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.

The effects of life stress and neural learning signals on fluid intelligence

Abstract: Fluid intelligence (fluid IQ), defined as the capacity for rapid problem solving and behavioral adaptation, is known to be modulated by learning and experience. Both stressful life events (SLES) and neural correlates of learning [specifically, a key mediator of adaptive learning in the brain, namely the ventral striatal representation of prediction errors (PE)] have been shown to be associated with individual differences in fluid IQ. Here, we examine the interaction between adaptive learning signals (using a well-characterized probabilistic reversal learning task in combination with fMRI) and SLES on fluid IQ measures. We find that the correlation between ventral striatal BOLD PE and fluid IQ, which we have previously reported, is quantitatively modulated by the amount of reported SLES. Thus, after experiencing adversity, basic neuronal learning signatures appear to align more closely with a general measure of flexible learning (fluid IQ), a finding complementing studies on the effects of acute stress on learning. The results suggest that an understanding of the neurobiological correlates of trait variables like fluid IQ needs to take socioemotional influences such as chronic stress into account.

Human complex exploration strategies are enriched by noradrenaline-modulated heuristics.

Abstract: An exploration-exploitation trade-off, the arbitration between sampling a lesser-known against a known rich option, is thought to be solved using computationally demanding exploration algorithms. Given known limitations in human cognitive resources, we hypothesised the presence of additional cheaper strategies. We examined for such heuristics in choice behaviour where we show this involves a value-free random exploration, that ignores all prior knowledge, and a novelty exploration that targets novel options alone. In a double-blind, placebo-controlled drug study, assessing contributions of dopamine (400 mg amisulpride) and noradrenaline (40 mg propranolol), we show that value-free random exploration is attenuated under the influence of propranolol, but not under amisulpride. Our findings demonstrate that humans deploy distinct computationally cheap exploration strategies and that value-free random exploration is under noradrenergic control.

Hypotheses About the Relationship of Cognition With Psychopathology Should be Tested by Embedding Them Into Empirical Priors

Abstract: Mechanistic hypotheses about psychiatric disorders are increasingly formalized as computational models. Model parameters, characterizing for example decision-making biases, are hypothesized to correlate with clinical constructs. This is promising (Moutoussis et al., 2016), but here we comment that techniques used in the literature tominimize noise in parameter estimationmay not be helpful. In addition, we point out related pitfalls whichmay lead to questionable research practices (Sijtsma, 2015). We advocate incorporating cross-domain, e.g., psychopathology-cognition relationships into the parameter inference itself. Maximum-likelihood techniques often provide noisy parameter estimates, in the sense of total error over an experimental group. In addition, in large studies brief tasks are often used, providing little data per participant. However, individual parameter estimation can be improved by using empirical priors (Efron, 2012). Here, parameter estimates are informed by, or conditioned upon, the population distribution that a case comes from. For an individual j with parameters θ j coming from a population distribution ppop:

Magnetoencephalography decoding reveals structural differences within integrative decision processes

Abstract: When confronted with complex inputs consisting of multiple elements, humans use various strategies to integrate the elements quickly and accurately. For instance, accuracy may be improved by processing elements one at a time1-4 or over extended periods5-8; speed can increase if the internal representation of elements is accelerated9,10. However, little is known about how humans actually approach these challenges because behavioural findings can be accounted for by multiple alternative process models11 and neuroimaging investigations typically rely on haemodynamic signals that change too slowly. Consequently, to uncover the fast neural dynamics that support information integration, we decoded magnetoencephalographic signals that were recorded as human subjects performed a complex decision task. Our findings reveal three sources of individual differences in the temporal structure of the integration process-sequential representation, partial reinstatement and early computation-each having a dissociable effect on how subjects handled problem complexity and temporal constraints. Our findings shed new light on the structure and influence of self-determined neural integration processes.

Right prefrontal cortex responds to item familiarity during a memory encoding task.

Abstract: In a previous word-pair encoding study (Dolan & Fletcher, 1997), we examined the effect of introducing novelty, either in studied words or in their mutual associations. A left medial temporal lobe (MTL) sensitivity to novel words and left prefrontal cortex (PFC) to novel associations was observed. In this further report on the data, we explored the extent to which the right PFC, more generally implicated in retrieval operations (Fletcher, Frith, & Rugg, 1997), was sensitive to these manipulations. Specifically, we characterised changes associated with increasing familiarity of study material. We demonstrate that the response in right ventrolateral PFC is preferentially sensitive to a condition in which all material was familiar (that is, in which all material had been presented prior to scanning). A more dorsal region in right PFC was found to be relatively more active in association with a condition in which one item in the pair was familiar but was paired with a novel associate. Our results suggest that sensitivity to stimulus familiarity is expressed in right PFC, even within the context of an encoding task. The data also provide further evidence for functional heterogeneity within right PFC, with a more ventral region responding to familiarity of complete word pairs and a more dorsal region responding to familiar single words occurring in the context of new associative relationships.

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.