Saki Tomioka
Papers
2
Total Citations
6
H-Index
2
About
Saki Tomioka investigates cognitive development and neurorobotics, focusing on how infants learn through dynamic caregiver interactions. Her research integrates predictive learning and uncertainty estimation to model how sensory inputs shape action, perception, and attention. In her 2015 study, she demonstrated that infants’ cognitive abilities can emerge from predictive learning of sensory inputs, including their uncertainty (inverse precision), offering a computational framework for developmental robotics. Her 2017 work extended this by showing how robots can mix actual and predicted sensory states based on uncertainty estimation, enabling flexible and robust behavior. Though her citation counts are modest (3 each), her contributions are foundational in bridging neuroscience, robotics, and developmental psychology. Tomioka’s neurorobotics experiments provide a novel platform for understanding embodied cognition and human-robot interaction, with implications for artificial intelligence and early childhood development. Her work stands out for its interdisciplinary approach, combining computational modeling with empirical insights into caregiver-infant dynamics.
Research Focus
Key Achievements
Top Papers
- 1
- 2