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 topic(s): Prefrontal cortex & Functional magnetic resonance imaging. The author has an hindex of 196, co-authored 919 publication(s) receiving 138540 citation(s). Previous affiliations of Raymond J. Dolan include VU University Amsterdam & McGovern Institute for Brain Research.
Papers published on a yearly basis
TL;DR: Only that part of the pain network associated with its affective qualities, but not its sensory qualities, mediates empathy, suggesting that the neural substrate for empathic experience does not involve the entire "pain matrix".
Abstract: Our ability to have an experience of another's pain is characteristic of empathy. Using functional imaging, we assessed brain activity while volunteers experienced a painful stimulus and compared it to that elicited when they observed a signal indicating that their loved one--present in the same room--was receiving a similar pain stimulus. Bilateral anterior insula (AI), rostral anterior cingulate cortex (ACC), brainstem, and cerebellum were activated when subjects received pain and also by a signal that a loved one experienced pain. AI and ACC activation correlated with individual empathy scores. Activity in the posterior insula/secondary somatosensory cortex, the sensorimotor cortex (SI/MI), and the caudal ACC was specific to receiving pain. Thus, a neural response in AI and rostral ACC, activated in common for "self" and "other" conditions, suggests that the neural substrate for empathic experience does not involve the entire "pain matrix." We conclude that only that part of the pain network associated with its affective qualities, but not its sensory qualities, mediates empathy.
01 Feb 2004-Nature Neuroscience
TL;DR: In right anterior insular/opercular cortex, neural activity predicted subjects' accuracy in the heartbeat detection task and local gray matter volume correlated with both interoceptive accuracy and subjective ratings of visceral awareness.
Abstract: Influential theories of human emotion argue that subjective feeling states involve representation of bodily responses elicited by emotional events. Within this framework, individual differences in intensity of emotional experience reflect variation in sensitivity to internal bodily responses. We measured regional brain activity by functional magnetic resonance imaging (fMRI) during an interoceptive task wherein subjects judged the timing of their own heartbeats. We observed enhanced activity in insula, somatomotor and cingulate cortices. In right anterior insular/opercular cortex, neural activity predicted subjects' accuracy in the heartbeat detection task. Furthermore, local gray matter volume in the same region correlated with both interoceptive accuracy and subjective ratings of visceral awareness. Indices of negative emotional experience correlated with interoceptive accuracy across subjects. These findings indicate that right anterior insula supports a representation of visceral responses accessible to awareness, providing a substrate for subjective feeling states.
01 Oct 1997-NeuroImage
TL;DR: Interactions among extrastriate, inferotemporal, and posterior parietal regions during visual processing, under different attentional and perceptual conditions, are focused on.
Abstract: ‡In this paper we introduce the idea of explaining responses, in one cortical area, in terms of an interaction between the influence of another area and some experimental (sensory or task-related) parameter. We refer to these effects as psychophysiological interactions and relate them to interactions based solely on experimental factors (i.e., psychological interactions), in factorial designs, and interactions among neurophysiological measurements (i.e., physiological interactions). We have framed psychophysiological interactions in terms of functional integration by noting that the degree to which the activity in one area can be predicted, on the basis of activity in another, corresponds to the contribution of the second to the first, where this contribution can be related to effective connectivity. A psychophysiological interaction means that the contribution of one area to another changes significantly with the experimental or psychological context. Alternatively these interactions can be thought of as a contribution-dependent change in regional responses to an experimental or psychological factor. In other words the contribution can be thought of as modulating the responses elicited by a particular stimulus or psychological process. The potential importance of this approach lies in (i) conferring a degree of functional specificity on this aspect of effective connectivity and (ii) providing a model of modulation, where the contribution from a distal area can be considered to modulate responses to the psychological or stimulusspecific factor defining the interaction. Although distinct in neurobiological terms, these are equivalent perspectives on the same underlying interaction. We illustrate these points using a functional magnetic resonance imaging study of attention to visual motion and a position emission tomography study of visual priming. We focus on interactions among extrastriate, inferotemporal, and posterior parietal regions during visual processing, under different attentional and perceptual conditions. r 1997 Academic Press
TL;DR: This work scanned human participants with functional magnetic resonance imaging while they engaged in instrumental conditioning to suggest partly dissociable contributions of the ventral and dorsal striatum to the critic and the actor.
Abstract: Instrumental conditioning studies how animals and humans choose actions appropriate to the affective structure of an environment. According to recent reinforcement learning models, two distinct components are involved: a "critic," which learns to predict future reward, and an "actor," which maintains information about the rewarding outcomes of actions to enable better ones to be chosen more frequently. We scanned human participants with functional magnetic resonance imaging while they engaged in instrumental conditioning. Our results suggest partly dissociable contributions of the ventral and dorsal striatum, with the former corresponding to the critic and the latter corresponding to the actor.
TL;DR: Direct in vivo evidence of a differential neural response in the human amygdala to facial expressions of fear and happiness is reported, providing direct evidence that the humangdala is engaged in processing the emotional salience of faces, with a specificity of response to fearful facial expressions.
Abstract: The amygdala is thought to play a crucial role in emotional and social behaviour. Animal studies implicate the amygdala in both fear conditioning and face perception. In humans, lesions of the amygdala can lead to selective deficits in the recognition of fearful facial expressions and impaired fear conditioning, and direct electrical stimulation evokes fearful emotional responses. Here we report direct in vivo evidence of a differential neural response in the human amygdala to facial expressions of fear and happiness. Positron-emission tomography (PET) measures of neural activity were acquired while subjects viewed photographs of fearful or happy faces, varying systematically in emotional intensity. The neuronal response in the left amygdala was significantly greater to fearful as opposed to happy expressions. Furthermore, this response showed a significant interaction with the intensity of emotion (increasing with increasing fearfulness, decreasing with increasing happiness). The findings provide direct evidence that the human amygdala is engaged in processing the emotional salience of faces, with a specificity of response to fearful facial expressions.
•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.
28 Jul 2005
01 Dec 1996-ACM Computing Surveys
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.).
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.
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