Plausible Model Research Articles

Depersonalization disorder as a systematic downregulation of interoceptive signals

Depersonalisation disorder (DPD) is a psychopathological condition characterised by a feeling of detachment from one's own body and surrounding, and it is understood as emerging from the downregulation of interoceptive afferents. However, the precise mechanisms that drive this ‘interoceptive silencing’ are yet to be clarified. Here we present a computational and neurobiologically plausible model of DPD within the active inference framework. Specifically, we describe DPD as arising from disrupted interoceptive processing at higher levels of the cortical hierarchy where the interoceptive and exteroceptive streams are integrated. We simulated the behaviour of an agent subjected to a situation of high interoceptive activation despite the absence of a perceivable threat in the external environment. The simulation showed how a similar condition, if perceived as inescapable, would result in a downregulation of interoceptive signals, whilst leaving the exteroceptive ones unaffected. Such interoceptive silencing would force the agent to over-rely on exteroceptive information and would ultimately lead to the DPD phenomenology. Finally, our simulation shows that repeated exposure to similar situations over time will lead the agent to increasingly disengage from bodily responses even in the face of a less triggering situation, explaining how a single episode of depersonalization can lead to chronic DPD.

A method for fine-scale vertical heterogeneity quantification from well data and its application to siliciclastic reservoirs of the UKCS

The character of a geological reservoir is critical to the behaviour of fluids that are extracted from it or injected into it, and thus can determine the economic viability of a subsurface project. There are a range of variables of interest when characterising a reservoir. One of these is the degree of heterogeneity, a function of the variability of rock properties resulting from various depositional and post-depositional processes. Heterogeneous reservoirs typically pose more challenges than homogenous reservoirs. The quantification of heterogeneity is therefore of importance for hydrocarbon exploration and production and will become increasingly important for evaluating “advantaged” hydrocarbons and the suitability of sites for subsurface storage. Numerous approaches have been used to quantify geological heterogeneity, but each only provides an estimate of the true heterogeneity and lacks the ability to quantify fine-scale variability. Here we propose an approach that can quantify the fine-scale vertical heterogeneity from well logs and demonstrate our approach on siliciclastic reservoirs of the United Kingdom Continental Shelf (UKCS). We analyse the Leman Sandstone Formation, Bunter Sandstone Formation, Maureen Sandstone Member, Andrew Sandstone Member, Balmoral Sandstone Member, and Forties Sandstone Member, which are important from both petroleum and energy transition perspectives. Our approach can be used to provide new insights into the differences between units and the geospatial nature of heterogeneity within each unit. This can be used to support evaluation/screening activity and to identify suitable analogues from which geologically plausible 3D reservoir models can be generated in a data-driven manner.

The mane factor: Compliance is associated with increased hair cortisol in the horse

Equine personality tests enable the selection of horses for roles based on their habitual behavioural responses to challenges, aiming to maximise performance and safeguard equine welfare. However, existing research has identified that behavioural responses to acute challenges do not correlate well with stress physiology in the horse. The aim of this study was to investigate whether chronic stress physiology is related to habitual compliance in horses. Hair cortisol concentration (HCC), a long-term biomarker of hypothalamic–pituitary–adrenal axis activity and stress, was assayed in mane hair from the withers, midpoint of the neck and poll of 24 riding school horses (66% male, mean age 13 ± 4 years). Three caregivers provided scores for Agreeableness, Neuroticism, Extroversion, Gregariousness Towards People and Gregariousness Towards Horses for all subjects using a validated equine personality questionnaire. A gamma GLMM was used to model HCC as a function of personality scores and potential confounders of HCC (age, sex, hair colour and sampling location). Individual horses were fitted as random intercepts in the model. Manual backward selection was used to identify the best-fitting plausible model. Mean HCC was 3.8 ± 1.2 pg/mg. The final model only retained Agreeableness and sampling location as explanatory variables of HCC. A positive association was found between Agreeableness (reflecting habitual compliance) and hair cortisol concentration (t = 2.7, p = 0.01). Basal cortisol levels are known to drive sensitivity to punishment in humans. Therefore, horses with higher basal cortisol may be more sensitive to the aversive cues associated with negative reinforcement and consequently be more compliant. However, it is also possible that habitually compliant behaviour leads to higher chronic stress levels through increased exposure to stressors, as more subtle expressions of discomfort are not recognised by handlers. This result has important implications for welfare in the context of horse-human interaction and warrants further investigation to clarify whether a causal link exists between habitual compliance and higher levels of chronic stress. There was also a weaker but significant impact of sampling location (t = 2.1, p = 0.03; higher HCC at the poll), highlighting the importance of carefully standardizing sampling location for HCC analysis.

Understanding 3D vision as a policy network.

It is often assumed that the brain builds 3D coordinate frames, in retinal coordinates (with binocular disparity giving the third dimension), head-centred, body-centred and world-centred coordinates. This paper questions that assumption and begins to sketch an alternative based on, essentially, a set of reflexes. A 'policy network' is a term used in reinforcement learning to describe the set of actions that are generated by an agent depending on its current state. This is an untypical starting point for describing 3D vision, but a policy network can serve as a useful representation both for the 3D layout of a scene and the location of the observer within it. It avoids 3D reconstruction of the type used in computer vision but is similar to recent representations for navigation generated through reinforcement learning. A policy network for saccades (pure rotations of the camera/eye) is a logical starting point for understanding (i) an ego-centric representation of space (e.g. Marr's (Marr 1982 Vision: a computational investigation into the human representation and processing of visual information) 2[Formula: see text]-D sketch) and (ii) a hierarchical, compositional representation for navigation. The potential neural implementation of policy networks is straightforward; a network with a large range of sensory and task-related inputs such as the cerebellum would be capable of implementing this input/output function. This is not the case for 3D coordinate transformations in the brain: no neurally implementable proposals have yet been put forward that could carry out a transformation of a visual scene from retinal to world-based coordinates. Hence, if the representation underlying 3D vision can be described as a policy network (in which the actions are either saccades or head translations), this would be a significant step towards a neurally plausible model of 3D vision. This article is part of the theme issue 'New approaches to 3D vision'.

Resolving the neural mechanism of core object recognition in space and time: A computational approach

The underlying mechanism of object recognition- a fundamental brain ability- has been investigated in various studies. However, balancing between the speed and accuracy of recognition is less explored. Most of the computational models of object recognition are not potentially able to explain the recognition time and, thus, only focus on the recognition accuracy because of two reasons: lack of a temporal representation mechanism for sensory processing and using non-biological classifiers for decision-making processing. Here, we proposed a hierarchical temporal model of object recognition using a spiking deep neural network coupled to a biologically plausible decision-making model for explaining both recognition time and accuracy. We showed that the response dynamics of the proposed model can resemble those of the brain. Firstly, in an object recognition task, the model can mimic human’s and monkey’s recognition time as well as accuracy. Secondly, the model can replicate different speed-accuracy trade-off regimes as observed in the literature. More importantly, we demonstrated that temporal representation of different abstraction levels (superordinate, midlevel, and subordinate) in the proposed model matched the brain representation dynamics observed in previous studies. We conclude that the accumulation of spikes, generated by a hierarchical feedforward spiking structure, to reach abound can well explain not even the dynamics of making a decision, but also the representations dynamics for different abstraction levels.

Interpersonal Behaviors Questionnaire (IBQ) applied to parenting of emerging adults: dimensional structure and criterion validity

BackgroundThe prolonged transitions to adulthood strengthen interest in parenting characteristics that can shape emerging adults’ development and progression to full-fledged adulthood. It also strengthens interest in instruments suitable for measuring such parenting characteristics. The current study contributes to this area of research by applying the Interpersonal Behavior Questionnaire (IBQ), to assess parenting behaviors that are pertinent for emerging adults’ development and wellbeing, and seeks evidence of its dimensional structure and criterion validity.MethodThe sample of the study consisted of 600 emerging adults (Mage = 24.94, SDage = 3.03, range 19–29 years; 52.3% women). The dimensionality of IBQ was assessed by testing a sequence of theoretically plausible models representing alternative factor score structures. Criterion validity was investigated by exploring the associations between the IBQ dimensions and basic psychological need satisfaction and frustration, negative affectivity, and thriving.ResultsThe findings provide evidence of a hypothesized six-factor structure, but advocate for the use of exploratory structural equation as it may more accurately reflect associations between the six dimensions. Findings also provide evidence of criterion validity.ConclusionsThe findings indicate that IBQ applied to parenting targets dimensions highly relevant for EAs’ flourishing or impoverished functioning. Findings also offer evidence of structure and criterion validity for the IBQ applied to emerging adults’ parent behaviors. As such, researchers may find IBQ attractive, as it is relatively concise yet also a holistic measure that captures the behaviors that both support and thwart an emerging adult’s need for autonomy, competence, and relatedness. Findings also shed light on the process of becoming an adult, the shift in parent–child relationships during this process, and emerging adults’ wellbeing.

Deep problems with neural network models of human vision.

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in predicting brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data.

The effect of socioeconomic status on cognitive and socioemotional processes in Latin American older adults

AbstractBackgroundSocioeconomic status (SES) negatively impacts cognitive and executive functioning in older adults, yet its effects on socioemotional abilities have not been studied in this population. Also, evidence on neurocognitive processes associated with aging primary comes from Western, educated, industrialized, rich, and democratic (WEIRD) populations, hindering the generalization of findings to those from upper‐middle‐ and low‐middle‐income countries, such as Latin America.MethodHere, we compared the performance of low‐ and high‐SES older adults from Argentina in cognitive state, executive functions, social cognition (emotion recognition and theory of mind), and counter‐empathic social emotions (envy and Schadenfreude; displeasure at others’ fortune and pleasure at others’ misfortune, respectively). Subsequently, we developed a path analysis to test the directionality of the relationship among those variables in a theoretically plausible model and tested the main paths via multiple regression analyses.ResultRelative to the high‐SES group, low‐SES older adults showed markedly poorer performance on all assessed domains. Convergent evidence from covariance, multiple regression, and path analyses suggested that SES impact on socioemotional processes was not primary but mediated by cognitive and executive decline.ConclusionThese findings offer the first characterization of SES impacts on cognitive and socioemotional processes in non‐WEIRD population and have relevant equity‐related implications for brain health.

Comparison of Three Mixed-Effects Models for Mass Movement Susceptibility Mapping Based on Incomplete Inventory in China

Generating an unbiased inventory of mass movements is challenging, particularly in a large region such as China. However, due to the enormous threat to human life and property caused by the increasing number of mass movements, it is imperative to develop a reliable nationwide mass movement susceptibility model to identify mass movement-prone regions and formulate appropriate disaster prevention strategies. In recent years, the mixed-effects models have shown their unique advantages in dealing with the biased mass movement inventory, yet there are no relevant studies to compare different mixed-effects models. This research compared three mixed-effects models to explore the most plausible and robust susceptibility mapping model, considering the inherently heterogeneously complete mass movement information. Based on a preliminary data analysis, eight critical factors influencing mass movements were selected as basis predictors: the slope, aspect, profile curvature, plan curvature, road density, river density, soil moisture, and lithology. Two additional factors, namely, the land use and geological environment division, representing the inventory bias were selected as random intercepts. Subsequently, three mixed-effects models—Statistical-based generalized linear mixed-effects model (GLMM), generalized additive mixed-effects model (GAMM), and machine learning-based tree-boosted mixed-effects model (TBMM)—were adopted. These models were used to evaluate the susceptibility of three distinct types of mass movements (i.e., 28,814 debris flows, 54,586 rockfalls and 108,432 landslides), respectively. The results were compared both from quantitative and qualitative perspectives. The results showed that TBMM performed best in all three cases with AUROCs (Area Under the Receiver Operating Characteristic curve) of cross-validation, spatial cross-validation, and predictions on simulated highly biased inventory, all exceeding 0.8. In addition, the spatial prediction patterns of TBMM were more in line with the natural geomorphological underlying process, indicating that TBMM can better reduce the impact of inventory bias than GLMM and GAMM. Finally, factor contribution analysis showed the key role of topographic factors in predicting the occurrence of mass movements, followed by road density and soil moisture. This study contributes to assessing China’s overall mass movement susceptibility situation and assisting policymakers in master planning for risk mitigation. Further, it demonstrates the tremendous potential of TBMM for mass movement susceptibility assessment, despite inherent biases in the inventory.

Retention Secured Nonlinear and Self-Rectifying Analog Charge Trap Memristor for Energy-Efficient Neuromorphic Hardware.

A memristive crossbar array (MCA) is an ideal platform for emerging memory and neuromorphic hardware due to its high bitwise density capability. A charge trap memristor (CTM) is an attractive candidate for the memristor cell of the MCA, because the embodied rectifying characteristic frees it from the sneak current issue. Although the potential of the CTM devices has been suggested, their practical viability needs to be further proved. Here, a Pt/Ta2 O5 /Nb2 O5- x /Al2 O3- y /Ti CTM stack exhibiting high retention and array-level uniformity is proposed, allowing a highly reliable selector-less MCA. It shows high self-rectifying and nonlinear current-voltage characteristics below 1µA of programming current with a continuous analog switching behavior. Also, its retention is longer than 105 s at 150°C, suggesting the device is highly stable for non-volatile analog applications. A plausible band diagram model is proposed based on the electronic spectroscopy results and conduction mechanism analysis. The self-rectifying and nonlinear characteristics allow reducing the on-chip training energy consumption by 71% for the MNIST dataset training task with an optimized programming scheme.

Detecting hypogenic karst features in the northeastern Delaware Basin, west Texas: Applications of Full Tensor Gradient (FTG) gravity data

Extensive karst development in carbonate and evaporate strata often occur in the Upper Permian formations of the Delaware Basin, west Texas. Subsurface karst feature detection is essential since they may cause severe drilling geohazards. In order to detect subsurface karst features, we present an interpretation approach that integrates high-resolution, high-precision airborne full tensor gradiometry (FTG) gravity data, structural interpretations, density and lithological variations derived from well logs, and published seismic refraction data, with regional cross sections to produce a geologically plausible 3D FTG model in the northeastern Delaware Basin. Both traditional gravity (Tz) and FTG (Tzz) density inversions were carried out on three shallow sedimentary layers of the Ochoan-lower Permian section, demonstrating the subtle density distribution in these strata. Detailed low-density karst boundaries were then interpretated by making use of the intrinsic properties of the gradient components, Txz and Tyz, which are: 1) edge detectors, because anomaly maxima and minima are located directly over source boundaries, and 2) indicators of the direction of density change across these boundaries. Source boundaries, or faults, mapped from these components were integrated and combined with regional geology and measured densities to verify existing faults, infer new faults, and predict probable locations and shapes of karst cavities in the southwestern and northeastern parts of the study area. Our results show that the central and southwestern parts of the study area may represent higher drilling risks due to an increase in shallow karst geohazards. This interpretation approach may be used to detect fault systems and geometries of low-density or high-density source geobodies worldwide.

Implementation of Kalman Filtering with Spiking Neural Networks.

A Kalman filter can be used to fill space-state reconstruction dynamics based on knowledge of a system and partial measurements. However, its performance relies on accurate modeling of the system dynamics and a proper characterization of the uncertainties, which can be hard to obtain in real-life scenarios. In this work, we explore how the values of a Kalman gain matrix can be estimated by using spiking neural networks through a combination of biologically plausible neuron models with spike-time-dependent plasticity learning algorithms. The performance of proposed neural architecture is verified with simulations of some representative nonlinear systems, which show promising results. This approach traces a path for its implementation in neuromorphic analog hardware that can learn and reconstruct partial and changing dynamics of a system without the massive power consumption that is typically needed in a Von Neumann-based computer architecture.

Theory of sleep/wake cycles affecting brain elastography

As elastography of the brain finds increasing clinical applications, fundamental questions remain about baseline viscoelastic properties of the brain in vivo. Furthermore, the underlying mechanisms of how and why elastographic measures can change over time are still not well understood. To study these issues, reverberant shear wave elastography using an optical coherence tomography scanner is implemented on a mouse model, both under awake conditions and in a sleep state where there are known changes in the glymphatic fluid flow system in the brain. We find that shear wave speed, a measure of stiffness, changes by approximately 12% between the two states, sleep versus awake, in the entire cortical brain imaging volume. Our microchannel flow model of biphasic (fluid plus solid) tissue provides a plausible rheological model based on the fractal branching vascular and perivascular system, plus a second parallel system representing the finer scale glymphatic fluid microchannels. By adjusting the glymphatic system fluid volume proportional to the known sleep/wake changes, we are able to approximately predict the measured shear wave speeds and their change with the state of the glymphatic system. The advantages of this model are that its main parameters are derived from anatomical measures and are linked to other major derivations of branching fluid structures including Murray’s Law. The implications for clinical studies are that elastography of the brain is strongly influenced by the regulation or dysregulation of the vascular, perivascular, and glymphatic systems.

Discretely Charged Dark Matter in Inflation Models Based on Holographic Space-Time

The holographic space-time (HST) model of inflation has a potential explanation for dark matter as tiny primordial black holes. Motivated by a recent paper of Barrau, we propose a version of this model where some of the inflationary black holes (IBHs), whose decay gives rise to the Hot Big Bang, carry the smallest value of a discrete symmetry charge. The fraction f of IBHs carrying this charge is difficult to estimate from first principles, but we determine it by requiring that the crossover between radiation and matter domination occurs at the correct temperature Teq∼1eV=10−28MP. The fraction is small, f∼2×10−9, so we believe this gives an extremely plausible model of dark matter.

The antimicrobial, antibiofilm, and wound healing properties of ethyl acetate crude extract of an endophytic fungus Paecilomyces sp. (AUMC 15510) in earthworm model

The endophytic fungus Paecilomyces sp. (AUMC 15510) was isolated from healthy stem samples of the Egyptian medicinal plant Cornulaca monacantha. We used GC–MS and HPLC analysis to identify the bioactive constituents of ethyl acetate crude extract of Paecilomyces sp. (PsEAE). Six human microbial pathogens have been selected to evaluate the antimicrobial activity of PsEAE. Our data showed that the extract has significant antimicrobial activity against all tested pathogens. However, the best inhibitory effect was observed against Bacillus subtilis ATCC 6633 and Pseudomonas aeruginosa ATCC 90274 with a minimum inhibitory concentration (MIC) of 3.9 μg/ml and minimum bactericidal concentration (MBC) of 15.6 μg/ml, for both pathogens. Also, PsEAE exerts a significant inhibition on the biofilm formation of the previously mentioned pathogenic strains. In addition, we evaluated the wound healing efficiency of PsEAE on earthworms (Lumbricus castaneus) as a feasible and plausible model that mimics human skin. Interestingly, PsEAE exhibited a promising wound healing activity and enhanced wound closure. In conclusion, Paecilomyces sp. (AUMC 15510) could be a sustainable source of antimicrobial agents and a potential therapeutic target for wound management.

"Descriptive Risk-Averse Bayesian Decision-Making," a Model for Complex Biological Motion Perception in the Human Dorsal Pathway.

Biological motion perception is integral not only to survival but also to the social life of human beings. Identifying the underlying mechanisms and their associated neurobiological substrates has been a matter of investigation and debate for some time. Although, in general, it is believed that the integration of local motion and dynamic form cues in the brain empowers the visual system to perceive/recognize biological motion stimuli, some recent studies have indicated the importance of dynamic form cues in such a process. Inspired by the previous neurophysiologically plausible biological motion perception models, a new descriptive risk-averse Bayesian simulation model, capable of discerning a ball's direction from a set of complex biological motion soccer kick stimuli, is proposed. The model represents only the dorsal pathway as a motion information processing section of the visual system according to the two-stream theory. The stimuli used have been obtained from a previous psychophysical study on athletes in our lab. Furthermore, the acquired psychophysical data from that study have been used to re-enact human behavior using our simulation model. By adjusting the model parameters, the psychometric function of athlete subjects has been mimicked. A correlation analysis between human and simulation data shows a significant and robust correlation between angular thresholds and slopes of the psychometric functions of both groups. Although it is established that the visual system optimally integrates all available information in the decision-making process, the results conform to the speculations favoring motion cue importance over dynamic form by testing the limits in which biological motion perception only depends on motion information processing.

Adaptation measures under the impacts of climate and land-use/land-cover changes using HSPF model simulation: Application to Gongola river basin, Nigeria

Recently, there is an upsurge in flood emergencies in Nigeria, in which their frequencies and impacts are expected to exacerbate in the future due to land-use/land cover (LULC) and climate change stressors. The separate and combined forces of these stressors on the Gongola river basin is feebly understood and the probable future impacts are not clear. Accordingly, this study uses a process-based watershed modelling approach – the Hydrological Simulation Program FORTRAN (HSPF) (i) to understand the basin's current and future hydrological fluxes and (ii) to quantify the effectiveness of five management options as adaptation measures for the impacts of the stressors. The ensemble means of the three models derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are employed for generating future climate scenarios, considering three distinct radiative forcing peculiar to the study area. Also, the historical and future LULC (developed from the hybrid of Cellular Automata and Markov Chain model) are used to produce the LULC scenarios for the basin. The effective calibration, uncertainty and sensitivity analyses are used for optimising the parameters of the model and the validated result implies a plausible model with efficiency of up to 75 %. Consequently, the results of individual impacts of the stressors yield amplification of the peak flows, with more profound impacts from climate stressor than the LULC. Therefore, the climate impact may trigger a marked peak discharge that is 48 % higher as compared to the historical peak flows which are equivalent to 10,000-year flood event. Whilst the combine impacts may further amplify this value by 27 % depending on the scenario. The proposed management interventions such as planned reforestation and reservoir at Dindima should attenuate the disastrous peak discharges by almost 36 %. Furthermore, the land management option should promote the carbon-sequestering project of the Paris agreement ratified by Nigeria. While the reservoir would serve secondary functions of energy production; employment opportunities, aside other social aspects. These measures are therefore expected to mitigate feasibly the negative impacts anticipated from the stressors and the approach can be employed in other river basins in Africa confronted with similar challenges.

Brain stimulation reveals distinct motives underlying reciprocal punishment and reward.

Reciprocal fairness, in the form of punishment and reward, is at the core of human societal order. Its underlying neural mechanisms are, however, not fully understood. We systemize suggestive evidence regarding the involvement of the right dorsolateral prefrontal cortex (rDLPFC) and medial prefrontal cortex (mPFC) in reciprocal fairness in three cognitive mechanisms (cognitive control, domain-general and self-reference). We test them and provide novel insights in a comprehensive behavioural experiment with non-invasive brain stimulation where participants can punish greedy actions and reward generous actions. Brain stimulation of either brain area decreases reward and punishment when reciprocation is costly but unexpectedly increases reward when it is non-costly. None of the hypothesized mechanisms fully accounts for the observed behaviour, and the asymmetric involvement of the investigated brain areas in punishment and reward suggests that different psychological mechanisms are underlying punishing selfishness and rewarding generosity. We propose that, for reciprocal punishment, the rDLPFC and the mPFC process self-relevant information, in terms of both personal cost and personal involvement; for reciprocal reward, these brain regions are involved in controlling selfish and pure reciprocity motives, while simultaneously promoting the enforcement of fairness norms. These insights bear importance for endeavours to build biologically plausible models of human behaviour.

A guideline for spatio‐temporal consistency in water quality modelling in rural areas

A guideline for spatio‐temporal consistency in water quality modelling in rural areas

GeoSim 2022 Workshop Report: The 5th ACM SIGSPATIAL International Workshop on Geospatial Simulation

Geospatial simulation is an effective tool to experience physical and/or cyber space (e.g., metaverse). New experiences gained from geospatial simulations can bring significant benefits including situation awareness, insight into environments, and entertainment. To take advantage of such simulations, it is crucial to advance methodology of modeling and simulation, develop plausible models and apply them to domains, leveraging big data and evolving technologies.