Attention, Value, and Decision Making
Sep
29
4:00pm 4:00pm

Attention, Value, and Decision Making

During value-based decision making, we often evaluate the value of each option sequentially by shifting our attention, even when the options are presented simultaneously. Here I will talk about the current evidence in favor of a sequential decision-making process in the brain. In particular, I will discuss how attention may play an important role during sequential decision making. We will focus our discussion on the orbitofrontal cortex (OFC), which has been suggested to encode value during value-based decision making. The new experiments from our lab show that the OFC neurons encode the value only one stimulus at a time, and attention is the guiding signal that chooses the stimulus. Attention modulates OFC activity through a winner-take-all mechanism and can be explained by a normalization model. Our results provide important insights toward the neural mechanism of value-based decision making

Biography

Dr. YANG Tianming obtained his B.S. degree in the Department of Biochemistry at Fudan University. He received his Ph. D. in neuroscience at the Baylor College of Medicine, Houston, Texas, investigating the neural plasticity in visual cortices under the advice of Dr. John Maunsell. He then did his postdoctoral research with Dr. Michael Shadlen then at the University of Washington, Seattle, studying the neural mechanism underlying probabilistic reasoning. In 2008, Dr. YANG became a sta scientist in the Section of Neuropsychology at the National Institute of Mental Health, USA, working on the reward circuitry in the brain. Since 2013, Dr. YANG works at the Institute of Neuroscience as Investigator and Head of the Laboratory of Neural Mechanisms of Decision Making and Cognition. He combines behavior, electrophysiology, and computational approaches to study the neural mechanisms of decision making and other higher cognitive functions.

Location

Room 1504, NYU Shanghai Pudong Campus 1555 Century Avenue, Pudong, Shanghai

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Predicting Unequal Treatment: The Role of Social Perception in Economic Valuation
May
18
4:00pm 4:00pm

Predicting Unequal Treatment: The Role of Social Perception in Economic Valuation

 

Ming Hsu
Haas School of Business and Helen Wills Neuroscience Institute, UC Berkeley

People’s concern for others is a long-celebrated feature of human sociality, yet people do not appear to extend this concern uniformly. How people treat others depends on who those others are—for example on their race, gender, or nationality—violating prevailing models of social preferences. Here, we offer a quantitative model that accounts for and predicts unequal treatment of others by integrating behavioral economic frameworks capturing how people value outcomes (social valuation) with psychological frameworks capturing how people see others (social perception). Using a battery of economic games, we show that this identity-based social valuation model robustly predicted unequal treatment across a diverse set of counterparts, even in out-of-sample scenarios involving novel counterparts and participants. These results elucidate how social perception influences well-studied components of social valuation, including preferences for fairness and reciprocity, with implications for scientific understanding of discrimination.

Biography
Ming Hsu is Associate Professor at the Haas School of Business and Helen Wills Neuroscience Institute, UC Berkeley.  Dr. Hsu’s lab takes an interdisciplinary approach to study the biological basis of economic and consumer decision-making at a number of different levels of analyses, including: (i) brain regions mediating computations involved in different aspects of decision-making, (ii) the underlying molecular, cellular, and genetic mechanisms, and (iii) how real-world outcomes are associated with variations in these processes. 

Location: Room 1504, NYU Shanghai | 1555 Century Avenue, Pudong New Area, Shanghai

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Is the Sub-cortical Colliculus Slave or Master during Value-based Orienting?
Apr
27
4:00pm 4:00pm

Is the Sub-cortical Colliculus Slave or Master during Value-based Orienting?

Michael Dorris

Institute of Neuroscience, CAS

Biography
Mike Dorris was born and raised in Trenton, Ont.  He obtained his B.Sc. in Life Sciences at Queen’s (1993), and then completed his M.Sc. (1995) and Ph.D. (2000) with Doug Munoz in the Department of Physiology. His graduate research launched a life-long interest in the role of pre-motor neural circuits in behavioural control.  Specifically, his research examines how the brain chooses between and efficiently prepares actions, when we are faced with uncertainty.  Dr. Dorris received the Governor General Gold Medal for his Ph.D. thesis. He then moved to New York University for his post-doctoral studies with Paul Glimcher. His pioneering work in Neuroeconomics examined the role of the parietal cortex in decision making during strategic games. In the Fall of 2003, Dr. Dorris joined the Department of Physiology at Queen’s as an assistant professor and recipient of a Tier II Canada Research Chair in Neural Control of Decision Making. Dr. Dorris was promoted to Associate Professor in 2009. In 2013, Dr. Dorris moved his laboratory to the Institute of Neuroscience within the Shanghai Institutes of Biological Sciences where he is Head Investigator of the Laboratory of Decision-Making. He continues to focus on elucidating brain areas involved in choosing motor actions based on their economic value.

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A Two-stage Model of Sensory Discrimination: An Alternative to Arift-diffusion
Mar
24
4:00pm 4:00pm

A Two-stage Model of Sensory Discrimination: An Alternative to Arift-diffusion

Michael S. Landy

Professor of Psychology and Neural Science - New York University

Biography
I have an enduring interest in the use of computational techniques to study human vision. My doctoral dissertation concerned the computer simulation of a neural network model of visual learning. For this work, I received the Ph.D. from the Department of Computer and Communication Sciences of the University of Michigan in 1981, having worked primarily with John Holland. I then moved to New York University and worked as a postdoctoral research associate with George Sperling, examining aspects of low bandwidth visual image sequences, in particular as applied to low bandwidth communication systems for the deaf (involving perceptual studies of American Sign Language). During that time I also co-wrote the HIPS image processing software. In 1984 I joined the faculty at NYU, and have continued to work on problems in visual perception, concentrating on perception of depth and texture. In 1992-3, I spent a sabbatical year as a National Research Council Senior Research Associate at NASA Ames Research Center. In the summer of 1998, I visited the Institut d'Ingénierie de la Vision, Université Jean Monnet de Saint-Étienne, collaborating on work on texture appearance. In 1999-2002, I spent a sabbatical year and much of the subsequent two years at the School of Optometry, University of California at Berkeley, working with Prof. Martin S. Banks on various projects in depth perception and stereopsis; that collaboration is ongoing.

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Self-control and the Dorsal Medial Prefrontal Cortex
Mar
23
4:00pm 4:00pm

Self-control and the Dorsal Medial Prefrontal Cortex

Gui Xue, PhD

State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research

Biography
Dr. Gui Xue is a 985 chief researcher and Changjiang Scholar chair professor in the State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University. He is also a Principle Investigator in the IDG/McGovern Institute for Brain Research at BNU. Dr. Xue received his Ph. D. in cognitive neuroscience from Beijing Normal University in 2004. He then spent three years at UCLA as a postdoctoral trainee, with the support of a fellowship from FPR-UCLA Center for Cultural, Brain and Development. He was a research assistant professor at the University of Southern California before landing at Beijing Normal University in 2011. Dr. Xue studies the cognitive and neural mechanisms of learning and memory, language learning, executive control and decision making, mainly using functional neuroimage techniques. He has published more than 80 research papers on academic journals such as Science, PNAS, Current Biology, Journal of Neuroscience, and Cerebral Cortex. His research is support by Natural Science Foundation of China, the 973 Project, the Thousand Young Scholar Program, and the New Century Excellent Talents Program. 

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Hippocampus and Decision Making
Feb
23
4:00pm 4:00pm

Hippocampus and Decision Making

Min Whan Jung

Center for Synaptic Brain Dysfunctions, Institute for Basic Science and Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea. 

Little attention has been paid to the hippocampus so far in formulating neural mechanisms underlying decision making. However, theoretical considerations suggest potential involvement of the hippocampus in value-based decision making, especially during model-based reinforcement learning in which values can be updated according to the decision-maker’s knowledge or model of the environment. To investigate whether the hippocampus processes value-related information, we examined how activity of hippocampal neurons in rats performing a dynamic foraging task was related to reward values that were estimated using a reinforcement learning model. CA1 neurons carried robust signals for the value of chosen action and they temporally overlapped with reward signals, indicating that signals necessary to evaluate the outcome of an experienced event converged in CA1. Compared to CA1, value signals were substantially weaker in CA3 and subiculum, the two neighbouring structures of CA1. Furthermore, selective inactivation of CA1, but not CA3, CA2, or dentate gyrus, impaired value learning without affecting value-dependent action selection. These results suggest a major role of CA1 in integrating value information with other elements of episodic memory. This way, the retrieval of a memory for a previously experienced event will automatically entail the retrieval of its associated value. Convergent factual and value information represented in CA1 might also be essential for simulating most probable and rewarding scenario for maximizing value. 

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