Educational neuroscience: progress and prospects

Educational neuroscience is an interdisciplinary research field that seeks to translate research findings on neural mechanisms of learning to educational practice and policy, and to understand the effects of education on the brain. Neuroscience and education can interact directly, by virtue of considering the brain as a biological organ that needs to be in the optimal condition to learn (‘brain health’); or indirectly, as neuroscience shapes psychological theory and psychology influences education. In this article, we trace the origins of educational neuroscience, its main areas of research activity, and the principal challenges it faces as a translational field. We consider how a pure psychology approach that ignores neuroscience is at risk of being misleading for educators. We address the major criticisms of the field, respectively comprising a priori arguments against the relevance of neuroscience to education, reservations with the current practical operation of the field, and doubts about the viability of neuroscience methods for diagnosing disorders or predicting individual differences. We consider future prospects of the field and ethical issues it raises. Finally, we discuss the challenge of responding to the (welcome) desire of education policymakers to include neuroscience evidence in their policymaking, while ensuring recommendations do not exceed the limitations of current basic science

Valuation of risky and uncertain choices

In this chapter, we describe how risk and ambiguity impact the value of choice options, how this impact can be modelled formally and how it is implemented in the brain. In particular, we give an overview of two distinct ways of how risky choice options can be decomposed – either into outcomes and probabilities as proposed in economics or into statistical moments of the probability distribution like mean, variance, or skewness, as proposed in finance theory. The components of either approach appear to be represented in common and, at least to some extent, in separate brain regions, which include the dopaminergic midbrain, striatum and the orbitofrontal cortex. Activity in different (prefrontal and striatal) brain regions also supports the distinction between decisions from experience, when knowledge about risk is learned through trial and error versus decisions from description, when it is described symbolically. The fact that the principal components of formal models from economics and finance theory and their behavioral versions that provide better descriptive fit are represented in the brain provides converging support for these models

Value learning through reinforcement: The basics of dopamine and reinforcement learning

This chapter provides an overview of reinforcement learning and temporal difference learning and relates these topics to the firing properties of midbrain dopamine neurons. First, we review the RescorlaWagner learning rule and basic learning phenomena, such as blocking, which the rule explains. Then we introduce the basic functional anatomy of the dopamine system and review studies that reveal a close correspondence between responses emitted by dopamine neurons and signals predicted by reinforcement learning. Finally, we introduce the generalization of the RescorlaWagner rule to sequential redictions as provided by temporal difference learning, and discuss its application to phasic activation changes of dopamine neurons. Subsequent chapters in this section deal with more advanced topics in reinforcement learning and presume that the reader is familiar with material covered in this chapter