Category: Resources

Join Us for Minds, Machines & Society

Join Us This Saturday for a free, open to the public event. Don’t miss Bob Mankoff, humorist and cartoon editor for the New Yorker & more recently for Esquire. Bob will provide a must-see talk about human and machine collaboration & creativity.

Posted in Events, Resources

CogSci 2018 – Why Changing Minds?

On the motivation for this year’s theme and its connection to current events. When we bid to organize CogSci three years ago the global erosion of faith in factual knowledge was already well under way. Scientific consensus was doing little to

Posted in Events, Resources

Interactive Science Communication

 by Scott Sievert and Purav “Jay” Patel Scientific research is still communicated with static text and images despite innovations in learning theories and technology. We are envisioning a future in which interactive simulations and visualizations are used to enhance how

Posted in LUCID, Machine Learning, Resources, Uncategorized

Singular Value Decomposition (SVD)

By Lowell Thompson and Ashley Hou This is a collaborative tutorial aimed at simplifying a common machine learning method known as singular value decomposition. Learn how these techniques impact computational neuroscience research as well! Singular value decomposition is a method

Posted in LUCID, Machine Learning, Resources

What is a Computational Cognitive Model?

By Rui Meng and Ayon Sen A computational cognitive model explores the essence of cognition and various cognitive functionalities through developing detailed, process-based understanding by specifying corresponding computation models. Sylvain Baillet discusses various aspects of cognitive computation models Computational model

Posted in LUCID Library, Resources

Martina Rau on Learning with Visuals

Lucid Faculty, Educational Psychology Professor, Director of Learning, Representations, & Technology Lab as well as Computer Sciences Affiliate, Martina Rau is interested in educational technologies to support more effective learning with visuals. While we generally think of visuals as helpful

Posted in LUCID Library, Resources

Poolmate: Pool-Based Machine Teaching

Poolmate provides a command-line interface to algorithms for searching teaching sets among a candidate pool. Poolmate is designed to work with any learner which can be communicated with through a file-based API. Developed by Ara Vartanian (aravart@cs.wisc.edu), Scott Alfeld (salfeld@amherst.edu),

Posted in Resources