7:01 PM
@vzn the main goal is operationalization (finding indicators, making it measureable) of deep vs shallow learning theory, which states the correlation of emotions with learning speed and type of learning.
The outcomes are: improved learning by checking human state, adjusted game for player (more personal, not purely random or the same every time), so reinforcement for human (the task does not matter, but psychology loves games, very good at measuring progress).
The best game for learning is poker, but this is hard to use.
Poker for real money, making real game is optimal in learning for human.
But any kind of token based poker is harmful for poker playing, and does not have this properties.
So making more human-like player, improving the AI or "trying to follow the human strategy" (I do not count on that, but we shall see) are things to cover in meanwhile.
Several years ago I made modification to learning curve, memory storage, short-term memory speed up for long-term memory. So instead of learning like 20h before the exam and than loosing everything one might spend 4 hours and remember it afterwards. This game X emotions is improving the learning pattern further.
Making it short: time pressured learning with improper repetition cycles (there are none) ends up in shallow learning, connected with danger, so avoidance mechanism kicks in - remember the situation to avoid danger in future, hence the memory storage is not decreased in ine month but several days - pass and forget. But even when people are systematic and make good, clean learning, there are obstacles in the process, I want to get rid of them.
Before eeg and emotions I was doing research in long-term memory, recall, memory recovery techniques and the quite recent results of "forgetting", which are proven (I also checked it) that we in fact not forget, just decrease in recall speed, which is degrading very fast, but memories are preserved.