Proceedings of the Annual Meeting of the Cognitive Science Society

Key to intelligence is the ability to flexibly compose and combine atomic concepts into representations that guide behavior, making "infinite use of finite means". However, modeling often assumes discrete concepts exist and focuses on their use, leaving it unclear how they are learned from complex sensory inputs and mapped to motor outputs. To address this limitation, we build on a drawing-like task and behavioral metrics that indicate discrete structure in motor behavior from Tian et al. (2025). Importantly, these discrete concepts were encoded in neural recordings from non-human primates that exhibited these behavioral metrics, validating the metrics. Here, we use this task and behavioral metrics as a modeling target, to quantify how discrete structure arises in a task-optimized neuro-symbolic model (Liang et al., 2022). Our model recapitulates these behavioral metrics, offering a foundation to compare with neural data and understand what drives the emergence of discrete conceptual structure.