Neural Mechanisms of Decision-Making and the Personal Level

2013 ◽  
Author(s):  
Nicholas Shea
Keyword(s):  
Decision Making ◽  
Cognitive Neuroscience ◽  
Neural Mechanisms ◽  
Voluntary Action ◽  
Mental Phenomenon ◽  
Neural Data ◽  
Personal Level ◽  
Mental Properties ◽  
Empirical Generalizations ◽  
The Brain

Can findings from psychology and cognitive neuroscience about the neural mechanisms involved in decision-making tell us anything useful about the commonly-understood mental phenomenon of making voluntary choices? Two philosophical objections are considered. First, that the neural data is subpersonal, and so cannot enter into illuminating explanations of personal-level phenomena like voluntary action. Secondly, that mental properties are multiply realized in the brain in such a way as to make them insusceptible to neuroscientific study. The chapter argues that both objections would be weakened by the discovery of empirical generalizations connecting subpersonal properties with personal-level phenomena. It gives three case studies that furnish evidence to that effect. It argues that the existence of such interrelations is consistent with a plausible construal of the personal-subpersonal distinction. Furthermore, there is no reason to suppose that the notion of subpersonal representation relied on in cognitive neuroscience illicitly imports personal-level phenomena like consciousness or normativity, or is otherwise explanatorily problematic.

scholarly journals Distributed context-dependent choice information in mouse dorsal-parietal cortex

2021 ◽  
Author(s):  
Javier Orlandi ◽  
Mohammad Adbolrahmani ◽  
Ryo Aoki ◽  
Dmitry Lyamzin ◽  
Andrea Benucci
Keyword(s):  
Decision Making ◽  
Decision Variable ◽  
Visual Stream ◽  
Posterior Cortex ◽  
Neural Data ◽  
Context Dependent ◽  
Cortical Regions ◽  
Low Dimensional ◽  
Independent Decision ◽  
The Brain

Abstract Choice information appears in the brain as distributed signals with top-down and bottom-up components that together support decision-making computations. In sensory and associative cortical regions, the presence of choice signals, their strength, and area specificity are known to be elusive and changeable, limiting a cohesive understanding of their computational significance. In this study, examining the mesoscale activity in mouse posterior cortex during a complex visual discrimination task, we found that broadly distributed choice signals defined a decision variable in a low-dimensional embedding space of multi-area activations, particularly along the ventral visual stream. The subspace they defined was near-orthogonal to concurrently represented sensory and motor-related activations, and it was modulated by task difficulty and contextually by the animals’ attention state. To mechanistically relate choice representations to decision-making computations, we trained recurrent neural networks with the animals’ choices and found an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. In conclusion, our results demonstrated an independent decision variable broadly represented in the posterior cortex, controlled by task features and cognitive demands. Its dynamics reflected decision computations, possibly linked to context-dependent feedback signals used for probabilistic-inference computations in variable animal-environment interactions.

scholarly journals Distributed context-dependent choice information in mouse dorsal-parietal cortex

2021 ◽  
Author(s):  
Javier G. Orlandi ◽  
Mohammad Abdolrahmani ◽  
Ryo Aoki ◽  
Dmitry R. Lyamzin ◽  
Andrea Benucci
Keyword(s):  
Decision Making ◽  
Decision Variable ◽  
Visual Stream ◽  
Posterior Cortex ◽  
Neural Data ◽  
Context Dependent ◽  
Cortical Regions ◽  
Low Dimensional ◽  
Independent Decision ◽  
The Brain

Choice information appears in the brain as distributed signals with top-down and bottom-up components that together support decision-making computations. In sensory and associative cortical regions, the presence of choice signals, their strength, and area specificity are known to be elusive and changeable, limiting a cohesive understanding of their computational significance. In this study, examining the mesoscale activity in mouse posterior cortex during a complex visual discrimination task, we found that broadly distributed choice signals defined a decision variable in a low-dimensional embedding space of multi-area activations, particularly along the ventral visual stream. The subspace they defined was near-orthogonal to concurrently represented sensory and motor-related activations, and it was modulated by task difficulty and contextually by the animals’ attention state. To mechanistically relate choice representations to decision-making computations, we trained recurrent neural networks with the animals’ choices and found an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. In conclusion, our results demonstrated an independent decision variable broadly represented in the posterior cortex, controlled by task features and cognitive demands. Its dynamics reflected decision computations, possibly linked to context-dependent feedback signals used for probabilistic-inference computations in variable animal-environment interactions.

scholarly journals Neural precursors of deliberate and arbitrary decisions in the study of voluntary action

2017 ◽  
Author(s):  
U. Maoz ◽  
G. Yaffe ◽  
C. Koch ◽  
L. Mudrik
Keyword(s):  
Decision Making ◽  
Free Will ◽  
Real Life ◽  
Neural Mechanisms ◽  
Causal Role ◽  
Voluntary Action ◽  
Readiness Potential ◽  
Neural Precursors ◽  
Readiness Potentials ◽  
Random Fluctuations

AbstractThe readiness potential (RP)—a key ERP correlate of upcoming action—is known to precede subjects’ reports of their decision to move. Some view this as evidence against a causal role for consciousness in human decision-making and thus against free-will. Yet those studies focused on arbitrary decisions—purposeless, unreasoned, and without consequences. It remains unknown to what degree the RP generalizes to deliberate, more ecological decisions. We directly compared deliberate and arbitrary decision-making during a $1000-donation task to non-profit organizations. While we found the expected RPs for arbitrary decisions, they were strikingly absent for deliberate ones. Our results and drift-diffusion model are congruent with the RP representing accumulation of noisy, random fluctuations that drive arbitrary—but not deliberate—decisions. They further point to different neural mechanisms underlying deliberate and arbitrary decisions, challenging the generalizability of studies that argue for no causal role for consciousness in decision-making to real-life decisions.Significance StatementThe extent of human free will has been debated for millennia. Previous studies demonstrated that neural precursors of action—especially the readiness potential—precede subjects’ reports of deciding to move. Some viewed this as evidence against free-will. However, these experiments focused on arbitrary decisions—e.g., randomly raising the left or right hand. We directly compared deliberate (actual $1000 donations to NPOs) and arbitrary decisions, and found readiness potentials before arbitrary decisions, but—critically—not before deliberate decisions. This supports the interpretation of readiness potentials as byproducts of accumulation of random fluctuations in arbitrary but not deliberate decisions and points to different neural mechanisms underlying deliberate and arbitrary choice. Hence, it challenges the generalizability of previous results from arbitrary to deliberate decisions.

scholarly journals Geometry of neural computation unifies working memory and planning

2021 ◽  
Author(s):  
Daniel B. Ehrlich ◽  
John D. Murray
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Working Memory ◽  
Prefrontal Cortex ◽  
Human Behavior ◽  
Real World ◽  
Recurrent Neural Networks ◽  
Neural Computation ◽  
Neural Data ◽  
The Brain

Real-world tasks require coordination of working memory, decision making, and planning, yet these cognitive functions have disproportionately been studied as independent modular processes in the brain. Here we propose that contingency representations, defined as mappings for how future behaviors depend on upcoming events, can unify working memory and planning computations. We designed a task capable of disambiguating distinct types of representations. Our experiments revealed that human behavior is consistent with contingency representations, and not with traditional sensory models of working memory. In task-optimized recurrent neural networks we investigated possible circuit mechanisms for contingency representations and found that these representations can explain neurophysiological observations from prefrontal cortex during working memory tasks. Finally, we generated falsifiable predictions for neural data to identify contingency representations in neural data and to dissociate different models of working memory. Our findings characterize a neural representational strategy that can unify working memory, planning, and context-dependent decision making.

Business Ethics and the Brain: Rommel Salvador and Robert G. Folger

2009 ◽  
Vol 19 (1) ◽  
pp. 1-31 ◽  
Author(s):  
Rommel Salvador ◽  
Robert G. Folger
Keyword(s):  
Decision Making ◽  
Business Ethics ◽  
Ethical Decision ◽  
Ethical Decision Making ◽  
Critical Role ◽  
Ethics Training ◽  
Neural Mechanisms ◽  
Field Of Study ◽  
Decision Making Processes ◽  
The Brain

ABSTRACT:Neuroethics, the study of the cognitive and neural mechanisms underlying ethical decision-making, is a growing field of study. In this review, we identify and discuss four themes emerging from neuroethics research. First, ethical decision-making appears to be distinct from other types of decision-making processes. Second, ethical decision-making entails more than just conscious reasoning. Third, emotion plays a critical role in ethical decision-making, at least under certain circumstances. Lastly, normative approaches to morality have distinct, underlying neural mechanisms. On the basis of these themes, we draw implications for research in business ethics and the practice of ethics training.

scholarly journals Distinct Roles of Dopamine D1 and D2 Receptor-expressing Neurons in the Nucleus Accumbens for a Strategy Dependent Decision Making

2021 ◽  
Author(s):  
Tadaaki Nishioka ◽  
Tom Macpherson ◽  
Kosuke Hamaguchi ◽  
Takatoshi Hikida
Keyword(s):  
Decision Making ◽  
Nucleus Accumbens ◽  
Dopamine D2 Receptor ◽  
D2 Receptor ◽  
Neural Mechanisms ◽  
Good Option ◽  
Psychological Study ◽  
Dopamine D2 ◽  
Different Types ◽  
The Brain

To optimize decision making, animals need to execute not only a strategy to choose a good option but sometimes also one to avoid a bad option. A psychological study indicates that positive and negative information is processed in a different manner in the brain. The nucleus accumbens (NAc) contains two different types of neurons, dopamine D1 and D2 receptor-expressing neurons which are implicated in reward-based decision making and aversive learning. However, little is known about the neural mechanisms by which D1 or D2 receptor-expressing neurons in the NAc contribute to the execution of the strategy to choose a good option or one to avoid a bad option under decision making. Here, we have developed two novel visual discrimination tasks for mice to assess the strategy to choose a good option and one to avoid a bad option. By chemogenetically suppressing the subpopulation of the NAc neurons, we have shown that dopamine D2 receptor-expressing neurons in the NAc selectively contribute to the strategy to avoid a bad option under reward-based decision making. Furthermore, our optogenetic and calcium imaging experiments indicate that dopamine D2 receptor-expressing neurons are activated by error choices and the activation following an error plays an important role in optimizing the strategy in the next trial. Our findings suggest that the activation of D2 receptor-expressing neurons by error choices through learning enables animals to execute the appropriate strategy.

scholarly journals How Reward and Aversion Shape Motivation and Decision Making: A Computational Account

2019 ◽  
Vol 26 (1) ◽  
pp. 87-99 ◽  
Author(s):  
Jeroen P. H. Verharen ◽  
Roger A. H. Adan ◽  
Louk J. M. J. Vanderschuren
Keyword(s):  
Decision Making ◽  
Substance Use ◽  
Neural Mechanisms ◽  
Behavioral Adaptation ◽  
Costs And Benefits ◽  
Substance Addiction ◽  
The Core ◽  
Brain Circuits ◽  
Motivated Behaviors ◽  
The Brain

Processing rewarding and aversive signals lies at the core of many adaptive behaviors, including value-based decision making. The brain circuits processing these signals are widespread and include the prefrontal cortex, amygdala and striatum, and their dopaminergic innervation. In this review, we integrate historic findings on the behavioral and neural mechanisms of value-based decision making with recent, groundbreaking work in this area. On the basis of this integrated view, we discuss a neuroeconomic framework of value-based decision making, use this to explain the motivation to pursue rewards and how motivation relates to the costs and benefits associated with different courses of action. As such, we consider substance addiction and overeating as states of altered value-based decision making, in which the expectation of reward chronically outweighs the costs associated with substance use and food consumption, respectively. Together, this review aims to provide a concise and accessible overview of important literature on the neural mechanisms of behavioral adaptation to reward and aversion and how these mediate motivated behaviors.

Aging and Neuroeconomics: Insights from Research on Neuromodulation of Reward-based Decision Making

2007 ◽  
Vol 29 (1) ◽  
Author(s):  
Shu-Chen Li ◽  
Guido Biele ◽  
Peter N. C. Mohr ◽  
Hauke R. Heekeren
Keyword(s):  
Decision Making ◽  
Empirical Evidence ◽  
Dopaminergic System ◽  
Neural Mechanisms ◽  
Future Research ◽  
Neurotransmitter Systems ◽  
Reward Prediction ◽  
Dopaminergic Modulation ◽  
The Brain ◽  
Expected Outcomes

Abstract‘Neuroeconomics’ can be broadly defined as the research of how the brain interacts with the environment to make decisions that are functional given individual and contextual constraints. Deciphering such brain-environment transactions requires mechanistic understandings of the neurobiological processes that implement value-dependent decision making. To this end, a common empirical approach is to investigate neural mechanisms of reward-based decision making. Flexible updating of choices and associated expected outcomes in ways that are adaptive for a given task (or a given set of tasks) at hand relies on dynamic neurochemical tuning of the brain’s functional circuitries involved in the representation of tasks, goals and reward prediction. Empirical evidence as well as computational theories indicate that various neurotransmitter systems (e.g., dopamine, norepinephrine, and serotonin) play important roles in reward-based decision making. In light of the apparent aging-related decline in various aspects of the dopaminergic system as well as the effects of neuromodulation on reward-related processes, this article focuses selectively on the literature that highlights the triadic relations between dopaminergic modulation, reward-based decision making, and aging. Directions for future research on aging and neuroeconomoics are discussed.

scholarly journals The brain and the law

2004 ◽  
Vol 359 (1451) ◽  
pp. 1727-1736 ◽  
Author(s):  
S. Zeki ◽  
O. R. Goodenough ◽  
Terrence Chorvat ◽  
Kevin McCabe
Keyword(s):  
Decision Making ◽  
Social Welfare ◽  
Cognitive Processes ◽  
Economic Models ◽  
Neural Mechanisms ◽  
Neuroscience Research ◽  
Legal Regimes ◽  
Human Decision ◽  
The Law ◽  
The Brain

Much has been written about how law as an institution has developed to solve many problems that human societies face. Inherent in all of these explanations are models of how humans make decisions. This article discusses what current neuroscience research tells us about the mechanisms of human decision making of particular relevance to law. This research indicates that humans are both more capable of solving many problems than standard economic models predict, but also limited in ways those models ignore. This article discusses how law is both shaped by our cognitive processes and also shapes them. The article considers some of the implications of this research for improving our understanding of how our current legal regimes operate and how the law can be structured to take advantage of our neural mechanisms to improve social welfare.

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