Here, transcranial direct current stimulation (tDCS) is a potent ial tool for alleviating various forms of cognitive decline, including memory loss, in older adults. However, past effects of tDCS on cognitive ability have been mixed. One important potential moderator of tDCS effects is the baseline level of cognitive performance. We tested the effects of tDCS on face-name associative memory in older adults, who suffer from performance deficits in this task relative to younger adults. Stimulation was applied to the left inferior prefrontal cortex during encoding of face-name pairs, and memory was assessed with both a recognition and recall task. As a result, face–name memory performance was decreased with the use of tDCS. This result was driven by increased false alarms when recognizing rearranged face–name pairs.
This summary of PANTHER Human Analytics work describes three of the team's major work activities: research with teams to elicit and document work practices; experimental studies of visual search performance and visual attention; and the application of spatio-temporal algorithms to the analysis of eye tracking data. Our intent is to provide basic introduction to the work area and a selected set of representative HA team publications as a starting point for readers interested our team's work.
In this study, eye tracking metrics and visual saliency maps were used to assess analysts' interactions with synthetic aperture radar (SAR) imagery. Participants with varying levels of experience with SAR imagery completed a target detection task while their eye movements and behavioral responses were recorded. The resulting gaze maps were compared with maps of bottom-up visual saliency and with maps of automatically detected image features The results showed striking differences between professional SAR analysis and novices in terms of how their visual search patterns related to the visual saliency of features in the imagery. They also revealed patterns that reflect the utility of various features in the images for the professional analysts These findings have implications for system design andfor the design and use of automatic feature classification algorithms.
In this study, eye tracking metrics and visual saliency maps were used to assess analysts' interactions with synthetic aperture radar (SAR) imagery. Participants with varying levels of experience with SAR imagery completed a target detection task while their eye movements and behavioral responses were recorded. The resulting gaze maps were compared with maps of bottom-up visual saliency and with maps of automatically detected image features The results showed striking differences between professional SAR analysis and novices in terms of how their visual search patterns related to the visual saliency of features in the imagery. They also revealed patterns that reflect the utility of various features in the images for the professional analysts These findings have implications for system design andfor the design and use of automatic feature classification algorithms.
A critical challenge in data science is conveying the meaning of data to human decision makers. While working with visualizations, decision makers are engaged in a visual search for information to support their reasoning process. As sensors proliferate and high performance computing becomes increasingly accessible, the volume of data decision makers must contend with is growing continuously and driving the need for more efficient and effective data visualizations. Consequently, researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles to assess the effectiveness of data visualizations. In this paper, we compare the performance of three different saliency models across a common set of data visualizations. This comparison establishes a performance baseline for assessment of new data visualization saliency models.
‘Big data’ is a phrase that has gained much traction recently. It has been defined as ‘a broad term for data sets so large or complex that traditional data processing applications are inadequate and there are challenges with analysis, searching and visualization’ [1]. Many domains struggle with providing experts accurate visualizations of massive data sets so that the experts can understand and make decisions about the data e.g., [2, 3, 4, 5]. Abductive reasoning is the process of forming a conclusion that best explains observed facts and this type of reasoning plays an important role in process and product engineering. Throughout a production lifecycle, engineers will test subsystems for critical functions and use the test results to diagnose and improve production processes. This paper describes a value-driven evaluation study [7] for expert analyst interactions with big data for a complex visual abductive reasoning task. Participants were asked to perform different tasks using a new tool, while eye tracking data of their interactions with the tool was collected. The participants were also asked to give their feedback and assessments regarding the usability of the tool. The results showed that the interactive nature of the new tool allowed the participants to gain new insights into their data sets, and all participants indicated that they would begin using the tool in its current state.
A critical challenge in data science is conveying the meaning of data to human decision makers. While working with visualizations, decision makers are engaged in a visual search for information to support their reasoning process. As sensors proliferate and high performance computing becomes increasingly accessible, the volume of data decision makers must contend with is growing continuously and driving the need for more efficient and effective data visualizations. Consequently, researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles to assess the effectiveness of data visualizations. In this paper, we compare the performance of three different saliency models across a common set of data visualizations. This comparison establishes a performance baseline for assessment of new data visualization saliency models.
‘Big data’ is a phrase that has gained much traction recently. It has been defined as ‘a broad term for data sets so large or complex that traditional data processing applications are inadequate and there are challenges with analysis, searching and visualization’ [1]. Many domains struggle with providing experts accurate visualizations of massive data sets so that the experts can understand and make decisions about the data e.g., [2, 3, 4, 5]. Abductive reasoning is the process of forming a conclusion that best explains observed facts and this type of reasoning plays an important role in process and product engineering. Throughout a production lifecycle, engineers will test subsystems for critical functions and use the test results to diagnose and improve production processes. This paper describes a value-driven evaluation study [7] for expert analyst interactions with big data for a complex visual abductive reasoning task. Participants were asked to perform different tasks using a new tool, while eye tracking data of their interactions with the tool was collected. The participants were also asked to give their feedback and assessments regarding the usability of the tool. The results showed that the interactive nature of the new tool allowed the participants to gain new insights into their data sets, and all participants indicated that they would begin using the tool in its current state.
