The work contributed a quantitative theory of how ACh and NE respectively signal expected and unexpected uncertainty in cortical computations.
Many subsequent experimental studies in electrophysiology, neuropsychology, and clinical neuroscience, done in various labs, have largely confirmed predictions of the theory.
Impact on Clinical Neuroscience
This work is not only important for its scientific contributions, but demonstrates that the theoretical approach taken in my research can have concrete and far-reaching impact on experimental and clinical neuroscience for many years down the line.
Recently, we have discovered, based on human behavioral data, that uncertainty-driven preference and learning are also present in learning, decision-making, and exploratory behavior related to face representation in the brain (Ryali et al, 2020). I’m currently actively looking for collaborators to test out some of the model predictions.
Related Papers
- Yu, A J & Dayan, P (2002). Acetylcholine in cortical inference. Neural Networks, 15 (4/5/6): 719-730.
- Yu, A J & Dayan, P (2005). Uncertainty, neuromodulation, and attention, Neuron, 46: 681-692.
- Yu, A J & Dayan, P (2005). Inference, attention, and decision in a Bayesian neural architecture. In Advances in Neural Information Processing Systems 17: 1577-84. MIT Press, Cambridge, MA.
- Dayan, P & Yu, A J (2006). Phasic norepinephrine: A neural interrupt signal for unexpected events. Network: Computation in Neural Systems, 17: 335-50.
- Ryali, C K, Goffin, S, Winkielman, P, Yu, A J (2020). From likely to likable: The role of statistical typicality in human social assessment of faces. Proceedings of the National Academy of Sciences
