This is a lesson I designed for the research assistants in the Eckstein lab. The learning objectives for this lesson were that students will be able to:
1: describe four of the main theories of category learning and how each theory improved upon the previous theory.
2: list the four key tenants of Computation Cognitive Neuroscience.
3: manipulate basic models of human category learning to model actual human performance.
The lesson involved four parts:
1:
Students watch the pre-recorded lecture. (embedded to the right)
The main learning objective was for students to be able to explain each theory and how it improved on the previous one (Learning Objective 1). This set the stage for the mini-lecture at the beginning of the live zoom meeting where I discussed why COVIS was able to improve so dramatically on previous theories.
2:
Students run themselves in a Category Learning experiment.
This helped engaged students by turning "category learning" from an abstract concept into something they have actually done and gave them their own data to model.
3:
Live Zoom Mini-lecture on Computational Cognitive Neuroscience.
I motivated this section by tying it back to the pre-recorded lecture (How was COVIS able to improve so dramatically on previous theories?). I then discussed Computational Cognitive Neuroscience and led a class discussion on how each of the four tenants contributed to COVIS' success (Learning Objective 2)
4:
Students model their own Category Learning performance.
I split the students up into zoom breakout rooms of 3 students to work on modeling their own data using code I provided. They were tasked with modeling their own individual learning curves as well as the average learning curve of their group (Learning Objective 3) while I switched between breakout rooms to help answer questions.