Relevant bibliographies by topics / Decision Neuroscience

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Decision Neuroscience'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 April 2022

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Journal articles on the topic "Decision Neuroscience"

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Sanfey, Alan G. "Decision Neuroscience." Current Directions in Psychological Science 16, no. 3 (June 2007): 151–55. http://dx.doi.org/10.1111/j.1467-8721.2007.00494.x.

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Shiv, Baba, Antoine Bechara, Irwin Levin, Joseph W. Alba, James R. Bettman, Laurette Dube, Alice Isen, et al. "Decision Neuroscience." Marketing Letters 16, no. 3-4 (December 2005): 375–86. http://dx.doi.org/10.1007/s11002-005-5899-8.

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Smith, David V., and Scott A. Huettel. "Decision neuroscience: neuroeconomics." Wiley Interdisciplinary Reviews: Cognitive Science 1, no. 6 (May 14, 2010): 854–71. http://dx.doi.org/10.1002/wcs.73.

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Yoon, Carolyn, Richard Gonzalez, Antoine Bechara, Gregory S. Berns, Alain A. Dagher, Laurette Dubé, Scott A. Huettel, et al. "Decision neuroscience and consumer decision making." Marketing Letters 23, no. 2 (May 26, 2012): 473–85. http://dx.doi.org/10.1007/s11002-012-9188-z.

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Hansen, Flemming, Peter Kenning, and Hilke Plassmann. "Contributions to decision neuroscience." Journal of Economic Psychology 31, no. 5 (October 2010): 764–66. http://dx.doi.org/10.1016/j.joep.2010.03.001.

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Hayden, Benjamin Y., and Patrick Haggard. "Neuroscience: Decision, Insight and Intention." Current Biology 27, no. 15 (August 2017): R750—R753. http://dx.doi.org/10.1016/j.cub.2017.06.065.

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Bossaerts, Peter. "What Decision Neuroscience Teaches Us About Financial Decision Making." Annual Review of Financial Economics 1, no. 1 (December 5, 2009): 383–404. http://dx.doi.org/10.1146/annurev.financial.102708.141514.

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Strle, Toma, and Olga Markič. "Looping effects of neurolaw, and the precarious marriage between neuroscience and the law." Balkan Journal of Philosophy 10, no. 1 (2018): 17–26. http://dx.doi.org/10.5840/bjp20181013.

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In the following article we first present the growing trend of incorporating neuroscience into the law, and the growing acceptance of and trust in neuroscience’s mechanistic and reductionistic explanations of the human mind. We then present and discuss some studies that show how nudging peoples’ beliefs about matters related to human agency (such as free will, decision-making, or self-control) towards a more deterministic, mechanistic and/or reductionistic conception, exerts an influence on their very actions, mentality, and brain processes. We suggest that the neuroscientific view of the human mind exerts an influence on the very cognitive phenomena neuroscience falsely believes to be studying objectively. This holds especially when we consider the systematic integration of neuroscience into the public domain, such as the law. For, such an integration acts as a reinforcement of the public’s and legal decision-makers’ endorsement of and trust in neuroscience’s view of human nature that further changes how people think and act. Such looping effects of neurolaw are probably inevitable. Accordingly, we should be aware of the scope of neuroscientific explanations and be careful not to overstate neuroscientific evidence and findings in legal contexts.
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Rilling, James K., and Alan G. Sanfey. "The Neuroscience of Social Decision-Making." Annual Review of Psychology 62, no. 1 (January 10, 2011): 23–48. http://dx.doi.org/10.1146/annurev.psych.121208.131647.

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Lee, Daeyeol. "Decision Making: From Neuroscience to Psychiatry." Neuron 78, no. 2 (April 2013): 233–48. http://dx.doi.org/10.1016/j.neuron.2013.04.008.

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Dissertations / Theses on the topic "Decision Neuroscience"

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Karlsson, Markus. "The Neuroscience of Decision Making : The Importance of Emotional Neural Circuits in Decision Making." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-16033.

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The neuroscience of decision making is laying the puzzle of how the brain computes decisions. It tries to sort out which factors are responsible for causing us to choose one way or the other. This thesis reviews to what extent emotional brain processes and their neural circuits impact decision making. The somatic marker hypothesis (SMH) provides a solid dual-system framework for decision making. Dissociating an impulsive system, in which the amygdala is central, and a reflective system mediated by the ventromedial prefrontal cortex(VMPFC). The SMH emphasizes the function of the VMPFC as necessary and crucial formaking favorable long-term decisions. Research on moral decision making also shows that similar systems as used by the SMH has a key role in how we think about moral dilemmas as well. Damage or maldevelopment of these neural circuits can cause myopia for the future and deeply immoral behavior. Abnormalities in emotional neuronal circuits can also be linked to addictive behavior and psychopathy. The findings on decision making and its neuralsubstrates dismantle the common sense notion of free will and moral responsibility. An explanation of how the feeling of free will arises is given using the Interpreter system theoryof consciousness. Moral responsibility without the need for a free will is defended by analternative approach with a framework of a brain in-control versus out-of-control.
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Blum, Bridget E. "Consumer Neuroscience: A Multi-disciplinary Approach to Marketing Leveraging Advances in Neuroscience, Psychology and Economics." Scholarship @ Claremont, 2016. http://scholarship.claremont.edu/cmc_theses/1414.

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For decades, neuroscience has greatly contributed to our foundational understanding of human behavior. More recently, the findings and methods of neuroscience have been applied to study the process of decision-making in order to offer advanced insights into the neural mechanisms that influence economic and consumer choices. In this thesis, I will address how customized marketing strategies can be enriched through the integration of consumer neuroscience, an integrative field anchored in the biological, cognitive and affective mechanisms of consumer behavior. By recognizing and utilizing these multidisciplinary interdependencies, marketers can enhance their advertising and promotional mix to elicit desired neural and affective consumer responses and measure these reactions in order to enhance purchasing decisions. The principal objective of this thesis is to present a comprehensive review of consumer neuroscience and to elucidate why it is an increasingly important area of study within the framework of human behavior. I will also describe how the insights gained from this emerging field can be leveraged to optimize marketing activities. Finally, I propose an experiment that illuminates key research questions, which may have considerable impact on the discipline of consumer neuroscience as well as the marketing industry.
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Topalidou, Meropi. "Neuroscience of decision making : from goal-directed actions to habits." Thesis, Bordeaux, 2016. http://www.theses.fr/2016BORD0174/document.

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Les processus de type “action-conséquence” (orienté vers un but) et stimulus-réponse sont deux composants importants du comportement. Le premier évalue le bénéfice d’une action pour choisir la meilleure parmi celles disponibles (sélection d’action) alors que le deuxième est responsable du comportement automatique, suscitant une réponse dès qu’un stimulus connu est présent. De telles habitudes sont généralement associées (et surtout opposées) aux actions orientées vers un but qui nécessitent un processus délibératif pour évaluer la meilleure option à prendre pour atteindre un objectif donné. En utilisant un modèle computationnel, nous avons étudié l’hypothèse classique de la formation et de l’expression des habitudes au niveau des ganglions de la base et nous avons formulé une nouvelle hypothèse quant aux rôles respectifs des ganglions de la base et du cortex. Inspiré par les travaux théoriques et expérimentaux de Leblois et al. (2006) et Guthrie et al. (2013), nous avons conçu un modèle computationnel des ganglions de la base, du thalamus et du cortex qui utilise des boucles distinctes (moteur, cognitif et associatif) ce qui nous a permis de poser l’hypothèse selon laquelle les ganglions de la base ne sont nécessaires que pour l’acquisition d’habitudes alors que l’expression de telles habitudes peut être faite par le cortex seul. En outre, ce modèle a permis de prédire l’existence d’un apprentissage latent dans les ganglions de la base lorsque leurs sorties (GPi) sont inhibées. En utilisant une tâche de bandit manchot à 2 choix, cette hypothèse a été expérimentalement testée et confirmée chez le singe; suggérant au final de rejeter l’idée classique selon laquelle l’automatisme est un trait subcortical
Action-outcome and stimulus-response processes are two important components of behavior. The former evaluates the benefit of an action in order to choose the best action among those available (action selection) while the latter is responsible for automatic behavior, eliciting a response as soon as a known stimulus is present. Such habits are generally associated (and mostly opposed) to goal-directed actions that require a deliberative process to evaluate the best option to take in order to reach a given goal. Using a computational model, we investigated the classic hypothesis of habits formation and expression in the basal ganglia and proposed a new hypothesis concerning the respective role for both the basal ganglia and the cortex. Inspired by previous theoretical and experimental works (Leblois et al., 2006; Guthrie et al., 2013), we designed a computational model of the basal ganglia-thalamus-cortex that uses segregated loops (motor, cognitive and associative) and makes the hypothesis that basal ganglia are only necessary for the acquisition of habits while the expression of such habits can be mediated through the cortex. Furthermore, this model predicts the existence of covert learning within the basal ganglia ganglia when their output is inhibited. Using a two-armed bandit task, this hypothesis has been experimentally tested and confirmed in monkey. Finally, this works suggest to revise the classical idea that automatism is a subcortical feature
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Li, Xiaogai. "Finite Element and Neuroimaging Techniques toImprove Decision-Making in Clinical Neuroscience." Doctoral thesis, KTH, Neuronik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-72345.

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Our brain, perhaps the most sophisticated and mysterious part of the human body, to some extent, determines who we are. However, it’s a vulnerable organ. When subjected to an impact, such as a traffic accident or sport, it may lead to traumatic brain injury (TBI) which can have devastating effects for those who suffer the injury. Despite lots of efforts have been put into primary injury prevention, the number of TBIs is still on an unacceptable high level in a global perspective. Brain edema is a major neurological complication of moderate and severe TBI, which consists of an abnormal accumulation of fluid within the brain parenchyma. Clinically, local and minor edema may be treated conservatively only by observation, where the treatment of choice usually follows evidence-based practice. In the first study, the gravitational force is suggested to have a significant impact on the pressure of the edema zone in the brain tissue. Thus, the objective of the study was to investigate the significance of head position on edema at the posterior part of the brain using a Finite Element (FE) model. The model revealed that water content (WC) increment at the edema zone remained nearly identical for both supine and prone positions. However, the interstitial fluid pressure (IFP) inside the edema zone decreased around 15% by having the head in a prone position compared with a supine position. The decrease of IFP inside the edema zone by changing patient position from supine to prone has the potential to alleviate the damage to axonal fibers of the central nervous system. These observations suggest that considering the patient’s head position during intensive care and at rehabilitation should be of importance to the treatment of edematous regions in TBI patients. In TBI patients with diffuse brain edema, for most severe cases with refractory intracranial hypertension, decompressive craniotomy (DC) is performed as an ultimate therapy. However, a complete consensus on its effectiveness has not been achieved due to the high levels of severe disability and persistent vegetative state found in the patients treated with DC. DC allows expansion of the swollen brain outside the skull, thereby having the potential in reducing the Intracranial Pressure (ICP). However, the treatment causes stretching of the axons and may contribute to the unfavorable outcome of the patients. The second study aimed at quantifying the stretching and WC in the brain tissue due to the neurosurgical intervention to provide more insight into the effects upon such a treatment. A nonlinear registration method was used to quantify the strain. Our analysis showed a substantial increase of the strain level in the brain tissue close to the treated side of DC compared to before the treatment. Also, the WC was related to specific gravity (SG), which in turn was related to the Hounsfield unit (HU) value in the Computerized Tomography (CT) images by a photoelectric correction according to the chemical composition of the brain tissue. The overall WC of brain tissue presented a significant increase after the treatment compared to the condition seen before the treatment. It is suggested that a quantitative model, which characterizes the stretching and WC of the brain tissue both before as well as after DC, may clarify some of the potential problems with such a treatment. Diffusion Weighted (DW) Imaging technology provides a noninvasive way to extract axonal fiber tracts in the brain. The aim of the third study, as an extension to the second study was to assess and quantify the axonal deformation (i.e. stretching and shearing)at both the pre- and post-craniotomy periods in order to provide more insight into the mechanical effects on the axonal fibers due to DC. Subarachnoid injection of artificial cerebrospinal fluid (CSF) into the CSF system is widely used in neurological practice to gain information on CSF dynamics. Mathematical models are important for a better understanding of the underlying mechanisms. Despite the critical importance of the parameters for accurate modeling, there is a substantial variation in the poroelastic constants used in the literature due to the difficulties in determining material properties of brain tissue. In the fourth study, we developed a Finite Element (FE) model including the whole brain-CSF-skull system to study the CSF dynamics during constant-rate infusion. We investigated the capacity of the current model to predict the steady state of the mean ICP. For transient analysis, rather than accurately fit the infusion curve to the experimental data, we placed more emphasis on studying the influences of each of the poroelastic parameters due to the aforementioned inconsistency in the poroelastic constants for brain tissue. It was found that the value of the specific storage term S_epsilon is the dominant factor that influences the infusion curve, and the drained Young’s modulus E was identified as the dominant parameter second to S_epsilon. Based on the simulated infusion curves from the FE model, Artificial Neural Network (ANN) was used to find an optimized parameter set that best fit the experimental curve. The infusion curves from both the FE simulations and using ANN confirmed the limitation of linear poroelasticity in modeling the transient constant-rate infusion. To summarize, the work done in this thesis is to introduce FE Modeling and imaging technologiesincluding CT, DW imaging, and image registration method as a complementarytechnique for clinical diagnosis and treatment of TBI patients. Hopefully, the result mayto some extent improve the understanding of these clinical problems and improve theirmedical treatments.
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Chang, Luke Joseph. "Deconstructing the Role of Expectations in Cooperative Behavior with Decision Neuroscience." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/223343.

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This project attempts to understand the role of expectations in cooperative behavior using the interdisciplinary approach of Decision Neuroscience. While cooperation provides the foundation for a successful society, the underlying bio-psycho-social mechanisms remain surprisingly poorly understood. This investigation deconstructs cooperation into the specific behaviors of trust, reciprocation, and norm enforcement using the Trust and Ultimatum Games from behavioral economics and combines formal modeling and functional magnetic resonance imaging to understand the neurocomputational role of expectations in these behaviors. The results indicate that people appear to use context specific shared expectations when making social decisions. These beliefs are malleable and appear to be dynamically updated after an interaction. Emotions such as guilt and anger can be formally operationalized in terms of others' expectations and appear to be processed by a specific neural system involving the anterior insula, anterior cingulate cortex, and supplemental motor cortex. Importantly, these neural signals appear to motivate people to not only behave consistent with these expectations, but also to help others update their beliefs when these expectations are violated. Further, violations of social expectations appear to promote enhanced memory for norm violators. This work demonstrates the neural and computational basis of moral sentiments.
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Lara, Lopez Luis Alejandro. "The role of neuroscience and neurotechnology: decision making in corporate enviroments." reponame:Repositório Institucional do FGV, 2016. http://hdl.handle.net/10438/17979.

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Decision making is a basic cognitive process and a critical skill set that can lead to a transformational change. This research is a study of the neurobiological basis of decision making, and how its results can be applied to the corporate world. Given the number of new discoveries in neuroscience due to technological advancements in neuroimaging, only partial knowledge about human brain have been utilized for business purposes. This research explores how this knowledge can be applied in business to improve decision making under uncertainty, and to find ways where business and science can be integrated and share best practices. It suggests to focus on cognition because its improvement is a key to high performance in business. Enhancement of cognition can be developed through training and practice of intelligence functions by creating virtual training environments that fully resemble real life, such as modelling and simulations. This practical training could also measure cognitive abilities and identify weak spots in the decision-making process on individuals. The results could be especially valuable to decision makers in fast-paced operating environment, under stress and uncertainty, and could offer vital insights for policy makers and business leaders making decisions in management, ethics, and marketing.
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Kolling, Nils Stephen. "Decision making, the frontal lobes and foraging behaviour." Thesis, University of Oxford, 2015. http://ora.ox.ac.uk/objects/uuid:ea509f5e-dca4-44e5-9f3f-f7d6550e5b45.

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The aim of this thesis was to understand the function of the frontal lobes during different types of decisions thusfar mostly neglected in cognitive neuroscience. Namely, I sought to understand how decisions are made when comparisons are not about a simple set of concrete options presented, but rather require a comparison with one specific encounter and a sense of the value of the current environment (Chapter 2-3). Additionally, I wanted to understand how decisions between concrete options can be contextualized by the current environment to allow considerations about changing environmental constraints to factor into the decision making process (Chapter 4-5). At last, I wanted to test how the potential for future behaviours within an environment has an effect on peoples decisions (Chapter 6). In other words, how do people construct prospective value when it requires a sense of own future behaviours? All this work was informed by concepts and models originating from optimal foraging theory, which seeks to understand animal behaviours using computational models for different ecological types of choices. Thus, this thesis offers a perspective on the neural mechanisms underlying human decision making capacities that relates them to common problems faced by animals and presumably humans in ecological environments (Chapter 1 and 7). As optimal foraging theory assumes that solving these problems efficiently is highly relevant for survival, it is possible that neural structures evolved in ways to particularly accommodate for the solution of those problems. Therefore, different prefrontal structures might be dedicated to unique ways of solving ecological kinds of decision problems. My thesis as a whole gives some evidence for such a perspective, as dACC and vmPFC were repeatedly identified as constituting unique systems for evaluation according to different reference frames. Their competition within a wider network of areas appeared to ultimately drive decisions under changing contexts. In the future, a better understanding of those changing interactions between these prefrontal areas which generate more complex and adaptive behaviours, will be crucial for understanding more natural choice behaviours. For this temporally resolved neural measurements as well as causal interference will be essential.
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Endres, Dominik M. "Bayesian and information-theoretic tools for neuroscience." Thesis, St Andrews, 2006. http://hdl.handle.net/10023/162.

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Zhou, Yating. "Dissection of navigation decision after mechanical stimulation in Drosophila." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=110655.

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Proper selection of new moving direction after external mechanical stimulation is essential for animals to avoid danger (i.e. predators), and thus is vital for survival. This process involves sensory inputs, central processing and motor outputs. Recent studies have made considerable progress in identifying mechanosensory neurons and mechanosensation receptor proteins. The molecular and cellular mechanisms that convert mechanosensation into proper navigation decision, however, remain largely undefined. In this study, I investigate the control of navigation decision in Drosophila. In response to gentle touch at the anterior segments, Drosophila larvae can re-orientate and select a new direction for forward movement. The extent of the change in moving direction is dependent on the intensity of tactile stimuli. Sensation of gentle touch requires chordotonal organs and class IV da neurons. Genetic analysis indicates a key role for the immunoglobulin (Ig) superfamily protein Turtle (Tutl) to regulate touch-initiated navigation decision. Tutl is required specifically in post-mitotic neurons at larval stage after the completion of embryonic development. Circuit breaking analysis identified a small subset of Tutl-positive neurons that are involved in modulating touch-initiated navigation decision.
Le choix d'une nouvelle direction de locomotion suite a une stimulation mécanique externe est un comportement animal de défense qui est essentiel pour éviter un danger (tel que prédateur), et ainsi est vital pour la survie. Ce mécanisme met en jeu une information sensorielle, le traitement de cette information au niveau central et enfin, la production d'effets moteurs. Des études récentes ont permis de progresser considérablement dans l'identification des neurones mécano-sensoriels et des protéines dites mécanorécepteurs. Cependant, les mécanismes moléculaires et cellulaires qui à partir d'un stimulus mécanique sensoriel génèrent un choix de navigation approprié restent vagues. Dans cette étude, nous nous intéressons aux mécanismes qui régulent la navigation chez la Drosophile. En réponse à un stimulus tactile léger appliqué au niveau des segments antérieurs, la larve de Drosophile peut se réorienter et avancer selon une nouvelle direction. L'amplitude de cette réorientation dépend de l'intensité du stimulus. La sensibilité au stimulus tactile requiert les organes chordotonaux et les neurones da de classe IV. Nos analyses génétiques suggèrent que la protéine Turtle, qui appartient à la superfamille des immunoglobulines, joue un rôle clef dans la décision à se réorienter suite à un stimulus tactile. Turtle est spécifiquement requise après le développement embryonnaire dans les neurones post-mitotiques de stade larvaire. Enfin, nos analyses pour décortiquer les circuits nous ont permis d'identifier un sous-type de neurones qui expriment Turtle et qui sont impliqués dans la modulation du choix de la direction de navigation initié par un stimulus tactile.
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Boldt, Annika. "Metacognition in decision making." Thesis, University of Oxford, 2015. http://ora.ox.ac.uk/objects/uuid:5d9b2036-cc42-4515-b40e-97bb3ddb1d78.

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Humans effortlessly and accurately judge their subjective probability of being correct in a given decision, leading to the view that metacognition is integral to decision making. This thesis reports a series of experiments assessing people’s confidence and error-detection judgements. These different types of metacognitive judgements are highly similar with regard to their methodology, but have been studied largely separately. I provide data indicating that these judgements are fundamentally linked and that they rely on shared cognitive and neural mechanisms. As a first step towards such a joint account of confidence and error detection, I present simulations from a computational model that is based on the notion these judgements are based on the same underlying processes. I next focus on how metacognitive signals are utilised to enhance cognitive control by means of a modulation of information seeking. I report data from a study in which participants received performance feedback, testing the hypothesis that participants will focus more on feedback when they are uncertain whether they were correct in the current trial, whilst ignoring feedback when they are certain regarding their accuracy. A final question addressed in this thesis asks which information contributes internally to the formation of metacognitive judgements, given that it remains a challenge for most models of confidence to explain the precise mechanisms by which confidence reflects accuracy, under which circumstances this correlation is reduced, and the role other influences might have, such as the inherent reliability of a source of evidence. The results reported here suggest that multiple variables – such as response time and reliability of evidence – play a role in the generation of metacognitive judgements. Inter-individual differences with regard to the utilisation of these cues to confidence are tested. Taken together, my results suggest that metacognition is crucially involved in decision making and cognitive control.
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Books on the topic "Decision Neuroscience"

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Neuroscience of decision making. New York, NY: Psychology Press, 2011.

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Reyna, Valerie F., and Vivian Zayas, eds. The neuroscience of risky decision making. Washington: American Psychological Association, 2014. http://dx.doi.org/10.1037/14322-000.

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Neuroscience and the economics of decision making. Abingdon, Oxon: Routledge, 2012.

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International Symposium on Attention and Performance (23th : 2008 : Stowe, Vermont ), ed. Decision making, affect, and learning: Attention and performance XXIII. Oxford: Oxford University Press, 2011.

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Graziano, Mario. Epistemology of decision: Rational choice, neuroscience and biological approaches. Dordrecht: Springer, 2013.

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Rolls, Edmund T. Memory, attention, and decision-making: A unifying computational neuroscience approach. Oxford: Oxford University Press, 2008.

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Maldonato, Mauro. The predictive brain: Consciousness, decision and embodied action. Brighton: Sussex Academic Press, 2014.

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Neuroscience of preference and choice: Cognitive and neural mechanisms. London: Academic Press, 2012.

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Simone, Shamay-Tsoory, and Chew Soo Hong 1954-, eds. From DNA to social cognition. Hoboken, N.J: Wiley-Blackwell, 2012.

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Emotion and reason: The cognitive science of decision making. Oxford: Oxford University Press, 2006.

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Book chapters on the topic "Decision Neuroscience"

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Moulin, Thierry, and Laurent Tatu. "Neuroscience and Decision-Making." In Deciding Where to Live, 113–25. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-15542-1_5.

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Salinas, Emilio. "Decision Making: Overview." In Encyclopedia of Computational Neuroscience, 1–3. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_248-6.

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Salinas, Emilio. "Decision-Making: Overview." In Encyclopedia of Computational Neuroscience, 1–3. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4614-7320-6_248-7.

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Miller, Paul. "Decision Making, Models." In Encyclopedia of Computational Neuroscience, 1–18. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7320-6_312-3.

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Yu, Angela J. "Decision-Making Tasks." In Encyclopedia of Computational Neuroscience, 1–8. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_314-1.

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Miller, Paul. "Decision Making, Threshold." In Encyclopedia of Computational Neuroscience, 1–4. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7320-6_315-5.

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Miller, Paul. "Decision Making, Threshold." In Encyclopedia of Computational Neuroscience, 1–4. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_315-6.

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Hauser, Christopher K., and Emilio Salinas. "Perceptual Decision Making." In Encyclopedia of Computational Neuroscience, 1–21. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_317-1.

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van Wingerden, Marijn, and Tobias Kalenscher. "Decision Making, Bias." In Encyclopedia of Computational Neuroscience, 1–6. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7320-6_746-1.

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van Wingerden, Marijn, and Tobias Kalenscher. "Decision-Making, Bias." In Encyclopedia of Computational Neuroscience, 937–42. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_746.

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Conference papers on the topic "Decision Neuroscience"

1

Szu, Harold, TP Jung, and Scott Makeig. "Enhanced decision making through neuroscience." In SPIE Defense, Security, and Sensing, edited by Harold Szu and Liyi Dai. SPIE, 2012. http://dx.doi.org/10.1117/12.926424.

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Schrater, Paul, Konrad Kording, and Gunnar Blohm. "Modeling in Neuroscience as a Decision Process." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1176-0.

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Krueger, Kai, Seth Herd, Ananta Nair, Jessica Mollick, and Randall O'Reilly. "Neural mechanisms of human decision-making." In 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1219-0.

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Huang, J., A. Isidori, L. Marconi, M. Mischiati, E. Sontag, and W. M. Wonham. "Internal Models in Control, Biology and Neuroscience." In 2018 IEEE Conference on Decision and Control (CDC). IEEE, 2018. http://dx.doi.org/10.1109/cdc.2018.8619624.

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Waskom, Michael, and Roozbeh Kiani. "Decision-making through evidence integration at long timescales." In 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1058-0.

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Höft, Nikolas, Rong Guo, Vaios Laschos, Sein Jeung, Dirk Ostwald, and Klaus Obermayer. "Risk Sensitivity under Partially Observable Markov Decision Processes." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1160-0.

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Jaskir, Alana, and Michael Frank. "Computational advantages of dopaminergic states for decision making." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1390-0.

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Bhui, Rahul, and Samuel J. Gershman. "Decision by Sampling Implements Efficient Coding of Psychoeconomic Functions." In 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1059-0.

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Bond, Krista, Kyle Dunovan, and Timothy Verstynen. "Value-conflict and volatility influence distinct decision-making processes." In 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1068-0.

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Mattar, Marcelo, Deborah Talmi, and Nathaniel Daw. "Memory mechanisms predict sampling biases in sequential decision tasks." In 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1164-0.

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Reports on the topic "Decision Neuroscience"

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Kwon, Wi-Suk, Gopikrishna Deshpande, Jeffrey Katz, and Sang-Eun Byun. What Does the Brain Tell about Scarcity Bias? Cognitive Neuroscience Evidence of Decision Making under Scarcity. Ames: Iowa State University, Digital Repository, 2017. http://dx.doi.org/10.31274/itaa_proceedings-180814-374.

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Social Influences on Decision-Making: Neuroscience Insights. IEDP Ideas for Leaders, July 2013. http://dx.doi.org/10.13007/184.

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Social Influences on Decision-Making: Neuroscience Insights. IEDP Ideas for Leaders, July 2013. http://dx.doi.org/10.13007/183.

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