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 neural underpinnings of an optimal exploitation of social information under uncertainty. Running Title: Neural basis of social information use.

Abstract: Social information influences decision-making through an integration of information derived from individual experience with that derived from observing the actions of others. This raises the question as to which extent one should utilize social information. One strategy is to make use of uncertainty estimates, leading to a copy-when-uncertain strategy that weights information from individual and social sources based on their respective reliabilities. Here we investigate this integration process by extending models of Bayes optimal integration of sensory information to a social decision context. We then use a key parameter of our behavioral model in conjunction with fMRI to identify the neural substrate that is specifically linked to the fidelity of this integration process. We show that individuals behave near Bayes optimal when integrating two distinct sources of social information but systematically deviate from Bayes optimal choice when integrating individual with social information. This systematic behavioral deviation from optimality is linked to activity of left inferior frontal gyrus. Thus, an ability to optimally exploit social information depends on processes that overcome an egocentric bias, and this regulatory role involves the left inferior prefrontal cortex. The findings provide a mechanistic explanation for observations wherein individuals neglect the benefits from exploiting social information. Introduction Our decisions benefit from a constant stream of social information that includes the vicarious observation of the actions and outcomes of actions of others (Rendell et al. 2011). This type of influence provides a rich underpinning to much of human culture (Dean et al. 2012). Under a broad range of conditions copying the actions of others results in the adoption of advantageous behavioral traits (Rendell et al. 2010). When observed actions, and their outcomes, are closely conjoined these stimulus response contingencies seem to be associatively learned using the very same prediction errors that underpin associative learning for non-social stimuli (Behrens et al. 2008; Burke et al. 2010; Heyes 2012). The influence of social information is often biased (Mesoudi et al. 2006) and modulated by context and individual predisposition (Alevy et al. 2007; Efferson et al. 2008; Toelch et al. 2009). This raises a fundamental question as to how individuals weight social and individual information in a decision-making context. One proposal is that a copy-when-uncertain strategy can account for situations wherein individually acquired information is imprecise. An unbiased account of this strategy suggests that each information source is weighted by its corresponding reliability with distinct information sources subsequently combined in a Bayes optimal manner. This type of integration process is evident in multi modal cue integration (Alais and Burr 2004; Ernst and Banks 2002; Knill and Pouget 2004), as well as in joint decision making where participants appear to use a confidence metric to arrive at a Bayes optimal response in perceptual tasks (Bahrami et al. 2010) cf. (Koriat 2012). When individuals have to integrate social and individual information, however, there appears to be a strong preference for individual information (Eriksson and Strimling 2009; Heyes 2012; Morgan et al. 2011) even when social information use is advantageous. Here we propose that individuals over discount social information proportional to the reliability of their own information to account for the uncertainty inherent in social information (Rieucau and Giraldeau 2011). We provide an experimental test for such a modified copy-whenuncertain strategy. The experimental manipulation of individual uncertainty estimates and with that the hypothesized differential use of social information opens up the possibility to index a neural basis for regulating the interplay of individual and social information. For this, we employ functional magnetic resonance imaging (fMRI) in conjunction with behavioral modeling of player choices where the focus is on individual differences in social information use. In brief, our task required players to solve a perceptual task where they had to guess the location of a briefly flashed stimulus (Fig. 1). In the first phase players could assess their own accuracy as well as the accuracy of two players, one with high and one with low accuracy, through feedback as to the correct location (after they made a choice). In a second phase, conducted in an fMRI scanner, participants received information (individual and/or social) generated in the first phase and made a second guess on the position of the stimulus but now without receiving feedback on its actual true position. We modelled players’ choices using Bayes optimal cue integration models. Parameters derived from these models were then used as regressors in a model based fMRI analysis to identify brain areas critically linked to this modulation of choice.

Integration of Retinal and Extraretinal Information across Eye Movements

Abstract: Visual perception is burdened with a highly discontinuous input stream arising from saccadic eye movements For successful integration into a coherent representation, the visuomotor system needs to deal with these self-induced perceptual changes and distinguish them from external motion Forward models are one way to solve this problem where the brain uses internal monitoring signals associated with oculomotor commands to predict the visual consequences of corresponding eye movements during active exploration Visual scenes typically contain a rich structure of spatial relational information, providing additional cues that may help disambiguate self-induced from external changes of perceptual input We reasoned that a weighted integration of these two inherently noisy sources of information should lead to better perceptual estimates Volunteer subjects performed a simple perceptual decision on the apparent displacement of a visual target, jumping unpredictably in sync with a saccadic eye movement In a critical test condition, the target was presented together with a flanker object, where perceptual decisions could take into account the spatial distance between target and flanker object Here, precision was better compared to control conditions in which target displacements could only be estimated from either extraretinal or visual relational information alone Our findings suggest that under natural conditions, integration of visual space across eye movements is based upon close to optimal integration of both retinal and extraretinal pieces of information

Oxytocin Effect on Collective Decision Making: A Randomized Placebo Controlled Study

Abstract: Collective decision making often benefits both the individuals and the group in a variety of contexts. However, for the group to be successful, individuals should be able to strike a balance between their level of competence and their influence on the collective decisions. The hormone oxytocin has been shown to promote trust, conformism and attention to social cues. We wondered if this hormone may increase participants' (unwarranted) reliance on their partners' opinion, resulting in a reduction in collective benefit by disturbing the balance between influence and competence. To test this hypothesis we employed a randomized double-blind placebo-controlled design in which male dyads self-administered intranasal oxytocin or placebo and then performed a visual search task together. Compared to placebo, collective benefit did not decrease under oxytocin. Using an exploratory time dependent analysis, we observed increase in collective benefit over time under oxytocin. Moreover, trial-by-trial analysis showed that under oxytocin the more competent member of each dyad was less likely to change his mind during disagreements, while the less competent member showed a greater willingness to change his mind and conform to the opinion of his more reliable partner. This role-dependent effect may be mediated by enhanced monitoring of own and other's performance level under oxytocin. Such enhanced social learning could improve the balance between influence and competence and lead to efficient and beneficial collaboration.

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.