We are interested in the neural mechanisms underlying decision-making and high cognitive functions mostly using non-human primate animals. Using single unit electrophysiology techniques, we record and analyze neural activities in different brain areas when animals are making complex decisions. Ultimately, our research leads to the complete understanding of the neural basis of human intelligence.

1. Neural mechanism of probabilistic reasoning

Many decisions rely on fuzzy and inaccurate information, which can be quantified with probabilities mathematically. How the brain utilizes probability information during decision making has been one of the hot topics in the decision-making field. We train monkeys to perform probabilistic reasoning tasks in which they make decisions by viewing pictures that are probabilistically associated with rewards. By recording from multiple areas in the prefrontal and parietal cortices, we are piecing together the neural circuitry that learns, processes, and uses probabilistic information during decision making.

2. Neural computation of value information

Animals use value information to guide their decisions when evaluating options. How value information is acquired and represented in the brain is still not well understood. We train monkeys to recognize pictures that predict different rewards. We focus our investigation on the orbitofrontal cortex and study its role in representing different aspects of value information associated with visual stimuli, including reward magnitude, probability, and volatility. In addition to the animal model work, we also collaborate with local hospitals to study value-based decision-making behavior in humans, including both healthy subjects and patients suffering from a variety of mental disorders. These studies may provide us clues on how neural computation of value information is affected in these disorders and ultimately provide insights in developing new treatments.

3. Neural network modeling of the prefrontal cortex 

The prefrontal cortex is critical for high cognitive brain functions. However, the neural computation it performs remains a mystery. One of the challenges is that our ability of recording from a large network of neurons is currently very limited. We are addressing this challenge by creating biologically-realistic neural network models based on experimental data to understand how neurons are wired together to achieve complex functions. By running simulations and studying network and neuron behavior, we use our models to not only explain experimental data collected from the real brain, but also provide testable ideas for future experimental investigations. Such study is essential for us to understand the neural computation principles underlying cognitive functions.

Lin Z*, Nie C*, Zhang Y*, Chen Y, Yang T (2020) Evidence accumulation for value computation in the prefrontal cortex during decision making. Proc Natl Acad Sci. 117(48): 30728-30737

Zhang Z, Cheng H, Yang T (2020) A recurrent neural network model for flexible and adaptive decision making based on sequence learning. PLoS Comput Biol. 16(11): e1008342

Chen X, Yang T (2020) A neural network model of basal ganglia’s decision-making circuitry. Cogn Neurodyn. https://doi.org/10.1007/s11571-020-09609-2

Liu D, Deng J, Zhang Z, Zhang Z, Sun Y, Yang T, Yao H (2020) Orbitofrontal control of visual cortex gain promotes visual associative learning. Nat. Commun. 11, 1–14.

Al Roumi F, Dotan D, Yang T, Wang L, Dehaene S. (2019) Acquisition and processing of an artificial mini-language combining semantic and syntactic elements. Cognition 185: 49-61.

Xie Y, Nie C, Yang T (2018) Covert shift of attention modulates the value encoding in the orbitofrontal cortex. eLife 7:e31507.

Zhang Z*, Cheng Z*, Lin Z, Nie C, Yang T (2018) A neural network model for the orbitofrontal cortex and task space acquisition during reinforcement learning. PLoS Comput Biol 14:e1005925 *Co-first authors.

Zhang C*, Chen Y*, Tian S, Wang T, Xie Y, Jin H, Lin G, Gong H, Zeljic K, Sun B, Yang T^#, Zhan S^# (2017) Effects of anterior capsulotomy on decision making in patients with refractory Obsessive–Compulsive Disorder. Front Psychol 8:1814 *Co-first authors  ^#Co-corresponding authors.

Shadlen MN, Kiani R, Newsome WT, Gold JI, Wolpert DM, Zylberberg A, Ditterich J, de Lafuente V, Yang T, Roitman J (2016) Comment on “Single-trial spike trains in parietal cortex reveal discrete steps during decision-making”. Science 351:1406.

Cheng Z, Deng Z, Hu X, Zhang B, Yang T (2015) Efficient reinforcement learning of a reservoir network model of parametric working memory achieved with a cluster population winner-take-all readout mechanism. J Neurophysiol 114:3296–3305.

Kira S*, Yang T*, Shadlen MN (2015) A neural implementation of Wald’s sequential probability ratio test. Neuron 85:861–873. *Co-first authors.

Wheeler ME, Woo SG, Ansel T, Tremel JJ, Collier AL, Velanova K, Ploran EJ, Yang T (2015) The strength of gradually accruing probabilistic evidence modulates brain activity during a categorical decision. J Cogn Neurosci.

Rudebeck PH, Putnam PT, Daniels TE, Yang T, Mitz AR, Rhodes SE V, Murray EA (2014) A role for primate subgenual cingulate cortex in sustaining autonomic arousal. Proc Natl Acad Sci 111:5391–5396.

Yang T, Bavley RL, Fomalont K, Blomstrom KJ, Mitz AR, Turchi J, Rudebeck PH, Murray EA (2014) Contributions of the hippocampus and entorhinal cortex to rapid visuomotor learning in rhesus monkeys. Hippocampus 24:1102–1111.

Yang T, Shadlen MN (2007) Probabilistic reasoning by neurons. Nature 447:1075–1080.

Shadlen MN, Hanks TD, Churchland AK, Kiani R, Yang T (2006) The speed and accuracy of a simple perceptual decision: a mathematical primer. Bayesian brain Probabilistic approaches to neural coding:209–237.

Yang T, Maunsell JHR (2004) The effect of perceptual learning on neuronal responses in monkey visual area V4. J Neurosci 24:1617–1626.

Ghose GM, Yang T, Maunsell JHR (2002) Physiological correlates of perceptual learning in monkey V1 and V2. J Neurophysiol 87:1867–1888.