State Inference Research Articles

Cue competition in mental state inference: Blocking effects in learning to interpret the behaviors of others

In the literature on learning, one of the most robust cue competition effects is blocking: the previous learning of a cue-outcome association prevents learning that other cues predict the same outcome if those cues are presented together with the first cue. In this research, we investigated blocking effects in mental state inference. Participants learned to diagnose the internal states of a target person based on the behaviors he displayed. Blocking effects were observed across several studies, such that, when participants had previously learned that a certain behavior predicted a certain internal state, they later failed to learn about the predictive value of other behaviors that were paired with the original behavioral cue. Implications are discussed for cognitive models of learning and cue competition, as well as for the social psychology of mental state inference.

Online Bayesian inference and learning of Gaussian-process state–space models

This paper addresses the recursive joint inference (state estimation) and learning (system identification) problem for nonlinear systems admitting a state–space formulation. We model the system as a Gaussian-process state–space model (GP-SSM) and leverage a recently developed reduced-rank formulation of GP-SSMs to enable efficient, online learning. The unknown dynamical system is expressed as a basis-function expansion, where a connection to the GP makes it possible to systematically assign priors to the basis-function weights. The approach is formulated within the sequential Monte Carlo framework, wherein each particle retains its own weights of the basis functions, which are updated recursively as measurements arrive. We report competitive results when compared to a state-of-the art offline Bayesian learning method. We also apply the method to a case study concerning tire-friction estimation. The results indicate that our method can accurately learn the tire friction using automotive-grade sensors in an online setting, and quickly detect sudden changes on the road surface.

Integrating empathic and mentalizing abilities with interpersonal sensitivity in people with eating disorders: A network analysis approach

IntroductionLiterature highlights that interpersonal sensitivity represents an important development and maintaining factor for Eating Disorder (ED). Mentalizing and empathy are two psychological constructs that play a crucial role in social functioning. However, the role of mentalizing and empathy in the socio-emotional processing deficits of ED patients has been under investigated.ObjectivesWe aimed to assess the complex interactions between the sub-components of mentalizing and empathy and ED symptoms through a network analysis approach.MethodsSeventy-seven women with EDs were included in our study. Eating disorder and affective symptomatology were investigated with self-report questionnaires. All patients underwent two computerized tasks: Movie for the Assessment of Social Cognition (MASC), assessing emotional and non-emotional mental state inferences; Empathic Accuracy Task-Revised (EAT-R), measuring accuracy in identifying and sharing others’ emotions. A partial correlation network and bridge function analyses were computed.ResultsIn the partial correlation network inference of cognitive mental states and shape concern were the nodes with the highest strength centrality. Inference of emotional mental states was the node with the highest bridge strength in the cluster of social cognition functions. Empathic and mentalizing abilities were directly connected with each other and with ED symptoms.ConclusionsThis is the first network analysis study which integrates self-reported symptoms and objective socio-cognitive performance in people with Eds. Our results provide evidence of the complex interactions between mentalizing, empathy and psychopathological symptoms in people with EDs. Therefore, confirm that the ability to infer others’ mental state may represent a useful target for clinical intervention in EDs.

Inference of thermodynamic state in the asthenosphere from anelastic properties, with applications to North American upper mantle

Inference of thermodynamic state and full-spectrum mechanical behavior of the lithosphere and asthenosphere is a central problem in geophysics, implicating our understanding of the convection patterns, transient responses and chemical composition of the planet. Anelasticity is responsible for significant relaxation of stress associated with seismic wave propagation in the asthenosphere, while irreversible transient creep may be important in the lithosphere. This paper focuses on the processes that may act at the time scales of seismic wave propagation, and current questions in the effort to determine the dependence of these effects on thermodynamic state. We introduce a free code library, the “Very Broadband Rheology calculator” (VBRc), designed to calculate frequency-dependent mechanical properties and easily compare different constitutive models favored by different laboratories. The methods operate only in the forward sense, starting with arrays of models of thermodynamic state, proceeding to arrays of mechanical properties. These calculations are incorporated into a Bayesian framework to infer variation in mantle thermodynamic state from Vs and Q, applied here to four locations in Western North America. The results demonstrate how well we can constrain the state, given the input models and the measurements. Results for sites in the Basin and Range, Colorado Plateau and interior craton east of the Rio Grande separate into distinct state variable ranges consistent with their tectonic environments.

Understanding the Links between Inferring Mental States, Empathy, and Burnout in Medical Contexts.

It is generally accepted that empathy should be the basis of patient care. However, this ideal may be unrealistic if healthcare professionals suffer adverse effects when engaging in empathy. The aim of this study is to explore the effect of inferring mental states and different components of empathy (perspective-taking; empathic concern; personal distress) in burnout dimensions (emotional exhaustion; depersonalization; personal accomplishment). A total of 184 healthcare professionals participated in the study (23% male, Mage = 44.60; SD = 10.46). We measured participants’ empathy, the inference of mental states of patients, and burnout. Correlation analyses showed that inferring mental states was positively associated with perspective-taking and with empathic concern, but uncorrelated with personal distress. Furthermore, emotional exhaustion was related to greater levels of personal distress and greater levels of inferences of mental states. Depersonalization was associated with greater levels of personal distress and lower levels of empathic concern. Personal accomplishment was associated with the inference of mental states in patients, lower levels of personal distress, and perspective-taking. These results provide a better understanding of how different components of empathy and mental state inferences may preserve or promote healthcare professionals’ burnout.

Maximal Entropy Approach for Quantum State Tomography

Quantum computation has been growing rapidly in both theory and experiments. In particular, quantum computing devices with a large number of qubits have been developed by IBM, Google, IonQ, and others. The current quantum computing devices are noisy intermediate-scale quantum $($NISQ$)$ devices, and so approaches to validate quantum processing on these quantum devices are needed. One of the most common ways of validation for an n-qubit quantum system is quantum tomography, which tries to reconstruct a quantum system's density matrix by a complete set of observables. However, the inherent noise in the quantum systems and the intrinsic limitations poses a critical challenge to precisely know the actual measurement operators which make quantum tomography impractical in experiments. Here, we propose an alternative approach to quantum tomography, based on the maximal information entropy, that can predict the values of unknown observables based on the available mean measurement data. This can then be used to reconstruct the density matrix with high fidelity even though the results for some observables are missing. Of additional contexts, a practical approach to the inference of the quantum mechanical state using only partial information is also needed.

When my wrongs are worse than yours: Behavioral and neural asymmetries in first-person and third-person perspectives of accidental harms

Research on third-party moral judgments highlights two mechanisms as central to moral judgments of accidental harms: the inference of intent and the perception of harm. However, little is known about how these mechanisms are recruited when people evaluate themselves for harm that they have accidentally caused. Here we explore how a person's perspective — as either actor or observer — influences their moral judgments of accidental harm. We use fMRI to investigate how brain regions involved in the inference of intent and the perception of harm differentially respond when participants either cause (first-person) or observe (third-person) accidental harm. First, we find that people judge their own accidental harms more harshly than they judge others' accidents, and hold themselves more responsible for the unintended harmful outcomes of their choices. Second, we find that regions responding to the first-hand experience of pain are also more sensitive to first-person harms relative to third-person harms, and brain-behavior relationships in a subset of these regions suggest that the tendency to judge oneself more harshly may be supported by a greater sensitivity to the victim's experience of harm. Third, though we find that first-person harms recruit regions for mental state inference to a lesser extent than third-person harms, this difference does not appear to account for the behavioral differences in moral judgment between first-person and third-person harms. The results of this experiment suggest that accidental harms are an important context for broadening our understanding of the relationship between agency, empathy, and moral judgments about the self.

Validation de la mini-SEA dans une population française variée : données de référence pour la pratique clinique

Social cognition, referring to humans’ abilities to process social information in order to efficiently interact with each other, is impaired in a wide range of clinical groups. Among them, patients with behavioral variant frontotemporal lobar degeneration (bvFTD) have been found to present deficits in emotion recognition and mental state inference. Based on these components of social cognition, the mini-SEA battery was developed a dozen years ago and is widely used today with patients suffering from a variety of conditions. However, national normative studies are still lacking. The aim of the present study is to provide such normative reference data for French participants and to evaluate the psychometric qualities of the mini-SEA. 150 healthy participants were assessed with the mini-SEA. We found that performance decreased with the age of participants, and we observed a general, though small, advantage for women participants. Sensitivity analyses revealed that both sub-tests are relatively easy for healthy participants. However, emotion recognition performance appeared to depend on the type of emotions displayed. Internal consistency and reliability both appeared to be satisfactory, although these dimensions should be investigated in greater depth in future studies. Finally, the mini-SEA showed good clinical validity, as reflected by excellent specificity and sensitivity values.

God's mind on morality.

Most research on cognition behind religious belief assumes that understanding of other minds is culturally uniform and follows the Western model of mind, which posits that (a) others' thoughts can be known and (b) action is best explained by mental state inference. This is potentially problematic if, as a growing body of evidence suggests, other populations view minds differently. We recruit Indigenous iTaukei Fijians who hold (a) a model of mind that discourages mental state inference and (b) co-existing Christian (Western) and traditional supernatural agent beliefs. Study 1 (N = 108), uses free-listing to examine how Western and local models of mind relate to beliefs. The Christian God cares about internal states and traits (aligning with the Western model of mind). Study 2 tests whether evoking God triggers intent focus in moral reasoning. Instead, God appears to enforce cultural models of mind in iTaukei (N = 151) and North Americans (N = 561). Expected divine judgement mirrors human judgement; iTaukei (N = 90) expect God to emphasise outcome, while Indo-Fijians (N = 219) and North Americans (N = 412) expect God to emphasise intent. When reminded to think about thoughts, iTaukei (N = 72) expect God to judge outcomes less harshly. Results suggest cultural/cognitive co-evolution: introduced cultural forms can spread new cognitive approaches, while Indigenous beliefs can persist as a reflection of local institutions.

Online Joint State Inference and Learning of Partially Unknown State-Space Models

A computationally efficient method for online joint state inference and dynamical model learning is presented. The dynamical model combines an a priori known, physically derived, state-space model with a radial basis function expansion representing unknown system dynamics and inherits properties from both physical and data-driven modeling. The method uses an extended Kalman filter approach to jointly estimate the state of the system and learn the unknown system dynamics, via the parameters of the basis function expansion. The key contribution is a computational complexity reduction compared to a similar approach with globally supported basis functions. By using compactly supported radial basis functions and an approximate Kalman gain, the computational complexity is considerably reduced and is essentially determined by the support of the basis functions. The approximation works well when the system dynamics exhibit limited correlation between points well separated in the state-space domain. The method is exemplified via two intelligent vehicle applications where it is shown to: (i) have competitive system dynamics estimation performance compared to the globally supported basis function method, and (ii) be real-time applicable to problems with a large-scale state-space.

Transfer learning of chaotic systems.

Can a neural network trained by the time series of system A be used to predict the evolution of system B? This problem, knowing as transfer learning in a broad sense, is of great importance in machine learning and data mining yet has not been addressed for chaotic systems. Here, we investigate transfer learning of chaotic systems from the perspective of synchronization-based state inference, in which a reservoir computer trained by chaotic system A is used to infer the unmeasured variables of chaotic system B, while A is different from B in either parameter or dynamics. It is found that if systems A and B are different in parameter, the reservoir computer can be well synchronized to system B. However, if systems A and B are different in dynamics, the reservoir computer fails to synchronize with system B in general. Knowledge transfer along a chain of coupled reservoir computers is also studied, and it is found that, although the reservoir computers are trained by different systems, the unmeasured variables of the driving system can be successfully inferred by the remote reservoir computer. Finally, by an experiment of chaotic pendulum, we demonstrate that the knowledge learned from the modeling system can be transferred and used to predict the evolution of the experimental system.

Supplemental Material for Spontaneous State Inferences

Supplemental Material for Spontaneous State Inferences

Towards Estimation of Emotions From Eye Pupillometry With Low-Cost Devices

Emotional care is important for some patients and their caregivers. Within a clinical or home care situation, technology can be employed to remotely monitor the emotional response of such people. This paper considers pupillometry as a non-invasive way of classifying an individual’s emotions. Standardized audio signals were used to emotionally stimulate the test subjects. Eye pupil images of up to 32 subjects of different genders were captured as video images by low-cost, infrared, Raspberry Pi board cameras. By processing of the images, a dataset of pupil diameters according to gender and age characteristics was established. Appropriate statistical tests for inference of the emotional state were applied to that dataset to establish the subjects’ emotional states in response to the audio stimuli. Results showed agreement between the test subjects’ opinions of their emotional state and the classification of emotions according to the range of pupil diameters found using the described method.

Diversity and dynamic changes of anelloviruses in plasma following allogeneic hematopoietic stem cell transplantation.

Monitoring of alphatorquevirus (torque teno virus [TTV]) DNA in plasma may prove to be useful to assess the net state of immune competence following allogeneic hematopoietic stem cell transplantation (allo-HSCT). There are scarce data published on the prevalence of beta (torque teno mini virus [TTMV]) and gammatorqueviruses (torque teno midi virus [TTMDV]) and, in particular, on the dynamics of anelloviruses in allo-HSCT patients. Twenty-five allo-HSCT recipients with available plasma specimens obtained before conditioning and after engraftment were included. Degenerated primers targeting a highly conserved genomic sequence across all anelloviruses were designed for genomic amplification and high-throughput sequencing. Co-detection of TTV, TTMV, and TTMDV both in pre-transplant and post-engraftment plasma specimens was documented in more than two-thirds of patients. The use of quantitative real-time polymerase chain reaction (PCR) assays targeting TTMV and TTMDV in addition to TTV may add value to TTV-specific PCR assays in the inference of the net state of immunosuppresion or immune competence in this clinical setting.

Attention in naïve psychology

In everyday life, mentalizing is nested in a rich context of cognitive faculties and background information that potentially contribute to its success. Yet, we know little about these modulating effects. Here we propose that humans develop a naïve psychological model of attention (featured as a goal-dependent, intentional relation to the environment) and use this to fine-tune their mentalizing attempts, presuming that the way people represent their environment is influenced by the cognitive priorities (attention) their current intentions create. The attention model provides an opportunity to tailor mental state inferences to the temporary features of the agent whose mind is in the focus of mentalizing. The ability to trace attention is an exceptionally powerful aid for mindreading. Knowledge about the partner's attention provides background information, however being grounded in his current intentions, attention has direct relevance to the ongoing interaction. Furthermore, due to its causal connection to intentions, the output of the attention model remains valid for a prolonged but predictable amount of time: till the evoking intention is in place. The naïve attention model theory is offered as a novel theory on social attention that both incorporates existing evidence and identifies new directions in research.

Solving quantum trajectories for systems with linear Heisenberg-picture dynamics and Gaussian measurement noise

We study solutions to the quantum trajectory evolution of $N$-mode open quantum systems possessing a time-independent Hamiltonian, linear Heisenberg-picture dynamics, and Gaussian measurement noise. In terms of the mode annihilation and creation operators, a system will have linear Heisenberg-picture dynamics under two conditions. First, the Hamiltonian must be quadratic. Second, the Lindblad operators describing the coupling to the environment (including those corresponding to the measurement) must be linear. In cases where we can solve the $2N$-degree polynomials that arise in our calculations, we provide an analytical solution for initial states that are arbitrary (i.e. they are not required to have Gaussian Wigner functions). The solution takes the form of an evolution operator, with the measurement-result dependence captured in $2N$ stochastic integrals over these classical random signals. The solutions also allow the POVM, which generates the probabilities of obtaining measurement outcomes, to be determined. To illustrate our results, we solve some single-mode example systems, with the POVMs being of practical relevance to the inference of an initial state, via quantum state tomography. Our key tool is the representation of mixed states of quantum mechanical oscillators as state vectors rather than state matrices (albeit in a larger Hilbert space). Together with methods from Lie algebra, this allows a more straightforward manipulation of the exponential operators comprising the system evolution than is possible in the original Hilbert space.

I2E: A Cognitive Architecture Based on Emotions for Assistive Robotics Applications

Emotions and personality play an essential role in human behavior, their considerations, and decision-making. Humans infer emotions from several modals, merging them all. They are an interface between a subject’s internal and external means. This paper presents the design, implementation, and tests of the Inference of an Emotional State (I2E): a cognitive architecture based on emotions for assistive robotics applications, which uses, as inputs, emotions recognized previously by four affective modals who inferred the emotional state to an assistive robot. Unlike solutions that classify emotions, with a single sign, the architecture proposed in this article will merge four sources of information about emotions into one. For this inference to be closer to a human being, a Fuzzy System Mamdani was used to infer the user’s personalities, and a MultiLayer Perceptron (MLP) was used to infer the robot’s personality. The hypothesis tested in this work was based on the Mehrabian studies and in addition to three experts in psychologists. The I2E architecture proved to be quite efficient for identifying an emotion with various types of input.

AI-Empowered Maritime Internet of Things: A Parallel-Network-Driven Approach

As one of the key technologies for realizing a fully digitalized world, the Internet of Things (IoT) requires ubiquitous connections across both land and sea. However, due to lack of infrastructure such as optical fibers and base stations, maritime communications inevitably face a highly complex and heterogeneous environment, which greatly challenges the connection reliability and traffic steering efficiency for future service-oriented maritime IoT. With the recent burgeoning application of artificial intelligence (AI) in many fields, an AI-empowered autonomous network for maritime IoT is envisioned as a promising solution. However, AI typically involves training/learning processes, which require realistic data/environment in order to obtain valuable outcomes. To this end, this article proposes the parallel network, which can be regarded as the "digital twin" of the real network and is responsible for realizing four key functionalities: self-learning and optimizing, state inference and network cognition, event prediction and anomaly detection, and knowledge database and snapshots. We then explain how various AI methods can facilitate the operation of the parallel- network-driven maritime network. A case study is provided to demonstrate the idea. Research directions are also outlined for further studies.

Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling.

Natural mitochondrial DNA (mtDNA) mutations enable the inference of clonal relationships among cells. mtDNA can be profiled along with measures of cell state, but has not yet been combined with the massively parallel approaches needed to tackle the complexity of human tissue. Here, we introduce a high-throughput, droplet-based mitochondrial single-cell Assay for Transposase Accessible Chromatin with sequencing (mtscATAC-seq), a method that combines high-confidence mtDNA mutation calling in thousands of single cells with their concomitant high-quality accessible chromatin profile. This enables the inference of mtDNA heteroplasmy, clonal relationships, cell state, and accessible chromatin variation in individual cells. We reveal single-cell variation in heteroplasmy of a pathologic mtDNA variant, which we associate with intra-individual chromatin variability and clonal evolution. We clonally trace thousands of cells from cancers, linking epigenomic variability to subclonal evolution and infer cellular dynamics of differentiating hematopoietic cells in vitro and in vivo. Taken together, our approach enables the study of cellular population dynamics and clonal properties in vivo.

Sparse multi-output Gaussian processes for online medical time series prediction

BackgroundFor real-time monitoring of hospital patients, high-quality inference of patients’ health status using all information available from clinical covariates and lab test results is essential to enable successful medical interventions and improve patient outcomes. Developing a computational framework that can learn from observational large-scale electronic health records (EHRs) and make accurate real-time predictions is a critical step. In this work, we develop and explore a Bayesian nonparametric model based on multi-output Gaussian process (GP) regression for hospital patient monitoring.MethodsWe propose MedGP, a statistical framework that incorporates 24 clinical covariates and supports a rich reference data set from which relationships between observed covariates may be inferred and exploited for high-quality inference of patient state over time. To do this, we develop a highly structured sparse GP kernel to enable tractable computation over tens of thousands of time points while estimating correlations among clinical covariates, patients, and periodicity in patient observations. MedGP has a number of benefits over current methods, including (i) not requiring an alignment of the time series data, (ii) quantifying confidence regions in the predictions, (iii) exploiting a vast and rich database of patients, and (iv) inferring interpretable relationships among clinical covariates.ResultsWe evaluate and compare results from MedGP on the task of online prediction for three patient subgroups from two medical data sets across 8,043 patients. We find MedGP improves online prediction over baseline and state-of-the-art methods for nearly all covariates across different disease subgroups and hospitals.ConclusionsThe MedGP framework is robust and efficient in estimating the temporal dependencies from sparse and irregularly sampled medical time series data for online prediction. The publicly available code is at https://github.com/bee-hive/MedGP.