CS295J/Proposal reviews from class 8

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Intellectual merit

(your paragraph here)

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Gideon

Intellectual merit

A meta-analysis of a subset of the cognitive psychology and human-computer interaction literature presents evidence that interactions between humans and computers can be improved by taking into account the cognitive resources required for different types of tasks. It is well known that humans and computers excel at different types of tasks, but the field has not made an explicit effort to standardize a set of guidelines that interface designers may use when developing computer systems. For people and computers to function in an optimized, complementary fashion, we still need a systematic way of distributing tasks amongst them.

It is often the case that what computers excel at, humans have difficulty with (e.g., arithmetic). While, the opposite is also true (e.g., pattern-recognition). After a preliminary search of the literature, we've explored common tasks in software today that have neglected consideration of this performance dichotomy. Designers have not been appropriately addressed these gaps in the computer industry due to a lack of multidisciplinary research. Our meta-analysis presents data that supports our view on two different tasks: 3D shape-rotation and face recognition.

We have demonstrated in just a 30-hour study that computer assisted 3D Shape Rotation is consistently preferred over human-only mental rotation. Complementarily, humans consistently outperform modern computer systems in face recognition. Sometimes these performance gaps are obvious due to a lack of technology or common-sense. However, that is not only the case. Our preliminary study clearly demonstrates that systems benefit from consistent and rule-based task distribution guidelines. Furthermore, the literature is rich with other types of tasks which await our systematic exploitation. Upon further study, the community will benefit from a tested and systematic approach for designing improved human-computer interfaces.

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EJ

Intellectual merit

While attempts have been made in the past to apply cognitive theory to the task of developing human-computer interfaces, there remains much work to be done. No standard and widespread model for the cognitive interaction with a computer exists. The roles of perception and cognition, while examined and studied independently, are often at odds with empirical and successful design guidelines in practice. Methods of study and evaluation, such as eye-tracking and workflow analysis, are still governed primarily by the needs at the end of the development process, with no quantitative model capable of influencing efficiency and consistency in the field.

We demonstrate in wide-ranging preliminary work that cognitive theory has a tangible and valuable role in all the stages of interface design and evaluation: models of distributed cognition can exert useful influence on the design of interfaces and the guidelines that govern it; algorithmic workflow analysis can lead to new interaction methods, including predictive options; a model of human perception can greatly enhance the usefulness of multimodal user study techniques; a better understanding of why classical strategies work will bring us closer to the "holy grail" of automated interface evaluation and recommendation. We can bring the field further down many of the only partially-explored avenues of the field in the years ahead.

Gaps

• Project summary needs expanding and context, but this may be impossible before we solidify other sections
• Background is strong, and does not have an excess of context, but that may be desirable here. Some notes/outlines/questions need to be answered/expanded/removed.
• Is "Significance" supposed to be significantly (ha) different from a projection of the influence and effects of the study? Perhaps these estimates should be parsed out of contributions and placed here.
• Need stronger distinction/clarification between "Aims" and "Contributions."

Eric

Intellectual merit

With the emergence of documented interaction histories in scientific visualization comes a new source of data for predicting user interactions. Correct prediction and corresponding UI modifications allow for a more personalized interface that can improve the user's efficiency in data exploration and enables groups of researchers working on the same type of task to more efficiently learn from one another.

In a 30-hour preliminary study, we have implemented a basic interaction prediction module using a relational markov model and shown through a series of user studies that it predicts on average 35% better than chance. We have also created a module that provides basic recommendations to the user based on these interaction predictions, and have shown that the user clicks on a recommended action 20% of the time, leading to an average task speedup of 8%.

The tasks for future work are twofold: first, we will improve and generalize our prediction module to allow for more accurate predictions in a wide variety of interfaces, including those with a larger number of possible actions and states. Second, we will further study the question of, given predictions of future interactions, how to modify the interface beyond giving basic recommendations. Ultimately, research in both of these directions will allow researchers to more efficiently glean information from complex data, enabling them to more quickly and easily contribute to their respective fields.

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