A computational model of infant sensorimotor exploration in the mobile paradigm

2025-04-24

AI Summary

This study investigates how infants acquire sensorimotor causal cognition through self-generated actions in the mobile dangling paradigm—a foundational developmental capacity. We propose the first computational model integrating action–outcome prediction, biologically constrained motor control, stochastic exploration, and motor noise. The model successfully reproduces classic findings as well as two recently reported behavioral patterns—gradual versus all-or-none contingency learning—along with characteristic movement preferences and burst-like activity. Ablation analyses confirm that each component is indispensable for capturing the full range of empirical phenomena. Our work provides the first computationally explicit, empirically testable account of early infant sensorimotor learning, overcoming prior models’ limitations in both biological plausibility and behavioral fidelity.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Modeling infant sensorimotor learning in mobile paradigm
• Understanding how infants detect action-outcome contingencies
• Replicating infant limb movement patterns computationally

Innovation

• Neural network models infant sensorimotor exploration
• Action-outcome prediction and motor noise included
• Biologically-inspired motor control replicates infant behavior