Amir Dezfouli

Adversarial manipulation of human decision-making

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.

Neural Network Poisson Models for Behavioural and Neural Spike Train Data

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

Disentangled behavioral representations

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

Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies

This paper introduces a hierarchical Bayesian inference framework for simultaneous model comparison and parameter estimation in group studies, showing improved accuracy and robustness.

Models that learn how humans learn: the case of decision-making and its disorders

The paper introduces a recurrent neural network approach to model human decision-making and shows it outperforms traditional methods in predicting behavior in depression and bipolar disorder.

Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models

The paper introduces a method using recurrent neural networks to jointly model behavioral and neuroimaging data, revealing new insights into the temporal patterns of brain activity related to decision-making.

The Algorithmic Neuroanatomy Of Action-Outcome Learning

The paper uses fMRI and computational modeling to show that a Kalman filter best explains action-outcome learning, with the medial prefrontal cortex distinguishing action effects from background noise.