Susan Bell Trickett

Note-taking for self-explanation and problem solving

We explore the effects of interfaces to take notes on problem solving and learning in a scientific discovery domain. In 2 experiments (1 correlational, 1 experimental), participants solved a series of 5 scientific reasoning problems in a computer environment. We provided some participants with access to an online notepad and found 3 main results: (a) Using the notepad helped participants solve the problems more accurately; (b) the benefits of using the notepad persisted after participants had stopped using it; and (c) participants who used the notepad for problem solving and self-explanation learned more, regardless of the type of notepad interface that was provided. Implications for learning systems with online notepads are discussed.

Toward a comprehensive model of graph comprehension: making the case for spatial cognition

We argue that a comprehensive model of graph comprehension must include spatial cognition. We propose that current models of graph comprehension have not needed to incorporate spatial processes, because most of the task/graph combinations used in the psychology laboratory are very simple and can be addressed using perceptual processes. However, data from our own research in complex domains that use complex graphs shows extensive use of spatial processing. We propose an extension to current models of graph comprehension in which spatial processing occurs a) when information is not explicitly represented in the graph and b) when simple perceptual processes are inadequate to extract that implicit information. We apply this model extension to some previously published research on graph comprehension from different labs, and find that it is able to account for the results.

The Relationship Between Spatial Transformations and Iconic Gestures

Current theories of gesture production all suggest that spatial working memory is a critical component of iconic gesture production. However, none of the models has a selection mechanism for what aspect of spatial working memory is gestured. We explored how expert and journeyman scientists gestured while discussing their work. Participants were most likely to make iconic gestures about change over time (spatial transformations), less likely to gesture about spatial relations and locations (geometric relations), and far less likely to gesture about the magnitude of spatial entities. We also found that experts were especially likely to have a high degree of association between iconic gestures and spatial transformations. These results show that different features of spatial language are gestured about at different rates. We suggest that current gesture production models need to be expanded to include selection mechanisms to account for these differences.

Extracting Explicit and Implict Information from Complex Visualizations

How do experienced users extract information from a complex visualization? We examine this question by presenting experienced weather forecasters with visualizations that did not show the needed information explicitly and examining their eye movements. We replicated Carpenter & Shah (1998) when the information was explicitly available on the visualization. However, when the information was not explicitly available, we found that forecasters used spatial reasoning in the form of spatial transformations. We also found a strong imagerial component for constructing meteorological information.

Visualizing uncertainty: the impact on performance.

Objective: This work investigated the impact of uncertainty representation on performance in a complex authentic visualization task, submarine localization. Background: Because passive sonar does not provide unique course, speed, and range information on a contact, the submarine operates under significant uncertainty. There are many algorithms designed to address this problem, but all are subject to uncertainty. The extent of this solution uncertainty can be expressed in several ways, including a table of locations (course, speed, range) or a graphical area of uncertainty. Method: To test the hypothesis that the representation of uncertainty that more closely matches the experts’ preferred representation of the problem would better support performance, even for the nonexpert., performance data were collected using displays that were either stripped of the spatial or the tabular representation. Results: Performance was more accurate when uncertainty was displayed spatially. This effect was only significant for the nonexperts for whom the spatial displays supported almost expert-like performance. This effect appears to be due to reduced mental effort. Conclusion: These results suggest that when the representation of uncertainty for this spatial task better matches the expert’s preferred representation of the problem even a nonexpert can show expert-like performance. Application: These results could apply to any domain where performance requires working with highly uncertain information.

How Do Scientists Respond to Anomalies? Different Strategies Used in Basic and Applied Science

We conducted two in vivo studies to explore how scientists respond to anomalies. Based on prior research, we identify three candidate strategies: mental simulation, mental manipulation of an image, and comparison between images. In Study 1, we compared experts in basic and applied domains (physics and meteorology). We found that the basic scientists used mental simulation to resolve an anomaly, whereas applied science practitioners mentally manipulated the image. In Study 2, we compared novice and expert meteorologists. We found that unlike experts, novices used comparison to address anomalies. We discuss the nature of expertise in the two kinds of science, the relationship between the type of science and the task performed, and the relationship of the strategies investigated to scientific creativity.

Spatial Transformations in Graph Comprehension

Although it is apparent that people are able to make inferences from graphs, it is presently unclear how they do so, even from simple graphs. Current theories of graph comprehension are largely silent about the processes by which such inferences are made (e.g., Freedman & Shah, 2002; Pinker, 1990). We propose that people use spatial reasoning, in the form of spatial transformations (Trafton, Trickett, & Mintz, in press), to answer inferential questions. Spatial transformations are cognitive operations that a person performs on internal or external visualizations, such as graphs. They occur when people must mentally create or delete something (e.g., a line) on the image in order to facilitate problem solving, and may be related to hypothetical drawing (Shimojima & Fukaya, 2003). This paper investigates the use of spatial transformations when people need to make inferences from graphs.

Unpacking the temporal advantage of distributing complex visual displays

Spatial arrangement of information can have large effects on problem solving. Although such effects have been observed in various domains (e.g., instruction and interface designs), little is known about the cognitive processing mechanisms underlying these effects, nor its applicability to complex visual problem solving. In three experiments, we showed that the impact of spatial arrangement of information on problem solving time can be surprisingly large for complex real world tasks. It was also found that the effect can be caused by large increases in slow, external information searches (Experiment 1), that the spatial arrangement itself is the critical factor and the effect is domain-general (Experiment 2a), and that the underlying mechanism can involve micro-strategy selection for information encoding in a response to differing information access cost (Experiment 2b). Overall, these studies show a large slowdown effect (i.e., approximately 30%) that stacking information produces over spatially distributed information, and multiple paths by which this effect can be produced.