Abstract
Previous studies have emphasized the role of task difficulty in decision performance while relatively neglecting the decision maker's subjective initiative and intrinsic learning process during task execution. This study manipulated the rule hierarchy factor, which reflects external task difficulty, and the block factor, which reflects the accumulation of intrinsic learning, and used analysis of variance (ANOVA), the drift-diffusion model (DDM), and random forest algorithms to systematically examine how task difficulty and learning jointly influence decision behavior and its underlying mechanisms. A total of 40 participants were recruited, and after strict exclusion criteria were applied, 34 valid datasets were included in the final analysis. The results showed that although rule hierarchy had a significant impact on decision performance in the early stage of the task (the first two blocks), this effect gradually diminished as task repetitions increased. Furthermore, the results revealed a clear dissociation in predictive mechanisms: intrinsic cognitive factors (specifically, evidence accumulation efficiency and decision bias) were the primary predictors of decision accuracy, whereas external task difficulty (rule hierarchy) acted as the dominant predictor for decision speed (reaction time). These findings provide a new perspective for understanding the dynamic relationship between external task demands and intrinsic learning processes, highlighting the necessity of distinguishing between accuracy and speed metrics in personalized education, training, and human-computer interaction design.