures rest on estimates of the model evidence, a principled trade-off between model accuracy and complexity. In the context of DCM, the log evidence is usually approximated using variational Bayes. Although this approach is highly efficient, it makes distributional assumptions and is vulnerable to local extrema. This paper introduces the use of thermodynamic integration (TI) for Bayesian model selection and averaging in the context of DCM. TI is based on Markov chain Monte Carlo sampling which is asymptotically exact but orders of magnitude slower than variational Bayes. In this paper, we explain the theoretical foundations of TI, covering key concepts such as the free energy and its origins in statistical physics. Our aim is to convey an in-depth understanding of the method starting from its historical origin in statistical physics. In addition, we demonstrate the practical application of TI via a series of examples which serve to guide the user in applying this method. Furthermore, these examples demonstrate that, given an efficient implementation and hardware capable of parallel processing, the challenge of high computational demand can be overcome successfully. The TI implementation presented in this paper is freely available as part of the open source software TAPAS., Cognitive Neurodynamics, 16 (1), ISSN:1871-4080, ISSN:1871-4099

Background Persecutory delusions (PD) are a prominent symptom in first episode psychosis and psychosis patients. PD have been linked to abnormalities in probabilistic reasoning and social inference (e.g., attribution styles). Predictive Coding theories of delusion formation suggest that rigid delusional beliefs could be formalized as precise (i.e. held with certainty) high-level prior beliefs, which were formed to explain away overly precise low-level prediction errors (PEs). Rigid reliance on high-level prior beliefs would in turn lead to diminished updating of high-level PEs, i.e. decreased learning and updating of high-level beliefs. Methods We tested the prediction that subclinical PD ideation is related to altered social inference and beliefs about others’ intentions. To that end, N=1’145 participants from the general population were pre-screened with the Paranoia Checklist (PCL) and assigned to groups of high (“high PD”) or low PD tendencies (“low PD”). Participants with intermediate scores were excluded, participants assigned to either group filled in the PCL again after four weeks, only individuals whose score remained inside the cut-offs for either group were subsequently invited to the study. We invited 162 participants and included 151 participants in the analyses based on exclusion criteria defined in an analysis plan, which was time-stamped before the conclusion of data acquisition. Participants performed a probabilistic advice-taking task with dynamic changes in the advice-outcome mapping (volatility) under one of two experimental frames. These frames differentially emphasised possible reasons behind unhelpful advice: (i) the adviser’s possible intentions (dispositional frame) or (ii) the rules of the game (situational frame). Our design was thus 2-by-2 factorial (high vs. low delusional ideation, dispositional vs. situational frame). Participants were matched regarding age, gender, and education in years. In addition to analyses of variance on participants’ behaviour, we applied computational modeling to test the predictions regarding prior beliefs and belief updating mentioned above. Results We found significant group-by-frame interactions, indicating that in the situational frame high PD participants took advice less into account than low scorers (df = (1,150), F = 5.77, p = 0.018, partial η2= 0.04). This was also reflected in the model parameters of the model explaining participants’ learning under uncertainty best in comparison to other learning models (e.g. tonic evolution rate omega2: df = (1,150), F = 4.75, p = 0.03). Discussion Our findings suggest that social inference in individuals with subclinical PD tendencies is shaped by rigid negative prior beliefs about the intentions of others. High PD participants were less sensitive to the attributional framing and updated their beliefs less vs. low PD participants thereby preventing them to make adaptive use of social information in “safe” contexts.

Subthalamic deep brain stimulation (DBS) for Parkinson’s disease (PD) may modulate chronometric and instrumental aspects of choice behaviour, including motor inhibition, decisional slowing, and value sensitivity. However, it is not well known whether subthalamic DBS affects more complex aspects of decision-making, such as the influence of subjective estimates of uncertainty on choices. In this study, 38 participants with PD played a virtual casino prior to subthalamic DBS (whilst ‘on’ medication) and again, 3-months postoperatively (whilst ‘on’ stimulation). At the group level, there was a small but statistically significant decrease in impulsivity postoperatively, as quantified by the Barratt Impulsiveness Scale (BIS). The gambling behaviour of participants (bet increases, slot machine switches and double or nothing gambles) was associated with this self-reported measure of impulsivity. However, there was a large variance in outcome amongst participants, and we were interested in whether individual differences in subjective estimates of uncertainty (specifically, volatility) were related to differences in pre- and postoperative impulsivity. To examine these individual differences, we fit a computational model (the Hierarchical Gaussian Filter, HGF), to choices made during slot machine game play as well as a simpler reinforcement learning model based on the Rescorla-Wagner formalism. The HGF was superior in accounting for the behaviour of our participants, suggesting that participants incorporated beliefs about environmental uncertainty when updating their beliefs about gambling outcome and translating these beliefs into action. A specific aspect of subjective uncertainty, the participant’s estimate of the tendency of the slot machine’s winning probability to change (volatility), increased subsequent to DBS. Additionally, the decision temperature of the response model decreased post-operatively, implying greater stochasticity in the belief-to-choice mapping of participants. Model parameter estimates were significantly associated with impulsivity; specifically, increased uncertainty was related to increased postoperative impulsivity. Moreover, changes in these parameter estimates were significantly associated with the maximum post-operative change in impulsivity over a six month follow up period. Our findings suggest that impulsivity in PD patients may be influenced by subjective estimates of uncertainty (environmental volatility) and implicate a role for the subthalamic nucleus in the modulation of outcome certainty. Furthermore, our work outlines a possible approach to characterising those persons who become more impulsive after subthalamic DBS, an intervention in which non-motor outcomes can be highly variable., Scientific Reports, 9 (1), ISSN:2045-2322

It has been suspected that abnormalities in social inference (e.g., learning others' intentions) play a key role in the formation of persecutory delusions (PD). In this study, we examined the association between subclinical PD and social inference, testing the prediction that proneness to PD is related to altered social inference and beliefs about others' intentions. We included 151 participants scoring on opposite ends of Freeman's Paranoia Checklist (PCL). The participants performed a probabilistic advice-taking task with a dynamically changing social context (volatility) under one of two experimental frames. These frames differentially emphasised possible reasons behind unhelpful advice: (i) the adviser's possible intentions (dispositional frame) or (ii) the rules of the game (situational frame). Our design was thus 2 × 2 factorial (high vs. low delusional tendencies, dispositional vs. situational frame). We found significant group-by-frame interactions, indicating that in the situational frame high PCL scorers took advice less into account than low scorers. Additionally, high PCL scorers believed more frequently that incorrect advice was delivered intentionally and that such misleading behaviour was directed towards them personally. Overall, our results suggest that social inference in individuals with subclinical PD tendencies is shaped by negative prior beliefs about the intentions of others and is thus less sensitive to the attributional framing of adviser-related information. These findings may help future attempts of identifying individuals at risk for developing psychosis and understanding persecutory delusions in psychosis. ISSN:0920-9964 ISSN:1573-2509

Background Patients with schizophrenia make more errors than healthy subjects in the antisaccade task. In this paradigm, participants are required to inhibit a reflexive saccade to a target and to select the correct action (a saccade in the opposite direction). While the precise origin of this deficit is not clear, it has been connected to aberrant dopaminergic and cholinergic neuromodulation. Methods To study the impact of dopamine and acetylcholine on inhibitory control and action selection, we administered two selective drugs (levodopa 200 mg/galantamine 8 mg) to healthy volunteers (N = 100) performing the antisaccade task. The computational model SERIA (stochastic early reaction, inhibition, and late action) was employed to separate the contribution of inhibitory control and action selection to empirical reaction times and error rates. Results Modeling suggested that levodopa improved action selection (at the cost of increased reaction times) but did not have a significant effect on inhibitory control. By contrast, according to our model, galantamine affected inhibitory control in a dose-dependent fashion, reducing inhibition failures at low doses and increasing them at higher levels. These effects were sufficiently specific that the computational analysis allowed for identifying the drug administered to an individual with 70% accuracy. Conclusions Our results do not support the hypothesis that elevated tonic dopamine strongly impairs inhibitory control. Rather, levodopa improved the ability to select correct actions. However, inhibitory control was modulated by cholinergic drugs. This approach may provide a starting point for future computational assays that differentiate neuromodulatory abnormalities in heterogeneous diseases like schizophrenia., Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 5 (3), ISSN:2451-9022, ISSN:2451-9030

In generative modeling of neuroimaging data, such as dynamic causal modeling (DCM), one typically considers several alternative models, either to determine the most plausible explanation for observed data (Bayesian model selection) or to account for model uncertainty (Bayesian model averaging). Both procedures rest on estimates of the model evidence, a principled trade-off between model accuracy and complexity. In DCM, the log evidence is usually approximated using variational Bayes (VB) under the Laplace approximation (VBL). Although this approach is highly efficient, it makes distributional assumptions and can be vulnerable to local extrema. An alternative to VBL is Markov Chain Monte Carlo (MCMC) sampling, which is asymptotically exact but orders of magnitude slower than VB. This has so far prevented its routine use for DCM.This paper makes four contributions. First, we introduce a powerful MCMC scheme – thermodynamic integration (TI) – to neuroimaging and present a derivation that establishes a theoretical link to VB. Second, this derivation is based on a tutorial-like introduction to concepts of free energy in physics and statistics. Third, we present an implementation of TI for DCM that rests on population MCMC. Fourth, using simulations and empirical functional magnetic resonance imaging (fMRI) data, we compare log evidence estimates obtained by TI, VBL, and other MCMC-based estimators (prior arithmetic mean and posterior harmonic mean). We find that model comparison based on VBL gives reliable results in most cases, justifying its use in standard DCM for fMRI. Furthermore, we demonstrate that for complex and/or nonlinear models, TI may provide more robust estimates of the log evidence. Importantly, accurate estimates of the model evidence can be obtained with TI in acceptable computation time. This paves the way for using DCM in scenarios where the robustness of single-subject inference and model selection becomes paramount, such as differential diagnosis in clinical applications.

An integral aspect of human cognition is the ability to inhibit stimulus-driven, habitual responses, in favour of complex, voluntary actions. In addition, humans can also alternate between different tasks. This comes at the cost of degraded performance when compared to repeating the same task, a phenomenon called the "task-switch cost." While task switching and inhibitory control have been studied extensively, the interaction between them has received relatively little attention. Here, we used the SERIA model, a computational model of antisaccade behaviour, to draw a bridge between them. We investigated task switching in two versions of the mixed antisaccade task, in which participants are cued to saccade either in the same or in the opposite direction to a peripheral stimulus. SERIA revealed that stopping a habitual action leads to increased inhibitory control that persists onto the next trial, independently of the upcoming trial type. Moreover, switching between tasks induces slower and less accurate voluntary responses compared to repeat trials. However, this only occurs when participants lack the time to prepare the correct response. Altogether, SERIA demonstrates that there is a reconfiguration cost associated with switching between voluntary actions. In addition, the enhanced inhibition that follows antisaccade but not prosaccade trials explains asymmetric switch costs. In conclusion, SERIA offers a novel model of task switching that unifies previous theoretical accounts by distinguishing between inhibitory control and voluntary action generation and could help explain similar phenomena in paradigms beyond the antisaccade task.

In the antisaccade task participants are required to saccade in the opposite direction of a peripheral visual cue (PVC). This paradigm is often used to investigate inhibition of reflexive responses as well as voluntary response generation. However, it is not clear to what extent different versions of this task probe the same underlying processes. Here, we explored with the Stochastic Early Reaction, Inhibition, and late Action (SERIA) model how the delay between task cue and PVC affects reaction time (RT) and error rate (ER) when pro- and antisaccade trials are randomly interleaved. Specifically, we contrasted a condition in which the task cue was presented before the PVC with a condition in which the PVC served also as task cue. Summary statistics indicate that ERs and RTs are reduced and contextual effects largely removed when the task is signaled before the PVC appears. The SERIA model accounts for RT and ER in both conditions and better so than other candidate models. Modeling demonstrates that voluntary pro- and antisaccades are frequent in both conditions. Moreover, early task cue presentation results in better control of reflexive saccades, leading to fewer fast antisaccade errors and more rapid correct prosaccades. Finally, high-latency errors are shown to be prevalent in both conditions. In summary, SERIA provides an explanation for the differences in the delayed and nondelayed antisaccade task.NEW & NOTEWORTHY In this article, we use a computational model to study the mixed antisaccade task. We contrast two conditions in which the task cue is presented either before or concurrently with the saccadic target. Modeling provides a highly accurate account of participants’ behavior and demonstrates that a significant number of prosaccades are voluntary actions. Moreover, we provide a detailed quantitative analysis of the types of error that occur in pro- and antisaccade trials.

In the antisaccade task, subjects are instructed to saccade in the opposite direction of a peripheral visual cue (PVC). Importantly, several psychiatric disorders are associated with increased error rates in this paradigm. Despite this observation, there is no consensus about the mechanism behind antisaccade errors: while often explained as inhibition failures, some studies have suggested that errors are caused by deficits in the ability to initiate voluntary saccades. Using a computational model, we recently showed that under some conditions high latency or late errors can be explained by a race process between voluntary pro- and antisaccades. A limitation of our findings is that in our previous experiment the PVC signaled the trial type, whereas in most studies, subjects are informed about the trial type before the PVC is presented. We refer to these task designs as asynchronous (AC) and synchronous cues (SC) conditions. Here, we investigated to which extent differences in design affect the type and frequency of errors in the antisaccade task. Twenty-four subjects participated in mixed blocks of pro- and antisaccade trials in both conditions. Our results demonstrate that error rates were highly correlated across task designs and a non-negligible fraction of them were classified as late errors in both conditions. In summary, our findings indicate that errors in the AC task are the result of both inhibition failures and inaccurate voluntary action initiation.

Despite the success of modern neuroimaging techniques in furthering our understanding of cognitive and pathophysiological processes, translation of these advances into clinically relevant tools has been virtually absent until now. Neuromodeling represents a powerful framework for overcoming this translational deadlock, and the development of computational models to solve clinical problems has become a major scientific goal over the last decade, as reflected by the emergence of clinically oriented neuromodeling fields like Computational Psychiatry, Computational Neurology, and Computational Psychosomatics. Generative models of brain physiology and connectivity in the human brain play a key role in this endeavor, striving for computational assays that can be applied to neuroimaging data from individual patients for differential diagnosis and treatment prediction. In this review, we focus on dynamic causal modeling (DCM) and its use for Computational Psychiatry. DCM is a widely used generative modeling framework for functional magnetic resonance imaging (fMRI) and magneto-/electroencephalography (M/EEG) data. This article reviews the basic concepts of DCM, revisits examples where it has proven valuable for addressing clinically relevant questions, and critically discusses methodological challenges and recent methodological advances. We conclude this review with a more general discussion of the promises and pitfalls of generative models in Computational Psychiatry and highlight the path that lies ahead of us. This article is categorized under: Neuroscience > Computation Neuroscience > Clinical Neuroscience.

The antisaccade task is a classic paradigm used to study the voluntary control of eye movements. It requires participants to suppress a reactive eye movement to a visual target and to concurrently initiate a saccade in the opposite direction. Although several models have been proposed to explain error rates and reaction times in this task, no formal model comparison has yet been performed. Here, we describe a Bayesian modeling approach to the antisaccade task that allows us to formally compare different models on the basis of their evidence. First, we provide a formal likelihood function of actions (pro- and antisaccades) and reaction times based on previously published models. Second, we introduce the Stochastic Early Reaction, Inhibition, and late Action model (SERIA), a novel model postulating two different mechanisms that interact in the antisaccade task: an early GO/NO-GO race decision process and a late GO/GO decision process. Third, we apply these models to a data set from an experiment with three mixed blocks of pro- and antisaccade trials. Bayesian model comparison demonstrates that the SERIA model explains the data better than competing models that do not incorporate a late decision process. Moreover, we show that the early decision process postulated by the SERIA model is, to a large extent, insensitive to the cue presented in a single trial. Finally, we use parameter estimates to demonstrate that changes in reaction time and error rate due to the probability of a trial type (pro- or antisaccade) are best explained by faster or slower inhibition and the probability of generating late voluntary prosaccades., Author summary One widely replicated finding in schizophrenia research is that patients tend to make more errors than healthy controls in the antisaccade task, a psychometric paradigm in which participants are required to look in the opposite direction of a visual cue. This deficit has been suggested to be an endophenotype of schizophrenia, as first order relatives of patients tend to show similar but milder deficits. Currently, most models applied to experimental findings in this task are limited to fit average reaction times and error rates. Here, we propose a novel statistical model that fits experimental data from the antisaccade task, beyond summary statistics. The model is inspired by the hypothesis that antisaccades are the result of several competing decision processes that interact nonlinearly with each other. In applying this model to a relatively large experimental data set, we show that mean reaction times and error rates do not fully reflect the complexity of the processes that are likely to underlie experimental findings. In the future, our model could help to understand the nature of the deficits observed in schizophrenia by providing a statistical tool to study their biological underpinnings.

Este artículo tiene como objetivo la estimación del trabajo doméstico (reproductivo) en Colombia, realizado al interior del hogar y su imputación equivalentea precios sombra, para lo cual se parte del enfoquede la Nueva Economía de la Familia desarrollado por Becker (1981; 1985) y la metodología de Heckman(1974; 1979). Dentro de los resultados de la presente investigación se pudo concluir, a través de la técnica econométrica de fronteras estocásticas, que las horas diarias por las que se maximiza el salario de reserva de la mujer en la cabecera municipal son de 11,9; mientras que en el área rural son de 12,3, lo que demuestra mayor competitividad de la mujer en la cabecera, frente al área rural. Por otra parte, las condiciones de vida de la ciudad (respecto a la crianza de los niños) frente a las condiciones rurales, hacen que el costo de oportunidad de salir al mercado sea más alto que en el área urbana y, por lo tanto, induce a que se eleve el salario de reserva de la mujer.

Patients with neuropsychiatric disorders, in particular schizophrenia, show a variety of eye movement abnormalities that putatively reflect alterations of perceptual inference, learning and cognitive control. While these abnormalities are consistently found at the group level, a particularly difficult and important challenge is to translate these findings into clinically useful tests for single patients. In this paper, we argue that generative models of eye movement data, which allow for inferring individual computational and physiological mechanisms, could contribute to filling this gap. We present a selective overview of eye movement paradigms with clinical relevance for schizophrenia and review existing computational approaches that rest on (or could be turned into) generative models. We conclude by outlining desirable clinical applications at the individual subject level and discuss the necessary validation studies.

Background Dynamic causal modeling (DCM) for fMRI is an established method for Bayesian system identification and inference on effective brain connectivity. DCM relies on a biophysical model that links hidden neuronal activity to measurable BOLD signals. Currently, biophysical simulations from DCM constitute a serious computational hindrance. Here, we present Massively Parallel Dynamic Causal Modeling (mpdcm), a toolbox designed to address this bottleneck. New method mpdcm delegates the generation of simulations from DCM's biophysical model to graphical processing units (GPUs). Simulations are generated in parallel by implementing a low storage explicit Runge–Kutta's scheme on a GPU architecture. mpdcm is publicly available under the GPLv3 license. Results We found that mpdcm efficiently generates large number of simulations without compromising their accuracy. As applications of mpdcm, we suggest two computationally expensive sampling algorithms: thermodynamic integration and parallel tempering. Comparison with existing method(s) mpdcm is up to two orders of magnitude more efficient than the standard implementation in the software package SPM. Parallel tempering increases the mixing properties of the traditional Metropolis–Hastings algorithm at low computational cost given efficient, parallel simulations of a model. Conclusions Future applications of DCM will likely require increasingly large computational resources, for example, when the likelihood landscape of a model is multimodal, or when implementing sampling methods for multi-subject analysis. Due to the wide availability of GPUs, algorithmic advances can be readily available in the absence of access to large computer grids, or when there is a lack of expertise to implement algorithms in such grids.

40 páginas Las últimas dos décadas han visto un resurgimiento de la estadística bayesiana, la cual fue vista como una disciplina marginal durante la mayor parte del siglo XX. Este fenómeno ha tenido un profundo efecto en la neurociencia, no solo en cuanto al tipo de métodos usados para analizar datos experimentales, sino también en la forma en que la percepción y la acción son conceptualizadas desde un punto de vista teórico. Este giro puede ser resumido en la hipótesis bayesiana del cerebro, según la cual una de las funciones centrales de este órgano es realizar inferencias estadísticas bayesianas. En este contexto, el principio de la energía libre, propuesto por Karl Friston, ha surgido como un posible candidato a una teoría unificada de la cognición. Son dos los propósitos de este artículo: primero presentar el principio de la energía libre desde una perspectiva filosófica y segundo aclarar si este principio debe ser visto como una teoría normativa de la cognición o si, al contrario, este puede realizar predicciones empíricas acerca del tipo de procesos computacionales que caracterizan a la cognición humana. En conclusión, el principio de la energía libre, como es frecuentemente presentado por Friston, corresponde a una teoría descriptiva del tipo de algoritmos computacionales implementados por el cerebro. Más aún, no hay todavía suficiente evidencia empírica en su favor y sí un gran número de hallazgos que apuntan en la dirección contraria. The last two decades have witnessed a resurgence of Bayesian statistics, which was regarded as a marginal discipline during most of the twentieth century. This phenomenon has had a profound effect on neuroscience, not only in terms of the kinds of methods used to analyze experimental data, but also in the way perception and action are conceptualized from a theoretical standpoint. This shift can be summarized in the Bayesian brain hypothesis, which holds that one of the central features of this organ is to mount Bayesian statistical inferences. In this context, the principle of free energy proposed by Karl Friston has emerged as a possible candidate for a unified theory of cognition. The purpose of this article is twofold. The first is to introduce the principle of free energy from a philosophical perspective; the second is to clarify whether this principle should be seen as a normative theory of cognition or if, on the contrary, it can be used to make empirical predictions about the sort computational processes that characterize human cognition. In conclusion, the principle of free energy, as often presented by Friston, is a descriptive theory on the type of computational algorithms the brain uses. Moreover, there is not enough empirical evidence in its favor and, in fact, a large number of findings point in the opposite direction.

Over the past decade, computational approaches to neuroimaging have increasingly made use of hierarchical Bayesian models (HBMs), either for inferring on physiological mechanisms underlying fMRI data (e.g., dynamic causal modelling, DCM) or for deriving computational trajectories (from behavioural data) which serve as regressors in general linear models. However, an unresolved problem is that standard methods for inverting the hierarchical Bayesian model are either very slow, e.g. Markov Chain Monte Carlo Methods (MCMC), or are vulnerable to local minima in non-convex optimisation problems, such as variational Bayes (VB). This article considers Gaussian process optimisation (GPO) as an alternative approach for global optimisation of sufficiently smooth and efficiently evaluable objective functions. GPO avoids being trapped in local extrema and can be computationally much more efficient than MCMC. Here, we examine the benefits of GPO for inverting HBMs commonly used in neuroimaging, including DCM for fMRI and the Hierarchical Gaussian Filter (HGF). Importantly, to achieve computational efficiency despite high-dimensional optimisation problems, we introduce a novel combination of GPO and local gradient-based search methods. The utility of this GPO implementation for DCM and HGF is evaluated against MCMC and VB, using both synthetic data from simulations and empirical data. Our results demonstrate that GPO provides parameter estimates with equivalent or better accuracy than the other techniques, but at a fraction of the computational cost required for MCMC. We anticipate that GPO will prove useful for robust and efficient inversion of high-dimensional and nonlinear models of neuroimaging data.