Author
Raymond J. Dolan
Other affiliations: VU University Amsterdam, McGovern Institute for Brain Research, UCL Institute of Neurology ...read more
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 published on a yearly basis
Papers
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TL;DR: The authors found that retrieval of emotional contexts elicited enhanced activity in right amygdala and a right-lateralized network that included extrastriate visual areas, while contextual retrieval was unsuccessful.
Abstract: There is considerable evidence that encoding and consolidation of memory are modulated by emotion, but the retrieval of emotional memories is not well characterized. Here we manipulated the emotional context with which affectively neutral stimuli were associated during encoding, allowing us to examine neural activity associated with retrieval of emotional memories without confounding the emotional attributes of cue items and the retrieved context. Using a source memory procedure we were also able to compare how retrieval processing was modulated when the emotional encoding context was recollected or not. An interaction between emotional encoding context and accuracy of source memory revealed that successful retrieval of emotional context was associated with activity in left amygdala, and a left frontotemporal network including anterior insula, prefrontal cortex and cingulate. In contrast, when contextual retrieval was unsuccessful, items encoded in emotional contexts elicited enhanced activity in right amygdala and a right-lateralized network that included extrastriate visual areas. These findings indicate distinct effects of emotion on successful and unsuccessful retrieval of source information, including lateralization of amygdala responses.
77 citations
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TL;DR: The findings show that the biological control of social behaviour is dynamically regulated not only by modulators promoting, but also by those diminishing a propensity to collaborate.
Abstract: Collaboration can provide benefits to the individual and the group across a variety of contexts. Even in simple perceptual tasks, the aggregation of individuals' personal information can enable enhanced group decision-making. However, in certain circumstances such collaboration can worsen performance, or even expose an individual to exploitation in economic tasks, and therefore a balance needs to be struck between a collaborative and a more egocentric disposition. Neurohumoral agents such as oxytocin are known to promote collaborative behaviours in economic tasks, but whether there are opponent agents, and whether these might even affect information aggregation without an economic component, is unknown. Here, we show that an androgen hormone, testosterone, acts as such an agent. Testosterone causally disrupted collaborative decision-making in a perceptual decision task, markedly reducing performance benefit individuals accrued from collaboration while leaving individual decision-making ability unaffected. This effect emerged because testosterone engendered more egocentric choices, manifest in an overweighting of one's own relative to others' judgements during joint decision-making. Our findings show that the biological control of social behaviour is dynamically regulated not only by modulators promoting, but also by those diminishing a propensity to collaborate.
77 citations
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TL;DR: It is found that choices altered preferences both immediately after being made and after the delay, providing evidence that making a decision can lead to enduring change in preferences.
Abstract: The idea that decisions alter preferences has had a considerable influence on the field of psychology and underpins cognitive dissonance theory Yet it is unknown whether choice-induced changes in preferences are long lasting or are transient manifestations seen in the immediate aftermath of decisions In the research reported here, we investigated whether these changes in preferences are fleeting or stable Participants rated vacation destinations before making hypothetical choices between destinations, immediately afterward, and 25 to 3 years later We found that choices altered preferences both immediately after being made and after the delay These changes could not be accounted for by participants' preexisting preferences, and they occurred only when participants made the choices themselves Our findings provide evidence that making a decision can lead to enduring change in preferences
77 citations
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TL;DR: Measureting regional cerebral blood flow changes whilst subjects performed memory tasks provides direct in vivo evidence for the involvement of the hippocampal formation in long-term memory in the intact human brain.
77 citations
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TL;DR: It is suggested that the responses of different brain regions do dissociate according to the phenomenology associated with memory retrieval, as seen in hemodynamic responses associated with both studying and recognizing words.
Abstract: The question of whether recognition memory judgments with
and without recollection reflect dissociable patterns of brain activity is unresolved. We used event-related, functional magnetic resonance imaging (fMRI) of 12 healthy volunteers to measure hemodynamic responses associated with both studying and recognizing words. Volunteers made one of three judgments to each word during recognition: whether they recollected seeing it during study (R judgments), whether they experienced a feeling of familiarity in the absence of recollection(K judgments), or whether they did not remember seeing it during study (N judgments). Both R and K judgments for studied words were associated with enhanced responses in left prefrontal and left parietal cortices relative to N judgments for unstudied words. The opposite pattern was observed in bilateral temporoccipital regions and amygdalae. R judgments for studied words were associated with enhanced responses in anterior left prefrontal, left parietal, and posterior cingulate regions relative to K judgments. At study, a posterior left prefrontal region exhibited an enhanced response to words subsequently given R versus K judgments, but the response of this region during recognition did not differentiate R and K judgments. K judgments for studied words were associated with enhanced responses in right lateral and medial prefrontal cortex relative to both R judgments for studied words and N judgments for unstudied words, a difference we attribute to greater monitoring demands when memory judgments are less certain.
These results suggest that the responses of different brain
regions do dissociate according to the phenomenology associated with memory retrieval.
77 citations
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01 Jan 1988TL;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
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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
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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
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