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A Probabilistic Model of Social Decision Making based on Reward Maximization
Our results suggest a probabilistic basis for human social decision making within the framework of expected reward maximization.
A Bayesian Framework for Modeling Confidence in Perceptual Decision Making
We show that this model, based on partially observable Markov decision processes (POMDPs), is able to predict confidence of a decision maker based only on the data available to the experimenter.
Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks
Each spike in the population of inference neurons represents a sample of a particular hidden world state.
How Prior Probability Influences Decision Making: A Unifying Probabilistic Model
To explain this data, a second model has been proposed which assumes a time-varying influence of the prior.
A rational decision making framework for inhibitory control
Intelligent agents are often faced with the need to choose actions with uncertain consequences, and to modify those actions according to ongoing sensory processing and changing task demands.
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