I've been reading about hierarchical learning (a variant of reinforcement learning from what I understand) and how it is shown to allow learning of a higher-level task (the main example is assembly). ...
Background: In many situations, people use to classify objects without knowing Machine Learning theory. For example, if small children see an unknown animal in the wild, (s)he tries to classify it as ...
I've recently became aware of the idea of "reverse learning" that might happen during REM sleep - the brain's attempt to eliminate pathological attractors that might appear in neural networks. The ...
I work with neural network models of human cognition a lot, and one thing that bugs me about them is the timescale: they learn over thousands of trials whereas humans seem to learn after a couple ...
In reinforcement learning, there is a stark distinction between model-based and model-free learning algorithms, where model-free methods don't make use any explicit information about the dynamics of ...
Need good example of two domains involving different procedural knowledge yet sharing same high-level strategies
Working in the domain of intelligent tutoring systems, I have to prove (or disprove) that explicit teaching of high-level strategies will allow students to use learned strategies across different ...
I am interested in the creation of chunks (aka configural nodes) from smaller chunks and input features (only interested in System 1 cognition). Unitization studies (e.g. Goldstone (pdf)), suggest ...