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The nativist approach to infant cognitive development proposes that infants are born with inherent knowledge structures that allow them to interpret the world almost immediately. This perspective contrasts with earlier developmental theories, such as those proposed by Jean Piaget, which emphasized a more gradual acquisition of cognitive abilities through interaction with the environment. One key concept in this approach is object permanence — the understanding that objects continue to...
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Related Experiment Video

Updated: Feb 20, 2026

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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Curiosity-based learning in infants: a neurocomputational approach.

Katherine E Twomey1, Gert Westermann2

  • 1Division of Human Communication, Development and Hearing, School of Health Sciences, University of Manchester, Manchester, UK.

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Infants learn best by seeking novel stimuli, a process modeled computationally. This curiosity-driven learning maximizes cognitive development by balancing environmental structure and learner plasticity.

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Area of Science:

  • Cognitive Science
  • Developmental Psychology
  • Computational Neuroscience

Background:

  • Infants actively structure their learning environments to drive cognitive development.
  • Understanding intrinsic motivation and information selection is crucial for developmental studies.

Purpose of the Study:

  • Propose an explicit mechanism for intrinsically motivated information selection in infants.
  • Develop a neurocomputational model for infant visual category learning.
  • Investigate how curiosity influences learning based on environmental complexity and learner plasticity.

Main Methods:

  • Developed a neurocomputational model of infant visual category learning.
  • Simulated a "free-learning" scenario where the model selected its own stimuli.
  • Formalized curiosity as a mechanism for maximizing stimulus novelty relative to internal states.

Main Results:

  • The model captured existing data on environmental complexity's role in infant learning.
  • Maximal learning occurred when the model prioritized stimulus novelty.
  • Learning effectiveness depended on the interplay between environmental structure and learner plasticity.

Conclusions:

  • Introduced a novel computational mechanism for curiosity-driven learning in infants.
  • Highlighted the importance of stimulus novelty for maximizing cognitive development.
  • Discussed implications for reinforcement learning models and understanding early development.