Uncertainty Estimation Research Articles

Impact of internal standard selection on measurement results for long chain fatty acids in blood

IntroductionInternal standards correct for measurement variation due to sample loss. Isotope labeled analytes are ideal internal standards for the measurement of fatty acids in human plasma but are not always readily available. For this reason, quantification of multiple analytes at once is most often done using only a single or few internal standards. The magnitude of the impact this has on method accuracy and precision is not well studied for gas chromatography-mass spectrometry systems. ObjectiveThis study aims to estimate bias and changes in uncertainty associated with using alternative fatty acid isotopologue internal standards for the estimation of similar or dissimilar long chain fatty acids. MethodUsing a previously reported method for the quantification of 27 fatty acids in human plasma using 18 internal standards we obtained estimates of bias and uncertainty at up to three levels of fatty acid concentration. ResultsWith some notable exceptions, method accuracy remained relatively stable when using an alternative internal standard (Median Relative Absolute Percent Bias: 1.76%, Median Spike-Recovery Absolute Percent Bias: 8.82%), with larger changes in method precision (Median Increase in Variance: 141%). Additionally, the degree of difference between analyte and internal standard structure was related to the magnitude of bias and uncertainty of the measurement. ConclusionThe data presented here show that the choice of internal standard used to estimate fatty acid concentration can affect the accuracy and reliability of measurement results and, therefore, needs to be assessed carefully when developing analytical methods for the measurement of fatty acid profiles.Disclaimer: The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention/the Agency for Toxic Substances and Disease Registry. Use of trade names is for identification only and does not imply endorsement by the Centers for Disease Control and Prevention, the Public Health Service, and the US Department of Health and Human Services.

Aerosol‐Calibrated Matched Filter Method for Retrievals of Methane Point Source Emissions Over the Los Angeles Basin

AbstractMethane, with a global warming potential roughly 86 times greater than carbon dioxide over a 20‐year timeframe, plays a crucial role in global warming. Remote sensing retrieval is a pivotal methodology for identifying methane emission sources, with accuracy influenced largely by surface and atmospheric properties, including aerosols. In this study, we propose an Aerosol‐Calibrated Matched Filter (ACMF) algorithm to improve the traditional Matched Filter (MF) method. Our new approach incorporates an aerosol scattering correction factor to reduce the aerosol‐induced bias on methane retrievals. Validating our algorithm through simulated spectra, we demonstrate that considering the aerosol scattering effect significantly reduces retrieval errors compared to MF methods by an average of approximately 90%. We apply our newly developed algorithm to hyperspectral data obtained from the Airborne Visible/Infrared Imaging Spectrometer—Next Generation in the Los Angeles Basin and focus on 11 plumes identified through case studies. Our results reveal that ACMF estimates of emission rates and inversion uncertainties exhibit an average reduction of approximately 4% compared to corresponding MF results, with deviation increasing with aerosol optical depth (AOD).

КЛИМАТИЧЕСКИЕ ПРОГНОЗЫ. ЧАСТЬ II. ВЕРОЯТНОСТНЫЕ ПОДХОДЫ

The main advantages of ensemble prediction systems and probabilistic climate forecasts as compared to deterministic ones are considered. Quality assessment has been performed for retrospective probabilistic forecasts of air temperature and precipitation on subseasonal timescales (the forecasts that were obtained with a new version of the SLAV072L96 global semi-Lagrangian atmospheric model developed in the Hydrometcenter of Russia and the Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences). Skill scores of probabilistic forecasts indicate the presence of a useful signal in the context of the forecasts of two extreme gradations of air temperature and precipitation at weekly and monthly integration intervals. It is concluded that the use of probabilistic approaches makes it possible to expand the time interval of the “usefulness” of forecasts from a week to a month, as well as to obtain estimates of uncertainty of a forecast and its potential economic value. The results of the study are expected to be used in the operational practice of the Hydrometcenter of Russia/North Eurasia Climate Centre, as well as in the preparation of consensus forecasts during sessions of regional climate forums.

CORTEX experiments, Part III: Experimental determination of the zero power transfer function of AKR-2 with reliable uncertainties

The transfer function is an important characteristic quantity of a nuclear reactor, since it contains the kinetic parameters. It expresses the response of a nuclear reactor to a disturbance of a certain frequency. If it is determined experimentally, it can be used to draw conclusions about the kinetic parameters. This article presents results of measurements of the zero power transfer function of the AKR-2 reactor at TU Dresden together with new data analysis methods. These measurements are compared to the theoretical zero power transfer function with kinetic parameters obtained via Monte Carlo simulations with MCNP and Serpent. To this end, advanced data analysis techniques based on a bootstrapping algorithms are employed. These techniques suppress the signal outside multiples of the fundamental frequency and additionally allow to obtain the full probability distribution of a peak in the frequency domain. This allowed for a reliable estimation of the mean value and uncertainty estimates of measured data of the zero power transfer function and the quantification of deviations between the experiments and the computations. It also made it possible to determine the phase of the zero power transfer function of AKR-2 for the first time. The experiments and computations are in agreement within the estimated uncertainties.

Modeling residual-vehicle effects on uncertainty estimation of the connected vehicle penetration rate

In the transition to full deployment of connected vehicles (CVs), the CV penetration rate plays a key role in bridging the gap between partial and complete traffic information. Several innovative methods have been proposed to estimate the CV penetration rate using only CV data. However, these methods, as point estimators, may lead to biased estimations or suboptimal solutions when applied directly in modeling or system optimization. To avoid these problems, the uncertainty and variability in the CV penetration rate must be considered. Recently, a probabilistic penetration rate (PPR) model was developed for estimating such uncertainties. The key model input is a constrained queue length distribution composed exclusively of queues formed by red signals in undersaturation conditions with no residual vehicles. However, in real-world scenarios, due to random arrivals, residual vehicles are commonly carried over from one cycle to another in temporary overflow cycles in undersaturation conditions, which seriously restricts the applicability of the PPR model. To address this limitation, this paper proposes a Markov-constrained queue length (MCQL) model that can model the complex effects of residual vehicles on the CV penetration rate uncertainty. A constrained queue with residual vehicles is decomposed into four vehicle groups: observable constrained residual vehicles, unobservable constrained residual vehicles, unconstrained residual vehicles, and new arrivals. Although the first vehicle group is observable in the former cycle, the focus of this work is to model the residual vehicles from the second and third vehicle groups in combination with the new arrivals. The MCQL model includes four sub-models, namely, the residual-vehicle model, convolutional constrained queue model, constrained residual queue model, and observable residual queue model, to isolate and derive the distribution of the constrained vehicle set formed by the three latter vehicle groups. This distribution is then substituted into the PPR model to estimate the uncertainty. Comprehensive VISSIM simulations and applications to real-world datasets demonstrate that the proposed MCQL model can accurately model the residual-vehicle effect and estimate the uncertainty. Thus, the applicability of the PPR model is truly extended to real-world settings, regardless of the presence of residual vehicles. A simple stochastic CV-based adaptive signal control example illustrates the potential of the proposed model in real-world applications.

Probabilistic Attention Based on Gaussian Processes for Deep Multiple Instance Learning.

Multiple instance learning (MIL) is a weakly supervised learning paradigm that is becoming increasingly popular because it requires less labeling effort than fully supervised methods. This is especially interesting for areas where the creation of large annotated datasets remains challenging, as in medicine. Although recent deep learning MIL approaches have obtained state-of-the-art results, they are fully deterministic and do not provide uncertainty estimations for the predictions. In this work, we introduce the attention Gaussian process (AGP) model, a novel probabilistic attention mechanism based on Gaussian processes (GPs) for deep MIL. AGP provides accurate bag-level predictions as well as instance-level explainability and can be trained end-to-end. Moreover, its probabilistic nature guarantees robustness to overfit on small datasets and uncertainty estimations for the predictions. The latter is especially important in medical applications, where decisions have a direct impact on the patient's health. The proposed model is validated experimentally as follows. First, its behavior is illustrated in two synthetic MIL experiments based on the well-known MNIST and CIFAR-10 datasets, respectively. Then, it is evaluated in three different real-world cancer detection experiments. AGP outperforms state-of-the-art MIL approaches, including deterministic deep learning ones. It shows a strong performance even on a small dataset with less than 100 labels and generalizes better than competing methods on an external test set. Moreover, we experimentally show that predictive uncertainty correlates with the risk of wrong predictions, and therefore it is a good indicator of reliability in practice. Our code is publicly available.

Global assessment of linear trend and seasonal variations of GNSS-IR sea level retrievals with nearby tide gauges

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) sea level retrievals and tide gauges at 40 globally distributed stations spanning from about 4.5 to 18 years are compared on a site-by-site basis, in terms of noise background, rate and seasonal variations by using the weighted least squares estimation (LSE) along with the Maximum likelihood estimation (MLE). The result shows that monthly GNSS-IR data agree well with tide gauges for most stations except the site Tuktoyaktuk, Canada. The mean correlation is 0.95 and the mean root mean square difference is 2.9 cm, respectively. The discrepancies of rate and seasonal amplitude estimates are within ± 1 cm for most stations. We confirm both the two sea level data exhibit temporal correlation, which has a great effect on the rate uncertainty estimates. Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) favor Matérn and first-order autoregressive (AR1) the preferred stochastic model for the daily and monthly mean sea level time series, respectively. Owing to the data span dependence for the rate uncertainty estimates, to get an accuracy of sub-mm/yr in linear rate using the weighted LSE, at least 30 years of data (depends on data quality) is required. We recommend using long time series and a proper stochastic model for the rate estimation of sea level.

140-year daily ensemble streamflow reconstructions over 661 catchments in France

Abstract. The recent development of FYRE (French hYdroclimate REanalysis) Climate, a high-resolution ensemble daily reanalysis of precipitation and temperature covering the 1871–2012 period and the whole of France, offers the opportunity to derive streamflow series over the country from 1871 onwards. The FYRE Climate dataset has been used as input for hydrological modelling over a large sample of 661 near-natural French catchments using the GR6J (Génie Rural à 6 Paramètres Journaliers) lumped conceptual model. This approach led to the creation of the 25-member hydrological reconstructions, HydRE (Hydrological REconstruction), spanning the 1871–2012 period. Two sources of uncertainties have been taken into account: (1) the climate uncertainty using forcings from all 25 ensemble members provided by FYRE Climate and (2) the streamflow measurement error by perturbing observations used during the calibration. Further, the hydrological model error based on the relative discrepancies between observed and simulated streamflow has been added to derive the HydREM (Hydrological REconstruction with Model error) streamflow reconstructions. These two reconstructions are compared to other hydrological reconstructions with different meteorological inputs, hydrological reconstructions from a machine learning algorithm, and independent and dependent observations. Overall, the results show the added value of the HydRE and HydREM reconstructions in terms of quality, uncertainty estimation, and representation of extremes, therefore allowing us to better understand the variability in past hydrology over France.

Novel scheme for uncertainty and disturbance estimation to control of spacecraft system with input constraint

In this paper, a novel scheme is presented to estimate the uncertainty and disturbance of the spacecraft system by a compensatory vector calculated online from the information of measured angular velocities. The compensatory vector is used in the constrained attitude control law to compensate for the perturbations of the spacecraft system. The attitude controller considering the input limitations of reaction wheels is developed by solving an optimization problem through the Karush-Kuhn-Tucker (KKT) theorem. By using noisy angular velocities in the observer design, the stochastic stability of the closed loop system under the constrained multivariable controller is demonstrated. In the results, at first, the open-loop performance of the uncertainty and disturbance observer is evaluated through computer simulations. Subsequently, the performance of the proposed controller in both constrained and unconstrained versions is evaluated. Finally, the proposed controller performance in compensating for uncertainties and disturbances is compared with the adaptive backstepping controller reported in the literature. The comparative results of two controllers show the superior performance for the proposed constrained controller in the presence of disturbances, uncertainties and input constraints. Furthermore, the proposed control system could attenuate the effect of measurement noises of angular velocities by weighting the compensatory vector in the observer design.

Adversarial EM for variational deep learning: Application to semi-supervised image quality enhancement in low-dose PET and low-dose CT

In positron emission tomography (PET) and X-ray computed tomography (CT), reducing radiation dose can cause significant degradation in image quality. For image quality enhancement in low-dose PET and CT, we propose a novel theoretical adversarial and variational deep neural network (DNN) framework relying on expectation maximization (EM) based learning, termed adversarial EM (AdvEM). AdvEM proposes an encoder–decoder architecture with a multiscale latent space, and generalized-Gaussian models enabling datum-specific robust statistical modeling in latent space and image space. The model robustness is further enhanced by including adversarial learning in the training protocol. Unlike typical variational-DNN learning, AdvEM proposes latent-space sampling from the posterior distribution, and uses a Metropolis–Hastings scheme. Unlike existing schemes for PET or CT image enhancement which train using pairs of low-dose images with their corresponding normal-dose versions, we propose a semi-supervised AdvEM (ssAdvEM) framework that enables learning using a small number of normal-dose images. AdvEM and ssAdvEM enable per-pixel uncertainty estimates for their outputs. Empirical analyses on real-world PET and CT data involving many baselines, out-of-distribution data, and ablation studies show the benefits of the proposed framework.

Using joint species distribution modelling to predict distributions of seafloor taxa and identify vulnerable marine ecosystems in New Zealand waters

Effective ecosystem-based management of bottom-contacting fisheries requires understanding of how disturbances from fishing affect seafloor fauna over a wide range of spatial and temporal scales. Spatial predictions of abundance for 67 taxa were developed, using an extensive dataset of faunal abundances collected using a towed camera system and spatially explicit predictor variables including bottom-trawl fishing effort, using a Joint Species Distribution Model (JSDM). The model fit metrics varied by taxon: the mean tenfold cross-validated AUC score was 0.70 ± 0.1 (standard deviation) for presence–absence and an R2 of 0.11 ± 0.1 (standard deviation) for abundance models. Spatial predictions of probability of occurrence and abundance (individuals per km2) varied by taxon, but there were key areas of overlap, with highest predicted taxon richness in areas of the continental shelf break and slope. The resulting joint predictions represent significant advances on previous predictions because they are of abundance, allow the exploration of co-occurrence patterns and provide credible estimates of taxon richness (including for rare species that are often not included in more commonly used single-species distribution modelling). Habitat-forming taxa considered to be Vulnerable Marine Ecosystem (VME) indicators (those taxa that are physically or functionally fragile to anthropogenic impacts) were identified in the dataset. Spatial estimates of likely VME distribution (as well as associated estimates of uncertainty) were predicted for the study area. Identifying areas most likely to represent a VME (rather than simply VME indicator taxa) provides much needed quantitative estimates of vulnerable habitats, and facilitates an evidence-based approach to managing potential impacts of bottom-trawling.

Self‐training with Bayesian neural networks and spatial priors for unsupervised domain adaptation in crack segmentation

AbstractThis study proposes a novel self‐training framework for unsupervised domain adaptation in the segmentation of concrete wall cracks using accumulated crack data. The proposed method incorporates Bayesian neural networks for uncertainty estimation of pseudo‐labels, and spatial priors of cracks for screening noisy labels. Experiments demonstrate that the proposed approach achieves significant improvements in F1 score. Comparing the F1 scores, Bayesian DeepLabv3+ and Bayesian U‐Net showed performance improvements of 0.0588 and 0.1501, respectively, after domain adaptation. Furthermore, the integration of Stable Diffusion for few‐shot image generation enhances domain adaptation performance by 0.0332. The proposed framework enables high‐precision crack segmentation with as few as 100 target images, which can be easily obtained at the site, reducing the cost of model deployment in infrastructure maintenance. The study also investigates the optimal number of iterations for domain adaptation based on the uncertainty score, providing insights for practical implementation. The proposed method contributes to the development of efficient and automated structural health monitoring using AI.

Uncertainty in REDD+ carbon accounting: a survey of experts involved in REDD+ reporting

BackgroundReducing Emissions from Deforestation and forest Degradation (REDD+) is a program established under the United Nations Framework Convention on Climate Change (UNFCCC) to reduce carbon emissions from forests in developing countries. REDD+ uses an incentive-based approach whereby participating countries are paid to reduce forest carbon loss and increase carbon storage. Country-level carbon accounting is challenging, and estimates of uncertainty in emission reductions are increasingly required in REDD+ reports. This requirement is hard to meet if countries lack the necessary resources, tools, and capabilities. Some REDD+ programs adjust their payments for the uncertainty reported, which presents a perverse incentive because uncertainties are larger if more sources of uncertainty are reported. We surveyed people involved in REDD+ reporting to assess current capacities and barriers to improving estimates of uncertainty.ResultsRepresentatives from 27 countries (44% of REDD+ countries at the time of survey implementation) responded to the survey. Nearly all respondents thought it important to include uncertainty in REDD+ reports, but most felt that the uncertainty reporting by their countries was inadequate. Our independent assessment of reports by these countries to the UNFCCC supported this opinion: Most countries reported uncertainty in activity data (91%) but not in emission factors (4–14%). Few countries use more advanced approaches to estimate uncertainty, such as Monte Carlo and Bayesian techniques, and many respondents indicated that they lack expertise, knowledge, or technical assistance. Other barriers include lack of financial resources and appropriate data. Despite these limitations, nearly all respondents indicated a strong desire to improve estimates of uncertainty in REDD+ reports.ConclusionsThe survey indicated that people involved in REDD+ reporting think it highly important to improve estimates of uncertainty in forest carbon accounting. To meet this challenge, it is essential to understand the obstacles countries face in quantifying uncertainty so we can identify where best to allocate efforts and funds. Investments in training and resources are clearly needed to better quantify uncertainty and would likely have successful outcomes given the strong desire for improvement. Tracking the efficacy of programs implemented to improve estimates of uncertainty would be useful for making further refinements.

The Road to Safety: A Review of Uncertainty and Applications to Autonomous Driving Perception.

Deep learning approaches have been gaining importance in several applications. However, the widespread use of these methods in safety-critical domains, such as Autonomous Driving, is still dependent on their reliability and trustworthiness. The goal of this paper is to provide a review of deep learning-based uncertainty methods and their applications to support perception tasks for Autonomous Driving. We detail significant Uncertainty Quantification and calibration methods, and their contributions and limitations, as well as important metrics and concepts. We present an overview of the state of the art of out-of-distribution detection and active learning, where uncertainty estimates are commonly applied. We show how these methods have been applied in the automotive context, providing a comprehensive analysis of reliable AI for Autonomous Driving. Finally, challenges and opportunities for future work are discussed for each topic.

Keeping an Eye Out for Change: Anxiety Disrupts Adaptive Resolution of Policy Uncertainty

BackgroundHuman learning unfolds under uncertainty. Uncertainty is heterogeneous with different forms exerting distinct influences on learning. While one can be uncertain about what to do to maximize rewarding outcomes, known as policy uncertainty, one can also be uncertain about general world knowledge, known as epistemic uncertainty (EU). In complex and naturalistic environments such as the social world, adaptive learning may hinge on striking a balance between attending to and resolving each type of uncertainty. Prior work illustrates that people with anxiety—those with increased threat and uncertainty sensitivity—learn less from aversive outcomes, particularly as outcomes become more uncertain. How does a learner adaptively trade-off between attending to these distinct sources of uncertainty to successfully learn about their social environment? MethodsWe developed a novel eye-tracking method to capture highly granular estimates of policy uncertainty and EU based on gaze patterns and pupil diameter (a physiological estimate of arousal). ResultsThese empirically derived uncertainty measures revealed that humans (N = 94) flexibly switched between resolving policy uncertainty and EU to adaptively learn about which individuals can be trusted and which should be avoided. However, those with increased anxiety (n = 49) did not flexibly switch between resolving policy uncertainty and EU and instead expressed less uncertainty overall. ConclusionsCombining modeling and eye-tracking techniques, we show that altered learning in people with anxiety emerged from an insensitivity to policy uncertainty and rigid choice policies, leading to maladaptive behaviors with untrustworthy people.

TUnA: an uncertainty-aware transformer model for sequence-based protein-protein interaction prediction.

Protein-protein interactions (PPIs) are important for many biological processes, but predicting them from sequence data remains challenging. Existing deep learning models often cannot generalize to proteins not present in the training set and do not provide uncertainty estimates for their predictions. To address these limitations, we present TUnA, a Transformer-based uncertainty-aware model for PPI prediction. TUnA uses ESM-2 embeddings with Transformer encoders and incorporates a Spectral-normalized Neural Gaussian Process. TUnA achieves state-of-the-art performance and, importantly, evaluates uncertainty for unseen sequences. We demonstrate that TUnA's uncertainty estimates can effectively identify the most reliable predictions, significantly reducing false positives. This capability is crucial in bridging the gap between computational predictions and experimental validation.

Estimating Uncertainty of Geographic Atrophy Segmentations with Bayesian Deep Learning

To apply methods for quantifying uncertainty of deep learning segmentation of geographic atrophy (GA). Retrospective analysis of OCT images and model comparison. One hundred twenty-six eyes from 87 participants with GA in the SWAGGER cohort of the Nonexudative Age-Related Macular Degeneration Imaged with Swept-Source OCT (SS-OCT) study. The manual segmentations of GA lesions were conducted on structural subretinal pigment epithelium en face images from the SS-OCT images. Models were developed for 2 approximate Bayesian deep learning techniques, Monte Carlo dropout and ensemble, to assess the uncertainty of GA semantic segmentation and compared to a traditional deep learning model. Model performance (Dice score) was compared. Uncertainty was calculated using the formula for Shannon Entropy. The output of both Bayesian technique models showed a greater number of pixels with high entropy than the standard model. Dice scores for the Monte Carlo dropout method (0.90, 95% confidence interval 0.87-0.93) and the ensemble method (0.88, 95% confidence interval 0.85-0.91) were significantly higher (P<0.001) than for the traditional model (0.82, 95% confidence interval 0.78-0.86). Quantifying the uncertainty in a prediction of GA may improve trustworthiness of the models and aid clinicians in decision-making. The Bayesian deep learning techniques generated pixel-wise estimates of model uncertainty for segmentation, while also improving model performance compared with traditionally trained deep learning models. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Calibrating the global network of gravitational wave observatories via laser power calibration at NIST and PTB

Current gravitational wave (GW) observatories rely on photon calibrators that use laser radiation pressure to generate displacement fiducials used to calibrate detector output signals. Reducing calibration uncertainty enables optimal extraction of astrophysical information such as source distance and sky position from detected signals. For the ongoing O4 observing run that started on 24 May 2023, the global GW detector network is employing a new calibration scheme with transfer standards calibrated at both the National Institute of Standards and Technology (NIST) and the Physikalisch-Technische Bundesanstalt (PTB). These transfer standards will circulate between the observatories and the metrology institutes to provide laser power calibration traceable to the International System of Units (SI) and enable assessment and reduction of relative calibration errors for the observatory network. The Laser Interferometer Gravitational-Wave Observatory (LIGO) project and the Virgo project are currently participating in the new calibration scheme. The Large-scale Cryogenic Gravitational-wave Telescope project (KAGRA) is expected to join in 2024, with the LIGO Aundha Observatory in India joining later. Before implementing this new scheme, a NIST-PTB bilateral comparison was conducted. It validated the scale representation by both laboratories, with a degree of equivalence of −0.2% and an associated expanded uncertainty of 0.32% (k = 2) which is significantly lower than previous studies. We describe the transfer of power sensor calibration, including detailed uncertainty estimates, from the transfer standards calibrated by NIST and PTB to the sensors operating continuously at the interferometer end stations. Finally, we discuss the ongoing calibration of Pcal-induced displacement fiducials for the O4 observing run. Achieved combined standard uncertainty levels as low as 0.3% facilitate calibrating the interferometer output signals with sub-percent accuracy.

Distinct computational mechanisms of uncertainty processing explain opposing exploratory behaviors in anxiety and apathy.

Decision-making in uncertain environments often leads to varied outcomes. Understanding how individuals interpret the causes of unexpected feedback is crucial for adaptive behavior and mental well-being. Uncertainty can be broadly categorized into two components: volatility and stochasticity. Volatility is about how quickly conditions change, impacting results. Stochasticity, on the other hand, refers to outcomes affected by random chance or "luck". Understanding these factors enables individuals to have more effective environmental analysis and strategy implementation (explore or exploit) for future decisions. This study investigates how anxiety and apathy, two prevalent affective states, influence the perceptions of uncertainty and exploratory behavior. Participants (N = 1001) completed a restless three-armed bandit task that was analyzed using latent state models. Anxious individuals perceived uncertainty as more volatile, leading to increased exploration and learning rates, especially after reward omission. Conversely, apathetic individuals viewed uncertainty as more stochastic, resulting in decreased exploration and learning rates. The perceived volatility-to-stochasticity ratio mediated the anxiety-exploration relationship post-adverse outcomes. Dimensionality reduction showed exploration and uncertainty estimation to be distinct but related latent factors shaping a manifold of adaptive behavior that is modulated by anxiety and apathy. These findings reveal distinct computational mechanisms for how anxiety and apathy influence decision-making, providing a framework for understanding cognitive and affective processes in neuropsychiatric disorders.

A Morphological Approach to Development of a Process for Measurement Uncertainty Estimation

The problem of increasing the reliability of uncertainty estimation of measurement results is considered. The purpose of this work was to justify the application of the process approach to the formation of the algorithm of uncertainty estimation on the basis of morphological analysis. It is theoretically substantiated that from the standpoint of the system approach to achieving an acceptable degree of reliability of measurement uncertainty estimates it is necessary to implement a process approach to the formation of the estimation method as an algorithm of actions. The main stages of the estimation process are defined. It is established that each stage of the estimation process can be realized by alternative methods. The morphological box method as a realization of morphological analysis is proposed as a basis for its solution. A morphological box design of the uncertainty estimation process with an open architecture is presented, based only on commonly accepted methods and approaches for realizing each step of the process. Two aspects of the application of the morphological box method are identified. On the one hand, the morphological box allows to form an algorithm of the uncertainty assessment process, maximally acceptable for the laboratory conditions, as a combination of process steps, based on the task at hand, combining different variants of realization of these steps. On the other hand, the morphological box acts as a tool for development of new methods of realization of various stages of the uncertainty assessment process. Examples of using the morphological box method to develop alternative algorithms of the uncertainty estimation process of the same measurement method and to develop new methods of realization of different stages of the estimation process are considered.