The Experts below are selected from a list of 249 Experts worldwide ranked by ideXlab platform
Dominic Palmerbrown - One of the best experts on this subject based on the ideXlab platform.
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2002 Rumelhart prizewinner
Trends in Cognitive Sciences, 2001Co-Authors: Dominic PalmerbrownAbstract:The next recipient of the Rumelhart Prize for formal analysis of human cognition is Richard Shiffrin of Indiana University. Shiffrin told TICS: ‘It is exceptionally pleasing to receive the recognition of one's peers, in the form of one of the highest prizes a field has to offer’. He explained that his theories of memory storage, retrieval and attention ‘have steadily built on earlier versions, adapting and improving whenever new data suggested failings of the older versions’. But he is also looking to the future: ‘A major current theme is the use of Bayesian, adaptive considerations to produce models that optimize performance… There is no necessary reason that humans would perform optimally in a given setting, but models developed with this assumption have been proving remarkably successful’. The presentation of the $100 000 Prize will take place at the next Annual Meeting of the Cognitive Science Society at George Mason University, in August 2002. DPB
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Rumelhart prize winner announced
Trends in Cognitive Sciences, 2001Co-Authors: Dominic PalmerbrownAbstract:The first recipient of the David E. Rumelhart Prize for contributions to the formal analysis of human cognition is Geoffrey E. Hinton, who will receive the award in August in Edinburgh, at the Annual Meeting of the Cognitive Science Society (http://www.hcrc.ed.ac.uk/cogsci2001). He will deliver the Prize Lecture, on designing generative models, at the meeting. Hinton is currently Director of the Gatsby Computational Neuroscience Unit at UCL and is an obvious choice for the award, having made several groundbreaking contributions to the analysis of representation, processing and learning in neural networks. The prize (http://www.cnbc.cmu.edu/derprize) will now be awarded annually, instead of biennially, because of the significant number of outstanding candidates, and the second recipient will be announced at the Edinburgh meeting. DPB
Thomas T Hills - One of the best experts on this subject based on the ideXlab platform.
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CogSci - Mechanistic Developmental Process: Rumelhart Prize Symposium in Honor of Linda Smith
Cognitive Science, 2017Co-Authors: Larissa K Samuelson, Anthony F Morse, Chen Yu, Eliana Colunga, Thomas T HillsAbstract:Mechanistic Developmental Process: Rumelhart Prize Symposium in Honor of Linda Smith Participants Larissa K. Samuelson (larissa-samuelson@uiowa.edu) Anthony Morse (anthony.morse@plymouth.ac.uk) Chen Yu (chenyu@indiana.edu) Eliana Colunga (colunga@psych.colorado.edu) Department of Psychology and DeLTA Center University of Iowa Iowa City, IA, 52242 Centre for Robotics and Neural Systems, Plymouth University Plymouth, Devon, PL4 8AA, UK Department of Psychological and Brain Sciences Indiana University Bloomington, IN, 47405 Department of Psychology and Neuroscience, University of Colorado Boulder Boulder, CO, 80309 Thomas Hills (T.T.Hills@warwick.ac.uk) Department of Psychology University of Warwick Coventry CV4 A word learning; computational models; robotics; dynamic systems. Motivation Traditional views of cognition, cognitive development, and word learning have viewed knowledge as divorced from processes of perceiving and acting. Linda Smith has championed a dynamic, mechanistic, and process- oriented view of cognition and focused on questions of development. She has shown how knowledge is embedded in, distributed across, and inseparable from the processes of perceiving and acting in the world. In so doing, she has enabled a new understanding of the nature of cognition and of how new ways of thinking come to be. This Rumelhart symposium in her honor illustrates how this focus on developmental process changes the questions asked and our resulting understanding of cognition. The five speakers will examine the developmental process of word learning from different vantage points ranging from perceptual to social to cognitive, and spanning multiple periods from the first words to rapid vocabulary growth to the building of semantic networks. A Unified View of Early Word Learning: Linking social interaction to sensory-motor Dynamics in Child-Parent Interaction Author: Chen Yu with Daniel Yurovsky Abstract: Many theories of early word learning begin with the uncertainty inherent to learning a word from its co- occurrence with a visual scene. However, the relevant visual scene for infant word learning is neither from the adult theorist’s view nor the mature partner’s view, but is rather from the learner’s personal view. To understand the mechanistic nature of early word learning, this talk focuses on micro-level behaviors as they unfold in real time in the dynamically complex interactions of child-parent interactions. We found that when infants interacted with Smart Behaviors from Simple Processes Author: Larissa Samuelson Abstract: The period between16-months and 3-years of age is one of rapid vocabulary growth and diversification. Children this age are often referred to as “amazing word
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mechanistic developmental process Rumelhart prize symposium in honor of linda smith
Cognitive Science, 2013Co-Authors: Larissa K Samuelson, Anthony F Morse, Chen Yu, Eliana Colunga, Thomas T HillsAbstract:Mechanistic Developmental Process: Rumelhart Prize Symposium in Honor of Linda Smith Participants Larissa K. Samuelson (larissa-samuelson@uiowa.edu) Anthony Morse (anthony.morse@plymouth.ac.uk) Chen Yu (chenyu@indiana.edu) Eliana Colunga (colunga@psych.colorado.edu) Department of Psychology and DeLTA Center University of Iowa Iowa City, IA, 52242 Centre for Robotics and Neural Systems, Plymouth University Plymouth, Devon, PL4 8AA, UK Department of Psychological and Brain Sciences Indiana University Bloomington, IN, 47405 Department of Psychology and Neuroscience, University of Colorado Boulder Boulder, CO, 80309 Thomas Hills (T.T.Hills@warwick.ac.uk) Department of Psychology University of Warwick Coventry CV4 A word learning; computational models; robotics; dynamic systems. Motivation Traditional views of cognition, cognitive development, and word learning have viewed knowledge as divorced from processes of perceiving and acting. Linda Smith has championed a dynamic, mechanistic, and process- oriented view of cognition and focused on questions of development. She has shown how knowledge is embedded in, distributed across, and inseparable from the processes of perceiving and acting in the world. In so doing, she has enabled a new understanding of the nature of cognition and of how new ways of thinking come to be. This Rumelhart symposium in her honor illustrates how this focus on developmental process changes the questions asked and our resulting understanding of cognition. The five speakers will examine the developmental process of word learning from different vantage points ranging from perceptual to social to cognitive, and spanning multiple periods from the first words to rapid vocabulary growth to the building of semantic networks. A Unified View of Early Word Learning: Linking social interaction to sensory-motor Dynamics in Child-Parent Interaction Author: Chen Yu with Daniel Yurovsky Abstract: Many theories of early word learning begin with the uncertainty inherent to learning a word from its co- occurrence with a visual scene. However, the relevant visual scene for infant word learning is neither from the adult theorist’s view nor the mature partner’s view, but is rather from the learner’s personal view. To understand the mechanistic nature of early word learning, this talk focuses on micro-level behaviors as they unfold in real time in the dynamically complex interactions of child-parent interactions. We found that when infants interacted with Smart Behaviors from Simple Processes Author: Larissa Samuelson Abstract: The period between16-months and 3-years of age is one of rapid vocabulary growth and diversification. Children this age are often referred to as “amazing word
Larissa K Samuelson - One of the best experts on this subject based on the ideXlab platform.
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CogSci - Mechanistic Developmental Process: Rumelhart Prize Symposium in Honor of Linda Smith
Cognitive Science, 2017Co-Authors: Larissa K Samuelson, Anthony F Morse, Chen Yu, Eliana Colunga, Thomas T HillsAbstract:Mechanistic Developmental Process: Rumelhart Prize Symposium in Honor of Linda Smith Participants Larissa K. Samuelson (larissa-samuelson@uiowa.edu) Anthony Morse (anthony.morse@plymouth.ac.uk) Chen Yu (chenyu@indiana.edu) Eliana Colunga (colunga@psych.colorado.edu) Department of Psychology and DeLTA Center University of Iowa Iowa City, IA, 52242 Centre for Robotics and Neural Systems, Plymouth University Plymouth, Devon, PL4 8AA, UK Department of Psychological and Brain Sciences Indiana University Bloomington, IN, 47405 Department of Psychology and Neuroscience, University of Colorado Boulder Boulder, CO, 80309 Thomas Hills (T.T.Hills@warwick.ac.uk) Department of Psychology University of Warwick Coventry CV4 A word learning; computational models; robotics; dynamic systems. Motivation Traditional views of cognition, cognitive development, and word learning have viewed knowledge as divorced from processes of perceiving and acting. Linda Smith has championed a dynamic, mechanistic, and process- oriented view of cognition and focused on questions of development. She has shown how knowledge is embedded in, distributed across, and inseparable from the processes of perceiving and acting in the world. In so doing, she has enabled a new understanding of the nature of cognition and of how new ways of thinking come to be. This Rumelhart symposium in her honor illustrates how this focus on developmental process changes the questions asked and our resulting understanding of cognition. The five speakers will examine the developmental process of word learning from different vantage points ranging from perceptual to social to cognitive, and spanning multiple periods from the first words to rapid vocabulary growth to the building of semantic networks. A Unified View of Early Word Learning: Linking social interaction to sensory-motor Dynamics in Child-Parent Interaction Author: Chen Yu with Daniel Yurovsky Abstract: Many theories of early word learning begin with the uncertainty inherent to learning a word from its co- occurrence with a visual scene. However, the relevant visual scene for infant word learning is neither from the adult theorist’s view nor the mature partner’s view, but is rather from the learner’s personal view. To understand the mechanistic nature of early word learning, this talk focuses on micro-level behaviors as they unfold in real time in the dynamically complex interactions of child-parent interactions. We found that when infants interacted with Smart Behaviors from Simple Processes Author: Larissa Samuelson Abstract: The period between16-months and 3-years of age is one of rapid vocabulary growth and diversification. Children this age are often referred to as “amazing word
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mechanistic developmental process Rumelhart prize symposium in honor of linda smith
Cognitive Science, 2013Co-Authors: Larissa K Samuelson, Anthony F Morse, Chen Yu, Eliana Colunga, Thomas T HillsAbstract:Mechanistic Developmental Process: Rumelhart Prize Symposium in Honor of Linda Smith Participants Larissa K. Samuelson (larissa-samuelson@uiowa.edu) Anthony Morse (anthony.morse@plymouth.ac.uk) Chen Yu (chenyu@indiana.edu) Eliana Colunga (colunga@psych.colorado.edu) Department of Psychology and DeLTA Center University of Iowa Iowa City, IA, 52242 Centre for Robotics and Neural Systems, Plymouth University Plymouth, Devon, PL4 8AA, UK Department of Psychological and Brain Sciences Indiana University Bloomington, IN, 47405 Department of Psychology and Neuroscience, University of Colorado Boulder Boulder, CO, 80309 Thomas Hills (T.T.Hills@warwick.ac.uk) Department of Psychology University of Warwick Coventry CV4 A word learning; computational models; robotics; dynamic systems. Motivation Traditional views of cognition, cognitive development, and word learning have viewed knowledge as divorced from processes of perceiving and acting. Linda Smith has championed a dynamic, mechanistic, and process- oriented view of cognition and focused on questions of development. She has shown how knowledge is embedded in, distributed across, and inseparable from the processes of perceiving and acting in the world. In so doing, she has enabled a new understanding of the nature of cognition and of how new ways of thinking come to be. This Rumelhart symposium in her honor illustrates how this focus on developmental process changes the questions asked and our resulting understanding of cognition. The five speakers will examine the developmental process of word learning from different vantage points ranging from perceptual to social to cognitive, and spanning multiple periods from the first words to rapid vocabulary growth to the building of semantic networks. A Unified View of Early Word Learning: Linking social interaction to sensory-motor Dynamics in Child-Parent Interaction Author: Chen Yu with Daniel Yurovsky Abstract: Many theories of early word learning begin with the uncertainty inherent to learning a word from its co- occurrence with a visual scene. However, the relevant visual scene for infant word learning is neither from the adult theorist’s view nor the mature partner’s view, but is rather from the learner’s personal view. To understand the mechanistic nature of early word learning, this talk focuses on micro-level behaviors as they unfold in real time in the dynamically complex interactions of child-parent interactions. We found that when infants interacted with Smart Behaviors from Simple Processes Author: Larissa Samuelson Abstract: The period between16-months and 3-years of age is one of rapid vocabulary growth and diversification. Children this age are often referred to as “amazing word
Peter Bossaerts - One of the best experts on this subject based on the ideXlab platform.
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CogSci - Neural Computations Supporting Cognition: Rumelhart Prize Symposium in Honor of Peter Dayan
Cognitive Science, 2017Co-Authors: Kenji Doya, Alexandre Pouget, John P. O'doherty, Peter BossaertsAbstract:Neural Computations Supporting Cognition: Rumelhart Prize Symposium in Honor of Peter Dayan Participants Kenji Doya (doya@oist.jap) John O’Doherty (jodoherty@caltech.edu) Neural Computation Unit, Okinawa Institute of Science and Technology, 1919-1 Tancha, Onna Okinawa 904-0495 Japan Division of the Humanities and Social Sciences, California Institute of Technology, MC 228-77 Pasadena, CA 91125 USA Alexandre Pouget (alex@cvs.rochester.edu) Peter Bossaerts (pbs@hss.caltech.edu) Departement de neuroscience fondementale, Universite de Geneve, 1 rue Michel-Servet CH-1211 Geneva 4, Switzerland Division of the Humanities and Social Sciences, California Institute of Technology, MC 228-77 Pasadena, CA 91125 USA Organizors Nathaniel Daw (daw@cns.nyu.edu) Center for Neural Science New York University New York, NY, 10003 Yael Niv (yael@princeton.edu) Princeton Neuroscience Institute and Psychology Department Princeton University, Princeton, NJ, 08544 Keywords: neural computation; reinforcement learning; inference; uncertainty and reward. After more than a decade from the discovery, however, there still remain questions to be answered, such as what striatal neuron firing represents, how and where an action is selected, and how negative reinforcement is realized. Here we review Peter Dayan's seminal contributions and recent developments. Motivation Principles of sound statistical inference underpin prominent accounts for a variety of cognitive phenomena, including perception, learning, and decision-making. Linking these building blocks of cognition to the biological substrate that supports them, recent work has investigated how the brain implements probabilistic inference and learning under uncertainty. The interplay between the psychological and biological levels of analysis has shed light on the structure of cognition and computation at both levels. This symposium builds on Peter Dayan’s seminal contributions to linking psychological, neural and computational phenomena. In particular, speakers will discuss recent work growing out of two areas where Dayan made early and fundamental contributions: the brain’s mechanisms for reinforcement learning, and neural representations supporting probabilistic inference under uncertainty. Fractionating model-based reinforcement- learning its component neural processes Author: John P. O’Doherty Abstract: It has recently been proposed that action- selection in the mammalian brain depends on at least two distinct mechanisms: a model-free reinforcement learning (RL) mechanism in which actions are selected on the basis of cached values acquired through trial and error, and a model-based RL system in which actions are chosen using values computed on-line by means of a rich cognitive model of the decision problem and knowledge of the current incentive value of goals. While much is now known about the putative neural substrates of the model-free RL system and its concomitant temporal difference prediction error, much less is known about how model-based RL is implemented at the neural level. In this talk I will review recent evidence from a series of functional neuroimaging studies in humans supporting the presence of neural signals within a wide expanse of cortex that are relevant to model- based RL. These include, a state-action based prediction error signal within a fronto-parietal network that could mediate learning of the cognitive model, a goal-value signal encoding the value of putative goal-outcomes within the Reinforcement learning and the basal ganglia Authors: Kenji Doya and Makoto Ito Abstract: The discovery of the parallel between the firing of dopamine neurons and the temporal difference error signal of the reinforcement theory in the 1990s brought a breakthrough in understanding the function of the basal ganglia. Previously the most enigmatic part of the brain is now considered as the center for linking perception, action,
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neural computations supporting cognition Rumelhart prize symposium in honor of peter dayan
Cognitive Science, 2012Co-Authors: Kenji Doya, John P Odoherty, Alexandre Pouget, Peter BossaertsAbstract:Neural Computations Supporting Cognition: Rumelhart Prize Symposium in Honor of Peter Dayan Participants Kenji Doya (doya@oist.jap) John O’Doherty (jodoherty@caltech.edu) Neural Computation Unit, Okinawa Institute of Science and Technology, 1919-1 Tancha, Onna Okinawa 904-0495 Japan Division of the Humanities and Social Sciences, California Institute of Technology, MC 228-77 Pasadena, CA 91125 USA Alexandre Pouget (alex@cvs.rochester.edu) Peter Bossaerts (pbs@hss.caltech.edu) Departement de neuroscience fondementale, Universite de Geneve, 1 rue Michel-Servet CH-1211 Geneva 4, Switzerland Division of the Humanities and Social Sciences, California Institute of Technology, MC 228-77 Pasadena, CA 91125 USA Organizors Nathaniel Daw (daw@cns.nyu.edu) Center for Neural Science New York University New York, NY, 10003 Yael Niv (yael@princeton.edu) Princeton Neuroscience Institute and Psychology Department Princeton University, Princeton, NJ, 08544 Keywords: neural computation; reinforcement learning; inference; uncertainty and reward. After more than a decade from the discovery, however, there still remain questions to be answered, such as what striatal neuron firing represents, how and where an action is selected, and how negative reinforcement is realized. Here we review Peter Dayan's seminal contributions and recent developments. Motivation Principles of sound statistical inference underpin prominent accounts for a variety of cognitive phenomena, including perception, learning, and decision-making. Linking these building blocks of cognition to the biological substrate that supports them, recent work has investigated how the brain implements probabilistic inference and learning under uncertainty. The interplay between the psychological and biological levels of analysis has shed light on the structure of cognition and computation at both levels. This symposium builds on Peter Dayan’s seminal contributions to linking psychological, neural and computational phenomena. In particular, speakers will discuss recent work growing out of two areas where Dayan made early and fundamental contributions: the brain’s mechanisms for reinforcement learning, and neural representations supporting probabilistic inference under uncertainty. Fractionating model-based reinforcement- learning its component neural processes Author: John P. O’Doherty Abstract: It has recently been proposed that action- selection in the mammalian brain depends on at least two distinct mechanisms: a model-free reinforcement learning (RL) mechanism in which actions are selected on the basis of cached values acquired through trial and error, and a model-based RL system in which actions are chosen using values computed on-line by means of a rich cognitive model of the decision problem and knowledge of the current incentive value of goals. While much is now known about the putative neural substrates of the model-free RL system and its concomitant temporal difference prediction error, much less is known about how model-based RL is implemented at the neural level. In this talk I will review recent evidence from a series of functional neuroimaging studies in humans supporting the presence of neural signals within a wide expanse of cortex that are relevant to model- based RL. These include, a state-action based prediction error signal within a fronto-parietal network that could mediate learning of the cognitive model, a goal-value signal encoding the value of putative goal-outcomes within the Reinforcement learning and the basal ganglia Authors: Kenji Doya and Makoto Ito Abstract: The discovery of the parallel between the firing of dopamine neurons and the temporal difference error signal of the reinforcement theory in the 1990s brought a breakthrough in understanding the function of the basal ganglia. Previously the most enigmatic part of the brain is now considered as the center for linking perception, action,
Eliana Colunga - One of the best experts on this subject based on the ideXlab platform.
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CogSci - Mechanistic Developmental Process: Rumelhart Prize Symposium in Honor of Linda Smith
Cognitive Science, 2017Co-Authors: Larissa K Samuelson, Anthony F Morse, Chen Yu, Eliana Colunga, Thomas T HillsAbstract:Mechanistic Developmental Process: Rumelhart Prize Symposium in Honor of Linda Smith Participants Larissa K. Samuelson (larissa-samuelson@uiowa.edu) Anthony Morse (anthony.morse@plymouth.ac.uk) Chen Yu (chenyu@indiana.edu) Eliana Colunga (colunga@psych.colorado.edu) Department of Psychology and DeLTA Center University of Iowa Iowa City, IA, 52242 Centre for Robotics and Neural Systems, Plymouth University Plymouth, Devon, PL4 8AA, UK Department of Psychological and Brain Sciences Indiana University Bloomington, IN, 47405 Department of Psychology and Neuroscience, University of Colorado Boulder Boulder, CO, 80309 Thomas Hills (T.T.Hills@warwick.ac.uk) Department of Psychology University of Warwick Coventry CV4 A word learning; computational models; robotics; dynamic systems. Motivation Traditional views of cognition, cognitive development, and word learning have viewed knowledge as divorced from processes of perceiving and acting. Linda Smith has championed a dynamic, mechanistic, and process- oriented view of cognition and focused on questions of development. She has shown how knowledge is embedded in, distributed across, and inseparable from the processes of perceiving and acting in the world. In so doing, she has enabled a new understanding of the nature of cognition and of how new ways of thinking come to be. This Rumelhart symposium in her honor illustrates how this focus on developmental process changes the questions asked and our resulting understanding of cognition. The five speakers will examine the developmental process of word learning from different vantage points ranging from perceptual to social to cognitive, and spanning multiple periods from the first words to rapid vocabulary growth to the building of semantic networks. A Unified View of Early Word Learning: Linking social interaction to sensory-motor Dynamics in Child-Parent Interaction Author: Chen Yu with Daniel Yurovsky Abstract: Many theories of early word learning begin with the uncertainty inherent to learning a word from its co- occurrence with a visual scene. However, the relevant visual scene for infant word learning is neither from the adult theorist’s view nor the mature partner’s view, but is rather from the learner’s personal view. To understand the mechanistic nature of early word learning, this talk focuses on micro-level behaviors as they unfold in real time in the dynamically complex interactions of child-parent interactions. We found that when infants interacted with Smart Behaviors from Simple Processes Author: Larissa Samuelson Abstract: The period between16-months and 3-years of age is one of rapid vocabulary growth and diversification. Children this age are often referred to as “amazing word
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mechanistic developmental process Rumelhart prize symposium in honor of linda smith
Cognitive Science, 2013Co-Authors: Larissa K Samuelson, Anthony F Morse, Chen Yu, Eliana Colunga, Thomas T HillsAbstract:Mechanistic Developmental Process: Rumelhart Prize Symposium in Honor of Linda Smith Participants Larissa K. Samuelson (larissa-samuelson@uiowa.edu) Anthony Morse (anthony.morse@plymouth.ac.uk) Chen Yu (chenyu@indiana.edu) Eliana Colunga (colunga@psych.colorado.edu) Department of Psychology and DeLTA Center University of Iowa Iowa City, IA, 52242 Centre for Robotics and Neural Systems, Plymouth University Plymouth, Devon, PL4 8AA, UK Department of Psychological and Brain Sciences Indiana University Bloomington, IN, 47405 Department of Psychology and Neuroscience, University of Colorado Boulder Boulder, CO, 80309 Thomas Hills (T.T.Hills@warwick.ac.uk) Department of Psychology University of Warwick Coventry CV4 A word learning; computational models; robotics; dynamic systems. Motivation Traditional views of cognition, cognitive development, and word learning have viewed knowledge as divorced from processes of perceiving and acting. Linda Smith has championed a dynamic, mechanistic, and process- oriented view of cognition and focused on questions of development. She has shown how knowledge is embedded in, distributed across, and inseparable from the processes of perceiving and acting in the world. In so doing, she has enabled a new understanding of the nature of cognition and of how new ways of thinking come to be. This Rumelhart symposium in her honor illustrates how this focus on developmental process changes the questions asked and our resulting understanding of cognition. The five speakers will examine the developmental process of word learning from different vantage points ranging from perceptual to social to cognitive, and spanning multiple periods from the first words to rapid vocabulary growth to the building of semantic networks. A Unified View of Early Word Learning: Linking social interaction to sensory-motor Dynamics in Child-Parent Interaction Author: Chen Yu with Daniel Yurovsky Abstract: Many theories of early word learning begin with the uncertainty inherent to learning a word from its co- occurrence with a visual scene. However, the relevant visual scene for infant word learning is neither from the adult theorist’s view nor the mature partner’s view, but is rather from the learner’s personal view. To understand the mechanistic nature of early word learning, this talk focuses on micro-level behaviors as they unfold in real time in the dynamically complex interactions of child-parent interactions. We found that when infants interacted with Smart Behaviors from Simple Processes Author: Larissa Samuelson Abstract: The period between16-months and 3-years of age is one of rapid vocabulary growth and diversification. Children this age are often referred to as “amazing word
