Maurits Adam
Papers
1
Total Citations
8
H-Index
1
About
Maurits Adam is a computational cognitive scientist whose research lies at the intersection of neural network modeling, event perception, and hierarchical prediction. His work explores how humans and artificial systems learn to segment continuous experience into meaningful events and use those representations to generate anticipations across multiple timescales. In his highly cited 2022 paper, "Developing hierarchical anticipations via neural network-based event segmentation," Adam introduced a hierarchical recurrent neural network model that autonomously learns latent event codes, offering a mechanistic account of how the brain might build structured predictions from unstructured sensory input. This contribution bridges cognitive science and artificial intelligence, providing a framework for understanding how hierarchical event cognition develops without explicit supervision. Though early in his career, his work has already garnered attention for its innovative integration of developmental psychology and deep learning, and it holds promise for advancing both theories of human cognition and the design of more adaptive AI systems. Adam’s research continues to shape how we think about the emergence of structured thought from raw experience.
Research Focus
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Top Papers
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