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      Go and no-go learning in reward and punishment: Interactions between affect and effect

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          Abstract

          Decision-making invokes two fundamental axes of control: affect or valence, spanning reward and punishment, and effect or action, spanning invigoration and inhibition. We studied the acquisition of instrumental responding in healthy human volunteers in a task in which we orthogonalized action requirements and outcome valence. Subjects were much more successful in learning active choices in rewarded conditions, and passive choices in punished conditions. Using computational reinforcement-learning models, we teased apart contributions from putatively instrumental and Pavlovian components in the generation of the observed asymmetry during learning. Moreover, using model-based fMRI, we showed that BOLD signals in striatum and substantia nigra/ventral tegmental area (SN/VTA) correlated with instrumentally learnt action values, but with opposite signs for go and no-go choices. Finally, we showed that successful instrumental learning depends on engagement of bilateral inferior frontal gyrus. Our behavioral and computational data showed that instrumental learning is contingent on overcoming inherent and plastic Pavlovian biases, while our neuronal data showed this learning is linked to unique patterns of brain activity in regions implicated in action and inhibition respectively.

          Highlights

          ► Expectation of valence interferes with action learning in human participants. ► Computational modeling disentangles influences of instrumental and Pavlovian systems. ► Striatum and SN/VTA track action values and bind them to the control of vigor. ► Successful control is associated with activity in the inferior prefrontal cortex.

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          Most cited references77

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          Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.

          All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms ("SPM2-type" and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http://www.mindboggle.info/papers/ website.
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            A neural substrate of prediction and reward.

            The capacity to predict future events permits a creature to detect, model, and manipulate the causal structure of its interactions with its environment. Behavioral experiments suggest that learning is driven by changes in the expectations about future salient events such as rewards and punishments. Physiological work has recently complemented these studies by identifying dopaminergic neurons in the primate whose fluctuating output apparently signals changes or errors in the predictions of future salient and rewarding events. Taken together, these findings can be understood through quantitative theories of adaptive optimizing control.
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              Emotion and motivation: the role of the amygdala, ventral striatum, and prefrontal cortex.

              Emotions are multifaceted, but a key aspect of emotion involves the assessment of the value of environmental stimuli. This article reviews the many psychological representations, including representations of stimulus value, which are formed in the brain during Pavlovian and instrumental conditioning tasks. These representations may be related directly to the functions of cortical and subcortical neural structures. The basolateral amygdala (BLA) appears to be required for a Pavlovian conditioned stimulus (CS) to gain access to the current value of the specific unconditioned stimulus (US) that it predicts, while the central nucleus of the amygdala acts as a controller of brainstem arousal and response systems, and subserves some forms of stimulus-response Pavlovian conditioning. The nucleus accumbens, which appears not to be required for knowledge of the contingency between instrumental actions and their outcomes, nevertheless influences instrumental behaviour strongly by allowing Pavlovian CSs to affect the level of instrumental responding (Pavlovian-instrumental transfer), and is required for the normal ability of animals to choose rewards that are delayed. The prelimbic cortex is required for the detection of instrumental action-outcome contingencies, while insular cortex may allow rats to retrieve the values of specific foods via their sensory properties. The orbitofrontal cortex, like the BLA, may represent aspects of reinforcer value that govern instrumental choice behaviour. Finally, the anterior cingulate cortex, implicated in human disorders of emotion and attention, may have multiple roles in responding to the emotional significance of stimuli and to errors in performance, preventing responding to inappropriate stimuli.
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                Author and article information

                Journal
                Neuroimage
                Neuroimage
                Neuroimage
                Academic Press
                1053-8119
                1095-9572
                01 August 2012
                01 August 2012
                : 62-334
                : 1
                : 154-166
                Affiliations
                [a ]Institute of Cognitive Neuroscience, University College London, London, W1CN 4AR, UK
                [b ]Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK
                [c ]Gatsby Computational Neuroscience Unit, University College London, London, W1CN 4AR, UK
                [d ]UCL Medical School, University College London, WC1N 4AR, UK
                [e ]Departament de Ciències Fisiològiques II, University of Barcelona, Institut d'Investigació Biomèdica de Bellvitge, 08907 L'Hospitalet de Llobregat (Barcelona), Spain
                [f ]Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University, Leipziger Strasse 44, 39120 Magdeburg, Germany
                Author notes
                [* ]Corresponding author at: Institute of Cognitive Neuroscience, University College London, London, W1CN 4AR, UK. m.guitart@ 123456ucl.ac.uk
                [1]

                These two authors equally contributed to this work.

                Article
                YNIMG9411
                10.1016/j.neuroimage.2012.04.024
                3387384
                22548809
                2788f1bb-8801-41a2-a1c9-cc9436a17a58
                © 2012 Elsevier Inc.

                This document may be redistributed and reused, subject to certain conditions.

                History
                : 11 April 2012
                Categories
                Article

                Neurosciences
                action,striatum,pavlovian,instrumental,learning
                Neurosciences
                action, striatum, pavlovian, instrumental, learning

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