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DOE OSTI2026

Attention-based explainability for structure–property relationships

Machine learning methods are emerging as a universal paradigm for constructing correlative structure–property relationships in materials science based on multimodal characterization. However, this necessitates the development of methods for the physical interpretability of the resulting correlative models. Here, we demonstrate the potential of attention-based neural networks for revealing structure–property relationships and the underlying physical mechanisms, using the ferroelectric properties of PbTiO3 thin films as a case study. Through the analysis of attention scores, we disentangle the influence of distinct domain patterns on the polarization switching process. The attention-based Transformer model is explored both as a direct interpretability tool and as a surrogate for explaining representations learned via unsupervised machine learning, enabling the identification of physically grounded correlations. We compare attention-derived interpretability scores with classical SHapley Additive exPlanations analysis and show that, in contrast to applications in natural language processing, attention mechanisms in materials science exhibit high efficiency in highlighting meaningful structural features.

Slautin, Boris [Independent Researcher]↗

NASA NTRS1975

Attention and its role in the operator's work

A complex attentiometer investigation of the distribution, redirection, and concentration of attention during an operator's work notes the following stages: (1) General attentiveness is still not adequately expressed in the beginning; and (2) operator self-control of actions develops and gradually decreases errors in redirecting and distributing attention. A definite relationship is found between the improvement of concentration, distribution and redirection of attention and automation of sensorimotor performance. Excercises prove less effective in redirection of attention.

Shvetsov, O. P.↗

NASA NTRS1980

The event-related brain potential as an index of attention allocation in complex displays

The advantages of employing the event-related brain potential (ERP) in the assessment of allocation of attention in dynamic environments are discussed. Three experiments are presented in which the P300 component of the ERP is demonstrated to be a useful index of subjects' locus of attention. The first two experiments were concerned with the allocation of attention during discrete and continuous visual monitoring tasks. The results indicated that a P300 was elicited only by stimuli to which the subject had to attend in order to perform successfully the task. The third experiment was conducted to assess the sensitivity of P300 to the manner in which attention is allocated to different aspects of a display during the performance of a 3-dimensional target acquisition task. The amplitude of the P300 was found to reflect differences between two levels of workload, as well as the task relevance of the stimuli. The results of the experiments are discussed in terms of their utility in the evaluation of the design of man-machine systems as well as in the study of the allocation of attention in operational environments.

Wickens, C. D.↗

NASA NTRS1989

Attention in dichoptic and binocular vision

The ability of human subjected to mobilize attention and cope with task requirements under dichoptic and binocular viewing was investigated in an experiment employing a target search task. Subjects were required to search for a target at either the global level, the local level, or at both levels of a compound stimulus. The tasks were performed in a focused attention condition in which subjects had to attend to the stimulus presented to one eye/field (under dichoptic and binocular viewings, respectively) and to ignore the stimulus presented to the irrelevant eye/field, and in a divided attention condition in which subjects had to attend to the stimuli presented to both eyes/fields. Subjects' performance was affected mainly by attention conditions which interacted with task requirements, rather than by viewing situation. An interesting effect of viewing was found for the local-directed search task in which the cost of dividing attention was higher under binocular than under dichoptic viewing.

Kimchi, Ruth↗

NASA NTRS1993

Performance under dichoptic versus binocular viewing conditions - Effects of attention and task requirements

Three experiments investigated subjects' ability to allocate attention and cope with task requirements under dichoptic versus binocular viewing conditions. Experiments 1 and 2 employed a target detection task in compound and noncompound stimuli, and Experiment 3 employed a relative-proximity judgment task. The tasks were performed in a focused attention condition in which subjects had to attend to the stimulus presented to one eye or field (under dichoptic and binocular viewing conditions, respectively) while ignoring the stimulus presented to the other eye or field, and in a divided attention condition in which subjects had to attend to the stimuli presented to both eyes or fields. Subjects' performance was affected by the interaction of attention conditions with task requirements, but it was generally the same under dichoptic and binocular viewing conditions. The more dependent the task was on finer discrimination, the more performance was impaired by divided attention. These results suggest that at least with discrete tasks and relatively short exposure durations, performance when each eye is presented with a separate stimulus is the same as when the entire field of stimulation is viewed by both eyes.

Kimchi, Ruth↗

NASA NTRS1994

Contingent Attentional Capture

Four experiments address the degree of top-down selectivity in attention capture by feature singletons through manipulations of the spatial relationship and featural similarity of target and distractor singletons in a modified spatial cuing paradigm. Contrary to previous studies, all four experiments show that when searching for a singleton target, an irrelevant featural singleton captures attention only when defined by the same feature value as the target. Experiments 2, 3, and 4 provide a potential explanation for this empirical discrepancy by showing that irrelevant singletons can produce distraction effects that are independent of shifts of spatial attention. The results further support the notion that attentional capture is contingent on top-down attention control settings but indicates that such settings can be instantiated at the level of feature values.

Remington, Roger↗

NASA NTRS2000

Spatial covert attention increases contrast sensitivity across the CSF: support for signal enhancement

This study is the first to report the benefits of spatial covert attention on contrast sensitivity in a wide range of spatial frequencies when a target alone was presented in the absence of a local post-mask. We used a peripheral precue (a small circle indicating the target location) to explore the effects of covert spatial attention on contrast sensitivity as assessed by orientation discrimination (Experiments 1-4), detection (Experiments 2 and 3) and localization (Experiment 3) tasks. In all four experiments the target (a Gabor patch ranging in spatial frequency from 0.5 to 10 cpd) was presented alone in one of eight possible locations equidistant from fixation. Contrast sensitivity was consistently higher for peripherally- than for neutrally-cued trials, even though we eliminated variables (distracters, global masks, local masks, and location uncertainty) that are known to contribute to an external noise reduction explanation of attention. When observers were presented with vertical and horizontal Gabor patches an external noise reduction signal detection model accounted for the cueing benefit in a discrimination task (Experiment 1). However, such a model could not account for this benefit when location uncertainty was reduced, either by: (a) Increasing overall performance level (Experiment 2); (b) increasing stimulus contrast to enable fine discriminations of slightly tilted suprathreshold stimuli (Experiment 3); and (c) presenting a local post-mask (Experiment 4). Given that attentional benefits occurred under conditions that exclude all variables predicted by the external noise reduction model, these results support the signal enhancement model of attention.

Non-NASA Center↗

NASA NTRS2012

Image Mapping and Visual Attention on the Sensory Ego-Sphere

The Sensory Ego-Sphere (SES) is a short-term memory for a robot in the form of an egocentric, tessellated, spherical, sensory-motor map of the robot s locale. Visual attention enables fast alignment of overlapping images without warping or position optimization, since an attentional point (AP) on the composite typically corresponds to one on each of the collocated regions in the images. Such alignment speeds analysis of the multiple images of the area. Compositing and attention were performed two ways and compared: (1) APs were computed directly on the composite and not on the full-resolution images until the time of retrieval; and (2) the attentional operator was applied to all incoming imagery. It was found that although the second method was slower, it produced consistent and, thereby, more useful APs. The SES is an integral part of a control system that will enable a robot to learn new behaviors based on its previous experiences, and that will enable it to recombine its known behaviors in such a way as to solve related, but novel, task problems with apparent creativity. The approach is to combine sensory-motor data association and dimensionality reduction to learn navigation and manipulation tasks as sequences of basic behaviors that can be implemented with a small set of closed-loop controllers. Over time, the aggregate of behaviors and their transition probabilities form a stochastic network. Then given a task, the robot finds a path in the network that leads from its current state to the goal. The SES provides a short-term memory for the cognitive functions of the robot, association of sensory and motor data via spatio-temporal coincidence, direction of the attention of the robot, navigation through spatial localization with respect to known or discovered landmarks, and structured data sharing between the robot and human team members, the individuals in multi-robot teams, or with a C3 center.

Fleming, Katherine Achim↗

NASA NTRS2017

Crew State Monitoring and Line-Oriented Flight Training for Attention Management

Loss of control - inflight (LOC-I) has historically represented the largest category of commercial aviation fatal accidents. A review of worldwide transport airplane accidents (2001-2010) indicated that loss of airplane state awareness (ASA) was responsible for the majority of the LOC-I fatality rate. The Commercial Aviation Safety Team (CAST) ASA study identified 12 major themes that were indicated across the ASA accident and incident events. One of the themes was crew distraction or ineffective attention management, which was found to be involved in all 18 events including flight crew channelized attention, startle/surprise, diverted attention, and/or confirmation bias. Safety Enhancement (SE)-211, "Training for Attention Management" was formed to conduct research to develop and assess commercial airline training methods and realistic scenarios that can address these attention-related human performance limitations. This paper describes NASA SE-211 research for new design approaches and validation of line-oriented flight training (LOFT). Recent accident and incident data suggests that Spatial Disorientation (SD) and Loss-of-Energy State Awareness (LESA) for transport category aircraft are becoming an increasingly prevalent safety concern in all domestic and international operations (Commercial Aviation Safety Team, 2014a). SD is defined as an erroneous perception of aircraft attitude that can lead directly to a Loss-of-Control Inflight (LOC-I) event and result in an accident or incident. LESA is typically characterized by a failure to monitor or understand energy state indications (e.g., airspeed, altitude, vertical speed, commanded thrust) and a resultant failure to maintain safe flight.

Stephens, Chad↗

DOE OSTI2025

Why Is Attention Sparse In Particle Transformer?

Transformer-based models have achieved state-of-the-art performance in jet tagging at the CERN Large Hadron Collider (LHC), with the Particle Transformer (ParT) representing a leading example of such models. A striking feature of ParT is its sparse, nearly binary, attention structure, raising questions about the origin of this behavior and whether it encodes physically meaningful correlations. In this work, we investigate the source of ParT's sparse attention by comparing models trained on multiple benchmark datasets and examine the relative contributions of the attention term and the physics-inspired interaction matrix before softmax. We find that binary sparsity arises primarily from the attention mechanism itself, with the interaction matrix playing a secondary role. Moreove, we show that ParT is able to identify key jet substructure elements, such as leptons in semileptonic top decays, even without explicit particle identification inputs. These results provide new insight into the interpretability of transformer-based jet taggers and clarify the conditions under which sparse attention patterns emerge in ParT.

Legge, Timothy [UC, San Diego]↗

DOE OSTI2024

AEPF: Attention-Enabled Point Fusion for 3D Object Detection

Current state-of-the-art (SOTA) LiDAR-only detectors perform well for 3D object detection tasks, but point cloud data are typically sparse and lacks semantic information. Detailed semantic information obtained from camera images can be added with existing LiDAR-based detectors to create a robust 3D detection pipeline. With two different data types, a major challenge in developing multi-modal sensor fusion networks is to achieve effective data fusion while managing computational resources. With separate 2D and 3D feature extraction backbones, feature fusion can become more challenging as these modes generate different gradients, leading to gradient conflicts and suboptimal convergence during network optimization. To this end, we propose a 3D object detection method, Attention-Enabled Point Fusion (AEPF). AEPF uses images and voxelized point cloud data as inputs and estimates the 3D bounding boxes of object locations as outputs. An attention mechanism is introduced to an existing feature fusion strategy to improve 3D detection accuracy and two variants are proposed. These two variants, AEPF-Small and AEPF-Large, address different needs. AEPF-Small, with a lightweight attention module and fewer parameters, offers fast inference. AEPF-Large, with a more complex attention module and increased parameters, provides higher accuracy than baseline models. Experimental results on the KITTI validation set show that AEPF-Small maintains SOTA 3D detection accuracy while inferencing at higher speeds. AEPF-Large achieves mean average precision scores of 91.13, 79.06, and 76.15 for the car class’s easy, medium, and hard targets, respectively, in the KITTI validation set. Results from ablation experiments are also presented to support the choice of model architecture.

Chemistry↗

NASA NTRS1976

Selective attention and the auditory vertex potential. I - Effects of stimulus delivery rate. II - Effects of signal intensity and masking noise

The effects of varying the rate of delivery of dichotic tone pip stimuli on selective attention measured by evoked-potential amplitudes and signal detectability scores were studied. The subjects attended to one channel (ear) of tones, ignored the other, and pressed a button whenever occasional targets - tones of a slightly higher pitch were detected in the attended ear. Under separate conditions, randomized interstimulus intervals were short, medium, and long. Another study compared the effects of attention on the N1 component of the auditory evoked potential for tone pips presented alone and when white noise was added to make the tones barely above detectability threshold in a three-channel listening task. Major conclusions are that (1) N1 is enlarged to stimuli in an attended channel only in the short interstimulus interval condition (averaging 350 msec), (2) N1 and P3 are related to different modes of selective attention, and (3) attention selectivity in multichannel listening task is greater when tones are faint and/or difficult to detect.

Schwent, V. L.↗

NASA NTRS1986

Division of attention as a function of the number of steps, visual shifts, and memory load

The effects on divided attention of visual shifts and long-term memory retrieval during a monitoring task are considered. A concurrent vigilance task was standardized under all experimental conditions. The results show that subjects can perform nearly perfectly on all of the time-shared tasks if long-term memory retrieval is not required for monitoring. With the requirement of memory retrieval, however, there was a large decrease in accuracy for all of the time-shared activities. It was concluded that the attentional demand of longterm memory retrieval is appreciable (even for a well-learned motor sequence), and thus memory retrieval results in a sizable reduction in the capability of subjects to divide their attention. A selected bibliography on the divided attention literature is provided.

Chechile, R. A.↗

NASA NTRS1993

Attentional tunneling and the head-up display

Twenty instrument-rated flight students flew simulated landing approaches to a realistic graphics generated airport, using either a head-up display (HUD) of instrument landing systems (ILS) instruments or the same display positioned 8.5 deg. head down. Responses to discrete events appearing either on the ground environment or on the instrument display assessed the pilot's ability to switch attention between these domains. Unexpected events also occurred once (per subject) in each domain. The results revealed that flight path control and attention switching to the instrumnet display was better supported by the HUD, while this advantage disappeared for attention switching to the environment, and for detection of unexpected events. The results are discussed in terms of the attention strategies employed in sampling multiple information sources.

Wickens, Christopher D.↗

NASA NTRS2001

Dynamics of the spatial scale of visual attention revealed by brain event-related potentials

The temporal dynamics of the spatial scaling of attention during visual search were examined by recording event-related potentials (ERPs). A total of 16 young participants performed a search task in which the search array was preceded by valid cues that varied in size and hence in precision of target localization. The effects of cue size on short-latency (P1 and N1) ERP components, and the time course of these effects with variation in cue-target stimulus onset asynchrony (SOA), were examined. Reaction time (RT) to discriminate a target was prolonged as cue size increased. The amplitudes of the posterior P1 and N1 components of the ERP evoked by the search array were affected in opposite ways by the size of the precue: P1 amplitude increased whereas N1 amplitude decreased as cue size increased, particularly following the shortest SOA. The results show that when top-down information about the region to be searched is less precise (larger cues), RT is slowed and the neural generators of P1 become more active, reflecting the additional computations required in changing the spatial scale of attention to the appropriate element size to facilitate target discrimination. In contrast, the decrease in N1 amplitude with cue size may reflect a broadening of the spatial gradient of attention. The results provide electrophysiological evidence that changes in the spatial scale of attention modulate neural activity in early visual cortical areas and activate at least two temporally overlapping component processes during visual search.

Non-NASA Center↗

NASA NTRS2010

System and method for image mapping and visual attention

A method is described for mapping dense sensory data to a Sensory Ego Sphere (SES). Methods are also described for finding and ranking areas of interest in the images that form a complete visual scene on an SES. Further, attentional processing of image data is best done by performing attentional processing on individual full-size images from the image sequence, mapping each attentional location to the nearest node, and then summing attentional locations at each node.

Peters, II, Richard A.↗

NASA NTRS2011

System and method for image mapping and visual attention

Peters, II, Richard A.↗

NASA NTRS2016

Psychophysiological Sensing and State Classification for Attention Management in Commercial Aviation

Attention-related human performance limiting states (AHPLS) can cause pilots to lose airplane state awareness (ASA), and their detection is important to improving commercial aviation safety. The Commercial Aviation Safety Team found that the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness, and that distraction of various forms was involved in all of them. Research on AHPLS, including channelized attention, diverted attention, startle / surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors has been implemented to simultaneously measure their physiological markers during high fidelity flight simulation human subject studies. Pilot participants were asked to perform benchmark tasks and experimental flight scenarios designed to induce AHPLS. Pattern classification was employed to distinguish the AHPLS induced by the benchmark tasks. Unimodal classification using pre-processed electroencephalography (EEG) signals as input features to extreme gradient boosting, random forest and deep neural network multiclass classifiers was implemented. Multi-modal classification using galvanic skin response (GSR) in addition to the same EEG signals and using the same types of classifiers produced increased accuracy with respect to the unimodal case (90 percent vs. 86 percent), although only via the deep neural network classifier. These initial results are a first step toward the goal of demonstrating simultaneous real time classification of multiple states using multiple sensing modalities in high-fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.

Harrivel, Angela R.↗