Judgment Patterns in Children’s Non-Symbolic Probabilistic Choices: A Hierarchical Drift-Diffusion Modeling Study

Children’s probabilistic reasoning yields seemingly conflicting results across task conditions. While children often succeed when heuristic cues match probabilistic structure (congruent condition), they usually perform at chance when these cues conflict with proportional information (incongruent condition). This apparent inconsistency reflects a broader tension between probabilistic and heuristic accounts of reasoning under uncertainty, raising questions about whether judgment patterns remain consistent across conditions and whether they reflect distinct underlying decision-making mechanisms.We studied probabilistic reasoning in six-to-ten-year-old children (N = 92) using a two-alternative forced-choice task with congruent and incongruent trials, with small or large probability differences. A Gaussian Mixture Model identified three clusters in the incongruent-large condition: a proportion-based group, a heuristic group relying on target numerosity, and a chance-level group. Distinct sensitivities to probability differences and numerosity indicated consistent judgment patterns across conditions. Diffusion modeling showed that the heuristic group had higher decision efficiency in congruent trials and a pre-decisional bias toward the jar with more targets, whereas the proportion-based group accumulated evidence more efficiently in incongruent trials and showed unbiased pre-decisional states.These findings suggest that developmental transitions in probabilistic reasoning may not follow a linear path toward formal logic but rather involve a shifting balance between coexisting cognitive mechanisms that remain stable at the individual level throughout childhood. This challenges a strict dichotomy between probabilistic and heuristic accounts, suggesting instead that multiple cognitive mechanisms and stable individual differences underlie the emergence of probabilistic intuition in children.