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Journal articles on the topic 'Decision Neuroscience'

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

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Hartley, Catherine A., and Leah H. Somerville. "The neuroscience of adolescent decision-making." Current Opinion in Behavioral Sciences 5 (October 2015): 108–15. http://dx.doi.org/10.1016/j.cobeha.2015.09.004.

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12

Sanfey, Alan G. "Social Decision-Making: Insights from Game Theory and Neuroscience." Science 318, no. 5850 (October 26, 2007): 598–602. http://dx.doi.org/10.1126/science.1142996.

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By combining the models and tasks of Game Theory with modern psychological and neuroscientific methods, the neuroeconomic approach to the study of social decision-making has the potential to extend our knowledge of brain mechanisms involved in social decisions and to advance theoretical models of how we make decisions in a rich, interactive environment. Research has already begun to illustrate how social exchange can act directly on the brain's reward system, how affective factors play an important role in bargaining and competitive games, and how the ability to assess another's intentions is related to strategic play. These findings provide a fruitful starting point for improved models of social decision-making, informed by the formal mathematical approach of economics and constrained by known neural mechanisms.
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13

Mateu, Guillermo, Lucas Monzani, and Roger Muñoz Navarro. "The role of the brain in financial decisions: A viewpoint on neuroeconomics." Mètode Revista de difusió de la investigació, no. 8 (June 5, 2018): 6. http://dx.doi.org/10.7203/metode.0.6923.

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In this article, we explain the important role neuroscience plays in economic and financial environments. Hence, we present neuroeconomics as a way to describe how decision-making processes affect brain activity, focusing especially on the importance of economic and financial decisions. We answer some questions regarding the role of emotions in finance, the psychological factors present in financial markets, and how neuropsychological stimuli affect our economic decisions. We conclude by citing the main research in the area of neuroscience in financial decision-making processes, and highlight further research projects in these areas.
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14

Beugre, Constant D., James Dulebohn, Richard E. Boyatzis, Sebastiano Massaro, David V. Smith, and Dongyuan Wu. "The Neuroscience of Decision Making in Organizations." Academy of Management Proceedings 2020, no. 1 (August 2020): 13893. http://dx.doi.org/10.5465/ambpp.2020.13893symposium.

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15

Bottalico, Barbara. "Cognitive Neuroscience, Decision Making and the Law." European Journal of Risk Regulation 2, no. 3 (September 2011): 427–32. http://dx.doi.org/10.1017/s1867299x00001458.

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Cognitive neuroscience was born when the theories and methods of cognitive psychology and neuropsychology were combined after a long period of parallel development. Over the last few decades, neuroscientific studies have begun to meet the challenge of understanding cognitive functions, thereby identifying the causal chain of neural events that underlies cognition. The development of powerful brain imaging technologies is now likely to present a range of opportunities in many spheres of public life, such as the criminal and civil justice system, and the business world.
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16

Dombrovski, Alexandre Y., and Michael N. Hallquist. "The decision neuroscience perspective on suicidal behavior." Current Opinion in Psychiatry 30, no. 1 (January 2017): 7–14. http://dx.doi.org/10.1097/yco.0000000000000297.

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17

Heydenfeldt, Jo Ann. "Decision Science and Applied Neuroscience: Emerging Possibilities." Performance Improvement 52, no. 6 (July 2013): 18–25. http://dx.doi.org/10.1002/pfi.21354.

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18

Herz, Damian M., Rafal Bogacz, and Peter Brown. "Neuroscience: Impaired Decision-Making in Parkinson’s Disease." Current Biology 26, no. 14 (July 2016): R671—R673. http://dx.doi.org/10.1016/j.cub.2016.05.075.

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19

Varón Sandoval, Alexander, and Lizeth Carolina Zapata Castillo. "A theoretical approach to neuroscience technologies’ contributions to administration in the digital transformation context." Cuadernos de Administración 37, no. 69 (June 21, 2021): e4010691. http://dx.doi.org/10.25100/cdea.v37i69.10691.

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This paper intends to make an approximation to the contributions of neuroscience technologies to administration in a digital transformation context, which will be done through a bibliographic search and document analysis based on a review of databases and the collection of those sources considered most relevant to the topic at hand concerning some of the most critical dimensions in this area, such as human resources, leadership, decision-making, and digital business ecosystems. Likewise, it seeks to make a theoretical reflection on the use of neurosciences as a management tool of relevance to organizations. Among the main findings is that applying neuroscience techniques and the traditional ones can improve critical processes within organizations.
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20

Mofatteh, Mohammad. "Neurosurgery and artificial intelligence." AIMS Neuroscience 8, no. 4 (2021): 477–95. http://dx.doi.org/10.3934/neuroscience.2021025.

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<abstract> <p>Neurosurgeons receive extensive and lengthy training to equip themselves with various technical skills, and neurosurgery require a great deal of pre-, intra- and postoperative clinical data collection, decision making, care and recovery. The last decade has seen a significant increase in the importance of artificial intelligence (AI) in neurosurgery. AI can provide a great promise in neurosurgery by complementing neurosurgeons' skills to provide the best possible interventional and noninterventional care for patients by enhancing diagnostic and prognostic outcomes in clinical treatment and help neurosurgeons with decision making during surgical interventions to improve patient outcomes. Furthermore, AI is playing a pivotal role in the production, processing and storage of clinical and experimental data. AI usage in neurosurgery can also reduce the costs associated with surgical care and provide high-quality healthcare to a broader population. Additionally, AI and neurosurgery can build a symbiotic relationship where AI helps to push the boundaries of neurosurgery, and neurosurgery can help AI to develop better and more robust algorithms. This review explores the role of AI in interventional and noninterventional aspects of neurosurgery during pre-, intra- and postoperative care, such as diagnosis, clinical decision making, surgical operation, prognosis, data acquisition, and research within the neurosurgical arena.</p> </abstract>
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21

Frydman, Cary, and Colin F. Camerer. "The Psychology and Neuroscience of Financial Decision Making." Trends in Cognitive Sciences 20, no. 9 (September 2016): 661–75. http://dx.doi.org/10.1016/j.tics.2016.07.003.

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22

Yoder, Keith J., and Jean Decety. "The neuroscience of morality and social decision-making." Psychology, Crime & Law 24, no. 3 (December 12, 2017): 279–95. http://dx.doi.org/10.1080/1068316x.2017.1414817.

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23

Chicurel, M. "NEUROSCIENCE: Neurons Weigh Options, Come to a Decision." Science 295, no. 5562 (March 15, 2002): 1995b—1997. http://dx.doi.org/10.1126/science.295.5562.1995b.

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24

Peterson, Andrew. "Should Neuroscience Inform Judgements of Decision-Making Capacity?" Neuroethics 12, no. 2 (May 9, 2018): 133–51. http://dx.doi.org/10.1007/s12152-018-9369-4.

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25

Jessup, Ryan K., and John P. O'Doherty. "Decision Neuroscience: Choices of Description and of Experience." Current Biology 20, no. 20 (October 2010): R881—R883. http://dx.doi.org/10.1016/j.cub.2010.09.017.

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26

Burns, Kelly, and Antoine Bechara. "Decision making and free will: a neuroscience perspective." Behavioral Sciences & the Law 25, no. 2 (2007): 263–80. http://dx.doi.org/10.1002/bsl.751.

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27

Serra, Daniel. "Decision-making: from neuroscience to neuroeconomics—an overview." Theory and Decision 91, no. 1 (June 28, 2021): 1–80. http://dx.doi.org/10.1007/s11238-021-09830-3.

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28

Roselli, Lucia Reis Peixoto, Adiel Teixeira de Almeida, and Eduarda Asfora Frej. "Decision neuroscience for improving data visualization of decision support in the FITradeoff method." Operational Research 19, no. 4 (January 3, 2019): 933–53. http://dx.doi.org/10.1007/s12351-018-00445-1.

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29

O'Connell, Redmond G., and Simon P. Kelly. "Neurophysiology of Human Perceptual Decision-Making." Annual Review of Neuroscience 44, no. 1 (July 8, 2021): 495–516. http://dx.doi.org/10.1146/annurev-neuro-092019-100200.

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The discovery of neural signals that reflect the dynamics of perceptual decision formation has had a considerable impact. Not only do such signals enable detailed investigations of the neural implementation of the decision-making process but they also can expose key elements of the brain's decision algorithms. For a long time, such signals were only accessible through direct animal brain recordings, and progress in human neuroscience was hampered by the limitations of noninvasive recording techniques. However, recent methodological advances are increasingly enabling the study of human brain signals that finely trace the dynamics of the unfolding decision process. In this review, we highlight how human neurophysiological data are now being leveraged to furnish new insights into the multiple processing levels involved in forming decisions, to inform the construction and evaluation of mathematical models that can explain intra- and interindividual differences, and to examine how key ancillary processes interact with core decision circuits.
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30

Schultz, Wolfram. "Introduction. Neuroeconomics: the promise and the profit." Philosophical Transactions of the Royal Society B: Biological Sciences 363, no. 1511 (October 2008): 3767–69. http://dx.doi.org/10.1098/rstb.2008.0153.

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Neuroeconomics investigates the neural mechanisms underlying decisions about rewarding or punishing outcomes (‘economic’ decisions). It combines the knowledge about the behavioural phenomena of economic decisions with the mechanistic explanatory power of neuroscience. Thus, it is about the neurobiological foundations of economic decision making. It is hoped that by ‘opening the box’ we can understand how decisions about gains and losses are directed by the brain of the individual decision maker. Perhaps we can even learn why some decisions are apparently paradoxical or pathological. The knowledge could be used to create situations that avoid suboptimal decisions and harm.
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31

Njegovanović, Ana. "Financial Decision Making in The Framework of Neuroscience / Anthropology with Review to The Pandemic and Climate Change." Financial Markets, Institutions and Risks 4, no. 4 (2020): 55–65. http://dx.doi.org/10.21272/fmir.4(4).55-65.2020.

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The purpose of this paper is interdisciplinary research of combinations of different disciplines of (natural) anthropology/neuroscience of consciousness and quantum physics and (social sciences) of financial decision making in the context of climate change and pandemics, which can be useful for finding new information, solving complex problems. The aim of this study is to provide insights into financial decision-making through the intertwining of anthropology/neuroscience and quantum physics in financial decision-making within COVID 19 and climate change and what their relationship/outcomes are. Human consciousness has slipped towards the collapse of convergent crises. Namely, health and climate change are intertwined. The causes of the COVID 19 crisis and climate change are common and their effects are approaching. The climatic situation and COVID-19, a zoonotic disease, are subject to human activity that has led to environmental degradation. Neither the climate crisis nor the zoonotic pandemic was unexpected. They have led to the loss of life that could have been prevented by delayed, insufficient, or wrong actions. Financial decision-making requires harmonizing public health improvements, creating a sustainable economic future, and better protecting remaining natural resources and biodiversity Perhaps in this context financial simplification could be defined as the coexistence of all options with different degrees of potential that we will choose (it is a superposition), other options cease to exist for us when we enter the so-called zero of the desired option (the brain prepares our decisions). The results of the research showed us that COVID 19 and climate change have caused economic risks and uncertainties that have far-reaching and profound implications for financial decision-making as well as the financial services industry and its institutions. Extending tools through anthropology/neuroscience and quantum physics has given us knowledge of the need to connect both the natural and social sciences to understand the complex world around us. Keywords: Anthropology, Neuroscience, Quantum physics, Financial Decision Making.
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32

WOLF, ANDREW B., and GIDON FELSEN. "How public health policy can be informed by neuroscience." Behavioural Public Policy 3, no. 1 (May 7, 2018): 37–46. http://dx.doi.org/10.1017/bpp.2017.9.

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AbstractMany public policies are designed to counteract commonly made decisions that result in poor health. These policies have primarily been informed by the behavioural economics of decision making. Underappreciated in this conversation has been the perspective from neuroscience, despite its recent success – and the likelihood of future progress – in advancing our understanding of the neural basis for health-related decisions. Using tobacco control as an example, we provide a concise overview of how public health policies can and should be informed by neuroscience. We propose that such input can improve policies by increasing their effectiveness, improving screening efficiency and informing relevant ethical considerations. Finally, we recognise limitations and highlight roles that key stakeholders can play in incorporating neuroscientific evidence for the benefit of public policy.
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33

Servant, Mathieu, Corey White, Anna Montagnini, and Borís Burle. "Linking Theoretical Decision-making Mechanisms in the Simon Task with Electrophysiological Data: A Model-based Neuroscience Study in Humans." Journal of Cognitive Neuroscience 28, no. 10 (October 2016): 1501–21. http://dx.doi.org/10.1162/jocn_a_00989.

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A current challenge for decision-making research is in extending models of simple decisions to more complex and ecological choice situations. Conflict tasks (e.g., Simon, Stroop, Eriksen flanker) have been the focus of much interest, because they provide a decision-making context representative of everyday life experiences. Modeling efforts have led to an elaborated drift diffusion model for conflict tasks (DMC), which implements a superimposition of automatic and controlled decision activations. The DMC has proven to capture the diversity of behavioral conflict effects across various task contexts. This study combined DMC predictions with EEG and EMG measurements to test a set of linking propositions that specify the relationship between theoretical decision-making mechanisms involved in the Simon task and brain activity. Our results are consistent with a representation of the superimposed decision variable in the primary motor cortices. The decision variable was also observed in the EMG activity of response agonist muscles. These findings provide new insight into the neurophysiology of human decision-making. In return, they provide support for the DMC model framework.
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34

Tormen, Federico. "Cognitive neuroscience applied to law. A neurolaw introduction." Neuropsychological Trends, no. 28 (November 2020): 83–91. http://dx.doi.org/10.7358/neur-2020-028-torm.

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Legal reasoning, from the decision of United Chambers of Supreme Court, through the legal assistance activity carried out by a lawyer, to the purchase of an object on Amazon by a common citizen (which integrates a contractual constraint), contains in itself assessments of opportunity and moral evaluation, problem solving, decision making and, in general, a cognitive activation very wide ranging. Thanks to the multidisciplinary vocation of neuroscience, in particular focused at the cognitive field in legal practice, the aim of the research in cognitive neuroscience applied to law is to help to bridge the lack of in-depth analysis in the decision-making processes that the main players of the law, such as judges and lawyers, are informed. And all this, taking into account the ethical issues that occur above all in the comparative analysis of neuroscience-law interaction.
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35

Kalenscher, Tobias. "Choosing is feeling—the cognitive neuroscience of decision making." Lancet Neurology 6, no. 1 (January 2007): 26–27. http://dx.doi.org/10.1016/s1474-4422(06)70673-1.

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36

Mobbs, Dean, Pete C. Trimmer, Daniel T. Blumstein, and Peter Dayan. "Foraging for foundations in decision neuroscience: insights from ethology." Nature Reviews Neuroscience 19, no. 7 (May 11, 2018): 419–27. http://dx.doi.org/10.1038/s41583-018-0010-7.

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37

Hallquist, Michael N., Nathan T. Hall, Alison M. Schreiber, and Alexandre Y. Dombrovski. "Interpersonal dysfunction in borderline personality: a decision neuroscience perspective." Current Opinion in Psychology 21 (June 2018): 94–104. http://dx.doi.org/10.1016/j.copsyc.2017.09.011.

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38

Bossaerts, Peter, and Carsten Murawski. "Decision Neuroscience: Why We Become More Cautious with Age." Current Biology 26, no. 12 (June 2016): R495—R497. http://dx.doi.org/10.1016/j.cub.2016.04.061.

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39

Carrillo, José Antonio, Stéphane Cordier, and Simona Mancini. "A decision-making Fokker–Planck model in computational neuroscience." Journal of Mathematical Biology 63, no. 5 (December 24, 2010): 801–30. http://dx.doi.org/10.1007/s00285-010-0391-3.

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40

Bollhagen, Andrew, and John Bickle. "Sounding the Call for External Validity in Decision Neuroscience." Science & Education 26, no. 3-4 (May 2017): 429–33. http://dx.doi.org/10.1007/s11191-017-9894-2.

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41

Van Duijvenvoorde, Anna C. K., Bernd Figner, Wouter D. Weeda, Maurits W. Van der Molen, Brenda R. J. Jansen, and Hilde M. Huizenga. "Neural Mechanisms Underlying Compensatory and Noncompensatory Strategies in Risky Choice." Journal of Cognitive Neuroscience 28, no. 9 (September 2016): 1358–73. http://dx.doi.org/10.1162/jocn_a_00975.

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Individuals may differ systematically in their applied decision strategies, which has critical implications for decision neuroscience but is yet scarcely studied. Our study's main focus was therefore to investigate the neural mechanisms underlying compensatory versus noncompensatory strategies in risky choice. Here, we compared people using a compensatory expected value maximization with people using a simplified noncompensatory loss-minimizing choice strategy. To this end, we used a two-choice paradigm including a set of “simple” items (e.g., simple condition), in which one option was superior on all attributes, and a set of “conflict” items, in which one option was superior on one attribute but inferior on other attributes. A binomial mixture analysis of the decisions elicited by these items differentiated between decision-makers using either a compensatory or a noncompensatory strategy. Behavioral differences were particularly pronounced in the conflict condition, and these were paralleled by neural results. That is, we expected compensatory decision-makers to use an integrated value comparison during choice in the conflict condition. Accordingly, the compensatory group tracked the difference in expected value between choice options reflected in neural activation in the parietal cortex. Furthermore, we expected noncompensatory, compared with compensatory, decision-makers to experience increased conflict when attributes provided conflicting information. Accordingly, the noncompensatory group showed greater dorsomedial PFC activation only in the conflict condition. These pronounced behavioral and neural differences indicate the need for decision neuroscience to account for individual differences in risky choice strategies and to broaden its scope to noncompensatory risky choice strategies.
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42

Hewig, Johannes, Thomas Straube, Ralf H. Trippe, Nora Kretschmer, Holger Hecht, Michael G. H. Coles, and Wolfgang H. R. Miltner. "Decision-making under Risk: An fMRI Study." Journal of Cognitive Neuroscience 21, no. 8 (August 2009): 1642–52. http://dx.doi.org/10.1162/jocn.2009.21112.

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Recent research has focused on decision-making under risk and its neural bases. Two kinds of bad decisions under risk may be defined: too risky decisions and too cautious decisions. Here we show that suboptimal decisions of both kinds lead to increased activity in the anterior cingulate cortex in a Blackjack gambling task. Moreover, this increased activity is related to the avoidance of the negatively evaluated decision under risk. These findings complement other results suggesting an important role of the dorsal anterior cingulate cortex in reward-based decision-making and conflict resolution.
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43

Njegovanović, Ana. "How Do We Decide? Thought Architecture Decision Making?" Financial Markets, Institutions and Risks 5, no. 2 (2021): 58–71. http://dx.doi.org/10.21272/fmir.5(2).58-71.2021.

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The study of decision-making is an intellectual discipline; mathematics, sociology, psychology, economics, political science, artificial intelligence, neuroscience and physics. Conventional decision theory tells us what choice of behavior should be made if we follow certain axioms. Scientific curiosity instructs us to reconsider beyond any area in which we have defined ourselves. We design the intertwining of brain, genetics, phylogenetics, and artificial and neural networks in financial trading to find the best combinations of parameter values in financial trading, incorporating them into ANN models for stock selection and trader identification. The purpose and goal of the paper is to make financial decisions in the intertwining of the brain, genetics, phylogenetics and artificial neural networks, focusing on opening new foundations, giving insights into the foundation rock that lies beneath that soil. Science seeks basic natural laws. Mathematics seeks new theorems to build on old ones. Engineering builds systems to address human needs. The three disciplines are interdependent, but different and yet Claude Shannon simultaneously makes a central contribution to all three disciplines, this was the guiding idea of our work (finance, neuroscience, artificial intelligence).
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44

Jankowska, Joanna. "Psychological Insights Into Decision‑Making." Kwartalnik Ekonomistów i Menedżerów 42, no. 4 (October 1, 2016): 0. http://dx.doi.org/10.5604/01.3001.0009.5485.

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This paper considers the widely approached problem of how individuals and groups make economic decisions. The author’s belief is that the answer to this question is highly interdisciplinary and lies not only in areas of study such as microeconomic theory and organisational behaviour, but also psychology, neuroscience and ethics. The author attempts to summarise a few chosen, existing models, which can help analyse both logical and psychological aspects of the process, and mentions a new, rising interdisciplinary field of neuroeconomics, which offers high potential for construction of new decision‐making models in the future.
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45

Manne, Sharon L., Barbara L. Smith, Sara Frederick, Anna Mitarotondo, Deborah A. Kashy, and Laurie J. Kirstein. "B-Sure: a randomized pilot trial of an interactive web-based decision support aid versus usual care in average-risk breast cancer patients considering contralateral prophylactic mastectomy." Translational Behavioral Medicine 10, no. 2 (January 4, 2019): 355–63. http://dx.doi.org/10.1093/tbm/iby133.

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Abstract The use of contralateral prophylactic mastectomy (CPM) is increasing among breast cancer patients who are at average or “sporadic” risk for contralateral breast cancer. Because CPM provides no survival benefit for these patients, it is not medically recommended for them. Decision support aids may facilitate more informed, higher quality CPM decision. The purpose of this study was to evaluate the feasibility and acceptability of B-Sure, an online decision support aid to facilitate informed decisions regarding CPM, and to compare the impact of B-Sure in increasing CPM knowledge, reducing decisional conflict, and increasing preparedness to make the CPM decision among breast cancer patients at sporadic risk who are considering CPM. Ninety-three patients with unilateral, nonhereditary breast cancer considering CPM completed a baseline survey, were randomized to receive B-Sure or Usual care, and completed a 4-week follow-up survey assessing decisional conflict, preparedness to make the CPM decision, and CPM knowledge as well as self-efficacy, perceived risk, worry, CPM motivations, and the surgical decision. Study participation was high. B-Sure was viewed by almost 80% of the participants and was evaluated positively. At follow-up, patients assigned to B-Sure reported significantly higher clarity regarding the personal values relevant to the CPM decision and higher knowledge about CPM. B-Sure had smaller effects on other aspects of decisional conflict. B-Sure improved CPM knowledge and reduced decisional conflict. Patients considering CPM may benefit from an online decision support aid, but may be sensitive to approaches that they perceive as biased against CPM.
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46

Dror, Itiel E. "Cognitive neuroscience in forensic science: understanding and utilizing the human element." Philosophical Transactions of the Royal Society B: Biological Sciences 370, no. 1674 (August 5, 2015): 20140255. http://dx.doi.org/10.1098/rstb.2014.0255.

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The human element plays a critical role in forensic science. It is not limited only to issues relating to forensic decision-making, such as bias, but also relates to most aspects of forensic work (some of which even take place before a crime is ever committed or long after the verification of the forensic conclusion). In this paper, I explicate many aspects of forensic work that involve the human element and therefore show the relevance (and potential contribution) of cognitive neuroscience to forensic science. The 10 aspects covered in this paper are proactive forensic science, selection during recruitment, training, crime scene investigation, forensic decision-making, verification and conflict resolution, reporting, the role of the forensic examiner, presentation in court and judicial decisions. As the forensic community is taking on the challenges introduced by the realization that the human element is critical for forensic work, new opportunities emerge that allow for considerable improvement and enhancement of the forensic science endeavour.
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Forte, Giuseppe, Matteo Morelli, and Maria Casagrande. "Heart Rate Variability and Decision-Making: Autonomic Responses in Making Decisions." Brain Sciences 11, no. 2 (February 15, 2021): 243. http://dx.doi.org/10.3390/brainsci11020243.

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Decision-making is one of the most crucial cognitive processes in daily life. An adaptable, rapid, and flexible decision requires integration between brain and body. Heart rate variability (HRV) indexes this brain–body connection and appears to be related to cognitive performance. However, its relationship with decision-making is poorly analyzed. This study investigates the relationship between HRV and the decision-making process, assessed through the Iowa Gambling Task (IGT). One hundred and thirty healthy university students (mean age = 23.35 ± 2.50) participated in the study. According to IGT performance, they were divided into high decision-makers (n = 79) and low decision-makers (n = 51). Heart rate variability was measured in the resting, reactivity (i.e., during IGT), and recovery phases. Higher vagally mediated HRV (vmHRV; indexed in frequency domain measures) was evidenced in good decision-makers in the resting, reactivity, and recovery phases. During the task, a higher vagal modulation after a first evaluation was highlighted in good decision-makers. In conclusion, HRV proves to be a valid index of inhibitory circuit functioning in the prefrontal cortex. The relationship with cognitive functions was also confirmed, considering the ability to inhibit disadvantageous responses and make better decisions.
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48

Borawska, Anna. "Cognitive Neuroscience Tools in Economic Experiments Investigating the Decision Making Process." Folia Oeconomica Stetinensia 17, no. 1 (June 27, 2017): 159–69. http://dx.doi.org/10.1515/foli-2017-0013.

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AbstractExperimental economics utilises a lot of different techniques to support its research. Applying computers and IT has already become common. As a novel approach the use of cognitive neuroscience tools is now being considered. Investigating the neurophysiological signals of experiment participants can give researchers a deeper insight into a decision making process. The aim of the article is to show how neuroscience techniques can contribute to economic experiments, especially those concerning decision making. The overview and presentation of the possibilities of such tools is shown regarding different stages of the decision making process and related experimental studies. The proposed analysis could allow for the better design of economic experiments conducted with the use of the most up-to date technology available.
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Camerer, Colin, George Loewenstein, and Drazen Prelec. "Neuroeconomics: How Neuroscience Can Inform Economics." Journal of Economic Literature 43, no. 1 (February 1, 2005): 9–64. http://dx.doi.org/10.1257/0022051053737843.

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Neuroeconomics uses knowledge about brain mechanisms to inform economic analysis, and roots economics in biology. It opens up the “black box” of the brain, much as organizational economics adds detail to the theory of the firm. Neuroscientists use many tools— including brain imaging, behavior of patients with localized brain lesions, animal behavior, and recording single neuron activity. The key insight for economics is that the brain is composed of multiple systems which interact. Controlled systems (“executive function”) interrupt automatic ones. Emotions and cognition both guide decisions. Just as prices and allocations emerge from the interaction of two processes—supply and demand— individual decisions can be modeled as the result of two (or more) processes interacting. Indeed, “dual-process” models of this sort are better rooted in neuroscientific fact, and more empirically accurate, than single-process models (such as utility-maximization). We discuss how brain evidence complicates standard assumptions about basic preference, to include homeostasis and other kinds of state-dependence. We also discuss applications to intertemporal choice, risk and decision making, and game theory. Intertemporal choice appears to be domain-specific and heavily influenced by emotion. The simplified ß-d of quasi-hyperbolic discounting is supported by activation in distinct regions of limbic and cortical systems. In risky decision, imaging data tentatively support the idea that gains and losses are coded separately, and that ambiguity is distinct from risk, because it activates fear and discomfort regions. (Ironically, lesion patients who do not receive fear signals in prefrontal cortex are “rationally” neutral toward ambiguity.) Game theory studies show the effect of brain regions implicated in “theory of mind”, correlates of strategic skill, and effects of hormones and other biological variables. Finally, economics can contribute to neuroscience because simple rational-choice models are useful for understanding highly-evolved behavior like motor actions that earn rewards, and Bayesian integration of sensorimotor information. Who knows what I want to do? Who knows what anyone wants to do? How can you be sure about something like that? Isn't it all a question of brain chemistry, signals going back and forth, electrical energy in the cortex? How do you know whether something is really what you want to do or just some kind of nerve impulse in the brain. Some minor little activity takes place somewhere in this unimportant place in one of the brain hemispheres and suddenly I want to go to Montana or I don't want to go to Montana. (White Noise, Don DeLillo)
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MacFadden, Robert J., and Dick Schoech. "Neuroscience, the Unconscious and Professional Decision Making: Implications for ICT." Journal of Technology in Human Services 28, no. 4 (October 29, 2010): 282–94. http://dx.doi.org/10.1080/15228835.2011.562636.

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