Keynote Speaker (Last name by alphabetical order)
Professor Luke CLARK
Professor Luke Clark, Director, Centre for Gambling Research at UBC, and Department of Psychology, University of British Columbia, Vancouver, Canada
Loss chasing, decision-making, and gambling addiction
Loss chasing is a diagnostic item for Gambling Disorder and is present in many screening instruments for problem gambling. It is often discussed as a defining feature for differentiating disordered gambling from recreational gambling. But loss chasing can also be expressed in many ways, both within and between gambling episodes, and this creates an obstacle for understanding the ‘centrality’ of loss chasing. This talk will summarize three lines of research characterizing loss chasing on laboratory tasks, in authentic slot machines, and in online behavioural tracking data, in order to better establish the psychological underpinnings of this symptom.
Professor Mingzhou Ding
Distinguished Professor and J. Crayton Pruitt Family Professor of Biomedical Engineering at University of Florida
Analyzing Coherent Brain Networks with Granger Causality
Multielectrode neurophysiological recording and functional brain imaging produce massive quantities of data. Multivariate time series analysis provides the basic framework for analyzing the patterns of neural interactions in these data. Neural interactions are directional. Being able to assess the directionality of neuronal interactions is thus a highly desired capability for understanding the cooperative nature of neural computation. Research over the last two decades has shown that Granger causality is a promising technique to furnish this capability. In this talk, I will introduce the concept of Granger causality and present results from applications of this technique to neuroimaging.
Professor Marc POTENZA
Marc N. Potenza, MD, PhD Director, Division on Addictions Research at Yale Director, Yale Center of Excellence in Gambling Research Director, Women and Addictions Core of Women’s Health Research at Yale Director, Yale Impulsivity Research Program Professor of Psychiatry, Neuroscience and Child Study Yale University School of Medicine
Substance and Behavioral Addictions: Reward, Control, and Decision-making and Their Relationships to Neural Processing and Treatment Outcomes
A core feature of addictions involves continued engagement in a behavior despite adverse consequences. In other words, addictions involve decisions to participate in specific motivated behaviors that are disadvantageous over the longer term. As behaviors with addictive potential are initially hedonic, decision-making involving reward/loss considerations are important to consider, as is cognitive control over behavioral engagement. In this presentation, reward/loss processing, cognitive and behavioral control and decision-making will be considered with respect to substance (e.g., tobacco, alcohol, cocaine and other drugs) and behavioral (e.g., gambling, internet gaming) addictions. Data regarding how brain correlates of these processes differ in individuals with and without addictions will be considered, as how these processes may operate transdiagnostically. Other transdiagnostic features linked to decision-making in addictions (e.g., impulsivity) will also be considered. Through studies that have integrated brain imaging measures into randomized clinical trials, data regarding how treatments for addictive disorders may alter such brain-behavior relationships will be presented. Implications for prevention, treatment, public health and policy will be discussed.
Professor Christian RUFF
Professor Christian Ruff, PhD, Chair of Decision Neuroscience, Zurich Center for Neuroeconomics, University of Zurich, Switzerland
Perceptual determinants of individual styles in risk-taking and preference-based choice
Humans vary considerably in their decision-making styles: Some of us shy away from financial risks whereas others appear to seek them, and different individuals show more or less stable choices over time. These individual decision-making styles are usually explained as differences in the way people assign value to the relevant features of the choice options. For instance, economic models of risk aversion assume that people assign lower utilities to uncertain large choice options, and neuroscience studies have focussed on affective brain processes signalling uncertainty and hedonic values of uncertain rewards. In my talk, I will provide evidence for a different neural origin of individual differences in risky and preference-based decision-making: I will provide evidence that risk aversion can arise mechanistically from purely perceptual limitations in how our brains represent magnitudes, and that it is in fact possible to predict individual risk attitudes from brain activity during purely perceptual tasks. Moreover, I will present a model of how the preferences guiding our choices may reflect noisy perception of internal signals, which in turn allows a good prediction of the stability of preference-based choices. Taken together, I hope that these data will convince the audience that we can improve our understanding of economic decision-making by explicitly accounting for our brain’s perceptual processing capacity.
Invited Speaker (last name by alphabetical order)
Professor Robin CHARK
Assistant Professor, Faculty of Business Administration / Centre for Cognitive and Brain Sciences, University of Macau
Non-equilibrium plays under strategic uncertainty
Most of the decisions we made involve uncertainty. Among these decisions, the uncertainty often arises from the indeterminacy of another person’s action. The outcomes depend on the randomness arising from another person. This interactiveness can be captured theoretically using game theory. At the core of game theory is the concept that players reason what other players will do by going through iterations of players guessing what other players think they will do. However, it has been often found that people do not follow the game-theoretical prediction when they play these games. In our neuroimaging study, we explore this question by using a behavioral economic task in which people make decisions under risk in an interpersonal environment. We consistently find that players deviate from the game theoretical prediction. There are two possibilities that why people do not play optimally. Decision makers could either prefer the risk arising from the strategic interaction or they may not be able to respond optimally in the face of uncertainty arising from another person. Decision makers may not always select the strategy with the highest expected payoff. They may even hold false beliefs of the actions of their opponents. The neuroimaging data allows us to disentangle these two possibilities by a computational model. In addition, the implications of the neural mechanism underlying how people make decisions under risk arising from another person on behavioral addictions will be discussed.
Professor Xiaowei GUAN
Professor Xiaowei Guan, Vice Dean of School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine
The brain circuits involved in addiction and the intervention of Chinese Medicine against addiction
Drug addiction is a chronic brain disease characterized by compulsive drug-craving and high risk of relapse, which is a kind of mental disorder syndrome with brain injury as the core and covering multiple factors such as psychological, physiological and social environment. At present, due to the complexity of brain circuit mechanism of addiction, there are no effective drugs and intervention measures to prevent and treat addiction in clinic. Traditional Chinese Medicine has a long history of treating psychiatric disorders including addiction with the advantages of holistic view and multi-targets. However, the unclear mechanism limits its clinical application and popularization. Using virus tracing, chemical genetics, optical fiber recording, electrophysiology, behavior, morphology and other technical methods, our team is currently engaged in exploring the brain circuit mechanism and acupuncture intervention strategy of addiction and related psychiatric comorbidity. Here, we’d like to introduce some our research progress in the neurobiological mechanisms and potential therapeutic strategy for addiction.
Professor Yong LIN
Professor Xun LIU
Professor Xun LIU, Institute of Psychology, Chinese Academy of Sciences; Department of Psychology, University of Chinese Academy of Sciences
Neurocognitive mechanism of addiction susceptibility
Various forms of addiction, including substance (e.g., drug, tobacco, and alcohol) and non-substance (e.g., gambling and internet gaming) are becoming a severe personal and societal burden. It is urgent and important to explore mechanisms of addiction susceptibility. Over the years, taking the approach of dual-system framework of risk taking behaviors, our lab has investigated this issue from the neurocognitive perspective. We have adopted and developed various paradigms to study reward processing (e.g., simple gambing task, roulette task, and mixed gamble task ) and cognitive control (e.g., Go/Nogo task, stop signal task, conditional stop signal task, and two-choice oddball task) in populations at risk of addiction (e.g., individuals with high sensation seeking, substance abuse, and internet gaming disorder), combining behavioral and neuroimaging indices. Our findings suggest that increased risky choices and abnormal reward processing can be used as biomarkers of addiction susceptibility, whereas impairment of inhibitory control can be used to assess the severity of addiction.
Professor Qiyang NIE
Assistant Professor, Centre for Cognitive and Brain Sciences / Department of Psychology, University of Macau
Illusory preference by association: Variability aversion in decision making is domain specific
Our past experience is structured by the statistics of events in the daily environment. Accurate estimation of current reward magnitude depends on the valence and distribution of these events. Rewards are often not singular events with constant magnitude but are elements of probability distributions that fluctuate from one moment to the next. Previous work mainly focused on the role of value magnitude rather than its distribution in decision making, and the present study employed a novel memory-based decision making task to investigate the influence of value distribution on decisional preference, we found a robust variability aversion effect – participants prefer options that are associated with values of less variance, and this effect is domain specific: it is much more pronounced in the loss domain than in the gain domain. A novel non-linear utility-difference accumulation model was developed to account for these data. These fundamental findings give a novel perspective for current theories of human choice and preference.
Professor Shenglin WEN
溫盛霖教授 中山大學附屬第五醫院（珠海） 精神心理科主任，主任醫師，博士生導師
Cognition and related biological techniques of nonhuman primates 非人靈長類動物的認知及相關生物學技術
Professor Haiyan WU
Prof Haiyan Wu, PI of Artificial Intelligence, Neuroscience and Decision Lab (https://andlab-um.com/).
Variations in EEG state during face perception predicts impression change and following decisions
People experience events and get an impression of others every day. Few studies have examined how and what can predict people’s bias to follow others’ opinions and the underlying neural mechanism. With a four experiment design, we showed that the amount of EEG variation when people experience viewing others’ faces can predict their following negative conformity (get the worse impression to others after knowing other’s negative evaluations) in a trustworthiness impression change task. Furthermore, we also found participants who showed higher variations in the independent task can predict their trust decisions in the trust game, which means they were more likely to update their impressions and following other’s judgments. Our findings suggest that the individual differences in independent face judgment may serve as a predictor of vulnerability to social-influenced mind changes at both behavioral and neural level.
Professor Zhen YUAN
Interim Head of Centre for Cognitive and Brain Sciences
Professor, Faculty of Health Sciences, University of Macau
Mapping of executive function and decision-making impairments in gambling addiction by using functional near-infrared spectroscopy
Functional near-infrared spectroscopy (fNIRS) has emerged as a highly promising brain mapping technique that enables continuously and noninvasively monitoring the hemodynamic responses in the human brain. In this study, fNIRS was utilized to identify the different brain activation patterns between pathological gamblers (PGs) and heathy controls (HCs). Specifically, we examined the hemodynamic changes in the prefrontal cortex using fNIRS recordings during the completion of executive function and decision making tasks for PGs and HCs. Our mapping results revealed that the groups exhibited notable differences in the brain activation and brain connectivity across the prefrontal region.
Professor Hang ZHANG
Professor Hang Zhang, Principal Investigator in the School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, and Peking-Tsinghua Center for Life Sciences of Peking University
The inductive biases in learning probability distributions
Probability distributions over perceptual features and action outcomes are central to many inference and decision-making problems in real life as well as in laboratory experiments. It should be noted that learning an arbitrary probability distribution from samples is itself an inductive inference problem that has no unique solution. The brain must choose among potentially infinite number of generative models that agree with the observed samples. What sorts of generative models does the brain favor? I will talk about some of our recent work, where we have developed a series of behavioral and neural measures to reveal the brain’s inductive biases in learning probability distributions. Contrary to the principle of parsimony that is generally believed to be true, we find that humans do not necessarily prefer simpler generative models. Instead, they often invent non-existent latent structures and favor generative models with medium-level complexity. These findings can have broad implications for human inference and decision-making, relating to both humans’ remarkable ability to discover latent causes and their vulnerableness to superstitious beliefs.
Professor Yingjie ZHU
Professor Yingjie Zhu, Director of Shenzhen Key Laboratory of Drug Addiction, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
Orchestrating Opiate-Associated Memories in Thalamic Circuits
Disrupting memories that associate environmental cues with drug experiences holds promise for treating addiction, yet accessing the distributed neural network that stores such memories is challenging. Here, we show that the paraventricular nucleus of the thalamus (PVT) orchestrates the acquisition and maintenance of opiate-associated memories via projections to the central nucleus of the amygdala (CeA) and nucleus accumbens (NAc). PVT→CeA activity associates morphine reward to the environment, whereas transient inhibition of the PVT→NAc pathway during retrieval causes enduring protection against opiate-primed relapse. Using brain-wide activity mapping, we revealed distributed network activities that are altered in non-relapsing mice, which enabled us to find that activating the downstream NAc→lateral hypothalamus (LH) pathway also prevents relapse. These findings establish the PVT as a key node in the opiate-associated memory network and demonstrate the potential of targeting the PVT→NAc→LH pathway for treating opioid addiction.
