Dr. Lei Zhang is looking for 2x PhD students interested in the cognitive, computational, and neural basis of (social) learning and decision-making in health and disease. The newly opened ALP(E)N Lab (Adaptive Learning Psychology and Neuroscience Lab) addresses the fundamental question of the “adaptive brain” by studying the cognitive, computational, and neurobiological basis of (social) learning and decision-making in healthy individuals (across the lifespan), and in psychiatric disorders. The lab combines an array of approaches including neuroimaging, patient studies and computational modelling (particularly hierarchical Bayesian modelling) with behavioural paradigms inspired by learning theories. The lab is based at the Centre for Human Brain Health and Institute of Mental Health at the University of Birmingham, UK, with access to exceptional facilities including MRI, MEG, TMS, and fNIRS. Funding is available through two competitive schemes from the BBSRC and MRC that provide a stipend, fees (at UK rate) and a research allowance, amongst other benefits. International (ie, outside UK) applicants are welcome.

Theories from reinforcement learning have been highly influential for interpreting neural activity in the biological circuits critical for animal and human learning. Central among these is the identification of phasic activity in dopamine neurons as a reward prediction error signal that drives learning in basal ganglia and prefrontal circuits. However, recent findings suggest that dopaminergic prediction error signals have access to complex, structured reward predictions and are sensitive to more properties of outcomes than learning theories with simple scalar value predictions might suggest. Here, I will present recent work in which we probed the identity-specific structure of reward prediction errors in an odor-guided choice task and found evidence for multiple predictive “threads” that segregate reward predictions, and reward prediction errors, according to the specific sensory features of anticipated outcomes. Our results point to an expanded class of neural reinforcement learning algorithms in which biological agents learn rich associative structure from their environment and leverage it to build reward predictions that include information about the specific, and perhaps idiosyncratic, features of available outcomes, using these to guide behavior in even quite simple reward learning tasks.

Since the theories of Marr, Ito, and Albus, the cerebellum has provided an attractive well-characterized model system to investigate biological mechanisms of learning. In recent years, theories have been developed that provide a normative account for many features of the anatomy and function of cerebellar cortex and cerebellum-like systems, including the distribution of parallel fiber-Purkinje cell synaptic weights, the expansion in neuron number of the granule cell layer and their synaptic in-degree, and sparse coding by granule cells. Typically, these theories focus on the learning of random mappings between uncorrelated inputs and binary outputs, an assumption that may be reasonable for certain forms of associative conditioning but is also quite far from accounting for the important role the cerebellum plays in the control of smooth movements. I will discuss in-progress work with Marjorie Xie, Samuel Muscinelli, and Kameron Decker Harris generalizing these learning theories to correlated inputs and general classes of smooth input-output mappings. Our studies build on earlier work in theoretical neuroscience as well as recent advances in the kernel theory of wide neural networks. They illuminate the role of pre-expansion structures in processing input stimuli and the significance of sparse granule cell activity. If there is time, I will also discuss preliminary work with Jack Lindsey extending these theories beyond cerebellum-like structures to recurrent networks.