Probabilistic Discrimination Research Articles

Discrimination of Copper Molten Marks through a Fire Reproduction Experiment Using Microstructure Features.

The copper molten marks at a fire site provide important clues for determining the causes of fire. Four factors have been presented to quantitatively discriminate copper molten marks, namely the fraction of (001) component perpendicular to the demarcation line, the grain aspect ratio, the fraction of Σ3 boundaries, and the fraction of maximum grain size. However, only laboratory-level results of these parameters have been presented, and their applicability in actual fires is yet to be verified. In this study, a fire reproduction experimental system was configured to generate molten marks similar to those in actual fire sites. The molten marks were measured by electron backscatter diffraction and applied to the four discriminant factors. The results obtained similar characteristics to those of the laboratory unit, confirming the applicability of the four discriminant factors. Discriminant equations and processes that can distinguish the primary and secondary arc beads were derived using the molten marks generated in the laboratory and reproduction experiments. Furthermore, a probabilistic discrimination method and classification model developed by machine learning were proposed. Therefore, the use of the discriminants in actual fires can improve the reliability of the statistics and prevent the recurrence of similar fires.

Unique features of stimulus-based probabilistic reversal learning.

Reversal learning paradigms are widely used assays of behavioral flexibility with their probabilistic versions being more amenable to studying integration of reward outcomes over time. Prior research suggests differences between initial and reversal learning, including higher learning rates, a greater need for inhibitory control, and more perseveration after reversals. However, it is not well-understood what aspects of stimulus-based reversal learning are unique to reversals, and whether and how observed differences depend on reward probability. Here, we used a visual probabilistic discrimination and reversal learning paradigm where male and female rats selected between a pair of stimuli associated with different reward probabilities. We compared accuracy, rewards collected, omissions, latencies, win-stay/lose-shift strategies, and indices of perseveration across two different reward probability schedules. We found that discrimination and reversal learning are behaviorally more unique than similar: Fit of choice behavior using reinforcement learning models revealed a lower sensitivity to the difference in subjective reward values (greater exploration) and higher learning rates for the reversal phase. We also found latencies to choose the better option were greater in females than males, but only for the reversal phase. Further, animals employed more win-stay strategies during early discrimination and increased perseveration during early reversal learning. Interestingly, a consistent reward probability group difference emerged with a richer environment associated with longer reward collection latencies than a leaner environment. Future studies should systematically compare the neural correlates of fine-grained behavioral measures to reveal possible dissociations in how the circuitry is recruited in each phase. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Probabilistic discrimination of relative stimulus features in mice

During perceptual decision-making, the brain encodes the upcoming decision and the stimulus information in a mixed representation. Paradigms suitable for studying decision computations in isolation rely on stimulus comparisons, with choices depending on relative rather than absolute properties of the stimuli. The adoption of tasks requiring relative perceptual judgments in mice would be advantageous in view of the powerful tools available for the dissection of brain circuits. However, whether and how mice can perform a relative visual discrimination task has not yet been fully established. Here, we show that mice can solve a complex orientation discrimination task in which the choices are decoupled from the orientation of individual stimuli. Moreover, we demonstrate a typical discrimination acuity of 9°, challenging the common belief that mice are poor visual discriminators. We reached these conclusions by introducing a probabilistic choice model that explained behavioral strategies in 40 mice and demonstrated that the circularity of the stimulus space is an additional source of choice variability for trials with fixed difficulty. Furthermore, history biases in the model changed with task engagement, demonstrating behavioral sensitivity to the availability of cognitive resources. In conclusion, our results reveal that mice adopt a diverse set of strategies in a task that decouples decision-relevant information from stimulus-specific information, thus demonstrating their usefulness as an animal model for studying neural representations of relative categories in perceptual decision-making research.

Conscious perception and the modulatory role of dopamine: no effect of the dopamine D2 agonist cabergoline on visual masking, the attentional blink, and probabilistic discrimination

RationaleConscious perception is thought to depend on global amplification of sensory input. In recent years, striatal dopamine has been proposed to be involved in gating information and conscious access, due to its modulatory influence on thalamocortical connectivity.ObjectivesSince much of the evidence that implicates striatal dopamine is correlational, we conducted a double-blind crossover pharmacological study in which we administered cabergoline—a dopamine D2 agonist—and placebo to 30 healthy participants. Under both conditions, we subjected participants to several well-established experimental conscious-perception paradigms, such as backward masking and the attentional blink task.ResultsWe found no evidence in support of an effect of cabergoline on conscious perception: key behavioral and event-related potential (ERP) findings associated with each of these tasks were unaffected by cabergoline.ConclusionsOur results cast doubt on a causal role for dopamine in visual perception. It remains an open possibility that dopamine has causal effects in other tasks, perhaps where perceptual uncertainty is more prominent.

Inhibitory and excitatory mechanisms in the human cingulate-cortex support reinforcement learning: A functional Proton Magnetic Resonance Spectroscopy study

The dorsal anterior cingulate cortex (dACC) is crucial for motivation, reward- and error-guided decision-making, yet its excitatory and inhibitory mechanisms remain poorly explored in humans. In particular, the balance between excitation and inhibition (E/I), demonstrated to play a role in animal studies, is difficult to measure in behaving humans. Here, we used functional magnetic-resonance-spectroscopy (1H-fMRS) to measure the brain's major inhibitory (GABA) and excitatory (Glutamate) neurotransmitters during reinforcement learning with three different conditions: high cognitive load (uncertainty); probabilistic discrimination learning; and a control null-condition. Participants learned to prefer the gain option in the discrimination phase and had no preference in the other conditions. We found increased GABA levels during the uncertainty condition, potentially reflecting recruitment of inhibitory systems during high cognitive load when trying to learn. Further, higher GABA levels during the null (baseline) condition correlated with improved discrimination learning. Finally, glutamate and GABA levels were correlated during high cognitive load. These results suggest that availability of dACC inhibitory resources enables successful learning. Our approach helps elucidate the potential contribution of the balance between excitation and inhibition to learning and motivation in behaving humans.

Probabilistic n/γ discrimination with robustness against outliers for use in neutron profile monitors.

A method to stochastically discriminate neutron and γ-ray signals measured with a stilbene organic scintillator is proposed. Each pulse signal was stochastically categorized into two groups: neutron and γ-ray. In previous work, the Expectation Maximization (EM) algorithm was used with the assumption that the measured data followed a Gaussian mixture distribution. It was shown that probabilistic discrimination between these groups is possible. Moreover, by setting the initial parameters for the Gaussian mixture distribution with a k-means algorithm, the possibility of automatic discrimination was demonstrated. In this study, the Student's t-mixture distribution was used as a probabilistic distribution with the EM algorithm to improve the robustness against the effect of outliers caused by pileup of the signals. To validate the proposed method, the figures of merit (FOMs) were compared for the EM algorithm assuming a t-mixture distribution and a Gaussian mixture distribution. The t-mixture distribution resulted in an improvement of the FOMs compared with the Gaussian mixture distribution. The proposed data processing technique is a promising tool not only for neutron and γ-ray discrimination in fusion experiments but also in other fields, for example, homeland security, cancer therapy with high energy particles, nuclear reactor decommissioning, pattern recognition, and so on.

Stochastic Nonlinear Model Predictive Control with Active Model Discrimination: A Closed-Loop Fault Diagnosis Application

This paper addresses the problem of model predictive control with multiple models for nonlinear systems subject to stochastic disturbances. The multiple models can represent various operating conditions such as system faults or failures, or arise from model structure uncertainty. The paper presents a stochastic nonlinear model predictive control (SNMPC) approach with endogenous learning for active discrimination between the competing models based on closed-loop system observations. The system learning is endogenized through explicit inclusion of a model discrimination measure into the stochastic optimal control problem, which facilitates probabilistic discrimination between the predictions of multiple models. The control approach uses a Bayesian estimation algorithm for recursive estimation of the probabilities that represent the degree to which each model predicts the online system observations. The performance of the proposed SNMPC approach with active model discrimination is demonstrated for closed-loop fault diagnosis.

Dopamine D3 Receptor Availability Is Associated with Inflexible Decision Making.

Dopamine D2/3 receptor signaling is critical for flexible adaptive behavior; however, it is unclear whether D2, D3, or both receptor subtypes modulate precise signals of feedback and reward history that underlie optimal decision making. Here, PET with the radioligand [(11)C]-(+)-PHNO was used to quantify individual differences in putative D3 receptor availability in rodents trained on a novel three-choice spatial acquisition and reversal-learning task with probabilistic reinforcement. Binding of [(11)C]-(+)-PHNO in the midbrain was negatively related to the ability of rats to adapt to changes in rewarded locations, but not to the initial learning. Computational modeling of choice behavior in the reversal phase indicated that [(11)C]-(+)-PHNO binding in the midbrain was related to the learning rate and sensitivity to positive, but not negative, feedback. Administration of a D3-preferring agonist likewise impaired reversal performance by reducing the learning rate and sensitivity to positive feedback. These results demonstrate a previously unrecognized role for D3 receptors in select aspects of reinforcement learning and suggest that individual variation in midbrain D3 receptors influences flexible behavior. Our combined neuroimaging, behavioral, pharmacological, and computational approach implicates the dopamine D3 receptor in decision-making processes that are altered in psychiatric disorders. Flexible decision-making behavior is dependent upon dopamine D2/3 signaling in corticostriatal brain regions. However, the role of D3 receptors in adaptive, goal-directed behavior has not been thoroughly investigated. By combining PET imaging with the D3-preferring radioligand [(11)C]-(+)-PHNO, pharmacology, a novel three-choice probabilistic discrimination and reversal task and computational modeling of behavior in rats, we report that naturally occurring variation in [(11)C]-(+)-PHNO receptor availability relates to specific aspects of flexible decision making. We confirm these relationships using a D3-preferring agonist, thus identifying a unique role of midbrain D3 receptors in decision-making processes.

Reservoir characterization using electrofacies analysis in the sandstone reservoir of the Norne Field (offshore Norway)

The Norne Field reservoir sandstone comprises Early–Middle Jurassic interbedded sandstones and shales to massive sandstones with some thin continuous cemented interlayers. A detailed characterization of the geological heterogeneities through electrofacies analysis, together with the simulation grid refinement, has been used to derive representative facies and petrophysical models (porosity, net-to-gross (NtG) and permeability). An electrofacies database was created comprising six rock types, ranging from cemented carbonates through shales and into clean sandstones. In the absence of available cored sections, the electrofacies scheme was validated by the geological and petrophysical reports of 26 wells using gamma-ray, neutron and density logs. An artificial neural network algorithm enabled the probabilistic discrimination of the different types of electrofacies, with a sampling rate of 0.125 m. This high-resolution electrofacies database, together with a high-resolution geomodel grid, enabled us to map the fine-scale heterogeneities mainly materialized by decimetre shales and cemented layers that could represent stratigraphic barriers to vertical fluid displacement. The high-resolution datasets created in this study will form the working basis on which to perform a probabilistic and multi-objective history matching guided by production and 4D seismic data, and assisted by geostatistical parameterization techniques.

Probabilistic Reversal Learning in Schizophrenia: Stability of Deficits and Potential Causal Mechanisms.

Although individuals with schizophrenia show impaired feedback-driven learning on probabilistic reversal learning (PRL) tasks, the specific factors that contribute to these deficits remain unknown. Recent work has suggested several potential causes including neurocognitive impairments, clinical symptoms, and specific types of feedback-related errors. To examine this issue, we administered a PRL task to 126 stable schizophrenia outpatients and 72 matched controls, and patients were retested 4 weeks later. The task involved an initial probabilistic discrimination learning phase and subsequent reversal phases in which subjects had to adjust their responses to sudden shifts in the reinforcement contingencies. Patients showed poorer performance than controls for both the initial discrimination and reversal learning phases of the task, and performance overall showed good test-retest reliability among patients. A subgroup analysis of patients (n = 64) and controls (n = 49) with good initial discrimination learning revealed no between-group differences in reversal learning, indicating that the patients who were able to achieve all of the initial probabilistic discriminations were not impaired in reversal learning. Regarding potential contributors to impaired discrimination learning, several factors were associated with poor PRL, including higher levels of neurocognitive impairment, poor learning from both positive and negative feedback, and higher levels of indiscriminate response shifting. The results suggest that poor PRL performance in schizophrenia can be the product of multiple mechanisms.

Role of Central Serotonin in Anticipation of Rewarding and Punishing Outcomes: Effects of Selective Amygdala or Orbitofrontal 5-HT Depletion.

Understanding the role of serotonin (or 5-hydroxytryptamine, 5-HT) in aversive processing has been hampered by the contradictory findings, across studies, of increased sensitivity to punishment in terms of subsequent response choice but decreased sensitivity to punishment-induced response suppression following gross depletion of central 5-HT. To address this apparent discrepancy, the present study determined whether both effects could be found in the same animals by performing localized 5-HT depletions in the amygdala or orbitofrontal cortex (OFC) of a New World monkey, the common marmoset. 5-HT depletion in the amygdala impaired response choice on a probabilistic visual discrimination task by increasing the effectiveness of misleading, or false, punishment and reward, and decreased response suppression in a variable interval test of punishment sensitivity that employed the same reward and punisher. 5-HT depletion in the OFC also disrupted probabilistic discrimination learning and decreased response suppression. Computational modeling of behavior on the discrimination task showed that the lesions reduced reinforcement sensitivity. A novel, unitary account of the findings in terms of the causal role of 5-HT in the anticipation of both negative and positive motivational outcomes is proposed and discussed in relation to current theories of 5-HT function and our understanding of mood and anxiety disorders.

Multiattribute Decision Making Based on Entropy under Interval-Valued Intuitionistic Fuzzy Environment

Multiattribute decision making (MADM) is one of the central problems in artificial intelligence, specifically in management fields. In most cases, this problem arises from uncertainty both in the data derived from the decision maker and the actions performed in the environment. Fuzzy set and high-order fuzzy sets were proven to be effective approaches in solving decision-making problems with uncertainty. Therefore, in this paper, we investigate the MADM problem with completely unknown attribute weights in the framework of interval-valued intuitionistic fuzzy (IVIF) set (IVIFS). We first propose a new definition of IVIF entropy and some calculation methods for IVIF entropy. Furthermore, we propose an entropy-based decision-making method to solve IVIF MADM problems with completely unknown attribute weights. Particular emphasis is put on assessing the attribute weights based on IVIF entropy. Instead of the traditional methods, which use divergence among attributes or the probabilistic discrimination of attributes to obtain attribute weights, we utilize the IVIF entropy to assess the attribute weights based on the credibility of the decision-making matrix for solving the problem. Finally, a supplier selection example is given to demonstrate the feasibility and validity of the proposed MADM method.

Probabilistic facies discrimination from simultaneous seismic-inversion results in clastic reservoirs in southwestern Venezuela

Facies volumes were obtained in southwestern Venezuela reservoirs integrating rock-physics analysis and seismic-inversion results. Facies volumes showed a sandstone-shale spatial distribution that matches log data and the conceptual sedimentologic model of the reservoir. After seismic-gather conditioning, applying a simple but robust processing sequence, the signal-to-noise ratio was increased. Two angle stacks were generated to perform a simultaneous seismic inversion to obtain P- and S-wave impedance volumes. Parallel to the inversion process, rock-physics analysis was done using rock-physics templates (RPT) for constant-cement models (CCM) to discriminate between sandstone and shale. P- and S-wave impedance bivariate probabilistic density functions (PDF) were computed from well-log data as an a priori probability. Finally, these PDFs were used to classify facies from P- and S-wave impedance volumes obtained from simultaneous inversion, using a Bayesian probabilistic approach. This classification produced two facies volumes, sandstone and shale, which honored the well information and rock-physics models.

Probabilistic spectral gain modification applied to beamformer-based noise reduction in a car environment

In this paper, we propose a new noise reduction technique for improving the performance of conventional beamformers used in cars. To this end, the probabilistic discrimination on target-directional signal presence is used as a parameter for the spectral gain modification (SGM) of a beamformer output. The direction of arrival (DOA)-based target-to-non-target-directional signal ratio (TNR) is first estimated by using spatial cues such as phase differences from multiple microphone signals. Next, the estimated TNR is utilized to estimate the target-directional signal presence probabilities (TDSPPs) that include global and local terms. The performance of the proposed SGM is evaluated by the degree of noise reduction, average and segmental signal-to-noise ratio (SNR), as well as perceptual evaluation of speech quality (PESQ) scores under car noise conditions whose SNR varies from -5 to 20 dB. As a result, it is shown that the proposed SGM significantly improves the target-directional signal enhancing performance against conventional beamformers, i.e., delay-and-sum beamformer (DSB), super directive beamformer (SDB) and generalized sidelobe canceller (GSC), for all input SNRs.

Determining objective weights with intuitionistic fuzzy entropy measures: A comparative analysis

In this paper, we propose a new objective weighting method that employs intuitionistic fuzzy (IF) entropy measures to solve multiple-attribute decision-making problems in the context of intuitionistic fuzzy sets. Instead of traditional fuzzy entropy, which uses the probabilistic discrimination of attributes to obtain attribute weights, we utilize the IF entropy to assess objective weights based on the credibility of the input data. We examine various measures for IF entropy with respect to hesitation degree, probability, non-probability, and geometry to calculate the attribute weights. A comparative analysis of different measures to generate attribute rankings is illustrated with both computational experiments as well as analyses of Pearson correlations, Spearman rank correlations, contradiction rates, inversion rates, and consistency rates. The experimental results indicate that ranking the outcomes of attributes not only depends on the type of IF entropy measures but is also affected by the number of attributes and the number of alternatives.

Probabilistic discrimination between large-scale environments of intensifying and decaying African Easterly Waves

About 50–60% of Atlantic tropical cyclones (TCs) including nearly 85% of intense hurricanes have their origins as African Easterly Waves (AEWs). However, predicting the likelihood of AEW intensification remains a difficult task. We have developed a Bayesian diagnostic methodology to understand genesis of North Atlantic TCs spawned by AEWs through the examination of the characteristics of the AEW itself together with the large-scale environment, resulting in a probabilistic discrimination between large-scale environments associated with intensifying and decaying AEWs. The methodology is based on a new objective and automatic AEW tracking scheme used for the period 1980 to 2001 based on spatio-temporally Fourier-filtered relative vorticity and meridional winds at different levels and outgoing long wave radiation. Using the AEW and Hurricane Best Track Files (HURDAT) data sets, probability density functions of environmental variables that discriminate between AEWs that decay, become TCs or become major hurricanes are determined. Results indicate that the initial amplitude of the AEWs is a major determinant for TC genesis, and that TC genesis risk increases when the wave enters an environment characterized by pre-existing large-scale convergence and moist convection. For the prediction of genesis, the most useful variables are column integrated heating, vertical velocity and specific humidity, and a combined form of divergence and vertical velocity and SST. It is also found that the state of the large-scale environment modulates the annual cycle and interannual variability of the AEW intensification efficiency.

Sequential probability ratio test for evaluating functionality in acid-base potentiometry

A procedure is proposed for evaluating the functionality of acids and bases using data from potentiometric titration. The sequential probability ratio test is used together with a numerical model involving the ionic strength varying during the course of the titration. As in all sequential procedures, the number and extent of titrant additions are a function of the probabilistic discrimination level reached after each addition. This involves an important saving of time and reagents. The procedure has been applied for distinguishing monofunctional from difunctional acids and bases in real and simulated experiments, and always gives very good results even in routine analysis.

Discrimination of alternative male phenotypes in Scapanes australis (Boisduval) (Coleoptera: Scarabaeidae: Dynastinae)

Abstract The palm pest Scapanes australis has been the object of considerable recent research to develop control measures that target male reproductive behaviour. This study investigated horn size scaling relationships and frequency distributions in two subspecies of S. australis for evidence of alternative male reproductive strategies. Indeed, a sigmoidal horn size allometry in both populations was detected as a significant vertical discontinuity by a piece‐wise regression model, which indicates that distinct minor and major male phenotypes are expressed in both taxa. A non‐linear regression algorithm showed that these subspecies differ significantly in two modelled parameters of horn allometry. A likelihood model was used for probabilistic discrimination of the alternative male phenotypes by estimating their ratios across the bimodal frequency distribution. The latter is intended to aid field investigation of the reproductive biology of S. australis by statistically discriminating the alternative male phenotypes on the basis of easily measured parameters in live samples.

Improved short-term spatial memory but impaired reversal learning following the dopamine D(2) agonist bromocriptine in human volunteers.

Studies in humans of cognitive effects of dopaminergic drugs have largely focused on tasks of working memory, with a few studies also examining executive function. This study was designed to investigate the effects of 1.25 mg of the dopamine D(2) agonist bromocriptine on spatial working memory, planning and discrimination reversal learning in young healthy volunteers. Twenty volunteers were tested in a double-blind, placebo-controlled, cross-over design. The cognitive assessment included tests taken from the Cambridge Neuropsychological Test Automated Battery (CANTAB) designed to test visuo-spatial recognition memory and spatial working memory. In addition, tests of spatial planning and discrimination reversal learning were used to assess the more general effects of bromocriptine. Tests of subjective feelings and motivation were also incorporated into the battery. Bromocriptine enhanced the spatial memory span of subjects, whilst impairing their ability to reverse a learned probabilistic discrimination. Tests of recognition memory and planning were unaffected by the drug. The findings were not explained by changes in subjective mood or motivational measures. The pattern of findings observed here mirror medication-dependent observations seen in Parkinson's disease. The results are discussed with reference to the different anatomical networks known to subserve performance of the differentially affected tasks.

Characterization of heterolithic deposits using electrofacies analysis in the tide-dominated Lower Jurassic Cook Formation (Gullfaks Field, offshore Norway)

A range of heterolithic facies, comprising thinly interbedded (millimetre–decimetre scale) sandstones and mudstones, characterizes the heterogeneous Lower Jurassic Cook Formation, including the productive Cook-3 reservoir in the Gullfaks Field. These heterolithic facies were deposited in a tide-dominated estuarine to deltaic setting and show up as massive ‘shaly-sands’ on conventional wireline logs. This makes the recognition and discrimination of different heterolithic facies types virtually impossible, which severely limits detailed reservoir geological and petrophysical predictions. This problem has been addressed by undertaking a high resolution electrofacies analysis using core facies as a ‘training set’ and applying this, through multivariate statistical techniques, to the interpretation of the conventional logs. An electrofacies database was created comprising five genetically linked rock types (ranging from lenticular-wavy bedding, through flaser bedding and into clean/massive sandstones). This electrofacies scheme was validated with reference to c . 125 m of cored section from five wells using gamma-ray, density and neutron logs. Multivariate statistical techniques have enabled probabilistic discrimination of the different types of heterolithic facies down to intervals of only 0.25 to 0.5 m thick, which is considerably greater than could be achieved using conventional well-log evaluation techniques alone.