Daniel McNamee - Academia.edu (original) (raw)

Papers by Daniel McNamee

Research paper thumbnail of Strong Homotopy Lie Algebras, Generalized Nahm Equations and Multiple M2-branes

We review various generalizations of the notion of Lie algebras, in particular those appearing in... more We review various generalizations of the notion of Lie algebras, in particular those appearing in the recently proposed Bagger-Lambert-Gustavsson model, and study their interrelations. We find that Filippov's n-Lie algebras are a special case of strong homotopy Lie algebras. Furthermore, we define a class of homotopy Maurer-Cartan equations, which contains both the Nahm and the Basu-Harvey equations as special cases. Finally, we show how the super Yang-Mills equations describing a Dp-brane and the Bagger-Lambert-Gustavsson equations supposedly describing M2-branes can be rewritten as homotopy Maurer-Cartan equations, as well.

Research paper thumbnail of Characterizing the associative content of brain structures involved in habitual and goal-directed actions in humans: a multivariate FMRI study

The Journal of neuroscience : the official journal of the Society for Neuroscience, Jan 4, 2015

While there is accumulating evidence for the existence of distinct neural systems supporting goal... more While there is accumulating evidence for the existence of distinct neural systems supporting goal-directed and habitual action selection in the mammalian brain, much less is known about the nature of the information being processed in these different brain regions. Associative learning theory predicts that brain systems involved in habitual control, such as the dorsolateral striatum, should contain stimulus and response information only, but not outcome information, while regions involved in goal-directed action, such as ventromedial and dorsolateral prefrontal cortex and dorsomedial striatum, should be involved in processing information about outcomes as well as stimuli and responses. To test this prediction, human participants underwent fMRI while engaging in a binary choice task designed to enable the separate identification of these different representations with a multivariate classification analysis approach. Consistent with our predictions, the dorsolateral striatum contained...

Research paper thumbnail of The structure of reinforcement-learning mechanisms in the human brain

Current Opinion in Behavioral Sciences, 2015

Here we review recent developments in the application of reinforcement-learning theory as a means... more Here we review recent developments in the application of reinforcement-learning theory as a means of understanding how the brain learns to select actions to maximize future reward, with a focus on human neuroimaging studies. We evaluate evidence for the distinction between model-based and model-free reinforcement-learning and their arbitration, and consider hierarchical reinforcement-learning schemes and structure learning. Finally we discuss the possibility of integrating across these different domains as a means of gaining a more complete understanding of how it is the brain learns from reinforcement.

Research paper thumbnail of Generalized Berezin quantization, Bergman metrics and fuzzy laplacians

Journal of High Energy Physics, 2008

We study extended Berezin and Berezin-Toeplitz quantization for compact Kähler manifolds, two rel... more We study extended Berezin and Berezin-Toeplitz quantization for compact Kähler manifolds, two related quantization procedures which provide a general framework for approaching the construction of fuzzy compact Kähler geometries. Using this framework, we show that a particular version of generalized Berezin quantization, which we baptize "Berezin-Bergman quantization", reproduces recent proposals for the construction of fuzzy Kähler spaces. We also discuss how fuzzy Laplacians can be defined in our general framework and study a few explicit examples. Finally, we use this approach to propose a general explicit definition of fuzzy scalar field theory on compact Kähler manifolds.

Research paper thumbnail of Generalized Berezin-Toeplitz quantization of Kähler supermanifolds

Journal of High Energy Physics, 2009

We extend the construction of generalized Berezin and Berezin-Toeplitz quantization to the case o... more We extend the construction of generalized Berezin and Berezin-Toeplitz quantization to the case of compact Hodge supermanifolds. Our approach is based on certain super-analogues of Rawnsley's coherent states. As applications, we discuss the quantization of affine and projective superspaces. Furthermore, we propose a definition of supersymmetric sigma-models on quantized Hodge supermanifolds. The corresponding quantum field theories are finite and thus yield supersymmetry-preserving regularizations for QFTs defined on flat superspace.

Research paper thumbnail of Category-dependent and category-independent goal-value codes in human ventromedial prefrontal cortex

Nature Neuroscience, 2013

To choose between manifestly distinct options, it is suggested that the brain assigns values to g... more To choose between manifestly distinct options, it is suggested that the brain assigns values to goals using a common currency. Although previous studies have reported activity in ventromedial prefrontal cortex (vmPFC) correlating with the value of different goal stimuli, it remains unclear whether such goal-value representations are independent of the associated stimulus categorization, as required by a common currency. Using multivoxel pattern analyses on functional magnetic resonance imaging (fMRI) data, we found a region of medial prefrontal cortex to contain a distributed goal-value code that is independent of stimulus category. More ventrally in the vmPFC, we found spatially distinct areas of the medial orbitofrontal cortex to contain unique category-dependent distributed value codes for food and consumer items. These results implicate the medial prefrontal cortex in the implementation of a common currency and suggest a ventral versus dorsal topographical organization of value signals in the vmPFC.

Research paper thumbnail of Evidence for Model-based Computations in the Human Amygdala during Pavlovian Conditioning

PLoS Computational Biology, 2013

Contemporary computational accounts of instrumental conditioning have emphasized a role for a mod... more Contemporary computational accounts of instrumental conditioning have emphasized a role for a model-based system in which values are computed with reference to a rich model of the structure of the world, and a model-free system in which values are updated without encoding such structure. Much less studied is the possibility of a similar distinction operating at the level of Pavlovian conditioning. In the present study, we scanned human participants while they participated in a Pavlovian conditioning task with a simple structure while measuring activity in the human amygdala using a high-resolution fMRI protocol. After fitting a model-based algorithm and a variety of model-free algorithms to the fMRI data, we found evidence for the superiority of a model-based algorithm in accounting for activity in the amygdala compared to the model-free counterparts. These findings support an important role for model-based algorithms in describing the processes underpinning Pavlovian conditioning, as well as providing evidence of a role for the human amygdala in model-based inference. Citation: Prévost C, McNamee D, Jessup RK, Bossaerts P, O'Doherty JP (2013) Evidence for Model-based Computations in the Human Amygdala during Pavlovian Conditioning. PLoS Comput Biol 9(2): e1002918.

Research paper thumbnail of Strong Homotopy Lie Algebras, Generalized Nahm Equations and Multiple M2-branes

We review various generalizations of the notion of Lie algebras, in particular those appearing in... more We review various generalizations of the notion of Lie algebras, in particular those appearing in the recently proposed Bagger-Lambert-Gustavsson model, and study their interrelations. We find that Filippov's n-Lie algebras are a special case of strong homotopy Lie algebras. Furthermore, we define a class of homotopy Maurer-Cartan equations, which contains both the Nahm and the Basu-Harvey equations as special cases. Finally, we show how the super Yang-Mills equations describing a Dp-brane and the Bagger-Lambert-Gustavsson equations supposedly describing M2-branes can be rewritten as homotopy Maurer-Cartan equations, as well.

Research paper thumbnail of Characterizing the associative content of brain structures involved in habitual and goal-directed actions in humans: a multivariate FMRI study

The Journal of neuroscience : the official journal of the Society for Neuroscience, Jan 4, 2015

While there is accumulating evidence for the existence of distinct neural systems supporting goal... more While there is accumulating evidence for the existence of distinct neural systems supporting goal-directed and habitual action selection in the mammalian brain, much less is known about the nature of the information being processed in these different brain regions. Associative learning theory predicts that brain systems involved in habitual control, such as the dorsolateral striatum, should contain stimulus and response information only, but not outcome information, while regions involved in goal-directed action, such as ventromedial and dorsolateral prefrontal cortex and dorsomedial striatum, should be involved in processing information about outcomes as well as stimuli and responses. To test this prediction, human participants underwent fMRI while engaging in a binary choice task designed to enable the separate identification of these different representations with a multivariate classification analysis approach. Consistent with our predictions, the dorsolateral striatum contained...

Research paper thumbnail of The structure of reinforcement-learning mechanisms in the human brain

Current Opinion in Behavioral Sciences, 2015

Here we review recent developments in the application of reinforcement-learning theory as a means... more Here we review recent developments in the application of reinforcement-learning theory as a means of understanding how the brain learns to select actions to maximize future reward, with a focus on human neuroimaging studies. We evaluate evidence for the distinction between model-based and model-free reinforcement-learning and their arbitration, and consider hierarchical reinforcement-learning schemes and structure learning. Finally we discuss the possibility of integrating across these different domains as a means of gaining a more complete understanding of how it is the brain learns from reinforcement.

Research paper thumbnail of Generalized Berezin quantization, Bergman metrics and fuzzy laplacians

Journal of High Energy Physics, 2008

We study extended Berezin and Berezin-Toeplitz quantization for compact Kähler manifolds, two rel... more We study extended Berezin and Berezin-Toeplitz quantization for compact Kähler manifolds, two related quantization procedures which provide a general framework for approaching the construction of fuzzy compact Kähler geometries. Using this framework, we show that a particular version of generalized Berezin quantization, which we baptize "Berezin-Bergman quantization", reproduces recent proposals for the construction of fuzzy Kähler spaces. We also discuss how fuzzy Laplacians can be defined in our general framework and study a few explicit examples. Finally, we use this approach to propose a general explicit definition of fuzzy scalar field theory on compact Kähler manifolds.

Research paper thumbnail of Generalized Berezin-Toeplitz quantization of Kähler supermanifolds

Journal of High Energy Physics, 2009

We extend the construction of generalized Berezin and Berezin-Toeplitz quantization to the case o... more We extend the construction of generalized Berezin and Berezin-Toeplitz quantization to the case of compact Hodge supermanifolds. Our approach is based on certain super-analogues of Rawnsley's coherent states. As applications, we discuss the quantization of affine and projective superspaces. Furthermore, we propose a definition of supersymmetric sigma-models on quantized Hodge supermanifolds. The corresponding quantum field theories are finite and thus yield supersymmetry-preserving regularizations for QFTs defined on flat superspace.

Research paper thumbnail of Category-dependent and category-independent goal-value codes in human ventromedial prefrontal cortex

Nature Neuroscience, 2013

To choose between manifestly distinct options, it is suggested that the brain assigns values to g... more To choose between manifestly distinct options, it is suggested that the brain assigns values to goals using a common currency. Although previous studies have reported activity in ventromedial prefrontal cortex (vmPFC) correlating with the value of different goal stimuli, it remains unclear whether such goal-value representations are independent of the associated stimulus categorization, as required by a common currency. Using multivoxel pattern analyses on functional magnetic resonance imaging (fMRI) data, we found a region of medial prefrontal cortex to contain a distributed goal-value code that is independent of stimulus category. More ventrally in the vmPFC, we found spatially distinct areas of the medial orbitofrontal cortex to contain unique category-dependent distributed value codes for food and consumer items. These results implicate the medial prefrontal cortex in the implementation of a common currency and suggest a ventral versus dorsal topographical organization of value signals in the vmPFC.

Research paper thumbnail of Evidence for Model-based Computations in the Human Amygdala during Pavlovian Conditioning

PLoS Computational Biology, 2013

Contemporary computational accounts of instrumental conditioning have emphasized a role for a mod... more Contemporary computational accounts of instrumental conditioning have emphasized a role for a model-based system in which values are computed with reference to a rich model of the structure of the world, and a model-free system in which values are updated without encoding such structure. Much less studied is the possibility of a similar distinction operating at the level of Pavlovian conditioning. In the present study, we scanned human participants while they participated in a Pavlovian conditioning task with a simple structure while measuring activity in the human amygdala using a high-resolution fMRI protocol. After fitting a model-based algorithm and a variety of model-free algorithms to the fMRI data, we found evidence for the superiority of a model-based algorithm in accounting for activity in the amygdala compared to the model-free counterparts. These findings support an important role for model-based algorithms in describing the processes underpinning Pavlovian conditioning, as well as providing evidence of a role for the human amygdala in model-based inference. Citation: Prévost C, McNamee D, Jessup RK, Bossaerts P, O'Doherty JP (2013) Evidence for Model-based Computations in the Human Amygdala during Pavlovian Conditioning. PLoS Comput Biol 9(2): e1002918.