If we pull on a string and something immediately moves, we infer a causal relation between our action and the result. Toss a stick into a bush and if something runs out we assume we caused that too. The acts of grabbing, hitting, pressing or squeezing all result in direct, contingent, visual changes in the state of the world. It is through such temporal contingencies that infants gain a basic understanding of the state of the world, and cognitive scientists argue that such experiences form the conceptual substrate on which even very abstract concepts are built. Causal linkages are ubiquitous and fundamental to our understanding of many diverse phenomena, including how people influence one another, how ecosystems function, and how physical systems function. Understanding causal linkages is, in many cases, the whole purpose of exploratory data analysis. Yet, currently, we do not have good general purpose interactive methods for exploring causal linkages. At present such linkages are generally expressed with static diagrams using simple arrows; on some occasions these arrows are labeled. Such diagrams are both difficult to understand and incapable of expressing the different kinds of phenomena that exist in an intuitive way. The PI's goal in this project is to develop visually compelling and easy to learn interactive representations of causal linkages. These will be built on simple physics-based visual metaphors that theory suggests form the substrate of our ability to cognitively model causal relationships. The interactive notation will be capable of expressing causal phenomena including negative causation, causal damping, causal amplification, causal blocking, as well as simple positive causation. In prior work, the PI has begun to develop a system of visual thinking design patterns that capture simple, effective interactive techniques, as well as basic perceptual and cognitive processes, in order to support the design processes of building tools for visual thinking. The current project will help further develop this theory, in addition to providing practical solutions to the problem of representing causal networks. Research activities will include design, design implementation, and evaluation studies of interactive causal diagramming notations. A multi-touch screen will provide the interface.
Broader Impacts: Modern theories of cognition have only just begun to take into account the fact that most real-world thinking occurs by people using interactive thinking tools, such as spreadsheets or computer aided design programs. Interactive diagrams have been shown to be extremely effective in data analysis. Techniques such as brushing, dynamic queries, and topological range highlighting provide powerful analytic tools, and multi-touch interaction is becoming increasingly available due to the widespread adoption of devices such as the iPhone. There is a need for interactive diagrams that effectively express different kinds of causal linkages in a way that can be easily understood. Such diagrams will find application in a wide variety of knowledge domains, including educational media and the interfaces that scientists and engineers use to explore causal networks.
If we pull on a string and something immediately moves, we infer a causal relation between our action and the result. Toss a stick into a bush, if something runs out we assume we caused that too. The acts of grabbing, hitting, pressing or squeezing all result in direct contingent visual changes in the state of the world. Cognitive scientists have hypothesized that it is through such temporal contingencies that infants gain a basic understanding of the state of the world. Furthermore, they propose that such experiences form the conceptual substrate on which even extremely abstract concepts are built. Interactive touch screen interfaces provide a much more immediate sensation of being directly in touch with objects on the screen. It seems reasonable to suppose that they may support a more immediate and compelling sensation of direct causation. The question asked by this project is can this be used to create interactive visual explanation of cause and effect relationships. More specifically, can it be used to help explain science in a way that is more direct, compelling and easy to understand? Dr Colin Ware at the University of New Hampshire, together with his students, carried out a series of studies to test the proposition that it will be possible to create very simple interactive animations showing such higher order effects as causal amplification or blocking, in ways that are very easy to interpret given the affordances of a multi-touch display. They invented a visual language for expressing these effects sing an interactive causal network diagram using a multi-touch interface. The results support the concept, showing that a variety of causal effects could be easily understood. In a more direct application of the work to science, Ware and graduate student Carmen St Jean also designed and built an interface to make it easier to understand a specific scientific problem, namely causal links in food webs. Our test project is in collaboration with Michael Fogarty and Robert Gamble of NOAA North East Fisheries who are developing an ecosystem based model of interactions between the key commercial species in the region. The model has 10 economically important fish species and incorporates both predation and competition between species. The model predicts that changing the catch of one species can sometimes result in changes in biomass of another species through multi-step causal chains. But because of the complex cause and effect chains, just looking at the outcomes of a change in fisheries policy can be confusing. The researchers developed dynamic change arcs as a device for interactively revealing causal linkages in the fisheries model. The results of an evaluation study showed that dynamic change arcs enabled participants to reason better about complex chains of causality than not showing linkages. Both fisheries scientists and fishermen found that it helped them interpret the possible outcomes of changes in fisheries policy.
