Raymond J. Dolan

Bio: Raymond J. Dolan is an academic researcher from University College London. The author has contributed to research in topics: Prefrontal cortex & Functional magnetic resonance imaging. The author has an hindex of 196, co-authored 919 publications receiving 138540 citations. Previous affiliations of Raymond J. Dolan include VU University Amsterdam & McGovern Institute for Brain Research.

Reward value of attractiveness and gaze - Making eye contact enhances the appeal of a pleasing face, irrespective of gender.

Abstract: 1 , with the eyes assuming particular importance 2,3. Here we show that the perceived attractiveness of an unfamiliar face increases brain activity in the ventral striatum of the viewer when meeting the person's eye, and decreases activity when eye gaze is directed away. Depending on the direction of gaze, attractiveness can thus activate dopaminergic regions that are strongly linked to reward prediction 4 , indicating that central reward systems may be engaged during the initiation of social interactions. In an event-related functional magnetic resonance imaging (fMRI) study, 16 subjects (8 male, 8 female) were shown colour images of 40 different faces, which had their eyes directed either at or away from the subject (Fig. 1). After the fMRI scanning session, subjects rated the attractiveness of the faces they had seen, and these ratings were normalized and parametrically entered into the analysis. Significant effects (P<0.05, corrected for multiple comparisons) were assessed using a t-test and displayed as statistical parametric maps using SPM99b software. No brain region showed any activation in response to facial attractiveness per se. We therefore investigated whether processing of attractiveness depends on gaze direction. When eye gaze was directed at the subjects, activity in the ventral striatum correlated positively with attractiveness (slope: +0.46% change in signal intensity per standard deviation increase in attractiveness rating). In contrast, this correlation was reversed when eye gaze was averted, causing activity in the ventral striatum to decrease with increasing attractiveness (slope: ǁ0.88% signal intensity per s.d. increase in attractiveness). Figure 2 shows the resulting activation map when these two contrasts are combined as an interaction — for example, the contrast (correlation with attractiveness when eye contact is made) minus (correlation with attractiveness when no eye contact is made). In a fixed-effects analysis, the effect was significant at a voxel level (the smallest volume tested in our study) in the right ventral striatum (Pǃ0.015) and at cluster level bilaterally (right, P<0.01; left, Pǃ0.01). We also implemented a second-level random-effects analysis to determine whether the results could be extended to the population at large. Significant effects persisted at a cluster level in the right ventral striatum (P<0.01; point of maximal activation: xǃ12, yǃǁ8, zǃ10) and approached significance in the left ventral striatum (Pǃ0.06, xǃǁ10, yǃ0, zǃ4). The gender of the observed face in relation to the gender of the observer did not influence the response in the ventral striatum. This might indicate that …

Deep brain stimulation of the subthalamic nucleus modulates sensitivity to decision outcome value in Parkinson’s disease

Abstract: Deep brain stimulation (DBS) of the subthalamic nucleus in Parkinson's disease is known to cause a subtle but important adverse impact on behaviour, with impulsivity its most widely reported manifestation. However, precisely which computational components of the decision process are modulated is not fully understood. Here we probe a number of distinct subprocesses, including temporal discount, outcome utility, instrumental learning rate, instrumental outcome sensitivity, reward-loss trade-offs, and perseveration. We tested 22 Parkinson's Disease patients both on and off subthalamic nucleus deep brain stimulation (STN-DBS), while they performed an instrumental learning task involving financial rewards and losses, and an inter-temporal choice task for financial rewards. We found that instrumental learning performance was significantly worse following stimulation, due to modulation of instrumental outcome sensitivity. Specifically, patients became less sensitive to decision values for both rewards and losses, but without any change to the learning rate or reward-loss trade-offs. However, we found no evidence that DBS modulated different components of temporal impulsivity. In conclusion, our results implicate the subthalamic nucleus in a modulation of outcome value in experience-based learning and decision-making in Parkinson's disease, suggesting a more pervasive role of the subthalamic nucleus in the control of human decision-making than previously thought.

Emotion, motivation and pain

Abstract: The perception of both pain intensity and pain aversiveness is not a simple feedforward process that reads out the amplitude of an ascending nociceptive signal to evoke a conscious unpleasant sensation ( Apkarian et al 2005 ). A wide variety of factors infl uence perception, including expectation, uncertainty, multisensory input, behavioral and environmental context, emotional and motivational state, self versus externally induced pain, and controllability ( Eccleston and Crombez 1999, Price 2000, Villemure and Bushnell 2002, Fields 2004, Wiech et al 2008, Ossipov et al 2010, Tracey 2010 ). This illustrates the complex process by which the brain constructs the sensory and emotional sensation of pain and challenges any standard “perception – action” model. The best-studied contribution to perception comes from expectation, not the least since this lies at the heart of the placebo and nocebo analgesic effect ( Price et al 2008 ). In brief, expectation generally biases perception in the direction of that expectation: if one expects a higher degree of pain than is infl icted, the pain is typically felt as more painful. An expectation of mild or no pain similarly reduces actual pain. The source of information from which an expectation is derived is diverse and ranges from the implicit information inherent in pavlovian conditioning to explicitly provided verbal instruction, and a multitude of experimental manipulations attest to the ubiquity and complexity of these effects and their biological correlates ( Voudouris et al 1989, Montgomery and Kirsch 1997, Price et al 1999, Benedetti et al 2005 ). Underneath this apparent complexity may lie a relatively simple model, which we propose here. An expectation can be considered as a belief, and this in turn can be formalized as a s0010 p0010

Reinforcement Learning: An Introduction

Abstract: Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

An integrative theory of prefrontal cortex function

Abstract: ▪ Abstract The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task. We review neurophysiological, neurobiological, neuroimaging, and computational studies that support this theory and discuss its implications as well as further issues to be addressed

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.