Abstract

The decision-making framework and computational models have been instrumental in uncovering the neurocognitive processes that underlie human behaviors, significantly enhancing our understanding of the human mind. However, traditional approaches often fall short in closely simulating real-world behaviors due to their oversimplified nature, inability to capture the dynamic aspects of human cognition, and reliance on secondary rewards. This article delves into the evolution and recent developments of computational approaches in understanding human cognition and decision-making, with a particular focus on addiction. We highlight the transition from conventional laboratory-based methods to innovative approaches that more effectively simulate real-world scenarios. These advanced approaches leverage state-of-the-art computational techniques to capture dynamic psychological processes, contributing to the ongoing development and advances in the field.