Learning from the Science of Cognition and Perception for Decision Making Proceedings of a Workshop (2018) / Chapter Skim
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5 Perceptual and Cognitive Constraints
5 Perceptual and Cognitive Constraints
Pages 35-46
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From page 35... ...
It would also consider tools designed to help process large-scale datasets so they can be understood by users, as well as the role of the human in human–machine partnerships. CAPACITY LIMITS IN ONLINE MEMORY AND ATTENTION Edward Awh, University of Chicago, discussed the concepts of working memory and attention and how they can be measured using robust scientific techniques.
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From page 36... ...
He added that studies have shown that as much as 30 to 40 percent of the observed variation in intelligence across individuals can be explained by this color memory test, clearly indicating that working memory is important for intelligent behavior. Since the 1970s, Awh continued, advances have been made in the ability to relate brain activity to the cognitive operations of working memory and attention, including developments in animal neurophysiology and human neuroimaging techniques.
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From page 37... ...
He pointed out that because of the high temporal resolution of this electrical approach, the brain activity data gathered can be plotted over time, providing a representation of not just what subjects are thinking about but also when they are thinking about it. Awh explained that the ability to look at the current content of memory and attention with temporal resolution will have practical applications in building powerful brain–computer interfaces, such as robotic limbs that can be controlled by signals from electrodes implanted in the brain.
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From page 38... ...
"The way we can deal with challenges and scalability in our current data economy," she said, "is to harness the power of the human visual system to help people find the right kinds of patterns in their data." Szafir described a ranking system, developed in the 1980s, that is still used in conventional visualization systems today.3 This system ranks the effectiveness of different aspects of the visual presentation of data, including position, length, orientation, area, lightness, and color. Szafir explained that the choice of the visual presentation can fundamentally affect the kinds of patterns people extract from the data, illustrating this point with research in which study participants were shown a simple bar graph with two bars, A and B, of different sizes.4 When asked to describe the data, participants generally said, "B is bigger than A," but when the same data were plotted as a line graph, participants said, "the values are increasing." According to Szafir, the results of this research demonstrate that the type of presentation affects the kinds of patterns people extract from visual displays.
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From page 39... ...
scales require multiple perspectives on the data to enable viewing both high-level and low-level information. Turning to the strategy of collaborating with computers to help people analyze large-scale data, Szafir described an ongoing project that involves analyzing satellite imagery data to detect targets of interest, such as intercontinental ballistic missiles.
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From page 40... ...
FROM VISION SCIENCE TO DATA SCIENCE Remco Chang, Tufts University, discussed how the physical limits of display technology and perceptual limits of humans can be understood to improve the data visualization pipeline -- for example, by decreasing the wait time experienced during searches of large databases. He began by describing the three current components of analysis of large amounts of data: machine learning techniques, storage of data in large databases, and visualization tools used to present data and analyses in ways that help users understand the data.
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From page 41... ...
He argued as well that perceptually driven computation should be able to highlight aspects of the data that the model predicts an analyst will miss. These advances, he said, will require collaboration between the fields of psychology, cognitive science, and computer science and the community of intelligence analysts.
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From page 42... ...
The model used in the study, Pirolli explained, can be used by researchers to understand how visual systems should be arranged so they can maximize the knowledge users gain from them. Pirolli then discussed trust, or the credibility of information sources, as another important component of successful human–AI interactions that can be studied using a multilevel cognitive model.
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From page 43... ...
Regarding directability, he stated that "we are nowhere near having very easily done interactive tasks between a human and AI system where I can direct it to do tasks that are reasonably complicated." Pirolli predicted that in 3 to 5 years, there will be better understanding of how to create and use multilevel models, explainable AI for visual analytics and simulated drone operations, and interactive task learning for simple robots with well-defined tasks. Looking 10 years out, he highlighted the 10 Canini, K., Suh, B., and Pirolli, P.L.
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From page 44... ...
He referred to a study on whether visualization tools could help users understand Bayesian or conditional probability. When participants were divided into groups of high and low spatial ability, he said, the tools could be improved using data from interactions of the group with high spatial ability, whereas it was difficult to use combined data derived from both groups to improve on the effectiveness of the tools.
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From page 45... ...
Expertise and long-term memories, argued Awh, can speed identification of the most relevant aspects of a situation and compensate for any limitations in working memory. Pirolli agreed, noting, for example, that younger analysts may on average score higher than their more experienced counterparts on cognitive measures of working memory and spatial reasoning, but experienced analysts can often work more efficiently with the knowledge they have accumulated.
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