Search NASA⌕ Search

SEARCH · Search NASA

Results for “Discrimination Learning”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays

Recently, quantitative models based on signal detection theory have been successfully applied to the prediction of human accuracy in visual search for a target that differs from distractors along a single attribute (feature search). The present paper extends these models for visual search accuracy to multidimensional search displays in which the target differs from the distractors along more than one feature dimension (conjunction, disjunction, and triple conjunction displays). The model assumes that each element in the display elicits a noisy representation for each of the relevant feature dimensions. The observer combines the representations across feature dimensions to obtain a single decision variable, and the stimulus with the maximum value determines the response. The model accurately predicts human experimental data on visual search accuracy in conjunctions and disjunctions of contrast and orientation. The model accounts for performance degradation without resorting to a limited-capacity spatially localized and temporally serial mechanism by which to bind information across feature dimensions.

Non-NASA Center↗
NASA NTRS1998

Nonverbal working memory of humans and monkeys: rehearsal in the sketchpad?

Investigations of working memory tend to focus on the retention of verbal information. The present experiments were designed to characterize the active maintenance rehearsal process used in the retention of visuospatial information. Rhesus monkeys (Macaca mulatta; N = 6) were tested as well as humans (total N = 90) because these nonhuman primates have excellent visual working memory but, unlike humans, cannot verbally recode the stimuli to employ verbal rehearsal mechanisms. A series of experiments was conducted using a distractor-task paradigm, a directed forgetting procedure, and a dual-task paradigm. No evidence was found for an active maintenance process for either species. Rather, it appears that information is maintained in the visuospatial sketchpad without active rehearsal.

Non-NASA Center↗
NASA NTRS1997

Uncertain responses by humans and rhesus monkeys (Macaca mulatta) in a psychophysical same-different task

The authors asked whether animals, like humans, use an uncertain response adaptively to escape indeterminate stimulus relations. Humans and monkeys were placed in a same-different task, known to be challenging for animals. Its difficulty was increased further by reducing the size of the stimulus differences, thereby making many same and different trials difficult to tell apart. Monkeys do escape selectively from these threshold trials, even while coping with 7 absolute stimulus levels concurrently. Monkeys even adjust their response strategies on short time scales according to the local task conditions. Signal-detection and optimality analyses confirm the similarity of humans' and animals' performances. Whereas associative interpretations account poorly for these results, an intuitive uncertainty construct does so easily. The authors discuss the cognitive processes that allow uncertainty's adaptive use and recommend further comparative studies of metacognition.

NASA Discipline Space Human Factors↗
NASA NTRS1996

Gravitational acceleration as a cue for absolute size and distance?

When an object's motion is influenced by gravity, as in the rise and fall of a thrown ball, the vertical component of acceleration is roughly constant at 9.8 m/sec2. In principle, an observer could use this information to estimate the absolute size and distance of the object (Saxberg, 1987a; Watson, Banks, von Hofsten, & Royden, 1992). In five experiments, we examined people's ability to utilize the size and distance information provided by gravitational acceleration. Observers viewed computer simulations of an object rising and falling on a trajectory aligned with the gravitational vector. The simulated objects were balls of different diameters presented across a wide range of simulated distances. Observers were asked to identify the ball that was presented and to estimate its distance. The results showed that observers were much more sensitive to average velocity than to the gravitational acceleration pattern. Likewise, verticality of the motion and visibility of the trajectory's apex had negligible effects on the accuracy of size and distance judgments.

NASA Discipline Space Human Factors↗
NASA NTRS1996

Haptic cues for orientation and postural control in sighted and blind individuals

Haptic cues from fingertip contact with a stable surface attenuate body sway in subjects even when the contact forces are too small to provide physical support of the body. We investigated how haptic cues derived from contact of a cane with a stationary surface at low force levels aids postural control in sighted and congenitally blind individuals. Five sighted (eyes closed) and five congenitally blind subjects maintained a tandem Romberg stance in five conditions: (1) no cane; (2,3) touch contact (< 2 N of applied force) while holding the cane in a vertical or slanted orientation; and (4,5) force contact (as much force as desired) in the vertical and slanted orientations. Touch contact of a cane at force levels below those necessary to provide significant physical stabilization was as effective as force contact in reducing postural sway in all subjects, compared to the no-cane condition. A slanted cane was far more effective in reducing postural sway than was a perpendicular cane. Cane use also decreased head displacement of sighted subjects far more than that of blind subjects. These results suggest that head movement control is linked to postural control through gaze stabilization reflexes in sighted subjects; such reflexes are absent in congenitally blind individuals and may account for their higher levels of head displacement.

NASA Discipline Neuroscience↗
NASA NTRS1996

Accuracy and adaptation of reaching and pointing in pitched visual environments

Visually perceived eye level (VPEL) and the ability of subjects to reach with an unseen limb to targets placed at VPEL were measured in a statically pitched visual surround (pitchroom). VPEL was shifted upward and downward by upward and downward room pitch, respectively. Accuracy in reaching to VPEL represented a compromise between VPEL and actual eye level. This indicates that VPEL shifts reflect in part a change in perceived location of objects. When subjects were provided with terminal visual feedback about their reaching, accuracy improved rapidly. Subsequent reaching, with the room vertical, revealed a negative aftereffect (i.e., reaching errors that were opposite those made initially in the pitched room). In a second study, pointing accuracy was assessed for targets located both at VPEL and at other positions. Errors were similar for targets whether located at VPEL or elsewhere. Additionally, pointing responses were restricted to a narrower range than that of the actual target locations. The small size of reaching and pointing errors in both studies suggests that factors other than a change in perceived location are also involved in VPEL shifts.

NASA Center ARC↗
NASA NTRS1990

Close binding of identity and location in visual feature perception

The binding of identity and location information in disjunctive feature search was studied. Ss searched a heterogeneous display for a color or a form target, and reported both target identity and location. To avoid better than chance guessing of target identity (by choosing the target less likely to have been seen), the difficulty of the two targets was equalized adaptively; a mathematical model was used to quantify residual effects. A spatial layout was used that minimized postperceptual errors in reporting location. Results showed strong binding of identity and location perception. After correction for guessing, no perception of identity without location was found. A weak trend was found for accurate perception of target location without identity. We propose that activated features generate attention-calling "interrupt" signals, specifying only location; attention then retrieves the properties at that location.

Orientation↗
NASA NTRS1989

Effect of structured visual environments on apparent eye level

Each of 12 subjects set a binocularly viewed target to apparent eye level; the target was projected on the rear wall of an open box, the floor of which was horizontal or pitched up and down at angles of 7.5 degrees and 15 degrees. Settings of the target were systematically biased by 60% of the pitch angle when the interior of the box was illuminated, but by only 5% when the interior of the box was darkened. Within-subjects variability of the settings was less under illuminated viewing conditions than in the dark, but was independent of box pitch angle. In a second experiment, 11 subjects were tested with an illuminated pitched box, yielding biases of 53% and 49% for binocular and monocular viewing conditions, respectively. The results are discussed in terms of individual and interactive effects of optical, gravitational, and extraretinal eye-position information in determining judgements of eye level.

NASA Center ARC↗
NASA NTRS2004

The relationship between perceived length and egocentric location in Muller-Lyer figures with one versus two chevrons

We examined the apparent dissociation of perceived length and perceived position with respect to the Muller-Lyer (M-L) illusion. With the traditional (two-chevron) figure, participants made accurate open-loop pointing responses at the endpoints of the shaft, despite the presence of a strong length illusion. This apparently non-Euclidean outcome replicated that of Mack, Heuer, Villardi, and Chambers (1985) and Gillam and Chambers (1985) and contradicts any theory of the M-L illusion in which mislocalization of shaft endpoints plays a role. However, when one of the chevrons was removed, a constant pointing error occurred in the predicted direction, as well as a strong length illusion. Thus, with one-chevron stimuli, perceived length and location were no longer completely dissociated. We speculated that the presence of two opposing chevrons suppresses the mislocalizing effects of a single chevron, especially for figures with relatively short shafts.

NASA Center ARC↗
DOE OSTI2025

ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning

The resilience of safety-critical systems is gaining importance due to the rise in cyber and physical threats, especially within critical infrastructure. Traditional static resilience metrics may not capture dynamic system states, leading to inaccurate assessments and ineffective responses to cyber threats. This work aims to develop a data-driven, adaptive method for resilience metric learning. We propose a data-driven approach using inverse reinforcement learning (IRL) to learn a single, adaptive resilience metric. The method infers a reward function from expert control actions. Unlike previous approaches using static weights or fuzzy logic, this work applies adversarial inverse reinforcement learning (AIRL), training a generator and discriminator in parallel to learn the reward structure and derive an optimal policy. The proposed approach is evaluated on multiple scenarios: optimal communication network rerouting, power distribution network reconfiguration, and cyber–physical restoration of critical loads using the IEEE 123-bus system. The adaptive, learned resilience metric enables faster critical load restoration in comparison to conventional RL approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Machine Learning–Augmented Laser-Induced Breakdown Spectroscopy for Spectral Discrimination of Iron Oxalates

Enhanced characterization and phase identification of post-PUREX Pu Oxalates (PuOXA) are pivotal for nonproliferation and pre-detonation nuclear forensics. Despite significant advances in the characterization of PuO 2 samples, little is known about the impact of both the chemical structure and oxidation states of PuOXA (i.e., Pu(III) and Pu(IV)) have on optical emission signatures. Here, we demonstrate the analytical capabilities of laser-induced breakdown spectroscopy (LIBS) applied to Fe(II) and Fe(III) oxalate samples as surrogates for PuOXA, highlighting the discriminating features in the LIBS emission spectra arising from differences in the oxidation states within mixed FeOXA samples. We report the enhancement of spectral feature selection using Principal Component Analysis (PCA), which enables the analytical superiority of machine learning algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR) over conventional univariate techniques for phase discrimination and chemometric analysis. Cluster analysis revealed how both matrix effects and laser ablation influence cluster separability by introducing spectral artifacts that misdirect the maximization of variance. PCA-selected emission lines were used in the regression models, demonstrating that both univariate and multivariate linear regression models (i.e., PLSR and SVR) can achieve acceptable performance, with machine learning models outperforming conventional calibration regressions. Furthermore, the application of non-linearly activated PCA-selected emission lines illustrates how simplifying the data while retaining captured variance enables the use of less complex and more computationally efficient models. Furthermore, this is particularly evident in the underperformance of RFR, which suffers from increased computational costs and overfitting owing to its high complexity.

Oxalates↗
DOE OSTI2024

Yet Another Discriminant Analysis (YADA): A Probabilistic Model for Machine Learning Applications

This paper presents a probabilistic model for various machine learning (ML) applications. While deep learning (DL) has produced state-of-the-art results in many domains, DL models are complex and over-parameterized, which leads to high uncertainty about what the model has learned, as well as its decision process. Further, DL models are not probabilistic, making reasoning about their output challenging. In contrast, the proposed model, referred to as Yet Another Discriminate Analysis(YADA), is less complex than other methods, is based on a mathematically rigorous foundation, and can be utilized for a wide variety of ML tasks including classification, explainability, and uncertainty quantification. YADA is thus competitive in most cases with many state-of-the-art DL models. Ideally, a probabilistic model would represent the full joint probability distribution of its features, but doing so is often computationally expensive and intractable. Hence, many probabilistic models assume that the features are either normally distributed, mutually independent, or both, which can severely limit their performance. YADA is an intermediate model that (1) captures the marginal distributions of each variable and the pairwise correlations between variables and (2) explicitly maps features to the space of multivariate Gaussian variables. Numerous mathematical properties of the YADA model can be derived, thereby improving the theoretic underpinnings of ML. Validation of the model can be statistically verified on new or held-out data using native properties of YADA. However, there are some engineering and practical challenges that we enumerate to make YADA more useful.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2003

Astrobiology Extends Biology into Deep Time and Space

To understand our own origins and to search for biospheres beyond Earth, we need a more robust concept of life itself. We must learn how to discriminate between attributes that are fundamental to all living systems versus those that represent principally local outcomes of long-term survival on Earth. We should identify the most basic environmental needs of life, chart the distribution of other habitable worlds, and understand the factors that created their distribution. Studies of microbial communities and the geologic record will be summarized that offer clues about the early evolution of our own biosphere as well as the signatures of life that we might find in the heavens.

Desmarais, David↗
DOE OSTI2025

Differential Seismic Phase Detection Probability as a Potential Discriminant of Explosions and Earthquakes

Deep learning models trained to estimate the probability of seismic P and S phases are rapidly expanding the scale of local event detections. Here, we evaluate the potential for deep learning model output phase detection probabilities to contribute to event‐type classification, particularly discrimination of single‐fired borehole explosions and earthquakes at local distances (<300 km). Motivated by the empirical success of P/S amplitude ratios, we consider the difference between P and S pick probability output from previously developed phase detection models, P prob −S prob ⁠, as a discriminant. Test data include M L ∼1–4 earthquakes and explosions observed by common seismographs in ten geologically diverse localities. Depending on the picking model and training data, binary classification using P prob −S prob with at least three stations can achieve approximately equivalent classification accuracy as P/S amplitude ratios without requiring any customization. Joint classification with P/S and P prob −S prob improves accuracy for most quality control scenarios. Pick probabilities are an efficient attribute to consider in explosion discrimination because they can be automated byproducts of event detection. They avoid the binary choice of picking or not picking weakly visible S waves common to explosions.

Duan, Chenglong [Rice Univ., Houston, TX (United S↗
DOE OSTI2025

Discriminative versus generative approaches to simulation-based inference

Most of the fundamental, emergent, and phenomenological parameters of particle and nuclear physics are determined through parametric template fits. Simulations are used to populate histograms which are then matched to data. This approach is inherently lossy, since histograms are binned and low-dimensional. Deep learning has enabled unbinned and high-dimensional parameter estimation through neural likelihood(-ratio) estimation. We compare two approaches for neural simulation-based inference (NSBI): one based on discriminative learning (classification) and one based on generative modeling. These two approaches are directly evaluated on the same datasets, with a similar level of hyperparameter optimization in both cases. In addition to a Gaussian dataset, we study NSBI using a Higgs boson dataset from the FAIR Universe Challenge. We find that both the direct likelihood and likelihood ratio estimation are able to effectively extract parameters with reasonable uncertainties. For the numerical examples and within the set of hyperparameters studied, we found that the likelihood ratio method is more accurate and/or precise. Both methods have a significant spread from the network training and would require ensembling or other mitigation strategies in practice.

high energy physics↗
NASA NTRS1988

Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.

Stclair, D. C.↗
DOE OSTI2025

Ultraheavy diquark decaying into vectorlike quarks at the LHC

We explore the discovery potential of ultraheavy (7–8.5 TeV) diquark scalars (𝑆 𝑢⁢𝑢 ) produced in the collision of two up quarks at the LHC. Assuming that the diquark scalar decays into two vectorlike quarks of mass around 2 TeV, each of them decaying into a 𝑊 + boson and a 𝑏 quark, we focus on the fully hadronic final state. We present a signal-from-background separation study based on a discriminator built with machine learning techniques. For this six-jet final state and a luminosity of 3000 fb −1 , we estimate that a diquark scalar of mass near 8 TeV may be discovered or ruled out even when its coupling to up quarks is as low as 0.2.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗