Evidence Accumulation

Evidence accumulation describes the process of integrating multiple pieces of information over time to reach a decision, mirroring how brains make choices under uncertainty. Current research focuses on developing computational models, including reinforcement learning algorithms and neuro-inspired architectures like Transformers, that capture this sequential evidence integration, often incorporating uncertainty quantification and adaptive decision thresholds. These models are being applied across diverse fields, from medical image analysis (e.g., lung cancer diagnosis) to robotics and machine learning, improving the robustness and efficiency of decision-making systems. The resulting advancements offer insights into both biological decision-making and the development of more reliable and intelligent artificial systems.