Curiosity-Based Learning in Infants: A Neurocomputational Approach

Autor/inn/en	Twomey, Katherine E.; Westermann, Gert
Titel	Curiosity-Based Learning in Infants: A Neurocomputational Approach
Quelle	In: Developmental Science, 21 (2018) 4, (15 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Twomey, Katherine E.) ORCID (Westermann, Gert)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1467-7687
DOI	10.1111/desc.12629
Schlagwörter	Infants; Infant Behavior; Child Development; Learning Processes; Visual Perception; Visual Stimuli; Reinforcement; Models; Novelty (Stimulus Dimension); Correlation; Cognitive Development; Neurosciences; Classification; Environmental Influences + Suchen Sie Ihr Suchwort? Infant; Toddler; Toddlers; Kleinkind; Kindesentwicklung; Learning process; Lernprozess; Visuelle Wahrnehmung; Positive Verstärkung; Analogiemodell; Korrelation; Kognitive Entwicklung; Neuroscience; Neurowissenschaften; Neurowissenschaft; Classification system; Klassifikation; Klassifikationssystem; Environmental influence; Umwelteinfluss
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01