Differential Contributions of Rostral and Caudal LIP to Reward-Guided Choice

Differential Contributions of Rostral and Caudal LIP to Reward-Guided Choice
Date & Time: 
Friday, December 6, 2019 - 12:00 to 13:00
Xinying Cai, NYU Shanghai
Room 385, Geography Building, Zhongbei Campus, East China Normal University


Increasing evidence suggests that the lateral intraparietal cortex (LIP) is a functionally heterogeneous area. Recent studies suggest that dorsal-ventral subdivisions of LIP may be preferentially involved in different aspects of visual-oculomotor processes. However, subdivisions along the rostral-caudal extent of LIP have been much less characterized, especially regarding their contributions to representing reward and visual perceptual salience in reward-guided choice. Here, we recorded neurons spanning the entire rostral-caudal extent of ventral LIP while the monkeys were performing a reward-guided choice task and contrasted the activity of the same neurons under a force choice task. At the behavior level, we discovered that the animal’s choice was dictated by the reward amount but not the intensity of perceptual salience associated with choice options. At the neural level, while the caudal LIP (cLIP) is modulated by both expected reward and perceptual salience, the rostral LIP (rLIP) is modulated by reward alone; Moreover, in cLIP, reward and perceptual salience were encoded by separate groups of neurons; while larger amount of reward generally boosts the activity of reward coding neurons, higher intensity of perceptual salience associated with the reward predicting cues suppresses the activity of the salience coding group. In addition, unlike cLIP, rLIP preferentially encodes the choice outcome in free choice but not the force choice task. In the meantime, rLIP also exhibits earlier and stronger choice probability representation under free choice. Lastly, two choice options produce anti-correlated modulation only in rLIP, suggesting that in free choice the effect of mutual inhibition occurs in rLIP but not cLIP. These results indicate that task-related information may be filtered through cLIP, yielding choicerelevant components for competition in rLIP.


Xinying Cai is an Assistant Professor of Neural and Cognitive Sciences at NYU Shanghai and a Global Network Assistant Professor of NYU. He holds a Ph.D. from Arizona State University and a B.S. from Zhejiang University. Professor Cai’s current research focuses on elucidating the neural underpinnings of economic decision making. Prior to joining NYU Shanghai, he was a postdoctoral fellow first at Yale University, then at Washington University in St. Louis.

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Sponsored by the NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai