Emotion Dynamics as Hierarchical Bayesian Inference in Time

What fundamental property of our environment would be most valuable and optimal in characterizing the emotional dynamics we experience in our daily life? Empirical work has shown that an accurate estimation of uncertainty is necessary for our optimal perception, learning, and decision-making. However, the role of this uncertainty in governing our affective dynamics remains unexplored. Using Bayesian encoding, decoding and computational modelling, we show that emotional experiences naturally arise due to ongoing uncertainty estimations in a hierarchical neural architecture. This hierarchical organization involves a number of prefrontal sub-regions, with the lateral orbitofrontal cortex having the highest representational complexity of uncertainty. Crucially, this representational complexity, was sensitive to temporal fluctuations in uncertainty and was predictive of participants predisposition to anxiety. Furthermore, the temporal dynamics of uncertainty revealed a distinct functional double dissociation within the OFC. Specifically, the medial OFC showed higher connectivity with the DMN, while the lateral OFC with that of the FPN in response to the evolving affect. Finally, we uncovered a temporally predictive code updating individual beliefs swiftly in the face of fluctuating uncertainty in the lateral OFC. A biologically relevant and computationally crucial parameter in theories of brain function, we extend uncertainty to be a defining component of complex emotions.

Population Properties of Neutron Stars in the Coalescing Compact Binaries

We perform a hierarchical Bayesian inference to investigate the population properties of the coalescing compact binaries involving at least one neutron star (NS). With the current gravitational-wave (GW) observation data, we can rule out none of the double Gaussian, single Gaussian, and uniform NS mass distribution models, though a specific double Gaussian model inferred from the Galactic NSs is found to be slightly more preferred. The mass distribution of black holes (BHs) in the neutron star–black hole (NSBH) population is found to be similar to that in the Galactic X-ray binaries. Additionally, the ratio of the merger rate densities between NSBHs and BNSs is estimated to be ∼3:7. The spin properties of the binaries, though constrained relatively poorly, play a nontrivial role in reconstructing the mass distribution of NSs and BHs. We find that a perfectly aligned spin distribution can be ruled out, while a purely isotropic distribution of spin orientation is still allowed. To evaluate the feasibility of reliably determining the population properties of NSs in the coalescing compact binaries with upcoming GW observations, we perform simulations with a mock population. We find that with 100 detections (including BNSs and NSBHs) the mass distribution of NSs can be well determined, and the fraction of BNSs can also be accurately estimated.

Structure in motion: visual motion perception as online hierarchical inference

Identifying the structure of motion relations in the environment is critical for navigation, tracking, prediction, and pursuit. Yet, little is known about the mental and neural computations that allow the visual system to infer this structure online from a volatile stream of visual information. We propose online hierarchical Bayesian inference as a principled solution for how the brain might solve this complex perceptual task. We derive an online Expectation-Maximization algorithm that explains human percepts qualitatively and quantitatively for a diverse set of stimuli, covering classical psychophysics experiments, ambiguous motion scenes, and illusory motion displays. We thereby identify normative explanations for the origin of human motion structure perception and make testable predictions for new psychophysics experiments. The algorithm furthermore affords a neural network implementation which shares properties with motion-sensitive cortical areas and motivates a novel class of experiments to reveal the neural representations of latent structure.

Inferring the dynamic of mutated hematopoietic stem and progenitor cells induced by IFNα in myeloproliferative neoplasms

Classical BCR-ABL-negative myeloproliferative neoplasms (MPN) are clonal disorders of hematopoietic stem cells (HSC) caused mainly by recurrent mutations in genes encoding JAK2 (JAK2), calreticulin (CALR), or the thrombopoietin receptor (MPL). Interferon alpha (IFNα) has demonstrated some efficacy in inducing molecular remission in MPN. In order to determine factors that influence molecular response rate, we evaluated the long-term molecular efficacy of IFNα in MPN patients by monitoring the fate of cells carrying driver mutations in a prospective observational and longitudinal study of 48 patients over more than 5 years. We measured several times per year the clonal architecture of early and late hematopoietic progenitors (84,845 measurements) and the global variant allele frequency in mature cells (409 measurements). Using mathematical modeling and hierarchical Bayesian inference, we further inferred the dynamics of IFNα-targeted mutated HSC. Our data support the hypothesis that IFNα targets JAK2V617F HSC by inducing their exit from quiescence and differentiation into progenitors. Our observations indicate that treatment efficacy is higher in homozygous than heterozygous JAK2V617F HSC and increases with high IFNα dosage in heterozygous JAK2V617F HSC. Besides, we found that the molecular responses of CALRm HSC to IFNα were heterogeneous, varying between type 1 and type 2 CALRm, and high dosage of IFNα correlates with worse outcomes. Together, our work indicates that the long-term molecular efficacy of IFNα implies an HSC exhaustion mechanism and depends on both the driver mutation type and IFNα dosage.

Hierarchical, Memory-Based Movement Models for Translocated Elk (Cervus canadensis)

The use of spatial memory is well-documented in many animal species and has been shown to be critical for the emergence of spatial learning. Adaptive behaviors based on learning can emerge thanks to an interdependence between the acquisition of information over time and movement decisions. The study of how spatio-ecological knowledge is constructed throughout the life of an individual has not been carried out in a quantitative and comprehensive way, hindered by the lack of knowledge of the information an animal already has of its environment at the time monitoring begins. Identifying how animals use memory to make beneficial decisions is fundamental to developing a general theory of animal movement and space use. Here we propose several mobility models based on memory and perform hierarchical Bayesian inference on 11-month trajectories of 21 elk after they were released in a completely new environment. Almost all the observed animals exhibited preferential returns to previously visited patches, such that memory and random exploration phases occurred. Memory decay was mild or negligible over the study period. The fact that individual elk rapidly become used to a relatively small number of patches was consistent with the hypothesis that they seek places with predictable resources and reduced mortality risks such as predation.

Minimal physicalism as a scale-free substrate for cognition and consciousness

Theories of consciousness and cognition that assume a neural substrate automatically regard phylogenetically basal, nonneural systems as nonconscious and noncognitive. Here, we advance a scale-free characterization of consciousness and cognition that regards basal systems, including synthetic constructs, as not only informative about the structure and function of experience in more complex systems but also as offering distinct advantages for experimental manipulation. Our “minimal physicalist” approach makes no assumptions beyond those of quantum information theory, and hence is applicable from the molecular scale upwards. We show that standard concepts including integrated information, state broadcasting via small-world networks, and hierarchical Bayesian inference emerge naturally in this setting, and that common phenomena including stigmergic memory, perceptual coarse-graining, and attention switching follow directly from the thermodynamic requirements of classical computation. We show that the self-representation that lies at the heart of human autonoetic awareness can be traced as far back as, and serves the same basic functions as, the stress response in bacteria and other basal systems.

Hierarchical, Memory-based Movement Models for Translocated Elk (Cervus canadensis)

The use of spatial memory is well documented in many animal species and has been shown to be critical for the emergence of spatial learning. Adaptive behaviors based on learning can emerge thanks to an interdependence between the acquisition of information over time and movement decisions. The study of how spatio-ecological knowledge is constructed throughout the life of an individual has not been carried out in a quantitative and comprehensive way, hindered by the lack of knowledge of the information an animal already has of its environment at the time monitoring begins. Identifying how animals use memory to make beneficial decisions is fundamental to developing a general theory of animal movement and space use. Here we propose several mobility models based on memory and perform hierarchical Bayesian inference on 11-month trajectories of 21 elk after they were released in a completely new environment. Almost all the observed animals exhibited preferential returns to previously visited patches, such that memory and random exploration phases occurred. Memory decay was mild or negligible over the study period. The fact that individual elk rapidly become used to a relatively small number of patches was consistent with the hypothesis that they seek places with predictable resources and reduced mortality risks such as predation.

Illusions, Delusions, and Your Backwards Bayesian Brain: A Biased Visual Perspective

The retinal image is insufficient for determining what is “out there,” because many different real-world geometries could produce any given retinal image. Thus, the visual system must infer which external cause is most likely, given both the sensory data and prior knowledge that is either innate or learned via interactions with the environment. We will describe a general framework of “hierarchical Bayesian inference” that we and others have used to explore the role of cortico-cortical feedback in the visual system, and we will further argue that this approach to “seeing” makes our visual systems prone to perceptual errors in a variety of different ways. In this deliberately provocative and biased perspective, we argue that the neuromodulator, dopamine, may be a crucial link between neural circuits performing Bayesian inference and the perceptual idiosyncrasies of people with schizophrenia.

Hierarchical Bayesian inference and spatial validation of socio-ecological system dynamics models: participatory modelling for Indigenous smallholder agriculture and food security in Guatemala

<p><span>Systems dynamics modelling is often used as a participatory modelling tool to model the long-term dynamics of socio-ecological systems, as well as to help in developing integrated policy decisions that take into account the unexpected and complex system behaviours that are often caused by the dynamic feedbacks between ecology and society. Actual use of these models in decision-making is, however, hindered by the frequent lack of high-quality temporal data on many key socioeconomic (and environmental) variables, which makes the application of traditional system dynamics model evaluation techniques difficult. This situation is particularly pronounced in the context of many Indigenous communities around the world, regions where improved access to decision support tools such as system dynamics modelling could be of particular use for supporting communities in their quest to make (and have implemented) their own resource management decisions. In the absence of rigorous quantification methods, however, these models are difficult to build and trust.</span></p><p><span>In this research, we present a novel methodology for calibrating hard-to-quantify relationships between socioeconomic variables of systems dynamics models. Based on hierarchical Bayesian inference, the methodology allows for the use of spatially explicit (but temporally poor) datasets to infer the quantitative, numerical relationships between socioeconomic variables, even when data in the precise region of interest is very scarce. We present, as a case study, a system dynamics model of small-scale agricultural systems and food security in two different regions of Guatemala (Tz'olöj Ya' and K'iche'), and analyse the impacts of different proposed policies in the face of socioeconomic shocks and water stress due to projected climate change. The hierarchical Bayesian inference calibration method allowed for the inference of key socioeconomic parameter values in a spatially explicit context to compensate for data scarcity, while spatial validation indicated which regions of the country the model was appropriate for.</span></p><p><span>Such a methodology, once incorporated into user-friendly system dynamics software, has the potential to facilitate participatory sociohydrological modelling even in quite data-scarce regions where modellers, up until now, have had to rely on educated guesses for the majority of the model's calibration.</span></p>

Hierarchical Bayesian Models to Estimate the Number of Losses of Separation between Aircraft in Flight

Air transport is considered to be the safest mode of mass transportation. Air traffic management (ATM) systems constitute one of the fundamental pillars that contribute to these high levels of safety. In this paper we wish to answer two questions: (i) What is the underlying safety level of ATM systems in Europe? and (ii) What is the dispersion, that is, how far does each ATM service provider deviate from this underlying safety level? To do this, we develop four hierarchical Bayesian inference models that allow us to infer and predict the common rate of occurrence of SMIs, as well as the specific rates of occurrence for each air navigation service provider (ANSP). This study shows the usefulness of hierarchical structures when it comes to obtaining parameters that enable risk to be quantified effectively. The models developed have been found to be useful in explaining and predicting the safety performance of 29 European ATM systems with common regulations and work procedures, but with different circumstances and numbers of aircraft, each managing traffic of differing complexity.