A computational model of infant sensorimotor exploration in the mobile paradigm

We present a computational model of the mechanisms that may determine infants' behavior in the "mobile paradigm". This paradigm has been used in developmental psychology to explore how infants learn the sensory effects of their actions. In this paradigm, a mobile (an articulated and movable object hanging above an infant's crib) is connected to one of the infant's limbs, prompting the infant to preferentially move that "connected" limb. This ability to detect a "sensorimotor contingency" is considered to be a foundational cognitive ability in development. To understand how infants learn sensorimotor contingencies, we built a model that attempts to replicate infant behavior. Our model incorporates a neural network, action-outcome prediction, exploration, motor noise, preferred activity level, and biologically-inspired motor control. We find that simulations with our model replicate the classic findings in the literature showing preferential movement of the connected limb. An interesting observation is that the model sometimes exhibits a burst of movement after the mobile is disconnected, casting light on a similar occasional finding in infants. In addition to these general findings, the simulations also replicate data from two recent more detailed studies using a connection with the mobile that was either gradual or all-or- none. A series of ablation studies further shows that the inclusion of mechanisms of action-outcome prediction, exploration, motor noise, and biologically-inspired motor control was essential for the model to correctly replicate infant behavior. This suggests that these components are also involved in infants' sensorimotor learning.

@Article{SPWHO25b, 
 	 author =  {Spisak, Josua and Popescu, Sergiu Tcaci and Wermter, Stefan and Hoffman, Matej and O'Regan, John Kevin},  
 	 title = {A computational model of infant sensorimotor exploration in the mobile paradigm}, 
 	 booktitle = {},
 	 journal = {https://arxiv.org/pdf/2504.17939},
 	 editors = {},
 	 number = {},
 	 volume = {},
 	 pages = {},
 	 year = {2025},
 	 month = {Apr},
 	 publisher = {},
 	 doi = {https://doi.org/10.48550/arXiv.2504.17939}, 
 }