Internal State Research Articles

Effects of mindfulness-based interventions on symptoms and interoception in trauma-related disorders and exposure to traumatic events: Systematic review and meta-analysis

Interoception is defined as the sense of the internal state of the body. Dysfunctions in interoception are found in several mental disorders, including trauma-related conditions. Mindfulness-Based Interventions (MBIs) have been shown to influence interoceptive processes. Randomised controlled trials (RCTs) have investigated whether MBIs impact symptoms and interoception in patients with trauma-related disorders. We undertook a systematic review and meta-analysis to synthesize these data. We included RCTs with an MBI arm which enrolled adult patients with trauma related-disorders or exposure to a traumatic experience, and addressed changes in interoception and trauma-related symptoms. A random-effects multivariate meta-analytic model was performed to quantify group differences in score change from baseline to follow-up. Twelve studies were included in the systematic review, and eleven in the meta-analysis. Overall, MBIs showed small to moderate positive effects on both interoception and symptoms. Despite a high heterogeneity in results, sensitivity analyses confirmed the robustness of the findings. We conclude that the efficacy of MBIs on trauma-related symptoms and interoception is supported by randomised evidence. However, further research is needed to understand whether changes in interoception might underpin the effectiveness of MBIs in trauma-related disorders.

Incorporating uncertainty within dynamic interoceptive learning.

Interoception, the perception of the internal state of the body, has been shown to be closely linked to emotions and mental health. Of particular interest are interoceptive learning processes that capture associations between environmental cues and body signals as a basis for making homeostatically relevant predictions about the future. One method of measuring respiratory interoceptive learning that has shown promising results is the Breathing Learning Task (BLT). While the original BLT required binary predictions regarding the presence or absence of an upcoming inspiratory resistance, here we extended this paradigm to capture continuous measures of prediction (un)certainty. Sixteen healthy participants completed the continuous version of the BLT, where they were asked to predict the likelihood of breathing resistances on a continuous scale from 0.0 to 10.0. In order to explain participants' responses, a Rescorla-Wagner model of associative learning was combined with suitable observation models for continuous or binary predictions, respectively. For validation, we compared both models against corresponding null models and examined the correlation between observed and modeled predictions. The model was additionally extended to test whether learning rates differed according to stimuli valence. Finally, summary measures of prediction certainty as well as model estimates for learning rates were considered against interoceptive and mental health questionnaire measures. Our results demonstrated that the continuous model fits closely captured participant behavior using empirical data, and the binarised predictions showed excellent replicability compared to previously collected data. However, the model extension indicated that there were no significant differences between learning rates for negative (i.e. breathing resistance) and positive (i.e. no breathing resistance) stimuli. Finally, significant correlations were found between fatigue severity and both prediction certainty and learning rate, as well as between anxiety sensitivity and prediction certainty. These results demonstrate the utility of gathering enriched continuous prediction data in interoceptive learning tasks, and suggest that the updated BLT is a promising paradigm for future investigations into interoceptive learning and potential links to mental health.

A systematic evaluation of text mining methods for short texts: Mapping individuals' internal states from online posts.

Short texts generated by individuals in online environments can provide social and behavioral scientists with rich insights into these individuals' internal states. Trained manual coders can reliably interpret expressions of such internal states in text. However, manual coding imposes restrictions on the number of texts that can be analyzed, limiting our ability to extract insights from large-scale textual data. We evaluate the performance of several automatic text analysis methods in approximating trained human coders' evaluations across four coding tasks encompassing expressions of motives, norms, emotions, and stances. Our findings suggest that commonly used dictionaries, although performing well in identifying infrequent categories, generate false positives too frequently compared to other methods. We show that large language models trained on manually coded data yield the highest performance across all case studies. However, there are also instances where simpler methods show almost equal performance. Additionally, we evaluate the effectiveness of cutting-edge generative language models like GPT-4 in coding texts for internal states with the help of short instructions (so-called zero-shot classification). While promising, these models fall short of the performance of models trained on manually analyzed data. We discuss the strengths and weaknesses of various models and explore the trade-offs between model complexity and performance in different applications. Our work informs social and behavioral scientists of the challenges associated with text mining of large textual datasets, while providing best-practice recommendations.

Neural correlates of recalled sadness, joy, and fear states: a source reconstruction EEG study.

The capacity to understand the others' emotional states, particularly if negative (e.g. sadness or fear), underpins the empathic and social brain. Patients who cannot express their emotional states experience social isolation and loneliness, exacerbating distress. We investigated the feasibility of detecting non-invasive scalp-recorded electrophysiological signals that correspond to recalled emotional states of sadness, fear, and joy for potential classification. The neural activation patterns of 20 healthy and right-handed participants were studied using an electrophysiological technique. Analyses were focused on the N400 component of Event-related potentials (ERPs) recorded during silent recall of subjective emotional states; Standardized weighted Low-resolution Electro-magnetic Tomography (swLORETA) was employed for source reconstruction. The study classified individual patterns of brain activation linked to the recollection of three distinct emotional states into seven regions of interest (ROIs). Statistical analysis (ANOVA) of the individual magnitude values revealed the existence of a common emotional circuit, as well as distinct brain areas that were specifically active during recalled sad, happy and fearful states. In particular, the right temporal and left superior frontal areas were more active for sadness, the left limbic region for fear, and the right orbitofrontal cortex for happy affective states. In conclusion, this study successfully demonstrated the feasibility of detecting scalp-recorded electrophysiological signals corresponding to internal and subjective affective states. These findings contribute to our understanding of the emotional brain, and have potential applications for future BCI classification and identification of emotional states in LIS patients who may be unable to express their emotions, thus helping to alleviate social isolation and sense of loneliness.

Social Isolation, Brain Food Cue Processing, Eating Behaviors, and Mental Health Symptoms

Perceived social isolation is associated with negative health outcomes, including increased risk for altered eating behaviors, obesity, and psychological symptoms. However, the underlying neural mechanisms of these pathways are unknown. To investigate the association of perceived social isolation with brain reactivity to food cues, altered eating behaviors, obesity, and mental health symptoms. This cross-sectional, single-center study recruited healthy, premenopausal female participants from the Los Angeles, California, community from September 7, 2021, through February 27, 2023. Participants underwent functional magnetic resonance imaging while performing a food cue viewing task. The main outcomes included brain reactivity to food cues, body composition, self-reported eating behaviors (food cravings, reward-based eating, food addiction, and maladaptive eating behaviors), and mental health symptoms (anxiety, depression, positive and negative affect, and psychological resilience). The study included 93 participants (mean [SD] age, 25.38 [7.07] years). Participants with higher perceived social isolation reported higher fat mass percentage, lower diet quality, increased maladaptive eating behaviors (cravings, reward-based eating, uncontrolled eating, and food addiction), and poor mental health (anxiety, depression, and psychological resilience). In whole-brain comparisons, the higher social isolation group showed altered brain reactivity to food cues in regions of the default mode, executive control, and visual attention networks. Isolation-related neural changes in response to sweet foods correlated with various altered eating behaviors and psychological symptoms. These altered brain responses mediated the connection between social isolation and maladaptive eating behaviors (β for indirect effect, 0.111; 95% CI, 0.013-0.210; P = .03), increased body fat composition (β, -0.141; 95% CI, -0.260 to -0.021; P = .02), and diminished positive affect (β, -0.089; 95% CI, -0.188 to 0.011; P = .09). These findings suggest that social isolation is associated with altered neural reactivity to food cues within specific brain regions responsible for processing internal appetite-related states and compromised executive control and attentional bias and motivation toward external food cues. These neural responses toward specific foods were associated with an increased risk for higher body fat composition, worsened maladaptive eating behaviors, and compromised mental health. These findings underscore the need for holistic mind-body-directed interventions that may mitigate the adverse health consequences of social isolation.

Integrating full fan morphology and configuration in three-dimensional simulation of air-cooled proton exchange membrane fuel cell stack

The internal water and thermal state and performance of an air-cooled proton exchange membrane (PEM) fuel cell stack are strongly determined by the fan arrangement and operational characteristics. This study presents an innovative method to investigate the influence of fan on stack performance through integration of fan’s full morphology, including the fan blade, grill between the fan and stack, etc. into a three-dimensional (3D) multiphase stack model. The multiple reference frame (MRF) model is implemented to simulate the air flow field caused by the rotation of fan and the multiple transfers coupled with electrochemical reactions is considered in the 3D stack model in detail. Through this integrated model, the influence of the air supply mode, fan number and duty ratio on stack performance and the distribution characteristics are investigated in detail. It is found that the air flowing into each cell in the fan-cooled stack is highly non-uniform, which increases the variations of oxygen concentration, temperature, and voltage in the stack. In comparison with the blowing mode, the suction mode helps to promote the uniformity of air supply with a lower air flow velocity.

An optical tweezer array of ultracold polyatomic molecules.

Polyatomic molecules have rich structural features that make them uniquely suited to applications in quantum information science1-3, quantum simulation4-6, ultracold chemistry7 and searches for physics beyond the standard model8-10. However, a key challenge is fully controlling both the internal quantum state and the motional degrees of freedom of the molecules. Here we demonstrate the creation of an optical tweezer array of individual polyatomic molecules, CaOH, with quantum control of their internal quantum state. The complex quantum structure of CaOH results in a non-trivial dependence of the molecules' behaviour on the tweezer light wavelength. We control this interaction and directly and non-destructively image individual molecules in the tweezer array with a fidelity greater than 90%. The molecules are manipulated at the single internal quantum state level, thus demonstrating coherent state control in a tweezer array. The platform demonstrated here will enable a variety of experiments using individual polyatomic molecules with arbitrary spatial arrangement.

Strain Relaxation Characterization on Lithium‐ion Batteries at Different States of Charge and Charging Rates

The strain on the cell casing can serve as an indicator of the internal state of the cell. Monitoring strain during the charging and discharging processes aids in determining optimal charging and discharging operations, thereby maximizing the lifespan and performance of the battery. Herein, strain dynamic curves are obtained for lithium‐ion batteries at low, medium, and high charge and discharge rates by affixing strain gauges to individual cells. The investigation delves into parameters such as strain relaxation time, maximum strain, and residual strain at various charge rates and states of charge. The experimental findings reveal distinctive patterns, indicating that the strain curve during high‐rate charging resembles a second‐order function, exhibiting more pronounced fluctuations as the rate increases. This stands in stark contrast to the strain exponential decay observed during conventional medium and low‐rate charging. Notably, the strain residual resulting from high‐rate charging proves to be several times higher than that observed in low‐rate charging, hinting at potential differences in the dynamic distribution of lithium ions within batteries during high‐rate versus medium‐low‐rate charging modes.

Non-negative scaled edge-consensus of saturated networked systems via adaptive output-feedback control

This paper investigates fully distributed scaled non-negative edge-consensus problems of networked systems with actuator saturation and unmeasurable internal states. By using the adaptive control method, output-feedback-based low-gain technique, and graph theory, a new adaptive algorithm is designed to obtain scaled edge-consensus conditions, under which the difficulties caused by the constraints on edge states and controllers are overcome. There are three interesting characteristics that any global information of networks is not used in this paper’s algorithm and result, including the number of vertexes and edges; the feasible solutions of the scaled edge-consensus conditions exist and can be easily obtained; the convergence rate of the controller is adjustable. Furthermore, the designed algorithm is expanded in the cases without actuator saturation. Finally, three examples are given to verify the theoretical results.

Entanglement signatures for quantum synchronization with single-ion phonon laser

The entanglement properties of quantum synchronization, based on a single-ion phonon laser subjected to an external drive, have been studied. It is found that the maximum value of steady-state entanglement between the ion’s internal and external states occurs near the noiseless boundary from synchronization to unsynchronization, accompanied by noticeable oscillatory behaviors during the corresponding time evolution of entanglement. In addition, the later time dynamics of entanglement also indicates the occurrence of frequency entrainment, as evidenced by the strong consistency between the bending of the observed frequency and the emergence of Liouvillian exceptional points (LEPs) in the first two eigenvalues of the Liouvillian eigenspectrum. Moreover, the emergence of LEPs, which is intimately associated with frequency entrainment, should be widely observed in quantum synchronization and can be explored in LEPs-based applications.

Subconductance states in a semimicroscopic model for a tetrameric pore.

A physical model for a structured tetrameric pore is studied. The pore is modeled as a device composed of four subunits, each one exhibiting two possible states (open and closed). The pore is located within a membrane that separates two reservoirs with ionic solutions. All variables of the model follow physical dynamical equationsaccounting for the internal structure of the pore, derived from a single energy functional and supplemented with thermal noises. An extensive study of the resulting ionic intensity is performed for different values of the control parameters, mainly membrane potential and reservoir ion concentrations. Two possible physical devices are studied: voltage-gated (including a voltage sensor in each subunit) and non-voltage-gated pores. The ionic flux through the pore exhibits several distinct dynamical configurations, in particular subconductance states, which indicate very different dynamical internal states of the subunits. Such subconductance states become much easier to observe in sensorless pores. These results are compared with available experimental data on tetrameric K channels and analytical predictions.

Diverse habitats shape the movement ecology of a top marine predator, the white shark Carcharodon carcharias

AbstractAn animal's movement is influenced by a plethora of internal and external factors, leading to individual‐ and habitat‐specific movement characteristics. This plasticity is thought to allow individuals to exploit diverse environments efficiently. We tested whether the movement characteristics of white sharks Carcharodon carcharias differ across ontogeny and among habitats along the coast of Central California. In doing so, we elucidate how changes in internal state (physiological changes coinciding with body size) and external environments (differing seascapes and/or diel phases) shape the movement of this globally distributed predator. Twenty‐one white sharks, from small juveniles to large adults, were equipped with motion‐sensitive biologging tags at four contrasting seascapes: two islands, a headland, and an inshore cove. From multisensor biologging data, 20 metrics characterizing movement (i.e., depth use, vertical velocities, activity, turning rates, and bursting events) were derived and subjected to multivariate analyses. Movement characteristics were most different across seascapes, followed by ontogeny and diel phase. Juvenile sharks, which were only encountered at the cove, displayed the most distinct movement characteristics. Sharks at this seascape remained close to the shore traveling over smaller areas, shallower depth ranges, and with lower levels of tail beat frequencies, when corrected for size, than sub‐adult and adult sharks tagged elsewhere. Distinct tortuous daytime versus linear nighttime horizontal movements were recorded from sharks at island seascapes but not from those at the headland or inshore cove. At the offshore islands, the linear nighttime swimming patterns coincided with repeated dives to and from deeper water. The availability of prey and access to deeper water are likely drivers of the differences in movement characteristics described, with varying demographics of pinniped prey found at the subadult and adult aggregation areas and juvenile sharks being piscivorous and their habitat neither adjacent to pinniped haul out areas nor deeper water. This study demonstrates plasticity in the movements of a top predator, which adapts its routine to suit the habitat it forages within.

Uneven Usage Battery State of Health Estimation via Fractional-Order Equivalent Circuit Model and AutoML Fusion

To accurately predict the State of Health (SOH) of lithium-ion batteries under the continuously changing charging and discharging conditions in practical applications, this study proposes a hybrid modeling approach that integrates a Fractional Order Equivalent Circuit Model (F-ECM) with the AutoGluon automatic machine learning framework. By leveraging Electrochemical Impedance Spectroscopy (EIS) to capture battery frequency response characteristics, F-ECM accurately fits EIS data to extract detailed internal state parameters. The integration of AutoGluon automates the machine learning process, enhancing the precision of SOH predictions. Through testing and analysis on real battery datasets, this method has demonstrated superior prediction precision and computational efficiency compared to existing mainstream modeling approaches. Specifically, the hybrid method achieved a Root Mean Square Error (RMSE) of 2.12% and a Mean Absolute Error (MAE) of 1.67%. This study presents a highly accurate, interpretable, and adaptable predictive framework for lithium-ion battery health assessment, offering valuable insights for battery health management system development.

The visualization of autism: Filming children at the Maudsley Hospital, London, 1957-8.

This article examines three films made during the 1950s by Elwyn James Anthony at the psychotic clinic for children at the Maudsley Hospital that marked an important transition in the purpose and practice of visual documentation in a clinical setting: film as a research tool was transitioning from the recording of external signs as indicators of internal subjective states, to the capture of the visual flow of communication between subjects. It is a shift that had a particular impact on the emergent classification of autism, a modality not yet properly separated from the broader term of psychosis, as a non-relational condition whose visual capture demonstrated a void of inter-human communicational exchange. Film was significant not only as a recording apparatus, but as a method of cutting and crafting sequences of movements into brief repetitive motifs. The filmed behaviour of children remained opaque to interpretation, a 'finding' that facilitated the modelling of an emergent autism as subjects who were isolated, alienated and automaton-like, inhabiting a separate temporality. The article situates this 'second', affectless autism, within a broader context of post-war research into gestures as a language of the body, developed largely through an intellectual network of German émigré psychoanalysts who had fled to the US and UK in the 1930s.

The Role of Motivation in the Batter Learning of Students

Psychological research has shown that among various factors influencing learning and academic progress, students’ motivation plays a prominent and perhaps the most important role. Proper recognition and analysis of students’ motivation are crucial for the effectiveness and efficiency of teaching and learning activities. Motivation is a factor that stimulates and directs specific behavior, or it refers to the internal states of living beings that lead to guiding or sustaining their behavior towards a certain goal. It is, in essence, an answer to the question of what the basis of scientific learning is. Even psychologists consider motivation more important than intelligence, and teachers still need motivated and enthusiastic students to achieve successful and effective learning, and they demand such students. The aim of this article is to enhance the reader’s knowledge and understanding of the importance and influencing factors of motivation and to assist in applying this knowledge in the field of teaching and learning. To this end, some relevant scientific sources have been studied, and the content of this article has been gathered and summarized from them. This article begins with the concepts of motivation, incentives, learning motivation, and progress, emphasizing the importance of motivation in the learning process, and concludes with a brief description of techniques for creating motivation in students.

Learning stability of partially observed switched linear systems

This paper deals with learning stability of partially observed switched linear systems under arbitrary switching. Such systems are widely used to describe cyber–physical systems which arise by combining physical systems with digital components. In many real-world applications, the internal states cannot be observed directly. It is thus more realistic to conduct system analysis using the outputs of the system. Stability is one of the most frequent requirement for safety and robustness of cyber–physical systems. Existing methods for analyzing stability of switched linear systems often require the knowledge of the parameters and/or all the states of the underlying system. In this paper, we propose an algorithm for deciding stability of switched linear systems under arbitrary switching based purely on observed output data. The proposed algorithm essentially relies on an output-based Lyapunov stability framework and returns an estimate of the joint spectral radius (JSR). We also prove a probably approximately correct error bound on the quality of the estimate of the JSR from the perspective of statistical learning theory.

Mathematical Models of Migration Balance and Migration Attitudes in Armenia

The purpose of the study is to provide a quantitative explanation of the migration balance and migration attitudes in Armenian society. A linear regression model has been constructed to explain the dependence of the migration balance on variables at the macro level, including annual GDP growth, the internal and external political state of Armenia. Data from the Armenian Statistical Service on statistics of border movements and annual GDP growth were used. The foreign policy state is represented by a variable taking the values “Truce”, “War”, and “Intense War”. The internal political state is represented by a variable taking the values “Open conflict”, “Latent conflict”, “Balanced”, “Positive expectations”, and “High expectations”. Based on the model, an estimate of the migration balance for 2023 was calculated. According to a sociological survey conducted in November 2022 using the structural modeling method (Structural Equation Modeling), two models were built that explain migration attitudes in society through the socio-economic characteristics of the respondent and characteristics of his political culture, including: the presence of a labor migrant in the family, the age of the respondent, approval of the 2018 revolution in Armenia, the realization of expectations from the revolution, a sense of political competence, political subjectivity and patriotism. The results of the study can be used to forecast the migration balance and migration attitudes in the society, as well as strategic planning and management of migration processes in Armenia.

Dynamic Investigation of Auxin on Germination of Sesamum Indicum Seed by 1H Spectroscopy and Chemical Shift Selection Imaging

Ger mination of sesame seeds in acidic and indole-3-acetic acid (IAA) liquid mediums was investigated using single pulse 1H spectroscopy and chemical shift selection imaging. The spectra and images demonstrated high correlation and reliability. Chemical shift selection imaging allowed for an intuitive comprehension of the dynamic distribution of metabolites and structural changes during germination, leading to a clearer understanding of IAA’s regulation on sesame metabolites, when combined with 1H spectra. Results showed that exogenous hormones accelerated the decomposition of lipid macromolecules before rapid radicle growth, while IAA delayed sesame development at this stage. However, post-growth delay, the addition of IAA effectively accelerated germination. This study confirms that 1H spectroscopy and chemical shift selection imaging can interpret the metabolic response of plants to the change in their internal physiological state and help to determine the optimal time to add IAA.

Deterministic observability calculations for zero-dimensional models of lithium–sulfur batteries

Among the various energy storage technologies under development, the lithium‑sulfur (Li–S) battery has considerable promise due to its higher theoretical energy density, small environmental footprint, and low projected costs. One of the main challenges posed by Li–S is the need for a battery management system (BMS) that can accommodate the system's complex multi-step redox behaviours; conventional approaches for lithium-ion batteries do not transfer. Most existing approaches rely on equivalent circuit network models, but there is growing interest in ‘zero-dimensional’ electrochemical models which can potentially give insights into the relative polysulfide species concentrations present at any given time. To be useful for state estimation, a model must be ‘observable’: it must be possible to uniquely determine the internal state through observation of the system's behaviour over time. Previous studies have assessed observability using numerical methods, which is an approximation. This study derives an analytic expression for the observability criterion, which allows greater confidence in the results. The analytic observability criterion is then validated against a numerical comparator. A zero-dimensional model from the literature is translated into an ordinary differential equation (ODE) form to define the state variables matrix A, the output matrix C, and subsequently the observability matrix O. These are compared to simulated numerical equivalents. In addition, the sensitivity of the numerical process has been demonstrated. The results have the potential to offer greater confidence in conclusions around observability, which in turn gives greater confidence in the effects of any algorithms based on them.

Drama during story time supports preschoolers’ understanding of story character feeling states

Inference generation, an emerging skill in preschool-aged children, is critical for story comprehension and often requires instruction and practice to develop. Drama-based instruction (DBI) is a promising strategy for supporting preschool students’ inferencing skills, emotion understanding, and overall story comprehension. The current study examined the effects of a DBI story time intervention on preschool students’ recall of story character feeling states. As part of the larger intervention, 196 students (ages 3-5) were randomly assigned by classroom to participate in DBI or traditional story time. After story time, students completed a brief story retelling task (free and prompted recall of the story). Recordings of students’ story retellings were coded for embodied behavior (i.e., gesture, facial expression, vocal change, and body movement) specific to character feelings. Embodied behaviors supported student recall of story character feelings and emotion words. DBI intervention students used more embodiment, specifically gesture, when recalling story character feelings compared to their control group peers. Finally, hearing a DBI story supported students’ emotion word recall, particularly for 3- and 4-year-old students. The findings from this study offer evidence for incorporating drama-based teaching strategies into story time to promote students’ inferencing skills regarding story character internal states and subsequent story comprehension.