The study of what actions an agent should take in a stochastic environment in order to maximize a cumulative reward.

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Cognitive Models of learning Working Memory usage

Recently in deep learning, there's been a surge in learning how to use memories as part of the optimisation process (i.e. LSTM's and Stacks). However, these aren't really analogous to how a cognitive ...
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How does goal-tracking and sign-tracking behaviour vary across species?

In Pavlonian (classical) conditioning, conditioned responses of an animal may vary. Some animals focus on the unconditioned stimulus (ie., food / location of food) while others may focus on the ...
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Why has behaviourism fallen out of favour?

Despite the catchy title, I'm not interested in personal opinions here. I am however, interested in feedback on how to better phrase the question so as to avoid personal opinions. After some ...
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Frequency estimation in binary prediction task

In a binary prediction task, people often match choice probabilities to outcome probabilities (a phenomenon known as probability matching). However, under certain circumstances (eg, existence of a ...
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How do mammals explore state spaces in reinforcement learning tasks?

Reinforcement learning is the act of learning how to preform a task given punishment and reward. A "state-space" is the space of choices in a context. When performing a reinforcement learning task, is ...
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Reinforcement learning for combining production rules

I'm trying to create a system that learns the Tower of Hanoi puzzle. The system I'm working off of uses a production system (similar to ACT-R), but uses hard-coded production rules. I know that Neil ...
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Brain areas active while learning hierarchical structure of a problem

There are multiple examples in the machine learning literature of trying to learn the hierarchical structure of a reinforcement learning problem, however have there been any papers tying this learning ...