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Leverhulme Doctoral Training Programme for the Ecological study of the Brain (3 Years) MPhil/PhD

Leverhulme Doctoral Training Programme for the Ecological study of the Brain (3 Years) MPhil/PhD

University College London

Location

United Kingdom

Qualification

PhD

Fees

£34,400

Duration

3 years

Next intake

Spring, Autumn, Winter

Entry score

English language requirements

Understanding how humans operate in the real-world, the ecological niche in which the brain has evolved, is critical to explaining the richness of human experiences. Most scientific knowledge about brain and behaviour, however, comes from laboratory studies focusing on a single domain (e.g., language processing) and sacrifice real-world context to achieve experimental control (e.g., recognition of isolated words rather than face-to-face communication). At the other end of the spectrum, scholars studying real-world phenomena often sacrifice experimental control in order to conduct studies in naturalistic settings. Laboratory experiments, however, often have limited external validity, while naturalistic approaches are descriptive, and therefore do not address causal relationships between brain, behaviour and the environment.

To explain the inherent richness of human experiences, a radical change in approach to brain and behavioural research is needed that enables future leaders to harness and further develop new methods and technologies – part of the digital revolution – to measure behaviour and brain activity in the wild (e.g., wearable devices and wireless electroencephalography), to bring real- world complexity into the lab (e.g., virtual and augmented reality, large-scale modifiable real- world facilities) and to analyse the wealth of data these methods produce (e.g., blind signal source separation, graph- theoretic analysis).

ECOLOGICAL BRAIN proposes to deliver such a change, moving from established patterns of working within single disciplines to an integrative science approach that brings together psychology, neuroscience, education, geography, computer science, engineering and architecture to bridge the gap between basic scientific discovery, and the application of this knowledge.

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