Richard Nock

This paper introduces a framework for generating adversarial opponents that influence human decision-making in specific tasks, demonstrating its effectiveness through experiments on action selection and response inhibition.

The paper introduces a neural network Poisson model to analyze neural spike train data and behavioral tasks, showing improved performance over previous methods.

The paper develops a framework to represent individual differences in decision-making using low-dimensional, interpretable latent spaces derived from recurrent neural networks.