Martin鈥檚 research examines behavioural and neural mechanisms of decision-making, auditory perception, communication, and reward processing. He uses psychophysical, computational, and neuroimaging methods to study how perceptual and cognitive systems support adaptive behaviour, and how these systems may be shaped by evolutionary pressures.
His academic interests include the intersection between neuroscience and artificial intelligence, especially how artificial neural networks can be understood in relation to biological neural networks such as the human brain. This includes questions of knowledge representation, learning, attention, decision-making, and the energy efficiency of intelligence. He is particularly interested in brain-inspired intelligence: how the human brain can inform the design, optimisation, and interpretation of AI systems. He also explores the cognitive and educational implications of generative AI, including the role of LLMs in communication and reasoning, the risks of cognitive offloading and over-reliance on AI, and the continuing importance of constructing, evaluating, and retaining one鈥檚 own arguments.
One key finding from his early work was that humans and other animals use vocal communication signals to estimate the body size of other individuals, a capacity that helps in judging the competence of potential mates and the threat posed by competitors. His work has also examined the evolutionary basis of developmental dyslexia and the brain mechanisms underlying suboptimal economic decision-making.
His research has been featured in , , , , and .
