Curiosity-based learning in infants: a neurocomputational approach.

Katherine E Twomey,Gert Westermann

doi:10.1111/desc.12629

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.

Highlights

For more than half a century, infants’ information selection has been documented in lab-based experiments
Wilcoxon rank-sum tests against chance (0.5) confirmed that the model formed a category in all conditions
In Experiment 1 we captured empirical data presented by Mather and Plunkett (2011), in which 10-month-old infants formed a robust category when familiarized with stimulus sequences that maximized overall perceptual distance, but not in sequences which minimized it

Summary

Introduction

For more than half a century, infants’ information selection has been documented in lab-based experiments. These carefully designed, rigorously controlled paradigms allow researchers to isolate a variable of interest while controlling for extraneous environmental influences, offering a fine-grained picture of the range of factors that affect early learning. Decades of developmental research have brought about a broad consensus that infants’ information selection and subsequent learning in empirical tasks are influenced by their existing representations, the learning environment, and discrepancies between the two (for a review, see Mather, 2013).

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Conclusion