Publications

Matzen, Laura E.

Abstract not provided.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

McNamara, Laura A.; Cole, Kerstan S.; Haass, Michael J.; Matzen, Laura E.; Morrow, James D.; Adams, Susan S.; McMichael, Stephanie N.

Researchers at Sandia National Laboratories are integrating qualitative and quantitative methods from anthropology, human factors and cognitive psychology in the study of military and civilian intelligence analyst workflows in the United States’ national security community. Researchers who study human work processes often use qualitative theory and methods, including grounded theory, cognitive work analysis, and ethnography, to generate rich descriptive models of human behavior in context. In contrast, experimental psychologists typically do not receive training in qualitative induction, nor are they likely to practice ethnographic methods in their work, since experimental psychology tends to emphasize generalizability and quantitative hypothesis testing over qualitative description. However, qualitative frameworks and methods from anthropology, sociology, and human factors can play an important role in enhancing the ecological validity of experimental research designs.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Matzen, Laura E.; Haass, Michael J.; McNamara, Laura A.; Adams, Susan S.; McMichael, Stephanie N.

Vision is one of the dominant human senses and most human-computer interfaces rely heavily on the capabilities of the human visual system. An enormous amount of effort is devoted to finding ways to visualize information so that humans can understand and make sense of it. By studying how professionals engage in these visual search tasks, we can develop insights into their cognitive processes and the influence of experience on those processes. This can advance our understanding of visual cognition in addition to providing information that can be applied to designing improved data visualizations or training new analysts. In this study, we investigated the role of expertise on performance in a Synthetic Aperture Radar (SAR) target detection task. SAR imagery differs substantially from optical imagery, making it a useful domain for investigating expert-novice differences. The participants in this study included professional SAR imagery analysts, radar engineers with experience working with SAR imagery, and novices who had little or no prior exposure to SAR imagery. Participants from all three groups completed a domain-specific visual search task in which they searched for targets within pairs of SAR images. They also completed a battery of domain-general visual search and cognitive tasks that measured factors such as mental rotation ability, spatial working memory, and useful field of view. The results revealed marked differences between the professional imagery analysts and the other groups, both for the domain-specific task and for some domain-general tasks. These results indicate that experience with visual search in non-optical imagery can influence performance on other domains.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Trumbo, Michael C.; Matzen, Laura E.; Silva, Austin R.; Haass, Michael J.; Divis, Kristin; Speed, Ann S.

Numerous domains, ranging from medical diagnostics to intelligence analysis, involve visual search tasks in which people must find and identify specific items within large sets of imagery. These tasks rely heavily on human judgment, making fully automated systems infeasible in many cases. Researchers have investigated methods for combining human judgment with computational processing to increase the speed at which humans can triage large image sets. One such method is rapid serial visual presentation (RSVP), in which images are presented in rapid succession to a human viewer. While viewing the images and looking for targets of interest, the participant’s brain activity is recorded using electroencephalography (EEG). The EEG signals can be time-locked to the presentation of each image, producing event-related potentials (ERPs) that provide information about the brain’s response to those stimuli. The participants’ judgments about whether or not each set of images contained a target and the ERPs elicited by target and non-target images are used to identify subsets of images that merit close expert scrutiny [1]. Although the RSVP/EEG paradigm holds promise for helping professional visual searchers to triage imagery rapidly, it may be limited by the nature of the target items. Targets that do not vary a great deal in appearance are likely to elicit useable ERPs, but more variable targets may not. In the present study, we sought to extend the RSVP/EEG paradigm to the domain of aviation security screening, and in doing so to explore the limitations of the technique for different types of targets. Professional Transportation Security Officers (TSOs) viewed bag X-rays that were presented using an RSVP paradigm. The TSOs viewed bursts of images containing 50 segments of bag X-rays that were presented for 100 ms each. Following each burst of images, the TSOs indicated whether or not they thought there was a threat item in any of the images in that set. EEG was recorded during each burst of images and ERPs were calculated by time-locking the EEG signal to the presentation of images containing threats and matched images that were identical except for the presence of the threat item. Half of the threat items had a prototypical appearance and half did not. We found that the bag images containing threat items with a prototypical appearance reliably elicited a P300 ERP component, while those without a prototypical appearance did not. These findings have implications for the application of the RSVP/EEG technique to real-world visual search domains.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Haass, Michael J.; Matzen, Laura E.; Adams, Susan S.; Roach, R.A.

The potential for bias to affect the results of knowledge elicitation studies is well recognized. Researchers and knowledge engineers attempt to control for bias through careful selection of elicitation and analysis methods. Recently, the development of a wide range of physiological sensors, coupled with fast, portable and inexpensive computing platforms, has added an additional dimension of objective measurement that can reduce bias effects. In the case of an abductive reasoning task, bias can be introduced through design of the stimuli, cues from researchers, or omissions by the experts. We describe a knowledge elicitation methodology robust to various sources of bias, incorporating objective and cross-referenced measurements. The methodology was applied in a study of engineers who use multivariate time series data to diagnose mance of devices throughout the production lifecycle. For visual reasoning tasks, eye tracking is particularly effective at controlling for biases of omission by providing a record of the subject’s attention allocation.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Silva, Austin; Avina, Glory E.; McClain, Jonathan T.; Matzen, Laura E.; Forsythe, James C.

There is a great need for creating cohesive, expert cybersecurity incident response teams and training them effectively. This paper discusses new methodologies for measuring and understanding expert and novice differences within a cybersecurity environment to bolster training, selection, and teaming. This methodology for baselining and characterizing individuals and teams relies on relating eye tracking gaze patterns to psychological assessments, human-machine transaction monitoring, and electroencephalography data that are collected during participation in the game-based training platform Tracer FIRE. We discuss preliminary findings from two pilot studies using novice and professional teams.

Matzen, Laura E.; Haass, Michael J.; Silva, Austin R.; Trumbo, Michael C.; Adams, Susan S.; McNamara, Laura A.; Speed, Ann S.

Abstract not provided.

McMichael, Stephanie N.; Matzen, Laura E.

Abstract not provided.

Matzen, Laura E.; Haass, Michael J.; McNamara, Laura A.

Abstract not provided.

Matzen, Laura E.; Trumbo, Michael C.

Training a person in a new knowledge base or skill set is extremely time consuming and costly, particularly in highly specialized domains such as the military and the intelligence community. Recent research in cognitive neuroscience has suggested that a technique called transcranial direct current stimulation (tDCS) has the potential to revolutionize training by enabling learners to acquire new skills faster, more efficiently, and more robustly (Bullard et al., 2011). In this project, we tested the effects of tDCS on two types of memory performance that are critical for learning new skills: associative memory and working memory. Associative memory is memory for the relationship between two items or events. It forms the foundation of all episodic memories, so enhancing associative memory could provide substantial benefits to the speed and robustness of learning new information. We tested the effects of tDCS on associative memory, using a real-world associative memory task: remembering the links between faces and names. Working memory refers to the amount of information that can be held in mind and processed at one time, and it forms the basis for all higher-level cognitive processing. We investigated the degree of transfer between various working memory tasks (the N-back task as a measure of verbal working memory, the rotation-span task as a measure of visuospatial working memory, and Raven's progressive matrices as a measure of fluid intelligence) in order to determine if tDCS-induced facilitation of performance is task-specific or general.

Matzen, Laura E.

Abstract not provided.

Matzen, Laura E.; Haass, Michael J.; Trumbo, Michael C.

Abstract not provided.

Current Biology

Matzen, Laura E.

Everyday problem solving requires the ability to go beyond experience by efficiently encoding and manipulating new information, i.e., fluid intelligence (Gf) [1]. Performance in tasks involving Gf, such as logical and abstract reasoning, has been shown to rely on distributed neural networks, with a crucial role played by prefrontal regions [2]. Synchronization of neuronal activity in the gamma band is a ubiquitous phenomenon within the brain; however, no evidence of its causal involvement in cognition exists to date [3]. Here, we show an enhancement of Gf ability in a cognitive task induced by exogenous rhythmic stimulation within the gamma band. Imperceptible alternating current [4] delivered through the scalp over the left middle frontal gyrus resulted in a frequency-specific shortening of the time required to find the correct solution in a visuospatial abstract reasoning task classically employed to measure Gf abilities (i.e., Raven’s matrices) [5]. Crucially, gamma-band stimulation (γ-tACS) selectively enhanced performance only on more complex trials involving conditional/logical reasoning. The finding presented here supports a direct involvement of gamma oscillatory activity in the mechanisms underlying higher-order human cognition.

Matzen, Laura E.

Abstract not provided.

Matzen, Laura E.; Haass, Michael J.; Trumbo, Michael C.; Adams, Susan S.; Silva, Austin R.

Abstract not provided.

Cole, Kerstan S.; Trumbo, Michael C.; Adams, Susan S.; Matzen, Laura E.; Forsythe, James C.

Abstract not provided.

Matzen, Laura E.; Haass, Michael J.; Silva, Austin R.; Peercy, David E.

Abstract not provided.

Communications in Computer and Information Science

Bandlow, Alisa; Matzen, Laura E.; Cole, Kerstan S.; Dornburg, Courtney S.; Geiseler, Charles J.; Greenfield, John A.; McNamara, Laura A.; Adams, Susan S.

Information visualization tools are being promoted to aid decision support. These tools assist in the analysis and comprehension of ambiguous and conflicting data sets. Formal evaluations are necessary to demonstrate the effectiveness of visualization tools, yet conducting these studies is difficult. Objective metrics that allow designers to compare the amount of work required for users to operate a particular interface are lacking. This in turn makes it difficult to compare workload across different interfaces, which is problematic for complicated information visualization and visual analytics packages. We believe that measures of working memory load can provide a more objective and consistent way of assessing visualizations and user interfaces across a range of applications. We present initial findings from a study using measures of working memory load to compare the usability of two graph representations. © 2011 Springer-Verlag.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Matzen, Laura E.

Technologies that augment human cognition have the potential to enhance human performance in a wide variety of domains. However, there are a number of individual differences in brain activity that must be taken into account during the development, validation, and application of augmented cognition tools. A growing body of research in cultural neuroscience has shown that there are substantial differences in how people from different cultural backgrounds approach various cognitive tasks. In addition, there are many other types of individual differences and even changes in a single individual over time that have implications for augmented cognition research and development. The aim of this session is to highlight a few of those differences and to discuss how they might impact augmented cognition technologies. © 2011 Springer-Verlag.

Matzen, Laura E.; Cole, Kerstan S.; Dornburg, Courtney S.; Bandlow, Alisa B.; Gieseler, Charles J.; McNamara, Laura A.; Adams, Susan S.

Abstract not provided.

Matzen, Laura E.; Haass, Michael J.

Abstract not provided.

McNamara, Laura A.; Matzen, Laura E.; Bandlow, Alisa B.; Gieseler, Charles J.

Abstract not provided.

Haass, Michael J.; Matzen, Laura E.

Abstract not provided.

Bandlow, Alisa B.; Cole, Kerstan S.; McNamara, Laura A.; Dornburg, Courtney S.; Adams, Susan S.; Matzen, Laura E.; Gieseler, Charles J.

Abstract not provided.

Bandlow, Alisa B.; Matzen, Laura E.; Cole, Kerstan S.; Dornburg, Courtney S.; Gieseler, Charles J.; McNamara, Laura A.; Adams, Susan S.

Abstract not provided.