Inferring on the intentions of others by hierarchical Bayesian learning

Inferring on others' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to "player" or "adviser" roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the adviser's incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players' behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers' intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the adviser's intentions were presently stable. Finally, our model of the players' inference predicted the players' interpersonal reactivity index (IRI) scores, explicit ratings of the advisers' helpfulness and the advisers' self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition

Uncertainty in perception and the Hierarchical Gaussian Filter

In its full sense, perception rests on an agent's model of how its sensory input comes about and the inferences it draws based on this model. These inferences are necessarily uncertain. Here, we illustrate how the Hierarchical Gaussian Filter (HGF) offers a principled and generic way to deal with the several forms that uncertainty in perception takes. The HGF is a recent derivation of one-step update equations from Bayesian principles that rests on a hierarchical generative model of the environment and its (in)stability. It is computationally highly efficient, allows for online estimates of hidden states, and has found numerous applications to experimental data from human subjects. In this paper, we generalize previous descriptions of the HGF and its account of perceptual uncertainty. First, we explicitly formulate the extension of the HGF's hierarchy to any number of levels; second, we discuss how various forms of uncertainty are accommodated by the minimization of variational free energy as encoded in the update equations; third, we combine the HGF with decision models and demonstrate the inversion of this combination; finally, we report a simulation study that compared four optimization methods for inverting the HGF/decision model combination at different noise levels. These four methods (Nelder-Mead simplex algorithm, Gaussian process-based global optimization, variational Bayes and Markov chain Monte Carlo sampling) all performed well even under considerable noise, with variational Bayes offering the best combination of efficiency and informativeness of inference. Our results demonstrate that the HGF provides a principled, flexible, and efficient-but at the same time intuitive-framework for the resolution of perceptual uncertainty in behaving agents

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups

Experience of the Successful Treatment With Canakinumab of a Patient With Systemic Juvenile Idiopathic Arthritis

The article presents the follow-up of a patient with severe systemic juvenile idiopathic arthritis (JIA) resistant to glucocorticosteroids, to the first genetically engineered biologic drug (GEBD) tocilizumab, a monoclonal antibody to the interleukin (IL) 6 receptor. Switching to the second GEBD — a monoclonal antibody to IL1β canakinumab — provided a remission of the disease. The first injection of the drug fully arrested the systemic symptoms of the disease, and the fourth one — the articular syndrome. The presented clinical example shows that switching to GEBD with a different mechanism of action — a monoclonal antibody to IL1β canakinumab — is highly effective and induces a remission of the disease in patients with systemic JIA resistant to tocilizumab. There were no adverse events under pressure of canakinumab therapy

Regression DCM for fMRI

The development of large-scale network models that infer the effective (directed) connectivity among neuronal populations from neuroimaging data represents a key challenge for computational neuroscience. Dynamic causal models (DCMs) of neuroimaging and electrophysiological data are frequently used for inferring effective connectivity but are presently restricted to small graphs (typically up to 10 regions) in order to keep model inversion computationally feasible. Here, we present a novel variant of DCM for functional magnetic resonance imaging (fMRI) data that is suited to assess effective connectivity in large (whole-brain) networks. The approach rests on translating a linear DCM into the frequency domain and reformulating it as a special case of Bayesian linear regression. This paper derives regression DCM (rDCM) in detail and presents a variational Bayesian inversion method that enables extremely fast inference and accelerates model inversion by several orders of magnitude compared to classical DCM. Using both simulated and empirical data, we demonstrate the face validity of rDCM under different settings of signal-to-noise ratio (SNR) and repetition time (TR) of fMRI data. In particular, we assess the potential utility of rDCM as a tool for whole-brain connectomics by challenging it to infer effective connection strengths in a simulated whole-brain network comprising 66 regions and 300 free parameters. Our results indicate that rDCM represents a computationally highly efficient approach with promising potential for inferring whole-brain connectivity from individual fMRI data

Correction: Inferring on the Intentions of Others by Hierarchical Bayesian Learning

ISSN:1553-734XISSN:1553-735

Efficacy and Safety of Immunization With Pneumococcal Polysaccharide Vaccine in Children With Juvenile Idiopathic Arthritis: Preliminary Results of a Prospective Open-Label Study

Juvenile idiopathic arthritis (JIA) is one of the most frequent and most disabling rheumatic diseases in children. Children with JIA receiving immunosuppressive and genetically engineered biologic drugs belong to the high-risk group for the development of bacterial and viral infections, including those administered by preventive vaccines.Objective: Our aim was to evaluate the efficacy and safety of 13-valent pneumococcal polysaccharide vaccine (PPV) in children with JIA.Methods. In a prospective open-label comparative study, the efficacy of vaccination was determined by the level of specific anti-pneumococcal antibodies (anti-SPP)IgG to Streptococcus pneumonia in the blood serum in patients with JIA. The safety of vaccination was assessed by determining a high-sensitivity C-reactive protein and S-100 protein as well as by the number of adverse events, by recording the number of infections of the upper respiratory tract and pneumonias, by the number of joints with active arthritis. Vaccination with 13-valent PPV was performed subcutaneously with one dose of 0.5 ml during therapy of the main disease with methotrexate or etanercept or 3 weeks before the appointment of methotrexate or etanercept. Patients were followed up for 1 year.Results. The study included 42 children with JIA: 21 with JIA in the active phase of the disease, 21 in remission of the disease. As a result of vaccination, the level of anti-pneumococcal antibodies (antiSPP)IgG increased in the group of children with JIA in the active phase from 26.1 (14.3; 52.1) to 73.0 (52.5; 156.0) mg/l (p = 0.001), with JIA in remission — from 27.4 (18.2; 59.1) to 54.6 (35.3; 96.0) mg/l (p = 0.029). The concentration of the predictor of S-100 protein high activity after vaccination was not increased (p = 0.192). JIA aggravation episodes were not fixed in any patient. Serious adverse events were not observed during the trial.Conclusion. The vaccination of children with JIA with 13-valent PPV is highly effective, not accompanied by exacerbation/increase in the activity of the disease and the development of serious adverse events

A generative model of whole-brain effective connectivity

The development of whole-brain models that can infer effective (directed) connection strengths from fMRI data represents a central challenge for computational neuroimaging. A recently introduced generative model of fMRI data, regression dynamic causal modeling (rDCM), moves towards this goal as it scales gracefully to very large networks. However, large-scale networks with thousands of connections are difficult to interpret; additionally, one typically lacks information (data points per free parameter) for precise estimation of all model parameters. This paper introduces sparsity constraints to the variational Bayesian framework of rDCM as a solution to these problems in the domain of task-based fMRI. This sparse rDCM approach enables highly efficient effective connectivity analyses in whole-brain networks and does not require a priori assumptions about the network's connectivity structure but prunes fully (all-to-all) connected networks as part of model inversion. Following the derivation of the variational Bayesian update equations for sparse rDCM, we use both simulated and empirical data to assess the face validity of the model. In particular, we show that it is feasible to infer effective connection strengths from fMRI data using a network with more than 100 regions and 10,000 connections. This demonstrates the feasibility of whole-brain inference on effective connectivity from fMRI data - in single subjects and with a run-time below 1 min when using parallelized code. We anticipate that sparse rDCM may find useful application in connectomics and clinical neuromodeling - for example, for phenotyping individual patients in terms of whole-brain network structure

Random effects family-level Bayesian model selection.

<p>(A) Posterior model probabilities pooled across all families of perceptual model families (i.e., HGF Volatility, HGF decision noise, No Volatility HGF and RW) indicate that the model class “HGF Volatility” explains participants' responses best. (B) Posterior model probabilities pooled across all response model families (i.e., Integrated (Cue and Advice), Reduced: Advice Only, and Reduced: Cue Only) indicate that the “Integrated” model family explains participants' responses best. (C) Posterior model probabilities across models that propose that the mapping of beliefs onto response probabilities is achieved via trial-by-trial adviser volatility estimates (Volatility models) or decision noise (Decision Noise). The former was the winning family.</p