1What is the main goal of the lecture regarding "reading minds"?
To build an Intelligent Tutor that understands why students make mistakes.
2Why is a "Process Model" useful for teaching computers?
It provides a map of the exact steps a brain takes to solve a problem, helping diagnose specific errors.
3What is the main advantage of using EEG over fMRI?
EEG is super fast (milliseconds) and can catch thoughts as they happen, unlike the slow fMRI.
4What is the "old solution" for dealing with noisy EEG data?
ERP (Event Related Potential), which averages hundreds of trials together to smooth out the noise.
5What is the major limitation of the ERP method?
It blurs details and smears everything together, so you can't see what happened on a single specific trial.
6What does the "Bump" theory suggest happens when the brain finishes a thinking step?
The brain releases a burst of energy that looks like a "bump" (half-circle shape) on the graph.
7How does the "Bottom-Up" approach analyze brain data?
The computer looks at the data without knowing anything and tries to guess patterns (like the number of bumps) on its own.
8What is the role of the ACT-R Process Model in this new method?
It acts as a "Cheat Sheet" or map that predicts exactly when specific cognitive stages (like Visual or Retrieval) should happen.
9What are the four specific stages mentioned in the ACT-R model?
Visual, Retrieval, Imaginal, and Manual.
10What happens when you combine Machine Learning (the bumps) with the ACT-R model?
It aligns the data perfectly (like stretching a rubber band), revealing a clear picture of thinking stages for single trials.
11In the word puzzle experiment, what difference was found between real words and non-words?
Non-words had a very short "Retrieval" stage because there was nothing to remember.
12How did the frequency of words affect the thinking steps?
Common words (like "Apple") were processed faster than rare words (like "Aardvark").
13What is ECoG?
A medical method where electrodes are placed directly on the brain.
14What is the "Linking Assumption" mentioned in the summary?
It is the feature that connects the mathematical "bump" in the data to a specific cognitive step in the theory.