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.

Neuronal correlates of familiarity-driven decisions in artificial grammar learning.

Abstract: It has been proposed on the basis of behavioural data that grammaticality judgments in implicit artificial grammar learning paradigms are largely driven by priming based on fragment familiarity. A prediction that follows from this account is that neural deactivation, a common correlate of repetition priming, should be observed for grammatical compared to ungrammatical stimuli. We conducted an event-related fMRI study to investigate neuronal correlates of such fragment-based priming. In a study phase, participants performed a short-term memory task on a series of strings of pseudofont characters. Scanning was performed in a subsequent test phase in which participants classified new strings as either grammatical or ungrammatical. Test strings differed systematically from training strings in terms of exemplar and fragment similarity. Behaviourally, participants classified strings as grammatical based on fragment familiarity. Differential activity was evident during string classification as reduced activity in left lateral occipital complex and bilateral lingual gyri for strings with high fragment familiarity compared to strings with low fragment familiarity. Thus, consistent with the hypothesis, neuronal facilitation in extrastriate occipital regions may constitute one basis of implicit grammaticality decisions based on fragment priming.

Neuroscience of Preference and Choice : Cognitive and Neural Mechanisms

Abstract: One of the most pressing questions in neuroscience, psychology and economics today is how does the brain generate preferences and make choices? With a unique interdisciplinary approach, this volume is among the first to explore the cognitive and neural mechanisms mediating the generation of the preferences that guide choice. From preferences determining mundane purchases, to social preferences influencing mating choice, through to moral decisions, the authors adopt diverse approaches to answer the question. Chapters explore the instability of preferences and the common neural processes that occur across preferences. Edited by one of the world’s most renowned cognitive neuroscientists, each chapter is authored by an expert in the field, with a host of international contributors. * Emphasis on common process underlying preference generation makes material applicable to a variety of disciplines – neuroscience, psychology, economics, law, philosophy, etc. * Offers specific focus on how preferences are generated to guide decision making, carefully examining one aspect of the broad field of neuroeconomics and complimenting existing volumes * Features outstanding, international scholarship, with chapter written by an expert in the topic area

Neurocomputational mechanisms underpinning aberrant social learning in young adults with low self-esteem

Abstract: Low self-esteem is a risk factor for a range of psychiatric disorders. From a cognitive perspective a negative self-image can be maintained through aberrant learning about self-worth derived from social feedback. We previously showed that neural teaching signals that represent the difference between expected and actual social feedback (i.e., social prediction errors) drive fluctuations in self-worth. Here, we used model-based functional magnetic resonance imaging (fMRI) to characterize learning from social prediction errors in 61 participants drawn from a population-based sample (n = 2402) who were recruited on the basis of being in the bottom or top 10% of self-esteem scores. Participants performed a social evaluation task during fMRI scanning, which entailed predicting whether other people liked them as well as the repeated provision of reported feelings of self-worth. Computational modeling results showed that low self-esteem participants had persistent expectations that others would dislike them, and a reduced propensity to update these expectations in response to social prediction errors. Low self-esteem subjects also displayed an enhanced volatility in reported feelings of self-worth, and this was linked to an increased tendency for social prediction errors to determine momentary self-worth. Canonical correlation analysis revealed that individual differences in self-esteem related to several interconnected psychiatric symptoms organized around a single dimension of interpersonal vulnerability. Such interpersonal vulnerability was associated with an attenuated social value signal in ventromedial prefrontal cortex when making predictions about being liked, and enhanced dorsal prefrontal cortex activity upon receipt of social feedback. We suggest these computational signatures of low self-esteem and their associated neural underpinnings might represent vulnerability for development of psychiatric disorder.

A role for the striatum in regret-related choice repetition

Abstract: "Regret aversion" is proposed to explain a tendency to avoid future choices that have induced past regret However, regret might also motivate us to repeat previous regret-related choices to make up for their previous selection, a behavior resembling "chasing" in the context of gambling In the current experiment, we acquired fMRI brain data while participants placed monetary bets on repeated gambles Behaviorally, participants showed a tendency to repeat previously regret-related choices (operationalized as those leading to an outcome worse than what might have been), an effect restricted to early runs of the task At gamble outcome, we show a reduction in ventral striatal activity for regret-related relative to relief-related outcomes Critically, this modulation was only seen when subjects were responsible for the bet choice Activity in dorsal striatum was associated with an influence of previous regret on participants' subsequent choices, which is evident in increased activity when regret-related choices were repeated, relative to avoided, on the next trial Our findings indicate that regret can lead to choice repetition as if seeking to make up for our mistakes and in so doing may lead to subsequent chasing behavior

Distinct Roles of Dopamine and Noradrenaline in Incidental Memory

Abstract: Episodic memory is sensitive to the influence of neuromodulators, such as dopamine and noradrenaline. These influences are considered important in the expression of several known memory biases, though their specific role in memory remains unclear. Using pharmacological agents with relatively high selectivity for either dopamine (400 mg amisulpride) or noradrenaline (40 mg propranolol) we examined their specific contribution to incidental memory. In a double-blind placebo-controlled human study (30 females, 30 males in total), we show that a memory selectivity bias was insensitive to propranolol but sensitive to amisulpride, consistent with a dominant influence from dopamine. By contrast, a putative arousal-induced memory boosting effect was insensitive to amisulpride but was sensitive to propranolol, consistent with a dominant noradrenaline effect. Thus, our findings highlight specific functional roles for dopamine and noradrenaline neurotransmission in the expression of incidental memory.SIGNIFICANCE STATEMENT Why some information is preferentially encoded into memory while other information is not is a central question in cognitive neuroscience. The neurotransmitters dopamine and noradrenaline are often assumed critical in influencing this selectivity, but their specific contributions remain obscure. In this double-blind, placebo-controlled, between-subjects drug study, we investigate the contributions of noradrenaline and dopamine to episodic memory. Using an incidental memory task, we find that blocking dopamine (400 mg amisulpride) eliminates a neural-gain related memory selectivity bias. Blocking noradrenaline function (40 mg propranolol), in contrast, abolishes an arousal-related memory enhancement. In this assessment of dopamine and noradrenaline neuromodulatory effects we reveal their specific contributions to episodic memory.

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.