CS295J/Boards from class 7.11

Identifying and Addressing Common Workflow Gaps in Brain Data Analysis (Caroline)

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Notes: Combine with some kind of cognitive load measurement, insight evaluation, to discover patterns that help explain evaluation results?

Comparison of user experience across different platform combos (Wenjun)

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Notes: Compare work efficiency, user experience and other factors in different platform combos for users who need to transfer data between softwares

The factors we can consider to compare :

• The type of users: are they multi-tasking people ? Or do they like better to spend long time on one software and then turn to another ? What is the percentage of each group of people? How does this habit difference influence the design of software combo ?

• The use of time : What is the time expense in the switching tasks ? Which is the better , AAABBB OR ABABAB ? (A and B stands for different softwares, and each character stands for an hour's work)

• For different features, what kind of softwares assist each other ? what kind of softwares conflict each other ? Can we apply some cognitive principles ?

• Given a task and a software in use , how to develop a strategy to select the best software match in a software pool for this given software?

• Could you design a graph or an experiment to measure how usable , how annoying and how ... among different combos .

• Hypothesis

1 We assume that consistency should be an important factor in platform combos. The universal identifier, function of shortcuts, transferred data forms, software operations should keep the same.

2 If impossible to keep the same , we should make them totally different. Because being similar is even worse. It will make a lot of confusions . We may design an experiment to testify that for the same function or feature , " the exactly same design" is better than " totally different design " , which are both better than " similar design ".

Predicting Analyst Performance at Acquisition Quality Assessment from Visual Scanning of Brain Image Montages (Steve)

Oct 18, 2011

Oct 13, 2011

Acquisition quality assessment is a first step in several brain imaging analysis workflows we've seen. This is true with Win and the folks who are using MRIcron and SPM. In both cases, they evaluate a sample of slices and visually inspect them to grade quality.

Hypotheses:

• Analysts who assess dataset quality (typically research assistants) have different skill levels are making these quality judgments, esp. under time constraints.
• We can measure the quality of that skill level. For instance, we can have a very experienced analyst score a set of acquisitions with infinite time and use this as "ground truth", then compare analysts' judgments of these sets against that ground truth.
• We can predict an analyst's skill level based on his/her strategy for looking at sampled slices (e.g., how much time spent with each slice, how many slices inspected, where a user looks in each image using eye-tracking).

Memory-Modulated Visual Search Patterns (Clara and Nathan)

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Task: Attempt to identify user search patterns while navigating an interface to find a given item that could be in multiple locations.

Hypothesis: 1. Users will check the most recent location that the item was found, then the next most recent, etc. 2. Users will check the location where the item is most frequently found, then the next most frequent, etc.

Optimizing Zoom in the Perception of Complex Networks (Michael)

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Integrating Multi Dimensions in EEG Data Analysis (Diem)

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Cognitive Task Analysis for EEG/fMRI Data Analysis (Hua)

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Measuring Cognitive Load During fMRI Data Analysis (Jenna)

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Memory-Modulated Visual Search Patterns on Touch Screen Interfaces (Clara and Nathan)

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