How the Brain Makes Decisions

2020 ◽  
Author(s):  
Thomas Boraud
Keyword(s):  
Decision Making ◽  
General Framework ◽  
Fundamental Question ◽  
Decision Making Process ◽  
Brain Functions ◽  
It Impacts ◽  
Combining Data ◽  
Neural Substrate ◽  
Human Decision ◽  
The Brain

The human decision-making process is tainted with irrationality. To address this issue, this book proposes a ‘bottom-up’ approach of the neural substrate of decision-making, starting from the fundamental question: What are the basic properties that a neural network of decision-making needs to possess? Combining data drawn from phylogeny and physiology, this book provides a general framework of the neurobiology of decision-making in vertebrates and explains how it evolved from the lamprey to the apes. It also addresses the consequences, examining how it impacts our capacity of reasoning and some aspects of the pathophysiology of high brain functions. To conclude, the text opens discussion to more philosophical concepts such as the question of free will.

scholarly journals Brain-inspired model for decision-making in the selection of beneficial information patterns among signals received by an unpredictable information-development environment

2021 ◽  
Author(s):  
Alireza Asgari ◽  
yvan beauregard
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Health And Safety ◽  
Vital Role ◽  
Successful Implementation ◽  
Development Environment ◽  
Brain Functions ◽  
Operational Costs ◽  
Industrial Operations ◽  
The Brain

With its diversification in products and services, today’s marketplace makes competition wildly dynamic and unpredictable for industries. In such an environment, daily operational decision-making has a vital role in producing value for products and services while avoiding the risk of loss and hazard to human health and safety. However, it makes a large portion of operational costs for industries. The main reason is that decision-making belongs to the operational tasks dominated by humans. The less involvement of humans, as a less controllable entity, in industrial operation could also favorable for improving workplace health and safety. To this end, artificial intelligence is proposed as an alternative to doing human decision-making tasks. Still, some of the functional characteristics of the brain that allow humans to make decisions in unpredictable environments like the current industry, especially knowledge generalization, are challenging for artificial intelligence. To find an applicable solution, we study the principles that underlie the human brain functions in decision-making. The relative base functions are realized to develop a model in a simulated unpredictable environment for a decision-making system that could decide which information is beneficial to choose. The method executed to build our model's neuronal interactions is unique that aims to mimic some simple functions of the brain in decision-making. It has the potential to develop for systems acting in the higher abstraction levels and complexities in real-world environments. This system and our study will help to integrate more artificial intelligence in industrial operations and settings. The more successful implementation of artificial intelligence will be the steeper decreasing operational costs and risks.

Central Language Hypothesis in the Decision-Making Process

2016 ◽  
pp. 66-83 ◽  
Author(s):  
Duygu Buğa
Keyword(s):  
Decision Making ◽  
Theoretical Framework ◽  
Future Research ◽  
Limbic Cortex ◽  
Decision Making Process ◽  
Discussion Section ◽  
Research Directions ◽  
Additional Information ◽  
Future Research Directions ◽  
The Brain

The purpose of this chapter is to explore the potential connection between neuroeconomics and the Central Language Hypothesis (CLH) which refers to the language placed within the subconscious mind of an individual. The CLH forwards that in the brains of bilingual and multilingual people, one language is more suppressive as it dominates reflexes, emotions, and senses. This central language (CL) is located at the centre of the limbic cortex of the brain. Therefore, when there is a stimulus on the limbic cortex (e.g., fear, anxiety, sadness), the brain produces the central language. The chapter begins with an Introduction followed by a Theoretical Framework. The next section discusses the neurolinguistic projection of the central language and includes the survey and the results used in this study. The Discussion section provides additional information regarding the questionnaire and the CLH, followed by Future Research Directions, Implications, and finally the Conclusion.

Probability and the Single Neuron: Decisions, Uncertainty and the Brain: The Science of Neuroeconomics, by P.-W. Glimcher. 2003. Cambridge, MA: MIT Press. 375 pp., $40.00.

2004 ◽  
Vol 10 (7) ◽  
pp. 1027-1029
Author(s):  
M. Kinsbourne
Keyword(s):  
Decision Making ◽  
Single Neuron ◽  
Prior Probability ◽  
Inclusive Fitness ◽  
Neural Circuitry ◽  
Inferior Parietal Lobule ◽  
Mathematical Methods ◽  
Economic Problems ◽  
Neural Substrate ◽  
The Brain

In an uncertain world, people and other animals make their living by predicting which of alternative courses of action is likely to yield the best return. For humans the return might take many forms, such as material, financial, social, or esthetic, but the underlying currency involved for any species is “inclusive fitness,” the rate at which an animal's genes are propagated. Professor Glimcher demonstrates that Economics methods are applicable to decision-making under conditions of uncertainty, both at the behavioral and the neuronal level. This approach has been called neuro-economics, although “econometrics” characterizes it more precisely. Econometrics is the application of statistical and mathematical methods in the field of economics to test and quantify economic theories and the solution to economic problems. Specifically, individuals' decision-making benefits from knowing how likely a response is to be reinforced, and knowing the reinforcement's value. Even single neurons are sensitive to these variables. Glimcher reaches beyond the heavily studied neural substrate for sensation and response to predictive neural circuitry that factors in the prior probability of reward, and its expected value. Indeed, he and his colleagues have identified neurons in monkey's inferior parietal lobule whose firing rates reflect both probability and value.

scholarly journals PathGame: Crowdsourcing Time-Constrained Human Solutions for the Travelling Salesperson Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Slaviša Dumnić ◽  
Đorđije Dupljanin ◽  
Vladimir Božović ◽  
Dubravko Ćulibrk
Keyword(s):  
Decision Making ◽  
Online Games ◽  
Initial Study ◽  
Decision Making Process ◽  
Human Decision ◽  
Travelling Salesperson Problem

Human strategies for solving the travelling salesperson problem (TSP) continue to draw the attention of the researcher community, both to further understanding of human decision-making and inspiration for the design of automated solvers. Online games represent an efficient way of collecting large amounts of human solutions to the TSP, and PathGame is a game focusing on non-Euclideanclosed-form TSP. To capture the instinctive decision-making process of the users, PathGame requires users to solve the problem as quickly as possible, while still favouring more efficient tours. In the initial study presented here, we have used PathGame to collect a dataset of over 16,000 tours, containing over 22,000,000 destinations. Our analysis of the data revealed new insights related to ways in which humans solve TSP and the time it takes them when forced to solve TSPs of large complexity quickly.

scholarly journals Effects of a Social Robot's Autonomy and Group Orientation on Human Decision-Making

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Pei-Luen Patrick Rau ◽  
Ye Li ◽  
Jun Liu
Keyword(s):  
College Students ◽  
Decision Making ◽  
Laboratory Experiment ◽  
Social Robot ◽  
Decision Making Process ◽  
Robot Design ◽  
Group Orientation ◽  
Intelligent Robots ◽  
Robot Systems ◽  
Human Decision

Social attributes of intelligent robots are important for human-robot systems. This paper investigates influences of robot autonomy (i.e., high versus low) and group orientation (i.e., ingroup versus outgroup) on a human decision-making process. We conducted a laboratory experiment with 48 college students and tested the hypotheses with MANCOVA. We find that a robot with high autonomy has greater influence on human decisions than a robot with low autonomy. No significant effect is found on group orientation or on the interaction between group orientation and autonomy level. The results provide implications for social robot design.

scholarly journals A Review of ‘Brain Computation as Hierarchical Abstraction’

2016 ◽  
Vol 9 (2) ◽  
pp. 293-300
Author(s):  
Bodo Herzog
Keyword(s):  
Social Sciences ◽  
Decision Making ◽  
Computational Neuroscience ◽  
Daily Life ◽  
Book Series ◽  
Brain Functions ◽  
Interdisciplinary Dialogue ◽  
Computational Aspects ◽  
Hierarchical Abstraction ◽  
The Brain

AbstractThis article is a review of the book ‘Brain Computation As Hierarchical Abstraction’ by Dana H. Ballard published by MIT press in 2015. The book series computational neuroscience familiarizes the reader with the computational aspects of brain functions based on neuroscientific evidence. It provides an excellent introduction of the functioning, i.e. the structure, the network and the routines of the brain in our daily life. The final chapters even discuss behavioral elements such as decision-making, emotions and consciousness. These topics are of high relevance in other sciences such as economics and philosophy. Overall, Ballard’s book stimulates a scientifically well-founded debate and, more importantly, reveals the need of an interdisciplinary dialogue towards social sciences.

scholarly journals Stochastic representation decision theory: How probabilities and values are entangled dual characteristics in cognitive processes

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243661
Author(s):  
Giuseppe M. Ferro ◽  
Didier Sornette
Keyword(s):  
Decision Making ◽  
Decision Maker ◽  
Time Pressure ◽  
Rational Expectation ◽  
Weighting Function ◽  
Objective Probability ◽  
Decision Making Process ◽  
Stochastic Representation ◽  
Human Decision ◽  
Expectation Theory

Humans are notoriously bad at understanding probabilities, exhibiting a host of biases and distortions that are context dependent. This has serious consequences on how we assess risks and make decisions. Several theories have been developed to replace the normative rational expectation theory at the foundation of economics. These approaches essentially assume that (subjective) probabilities weight multiplicatively the utilities of the alternatives offered to the decision maker, although evidence suggest that probability weights and utilities are often not separable in the mind of the decision maker. In this context, we introduce a simple and efficient framework on how to describe the inherently probabilistic human decision-making process, based on a representation of the deliberation activity leading to a choice through stochastic processes, the simplest of which is a random walk. Our model leads naturally to the hypothesis that probabilities and utilities are entangled dual characteristics of the real human decision making process. It predicts the famous fourfold pattern of risk preferences. Through the analysis of choice probabilities, it is possible to identify two previously postulated features of prospect theory: the inverse S-shaped subjective probability as a function of the objective probability and risk-seeking behavior in the loss domain. It also predicts observed violations of stochastic dominance, while it does not when the dominance is “evident”. Extending the model to account for human finite deliberation time and the effect of time pressure on choice, it provides other sound predictions: inverse relation between choice probability and response time, preference reversal with time pressure, and an inverse double-S-shaped probability weighting function. Our theory, which offers many more predictions for future tests, has strong implications for psychology, economics and artificial intelligence.

Decision-Making Cognitive Process

2018 ◽  
Vol 3 (1) ◽  
pp. 1-12
Author(s):  
Thais Spiegel ◽  
Ana Carolina P V Silva
Keyword(s):  
Decision Making ◽  
Cognitive Process ◽  
Decision Makers ◽  
Time Constraints ◽  
Decision Making Process ◽  
Cognitive Limitations ◽  
The World ◽  
Human Decision ◽  
Per Se ◽  
Rationality Theory

In the study of decision-making, the classical view of behavioral appropriateness or rationality was challenged by neuro and psychological reasons. The “bounded rationality” theory proposed that cognitive limitations lead decision-makers to construct simplified models for dealing with the world. Doctors' decisions, for example, are made under uncertain conditions, as without knowing precisely whether a diagnosis is correct or whether a treatment will actually cure a patient, and often under time constraints. Using cognitive heuristics are neither good nor bad per se, if applied in situations to which they have been adapted to be helpful. Therefore, this text contextualizes the human decision-making perspective to find descriptions that adhere more closely to the human decision-making process. Then, based on a literature review of cognition during decision-making, particularly in healthcare context, it addresses a model that identifies the roles of attention, categorization, memory, emotion, and their inter-relations, during the decision-making process.

The Role of Executive Function in Shaping Reinforcement Learning

2020 ◽  
Author(s):  
Milena Rmus ◽  
Samuel McDougle ◽  
Anne Collins
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Instrumental Behavior ◽  
Complex Environments ◽  
Neural Computations ◽  
Human Decision ◽  
Brain And Behavior ◽  
And Behavior ◽  
The Brain

Reinforcement learning (RL) models have advanced our understanding of how animals learn and make decisions, and how the brain supports some aspects of learning. However, the neural computations that are explained by RL algorithms fall short of explaining many sophisticated aspects of human decision making, including the generalization of learned information, one-shot learning, and the synthesis of task information in complex environments. Instead, these aspects of instrumental behavior are assumed to be supported by the brain’s executive functions (EF). We review recent findings that highlight the importance of EF in learning. Specifically, we advance the theory that EF sets the stage for canonical RL computations in the brain, providing inputs that broaden their flexibility and applicability. Our theory has important implications for how to interpret RL computations in the brain and behavior.

Sign in / Sign up

Export Citation Format

Share Document