25 resources related to Neuroeconomics
- Topics related to Neuroeconomics
- IEEE Organizations related to Neuroeconomics
- Conferences related to Neuroeconomics
- Periodicals related to Neuroeconomics
- Most published Xplore authors for Neuroeconomics
Biomedical signal processing, Biomedical imaging and image processing, Bioinstrumentation, Bio-robotics and biomechanics, Biosensors and Biomaterials, Cardiovascular and respiratory systems engineering, Cellular and Tissue Engineering, Healthcare information systems, Human machine/computer interface, Medical device design, Neural and rehabilitation engineering, Technology commercialization, industry, education, and society, Telemedicine, Therapeutic and diagnostics systems, Recent advancements in biomedical engineering
The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications.
Imaging methods applied to living organisms with emphasis on innovative approaches that use emerging technologies supported by rigorous physical and mathematical analysis and quantitative evaluation of performance.
Rehabilitation aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation, and hardware and software applications for rehabilitation engineering and assistive devices.
2017 25th Signal Processing and Communications Applications Conference (SIU), 2017
In neuroeconomics experiments many ocular artifacts are encountered during long trial durations. In this study, results from algorithms used to remove artifacts in EEG measurements are presented. The study consists of three parts. In the first part, EEG signals were band-pass filtered to remove high frequency noise and low frequency drift. Next, the artifacts were removed by using traditional regression ...
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2008
The basic reciprocity between individual parts and collective organization constitutes a key scientific question spanning the biological and social sciences. Such reciprocity is accompanied by the absence of direct linkages between levels of description giving rise to what is often referred to as the <i>aggregation</i> or <i>nonequivalence</i> <i>problem</i> between levels of analysis. This issue is encountered both in neuroscience and ...
The 2013 International Joint Conference on Neural Networks (IJCNN), 2013
Behavioral economics and neuroeconomics concern how humans process multiple alternatives to make their decisions, and propose how discoveries about how the brain works can inform models of economic behavior. This lecture will survey how results about cooperative-competitive and cognitive-emotional dynamics that were discovered to better understand how brains control behavior can shed light on issues of importance in economics, including ...
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2008
Recently, a rapidly growing approach within consumer research has developed under the label of ldquoconsumer neuroscience.rdquo Its goal is to use insights and methods from neuroscience to enhance the understanding of consumer behavior. In this paper we aim to provide an overview of questions of interest to consumer researchers, to present initial research findings, and to outline potential implications for ...
The 5th 2012 Biomedical Engineering International Conference, 2012
This paper proposes an investigation on classification of the positive and negative emotions via the use of electroencephalogram (EEG). EEG bandpowers are extracted as the feature of interest. Two simple decision rules to classify positive and negative emotions are proposed, i.e. 1) using both the left and right frontal information and 2) using only one side of the left or ...
In neuroeconomics experiments many ocular artifacts are encountered during long trial durations. In this study, results from algorithms used to remove artifacts in EEG measurements are presented. The study consists of three parts. In the first part, EEG signals were band-pass filtered to remove high frequency noise and low frequency drift. Next, the artifacts were removed by using traditional regression method and independent component analysis (ICA). Finally, the performances of the two artifact removal methods were compared. Although artifacts were suppressed better by ICA than regression, ICA caused decrease in root mean square (RMS) values of the non-artifactual parts of some channels.
The basic reciprocity between individual parts and collective organization constitutes a key scientific question spanning the biological and social sciences. Such reciprocity is accompanied by the absence of direct linkages between levels of description giving rise to what is often referred to as the <i>aggregation</i> or <i>nonequivalence</i> <i>problem</i> between levels of analysis. This issue is encountered both in neuroscience and economics. So far, in spite of being identified and extensively discussed in various (other) scientific fields, the problem of understanding the nature of the interactions and coordination dynamics between individual (neuron ~ agent) and collective (neural networks ~ population of humans) behaviors has received little, if any attention in the growing field of <i>neuroeconomics</i>. The present contribution focuses on bringing a theoretical perspective to the interpretation of experiments recently published in this field and addressing how the concepts and methods of <i>coordination</i> <i>dynamics</i> may impact future research. First, we very briefly discuss the links between biology and economics. Second, we address the nonequivalence problem between different levels of analysis and the concept of <i>reciprocal</i> <i>causality</i>. Third, neuroeconomics studies that investigate the neural underpinnings of social decision making in the context of two economic games (trust and ultimatum) are reviewed to highlight issues that arise when experimental results exist at multiple scales of observation and description. Finally, in the last two sections, we discuss how coordination dynamics might provide novel routes to studying and modelling the relation between brain activity and decision making.
Behavioral economics and neuroeconomics concern how humans process multiple alternatives to make their decisions, and propose how discoveries about how the brain works can inform models of economic behavior. This lecture will survey how results about cooperative-competitive and cognitive-emotional dynamics that were discovered to better understand how brains control behavior can shed light on issues of importance in economics, including results about the voting paradox, how to design stable economic markets, irrational decision making under risk (Prospect Theory), probabilistic decision making, preferences for previously unexperienced alternatives over rewarded experiences, and bounded rationality.
Recently, a rapidly growing approach within consumer research has developed under the label of ldquoconsumer neuroscience.rdquo Its goal is to use insights and methods from neuroscience to enhance the understanding of consumer behavior. In this paper we aim to provide an overview of questions of interest to consumer researchers, to present initial research findings, and to outline potential implications for consumer research. In order to do so, we first discuss the term ldquoconsumer neurosciencerdquo and give a brief description of recently discussed issues in consumer research. We then provide a review and short description of initial empirical evidence from past studies in consumer neuroscience. Next, we present an example of how consumer research or, more specifically, customer loyalty research, may benefit from the consumer neuroscience approach. The paper concludes with a discussion of potential implications and suggestions for future research in the nascent field of consumer neuroscience.
This paper proposes an investigation on classification of the positive and negative emotions via the use of electroencephalogram (EEG). EEG bandpowers are extracted as the feature of interest. Two simple decision rules to classify positive and negative emotions are proposed, i.e. 1) using both the left and right frontal information and 2) using only one side of the left or right frontal information. First decision reports low accuracy while the second decision rule can achieve higher accuracy between 80 to 90%. This can be concluded that the proposed method is possible for the real-time emotion classification in neuroeconomics.
One of the most fascinating and promising research areas in economics is the integration of the following three fields of economics: experimental economics, computational economics and neuroeconomics. Due to their methodologically interdisciplinary nature, the development of each of the three should interest computer scientists and engineering people. The relationship among experimental economics, agent-based computational economics and neuroeconomics is, in essence, a relationship between human agents and software agents. Software agents can be regarded as the effective abstraction or model of human agents "which we learn from experimental economics or neural economics.
The standard modeling framework in functional magnetic resonance imaging (fMRI) is predicated on assumptions of linearity, time invariance and stationarity. These assumptions are rarely checked because doing so requires specialized software, although failure to do so can lead to bias and mistaken inference. Identifying model violations is an essential but largely neglected step in standard fMRI data analysis. Using Lagrange multiplier testing methods we have developed simple and efficient procedures for detecting model violations such as nonlinearity, nonstationarity and validity of the common double gamma specification for hemodynamic response. These procedures are computationally cheap and can easily be added to a conventional analysis. The test statistic is calculated at each voxel and displayed as a spatial anomaly map which shows regions where a model is violated. The methodology is illustrated with a large number of real data examples.
We investigated brain activity during the observation of TV commercials by tracking the cortical activity and the functional connectivity changes in normal subjects. The aim was to elucidate if the TV commercials that were remembered by the subjects several days after their first observation elicited particular brain activity and connectivity compared with those generated during the observation of TV commercials that were quickly forgotten. High- resolution electroencephalogram (EEG) recordings were performed in a group of healthy subjects and the cortical activity during the observation of TV commercials was evaluated in several regions of interest coincident with the Brodmann areas (BAs). The patterns of cortical connectivity were obtained in the four principal frequency bands, Theta (3-7 Hz), Alpha (8-12 Hz), Beta (13-30 Hz), Gamma (30-40 Hz) and the directed influences between any given pair of the estimated cortical signals were evaluated by use of a multivariate spectral technique known as partial directed coherence. The topology of the cortical networks has been identified with tools derived from graph theory. Results suggest that the cortical activity and connectivity elicited by the viewing of the TV commercials that were remembered by the experimental subjects are markedly different from the brain activity elicited during the observation of the TV commercials that were forgotten. In particular, during the observation of the TV commercials that were remembered, the amount of cortical spectral activity from the frontal areas (BA 8 and 9) and from the parietal areas (BA 5, 7, and 40) is higher compared with the activity elicited by the observation of TV commercials that were forgotten. In addition, network analysis suggests a clear role of the parietal areas as a target of the incoming flow of information from all the other parts of the cortex during the observation of TV commercials that have been remembered. The techniques presented here shed new light on all the cortical networks and their behavior during the memorization of TV commercials. Such techniques could also be relevant in neuroeconomics and neuromarketing for the investigation of the neural substrates subserving other decision-making and recognition tasks.
Recent research in neuroeconomics reveals that people show different behavior and lower activation of brain regions associated with mentalizing (i.e., the inference of other's mental states) when engaged in decision making tasks with a computer, when compared to a human. These findings are important for affective computing because they suggest people's decision making might be influenced differently according to whether they believe the emotional expressions shown by a computer are being generated by a computer algorithm or a human. To test this, we had people engage in a social dilemma (Experiment 1) or a negotiation (Experiment 2) with virtual humans that were either agents (i.e., controlled by computers) or avatars (i.e., controlled by humans). The results show a clear agency effect: in Experiment 1, people cooperated more with virtual humans that showed facial cooperative displays (e.g., joy after mutual cooperation) rather than competitive displays (e.g., joy when the participant was exploited) but, the effect was only significant with avatars, in Experiment 2, people conceded more to an angry than a neutral virtual human but, once again, the effect was only significant with avatars.
This presentation concerns some idea of what could be done, in the author's view, to help make Wang's cognitive informatics a powerful and viable source of tools and techniques for solving various real life problems. First, we give a brief account of cognitive informatics meant as a multidisciplinary field within informatics, or computer science, that is based on results of cognitive and information sciences, and which deals with human information processing mechanisms and processes and their decision theoretic, engineering, etc. applications in broadly perceived computing. We focus on its purpose, i.e. to develop and implement technologies to facilitate and extend the information acquisition, comprehension and processing capacity of humans. Emphasis is on underlying processes in the brain. However, we advocate an extended approach in which though the very cognitive informatics is the foundation, as those processes in the brain are crucial, some sort of an “outer” cognitive informatics is needed which explicitly makes reference not what proceeds “internally” in the brain, because we do not “see” this, but “externally”, i.e. what people can see, judge, evaluate, etc., and what is clearly a result of cognitive information specific processes in the brain. This line of reasoning is in line with the very essence of comprehension, memorizing, learning, choice and decision making, satisfaction with partial truth, allowing for not perfect solutions, etc. dealt with using tools and techniques derived from many areas like psychology, behavioral science, neuroscience, artificial intelligence, linguistics, neuroeconomics etc. In our case, we will concentrate on some cognitive informatics type elements that mostly have been inspired by psychology and behavioral sciences, as our problem is inherently related to human judgments and perceptions, but we will mentioned some inspirations from neuroscience, notably along the lines of neuroeconomics. Cognitive informatics constitutes a foundation of its related new field, cognitive computing, which is basically a new direction in broadly perceived intelligent computing and systems that synergistically combines results from many areas, e.g., information science, computational sciences, computer science, artificial and computational intelligence, cybernetics, systems science, cognitive science, (neuro)psychology, brain science, linguistics, etc. to just mention a few. We try to show on an example of a dynamic systems modeling, more specifically scenario based regional development planning, that cognitive computing can provide new conceptual and implementation vistas. Basically, we consider a region that is characterized by 7 life quality indicators related to economic, social, environmental, etc. qualities, which evolve over some planning horizon due to some investments, mostly by some regional or governmental agencies. There are some scenarios of investment levels over the planning horizon, meant for the development of the particular life quality indexes, and some desired levels of these indexes, both objective, i.e. set by authorities, and subjective, i.e. perceived by the inhabitant groups. As a result of a particular investment scenario, the life quality indexes evolve over the planning horizon, and their temporal evolution is evaluated by the authorities and inhabitants. This evaluation has both an objective, i.e. against the “officially” set thresholds, and subjective, i.e. as perceived by various humans and their groups. Basically, we employ Kacprzyk's fuzzy dynamic programming based approach to the modeling and planning/programming of sustainable regional development, with soft constraints and goals, but we advocated a more sophisticated assessment of variability, stability, balancedness of consecutive investments. In this process we try to develop evaluation measures, and then the optimization type model using concepts that can be effectively and efficiently handled by cognitive computing, notably the inclusion of the so called decision making and behavioral biases, biases in probability and belief, social biases, memory errors, etc. Moreover, we strongly reflect the so called status quo and minimal change biases. By using many results from social sciences, psychology, behavioral economics, neuroeconomics, etc. on human judgments and human centric evaluations, we augment a traditional purely effectiveness and efficiency oriented analysis by a more sophisticated analysis of effects of variability of temporal evolution of some life quality indicators on the human perception of its goodness. The model presented, which has been employed for years as part of large mathematical modeling projects for sustainable regional development in many regions in Asia and Europe, is illustrated on an example with scenario analysis for a rural region plagued by social and economic difficulties in which subsidies should properly be distributed over time to obtain a best overall socioeconomic effect. In this talk we present the model in a different perspective, based first on the basic Wang's cognitive informatics and its Wang and Ruhe's decision making application, and then based on new, more comprehensive cognitive computing. We show that this provides a novel insight.
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