Majid Nili Ahmadabadi

This paper introduces a deformation in probability estimation to a reinforcement learning model, linking it to anxious-depression symptoms in psychiatric disorders.

The paper uses a neuron model to infer hidden microcircuit structures from neuronal activity data by analyzing input-output relations and interaction patterns.

This study compares two reinforcement learning models in volatile environments, finding that separate reward and punishment learning rates improve performance, with higher reward learning rates being more beneficial.

This study shows humans can learn to integrate artificial tactile information with visual stimuli using Bayesian models, improving discrimination and confidence over time.

The paper shows that higher meta-cognitive ability is linked to reduced reliance on overconfident model-free learning in variable environments.

The paper proposes a k-nearest neighbor method to improve parameter estimation in reinforcement learning models, showing better reliability than maximum likelihood approaches.