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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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
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 NTRS2002

The footprints of visual attention in the Posner cueing paradigm revealed by classification images

In the Posner cueing paradigm, observers' performance in detecting a target is typically better in trials in which the target is present at the cued location than in trials in which the target appears at the uncued location. This effect can be explained in terms of a Bayesian observer where visual attention simply weights the information differently at the cued (attended) and uncued (unattended) locations without a change in the quality of processing at each location. Alternatively, it could also be explained in terms of visual attention changing the shape of the perceptual filter at the cued location. In this study, we use the classification image technique to compare the human perceptual filters at the cued and uncued locations in a contrast discrimination task. We did not find statistically significant differences between the shapes of the inferred perceptual filters across the two locations, nor did the observed differences account for the measured cueing effects in human observers. Instead, we found a difference in the magnitude of the classification images, supporting the idea that visual attention changes the weighting of information at the cued and uncued location, but does not change the quality of processing at each individual location.

Non-NASA Center↗
NASA NTRS2015

A Metric to Quantify Shared Visual Attention in Two-Person Teams

Introduction: Critical tasks in high-risk environments are often performed by teams, the members of which must work together efficiently. In some situations, the team members may have to work together to solve a particular problem, while in others it may be better for them to divide the work into separate tasks that can be completed in parallel. We hypothesize that these two team strategies can be differentiated on the basis of shared visual attention, measured by gaze tracking. 2) Methods: Gaze recordings were obtained for two-person flight crews flying a high-fidelity simulator (Gontar, Hoermann, 2014). Gaze was categorized with respect to 12 areas of interest (AOIs). We used these data to construct time series of 12 dimensional vectors, with each vector component representing one of the AOIs. At each time step, each vector component was set to 0, except for the one corresponding to the currently fixated AOI, which was set to 1. This time series could then be averaged in time, with the averaging window time (t) as a variable parameter. For example, when we average with a t of one minute, each vector component represents the proportion of time that the corresponding AOI was fixated within the corresponding one minute interval. We then computed the Pearson product-moment correlation coefficient between the gaze proportion vectors for each of the two crew members, at each point in time, resulting in a signal representing the time-varying correlation between gaze behaviors. We determined criteria for concluding correlated gaze behavior using two methods: first, a permutation test was applied to the subjects' data. When one crew member's gaze proportion vector is correlated with a random time sample from the other crewmember's data, a distribution of correlation values is obtained that differs markedly from the distribution obtained from temporally aligned samples. In addition to validating that the gaze tracker was functioning reasonably well, this also allows us to compute probabilities of coordinated behavior for each value of the correlation. As an alternative, we also tabulated distributions of correlation coefficients for synthetic data sets, in which the behavior was modeled as a first-order Markov process, and compared correlation distributions for identical processes with those for disparate processes, allowing us to choose criteria and estimate error rates. 3) Discussion: Our method of gaze correlation is able to measure shared visual attention, and can distinguish between activities involving different instruments. We plan to analyze whether pilots strategies of sharing visual attention can predict performance. Possible measurements of performance include expert ratings from instructors, fuel consumption, total task time, and failure rate. While developed for two-person crews, our approach can be applied to larger groups, using intra-class correlation coefficients instead of the Pearson product-moment correlation.

Gontar, Patrick↗
NASA NTRS2016

Measuring and Modeling Shared Visual Attention

Multi-person teams are sometimes responsible for critical tasks, such as flying an airliner. Here we present a method using gaze tracking data to assess shared visual attention, a term we use to describe the situation where team members are attending to a common set of elements in the environment. Gaze data are quantized with respect to a set of N areas of interest (AOIs); these are then used to construct a time series of N dimensional vectors, with each vector component representing one of the AOIs, all set to 0 except for the component corresponding to the currently fixated AOI, which is set to 1. The resulting sequence of vectors can be averaged in time, with the result that each vector component represents the proportion of time that the corresponding AOI was fixated within the given time interval. We present two methods for comparing sequences of this sort, one based on computing the time-varying correlation of the averaged vectors, and another based on a chi-square test testing the hypothesis that the observed gaze proportions are drawn from identical probability distributions.We have evaluated the method using synthetic data sets, in which the behavior was modeled as a series of activities, each of which was modeled as a first-order Markov process. By tabulating distributions for pairs of identical and disparate activities, we are able to perform a receiver operating characteristic (ROC) analysis, allowing us to choose appropriate criteria and estimate error rates.We have applied the methods to data from airline crews, collected in a high-fidelity flight simulator (Haslbeck, Gontar Schubert, 2014). We conclude by considering the problem of automatic (blind) discovery of activities, using methods developed for text analysis.

attention↗
NASA NTRS2016

Measuring and Modeling Shared Visual Attention

Multi-person teams are sometimes responsible for critical tasks, such as flying an airliner. Here we present a method using gaze tracking data to assess shared visual attention, a term we use to describe the situation where team members are attending to a common set of elements in the environment. Gaze data are quantized with respect to a set of N areas of interest (AOIs); these are then used to construct a time series of N dimensional vectors, with each vector component representing one of the AOIs, all set to 0 except for the component corresponding to the currently fixated AOI, which is set to 1. The resulting sequence of vectors can be averaged in time, with the result that each vector component represents the proportion of time that the corresponding AOI was fixated within the given time interval. We present two methods for comparing sequences of this sort, one based on computing the time-varying correlation of the averaged vectors, and another based on a chi-square test testing the hypothesis that the observed gaze proportions are drawn from identical probability distributions. We have evaluated the method using synthetic data sets, in which the behavior was modeled as a series of "activities," each of which was modeled as a first-order Markov process. By tabulating distributions for pairs of identical and disparate activities, we are able to perform a receiver operating characteristic (ROC) analysis, allowing us to choose appropriate criteria and estimate error rates. We have applied the methods to data from airline crews, collected in a high-fidelity flight simulator (Haslbeck, Gontar & Schubert, 2014). We conclude by considering the problem of automatic (blind) discovery of activities, using methods developed for text analysis.

attention↗
NASA NTRS2015

Measuring and Modeling Shared Visual Attention

Multi-person teams are sometimes responsible for critical tasks, such as flying an airliner. Here we present a method using gaze tracking data to assess shared visual attention, a term we use to describe the situation where team members are attending to a common set of elements in the environment. Gaze data are quantized with respect to a set of N areas of interest (AOIs); these are then used to construct a time series of N dimensional vectors, with each vector component representing one of the AOIs, all set to 0 except for the component corresponding to the currently fixated AOI, which is set to 1. The resulting sequence of vectors can be averaged in time, with the result that each vector component represents the proportion of time that the corresponding AOI was fixated within the given time interval.We present two methods for comparing sequences of this sort, one based on computing the time varying correlation of the averaged vectors, and another based on a chi-square test testing the hypothesis that the observed gaze proportions are drawn from identical probability distributions.We have evaluated the method using synthetic data sets, in which the behavior was modeled as a series of activities, each of which was modeled as a first-order Markov process. By tabulating distributions for pairs of identical and disparate activities, we are able to perform a receiver operating characteristic (ROC) analysis, allowing us to choose appropriate criteria and estimate error rates. Using these criteria, we have applied the methods to data from airline crews, collected in a high-fidelity flight simulator (Gontar Hoermann, 2014). We conclude by considering the problem of automatic (blind) discovery of activities, using methods developed for text analysis.

Mulligan, Jeffrey B.↗
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 NTRS2011

Simulating Visual Attention Allocation of Pilots in an Advanced Cockpit Environment

This paper describes the results of experiments conducted with human line pilots and a cognitive pilot model during interaction with a new 40 Flight Management System (FMS). The aim of these experiments was to gather human pilot behavior data in order to calibrate the behavior of the model. Human behavior is mainly triggered by visual perception. Thus, the main aspect was to setup a profile of human pilots' visual attention allocation in a cockpit environment containing the new FMS. We first performed statistical analyses of eye tracker data and then compared our results to common results of familiar analyses in standard cockpit environments. The comparison has shown a significant influence of the new system on the visual performance of human pilots. Further on, analyses of the pilot models' visual performance have been performed. A comparison to human pilots' visual performance revealed important improvement potentials.

Frische, F.↗
NASA NTRS2015

Visual Attention Allocation Between Robotic Arm and Environmental Process Control: Validating the STOM Task Switching Model

Fifty six participants time shared a spacecraft environmental control system task with a realistic space robotic arm control task in either a manual or highly automated version. The former could suffer minor failures, whose diagnosis and repair were supported by a decision aid. At the end of the experiment this decision aid unexpectedly failed. We measured visual attention allocation and switching between the two tasks, in each of the eight conditions formed by manual-automated arm X expected-unexpected failure X monitoring- failure management. We also used our multi-attribute task switching model, based on task attributes of priority interest, difficulty and salience that were self-rated by participants, to predict allocation. An un-weighted model based on attributes of difficulty, interest and salience accounted for 96 percent of the task allocation variance across the 8 different conditions. Task difficulty served as an attractor, with more difficult tasks increasing the tendency to stay on task.

STOM attention allocation model validation↗
NASA NTRS2015

A Metric to Quantify Shared Visual Attention in Two-Person Teams

Critical tasks in high-risk environments are often performed by teams, the members of which must work together efficiently. In some situations, the team members may have to work together to solve a particular problem, while in others it may be better for them to divide the work into separate tasks that can be completed in parallel. We hypothesize that these two team strategies can be differentiated on the basis of shared visual attention, measured by gaze tracking.

Gontar, Patrick↗
NASA NTRS2012

Quantifying Pilot Visual Attention in Low Visibility Terminal Operations

Quantifying pilot visual behavior allows researchers to determine not only where a pilot is looking and when, but holds implications for specific behavioral tracking when these data are coupled with flight technical performance. Remote eye tracking systems have been integrated into simulators at NASA Langley with effectively no impact on the pilot environment. This paper discusses the installation and use of a remote eye tracking system. The data collection techniques from a complex human-in-the-loop (HITL) research experiment are discussed; especially, the data reduction algorithms and logic to transform raw eye tracking data into quantified visual behavior metrics, and analysis methods to interpret visual behavior. The findings suggest superior performance for Head-Up Display (HUD) and improved attentional behavior for Head-Down Display (HDD) implementations of Synthetic Vision System (SVS) technologies for low visibility terminal area operations. Keywords: eye tracking, flight deck, NextGen, human machine interface, aviation

Ellis, Kyle K.↗
NASA NTRS1997

Superimposition, symbology, visual attention, and the head-up display

In two experiments we examined a number of related factors postulated to influence head-up display (HUD) performance. We addressed the benefit of reduced scanning and the cost of increasing the number of elements in the visual field by comparing a superimposed HUD with an identical display in a head-down position in varying visibility conditions. We explored the extent to which the characteristics of HUD symbology support a division of attention by contrasting conformal symbology (which links elements of the display image to elements of the far domain) with traditional instrument landing system (ILS) symbology. Together the two experiments provide strong evidence that minimizing scanning between flight instruments and the far domain contributes substantially to the observed HUD performance advantage. Experiment 1 provides little evidence for a performance cost attributable to visual clutter. In Experiment 2 the pattern of differences in lateral tracking error between conformal and traditional ILS symbology supports the hypothesis that, to the extent that the symbology forms an object with the far domain, attention may be divided between the superimposed image and its counterpart in the far domain.

Attention↗
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

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 all attentional locations at each node.

Peters, II, Richard A.↗
NASA NTRS1984

Visual Attention to Radar Displays

A model is described which predicts the allocation of attention to the features of a radar display. It uses the growth of uncertainty and the probability of near collision to call the eye to a feature of the display. The main source of uncertainty is forgetting following a fixation, which is modelled as a two dimensional diffusion process. The model was used to predict information overload in intercept controllers, and preliminary validation obtained by recording eye movements of intercept controllers in simulated and live (practice) interception.

Moray, N.↗
NASA NTRS1984

Moving attention - Evidence for time-invariant shifts of visual selective attention

Two experiments measured the time to shift spatial selective attention across the visual field to targets 2 or 10 deg from central fixation. A central arrow cued the most likely target location. The direction of attention was inferred from reaction times to expected, unexpected, and neutral locations. The development of a spatial attentional set with time was examined by presenting target probes at varying times after the cue. There were no effects of distance on the time course of the attentional set. Reaction times for far locations were slower than for near, but the effects of attention were evident by 150 msec in both cases. Spatial attention does not shift with a characteristic, fixed velocity. Rather, velocity is proportional to distance, resulting in a movement time that is invariant over the distances tested.

Remington, R.↗