Exploring the Unknown: How the Brain Explores, Learns, and Sometimes Goes Astray

Exploration under uncertainty is a core component of adap- tive behaviour, shaping how we learn when outcomes are unknown and sampling is costly. This thesis examines the neurocognitive mechanisms that support human exploration across increasing levels of complexity, using a cascade frame- work that separates three components: gating processes that prioritise what enters learning, policy mechanisms that trans- late uncertainty into choice, and scaling mechanisms that en- able generalisation in large, structured spaces. Study 1 (Gat- ing) uses Activation Likelihood Estimation to characterise the neural systems supporting novelty and deviance processing. The results identify a distributed network with convergence in medial temporal and salience regions and reveal system- atic differences between active and passive detection paradigms. Study 2 (Policy) applies behavioural and computational mod- elling to show that sensation seeking is associated with greater tolerance for uncertainty and increased exploratory variabil- ity, and that sensation seeking and impulsivity map onto dis- sociable latent components of choice. Study 3 (Scaling) com- bines behavioural modelling with functional neuroimaging to test how exploration remains tractable in complex environ- ments. The findings implicate hippocampal–prefrontal sys- tems in structure representation that supports generalisation, allowing information to propagate to novel options. Together, I provide a component–level account of exploration under uncertainty, clarifying how similar exploratory behaviours can arise from different computations and highlighting routes through which disruptions to gating, policy, or scaling may contribute to maladaptive behaviour.