Hi! I'm Caleb Strait

I'm a cognitive scientist & data scientist specializing in human decision-making processes and their shortfalls. My doctoral work focused on applying machine learning techniques to neural data collected mid-decision. Professionally I build models to support medical decisions in healthcare and clinical trial settings. In my spare time I work on my conversation framework mobile app, Discourser, which I've designed from the ground-up to encourage opinion convergance among debate participants.


A mobile app framework for productive discussions

Discourser uses a tree framework for the visualization of an argument’s structure. The argument begins, in this case, as a central statement to be accepted or rejected. The central statement can be given any number of sub-statements, which are each explicitly designated as a supporting statement or a dissenting statement. Each sub-statement can also have any number of its own supporting or dissenting sub-statements. This recursive structure allows participants to fully document the intricacies of not only the supporting and dissenting sub-statements pertaining to a central statement, but also to suss out the strength of each of those sub-statements through the same system as with the central statement: a series of supporting and dissenting sub-statements.
Discourser maintains agreement scores for each statement that is added to a conversation, allowing for full documentation of participants’ relevant opinions on each part of the argument. Similar to how online comment sections allow for the vote-based finding of the best text-based argument, agreement scores give us a way to sort out how convincing parts of the argument are separately from one another, as segmented via the tree framework.

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