ArticleDevelopmental science2018
Curiosity-based learning in infants: a neurocomputational approach.
Article in Developmental science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
29 citing papers in PubMed, 90 citations in OpenAlex.
- Navigating Knowledge: Effects of State Curiosity on Children's Word Learning and Information Seeking.Developmental science · 2026Article
- Inquiry and innovation: Considering curiosity and creativity in autism.Acta psychologica · 2026Review
- Article
- What do people want to know about another person? The answer depends on whether that person is an adult or a child.PloS one · 2026Article
- No Evidence for Curiosity-Driven Information Selection Advantage in Infants' Novel Word Learning.Developmental science · 2026Article
- Individual Differences in Infants' Curiosity Are Linked to Cognitive Capacity in Early Childhood.Developmental science · 2026Article
- Leveling up fun: learning progress, expectations, and success influence enjoyment in video games.Scientific reports · 2025Article
- Chimpanzees and children are curious about social interactions.Proceedings. Biological sciences · 2025Article
- Postnatal dependency as the foundation of social learning in humans.Proceedings. Biological sciences · 2025Review
- The limits of curiosity? New evidence for the roles of metacognitive abilities and curiosity in learning.Metacognition and learning · 2025Article
- Infants evaluate informativeness of evidence and predict causal events as revealed in theta oscillations and predictive looking.Communications psychology · 2024Article
- Helpless infants are learning a foundation model.Trends in cognitive sciences · 2024Review
- NewAbstractConcepts: A Database of 42 Normed Abstract Concepts and Exemplars.Journal of cognition · 2024Article
- Article
- Children's affective involvement in early word learning.Scientific reports · 2023Article
- Epidemiologic trends in pediatric ocular injury in the USA from 2010 to 2019.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022Observational
- Humans monitor learning progress in curiosity-driven exploration.Nature communications · 2021Article
- Parent-Child Joint Behaviors in Novel Object Play Create High-Quality Data for Word Learning.Child development · 2021Article
- Children's exploratory play tracks the discriminability of hypotheses.Nature communications · 2021Article
- Sampling to learn words: Adults and children sample words that reduce referential ambiguity.Developmental science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infants are curious learners who drive their own cognitive development by imposing structure on their learning environment as they explore. Understanding the mechanisms by which infants structure their own learning is therefore critical to our understanding of development. Here we propose an explicit mechanism for intrinsically motivated information selection that maximizes learning. We first present a neurocomputational model of infant visual category learning, capturing existing empirical data on the role of environmental complexity on learning. Next we "set the model free", allowing it to select its own stimuli based on a formalization of curiosity and three alternative selection mechanisms. We demonstrate that maximal learning emerges when the model is able to maximize stimulus novelty relative to its internal states, depending on the interaction across learning between the structure of the environment and the plasticity in the learner itself. We discuss the implications of this new curiosity mechanism for both existing computational models of reinforcement learning and for our understanding of this fundamental mechanism in early development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.