Artificial intelligence and neuroscience

Joshua Bolam and Jessica Ann Diaz

Chapter 5 in Elgar Concise Encyclopedia of Neuroscience and Management, 2026, pp 18-21 from Edward Elgar Publishing

Abstract: This chapter surveys how artificial intelligence (AI) serves as an analytic engine for neuroscience and its applications in business. After outlining AI's evolution from symbolic systems to modern deep learning, we show how machine-learning methods – supervised, unsupervised, and deep learning – extract structure from EEG, fMRI, and fNIRS data to predict cognition and behaviour. Use cases include neuromarketing, where neural signals forecast preference and willingness to pay, and management contexts involving decision-making and organisational dynamics. We address the “black box” problem and demonstrate how explainable AI improves interpretability and generalisation across populations. We conclude with ethical and practical considerations, arguing for transparent, rigorously validated pipelines that translate neural insights into actionable business value.

Keywords: Artificial intelligence (AI); Machine learning (ML); Deep learning; Neuroscience; Neuromarketing; Decision-making (search for similar items in EconPapers)
Date: 2026
ISBN: 9781035332885
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