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Author

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.


Papers
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Journal ArticleDOI
TL;DR: Multivariate techniques embedded in a novel machine learning framework are promising tools to link behavior and/or symptoms to neurobiology and thus have great potential to contribute to a biologically grounded definition of psychiatric disorders.

28 citations

Journal ArticleDOI
TL;DR: The authors found a significant negative correlation between human growth hormone (HGH) response to clonidine and urinary free cortisol level in 14 depressed patients.
Abstract: The authors found a significant negative correlation between human growth hormone (HGH) response to clonidine and urinary free cortisol level in 14 depressed patients. The HGH response did not distinguish endogenous depression from nonendogenous depression.

28 citations

Journal ArticleDOI
TL;DR: It is argued that a psychiatry informed by computational neuroscience, computational psychiatry, can obviate this danger of research into the biological basis of emotional and motivational disorders by rendering obsolete the polarity between biological and psychosocial conceptions of illness.
Abstract: Research into the biological basis of emotional and motivational disorders is in danger of riding roughshod over a patient-centered psychiatry and falling into the dualist errors of the past, i.e., by treating mind and brain as conceptually distinct. We argue that a psychiatry informed by computational neuroscience, computational psychiatry, can obviate this danger. Through a focus on the reasoning processes by which humans attempt to maximize reward (and minimize punishment), and how such reasoning is expressed neurally, computational psychiatry can render obsolete the polarity between biological and psychosocial conceptions of illness. Here, the term 'psychological' comes to refer to information processing performed by biological agents, seen in light of underlying goals. We reflect on the implications of this perspective for a definition of mental disorder, including what is entailed in asserting that a particular disorder is 'biological' or 'psychological' in origin. We propose that a computational approach assists in understanding the topography of mental disorder, while cautioning that the point at which eccentric reasoning constitutes disorder often remains a matter of cultural judgment.

28 citations

Journal ArticleDOI
TL;DR: Brain imaging continues to provide important data about brain structure, neurotransmitter function and the physiological basis of cognitive processes, as these relate to schizophrenia and mood disorders, but a unifying theoretical perspective that can clarify the precise nature of the biological basis is lacking.

28 citations

Journal ArticleDOI
TL;DR: Greater average reward was associated with enhanced activity in dopaminergic midbrain to a degree that correlated with the relationship between average reward and pressing vigor, and an opposite pattern was observed in subgenual cingulate cortex, a region implicated in negative mood and motivational inhibition.
Abstract: Dopamine plays a key role in motivation. Phasic dopamine response reflects a reinforcement prediction error RPE, whereas tonic dopamine activity is postulated to represent an average reward that mediates motivational vigor. However, it has been hard to find evidence concerning the neural encoding of average reward that is uncorrupted by influences of RPEs. We circumvented this difficulty in a novel visual search task where we measured participants' button pressing vigor in a context where information underlying an RPE about future average reward was provided well before the average reward itself. Despite no instrumental consequence, participants' pressing force increased for greater current average reward, consistent with a form of Pavlovian effect on motivational vigor. We recorded participants' brain activity during task performance with fMRI. Greater average reward was associated with enhanced activity in dopaminergic midbrain to a degree that correlated with the relationship between average reward and pressing vigor. Interestingly, an opposite pattern was observed in subgenual cingulate cortex, a region implicated in negative mood and motivational inhibition. These findings highlight a crucial role for dopaminergic midbrain in representing aspects of average reward and motivational vigor.

27 citations


Cited by
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Book
01 Jan 1988
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
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.

37,989 citations

28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组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�ly.
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。

18,940 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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.).

13,246 citations

Journal ArticleDOI
TL;DR: It is proposed 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, which 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.
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

10,943 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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.

10,141 citations