Search Results (14)

In the field of neuroeconomics, the assessment of cognitive workload is a crucial issue with significant implications for real-world applications. Previous research has made progress in task-based germane cognitive load classification, but decentralized studies focusing on task-independent assessment have often produced less than optimal results. In this study, we present a stacked graph attention convolutional networks (SGATCNs) model to tackle the challenges related to task-independent cognitive workload assessment using EEG spatial information. The model employs the differential entropy (DE) and power spectral density (PSD) features of each EEG channel across four frequency bands (delta, theta, alpha, and beta) as node information. For the construction of the network structure, phase-locked values (PLVs), phase-lag indices (PLIs), Pearson correlation coefficients (PCCs), and mutual information (MI) are utilized and evaluated to generate a functional brain network. Specifically, the model aggregates spatial information on the dynamic map by stacking the graph attention layers and utilizes the convolution module to extract the frequency domain information from between the networks under each frequency band. We conducted a cognitive workload experiment with 15 subjects and selected three representative psychological experimental task paradigms (N-back, mental arithmetic, and Sternberg) to induce different levels of cognitive workload (low, medium, and high). Our framework achieved an average accuracy of 65.11% in recognizing the task-independent cognitive workload across the three scenarios. Full article

The brain receives information via sensory inputs through the peripheral nervous system and stores a small subset as memories within the central nervous system. Short-term, working memory is present in the hippocampus whereas long-term memories are distributed within neural networks throughout the brain. Elegant studies on the mechanisms for memory storage and the neuroeconomic formulation of human decision making have been recognized with Nobel Prizes in Physiology or Medicine and in Economics, respectively. There is a wide gap, however, in our understanding of how memories of disparate bits of information translate into “knowledge”, and the neural mechanisms by which knowledge is used to make decisions. I propose that the conceptualization of a “knowledge network” for the creation, storage and recall of knowledge is critical to start bridging this gap. Knowledge creation involves value-driven contextualization of memories through cross-validation via certainty-seeking behaviors, including rumination or reflection. Knowledge recall, like memory, may occur via oscillatory activity that dynamically links multiple networks. These networks may show correlated activity and interactivity despite their presence within widely separated regions of the nervous system, including the brainstem, spinal cord and gut. The hippocampal–amygdala complex together with the entorhinal and prefrontal cortices are likely components of multiple knowledge networks since they participate in the contextual recall of memories and action selection. Sleep and reflection processes and attentional mechanisms mediated by the habenula are expected to play a key role in knowledge creation and consolidation. Unlike a straightforward test of memory, determining the loci and mechanisms for the storage and recall of knowledge requires the implementation of a naturalistic decision-making paradigm. By formalizing a neuroscientific concept of knowledge networks, we can experimentally test their functionality by recording large-scale neural activity during decision making in awake, naturally behaving animals. These types of studies are difficult but important also for advancing knowledge-driven as opposed to big data-driven models of artificial intelligence. A knowledge network-driven understanding of brain function may have practical implications in other spheres, such as education and the treatment of mental disorders. Full article

Neuroeconomics merges neuroscience, economics, and psychology to investigate the neural basis of decision making. Decision making involves assessing outcomes with subjective value, shaped by emotions and experiences, which are crucial in economic decisions. Functional MRI (fMRI) reveals key areas of the brain, including the ventro-medial prefrontal cortex, that are involved in subjective value representation. Collaborative interdisciplinary efforts are essential for advancing the field of neuroeconomics, with implications for clinical interventions and policy design. This review explores subjective value in neuroeconomics, highlighting brain regions identified through fMRI studies. Full article

The probabilistic reward task (PRT) has identified reward learning impairments in those with major depressive disorder (MDD), as well as anhedonia-specific reward learning impairments. However, attempts to validate the anhedonia-specific impairments have produced inconsistent findings. Thus, we seek to determine whether the Reward Behavior Disengagement (RBD), our proposed economic augmentation of PRT, differs between MDD participants and controls, and whether there is a level at which RBD is high enough for depressed participants to be considered objectively disengaged. Data were gathered as part of the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a double-blind, placebo-controlled clinical trial of antidepressant response. Participants included 195 individuals with moderate to severe MDD (Quick Inventory of Depressive Symptomatology (QIDS–SR) score ≥ 15), not in treatment for depression, and with complete PRT data. Healthy controls (n = 40) had no history of psychiatric illness, a QIDS–SR score < 8, and complete PRT data. Participants with MDD were treated with sertraline or placebo for 8 weeks (stage I of the EMBARC trial). RBD was applied to PRT data using discriminant analysis, and classified MDD participants as reward task engaged (n = 137) or reward task disengaged (n = 58), relative to controls. Reward task engaged/disengaged groups were compared on sociodemographic features, reward–behavior, and sertraline/placebo response (Hamilton Depression Rating Scale scores). Reward task disengaged MDD participants responded only to sertraline, whereas those who were reward task engaged responded to sertraline and placebo (F(1293) = 4.33, p = 0.038). Reward task engaged/disengaged groups did not differ otherwise. RBD was predictive of reward impairment in depressed patients and may have clinical utility in identifying patients who will benefit from antidepressants. Full article

Theoretical accounts on social decision-making under uncertainty postulate that individual risk preferences are context dependent. Generalization of models of decision-making to dyadic interactions in the personal health context remain to be experimentally addressed. In economic utility-based models, interactive behavioral games provide a framework to investigate probabilistic learning of sequential reinforcement. Here, we model an economic trust game in the context of a chronic disease (Diabetes Type 1) which involves iterated daily decisions in complex social contexts. Ninety-one patients performed experimental trust games in both economic and health settings and were characterized by a multiple self-report set of questionnaires. We found that although our groups can correctly infer pay-off contingencies, they behave differently because patients with a biological profile of preserved glycemic control show adaptive choice behavior both in economic and health domains. On the other hand, patients with a biological profile of loss of glycemic control presented a contrasting behavior, showing non-adaptive choices on both contexts. These results provide a direct translation from neuroeconomics to decision-making in the health domain and biological risk profiles, in a behavioral setting that requires difficult and self-consequential decisions with health impact. Our findings also provide a contextual generalization of mechanisms underlying individual decision-making under uncertainty. Full article

The goal of this study is to determine the level of adaptation of agro-industrial cooperatives of small producers of alternative crops, and it considers the hierarchy of patterns to evaluate their systemic responses to accelerated change following the COVID-19 pandemic by evaluating the risk of their structures adapting to the digital environment. With a total of (n = 90) volunteer responders, the study is experimental, transactional, descriptive, and correlational, with a control group (CENFROCAFE) and an experimental group (ACEPAT) (24 producer partners, 14 producer managers, and 7 employees for each cooperative). In Step 1 (SOFT aspect), it measures the organizational memory (OM) of Y0 = 0.32 in the (control group) and Y1 = 0.59 in the (experimental group) by measuring hidden plots in the formal and informal interrelations of its members with the correlation of the holistic competencies of innovation. In Stage 2 (HARD aspect), the impact of the digital operational risk (DOR) is measured in the adaptation of the organization structure, which results in the control group with a Digital Operational Risk (DOR) = (3.4), which is “High” and greater than the experimental group with DOR = (3.3), which is “Moderate”. In conclusion, Hypothesis 1 is met with a greater adaptation of the experimental group, greater organizational memory, and lower digital operational risk, which reflects that the memory of the organization would reflect the temporal memories of the human brains of its members, and that, in the same way, its behavior could be predicted linearly. Full article

This article focuses on analyzing the neuroeconomic decisions in cocoa producers and the impact of this methodology on the productivity of fair trade organic cocoa producers on the population of Nuevo Bambamarca, province of Tocache, Peru. The main elements of the methodology are the incentive phase of associativity, the alignment phase to macro trends, the prospecting phase of the country to be exported to, the prospecting phase of the type of niche market, the prospecting phase of fair participation, the innovation and design phase of the prototype, the standardization phase of the raw material technical specifications for collection, the strengthening phase the producer’s commitment, the learning phase of the producer in crop management, and the evaluation phase of productivity in the field. This research study is pre-experimental, cross-sectional, explanatory, and descriptive. The experimental group made up of 20 fair trade organic cocoa producers of the Cooperativa Agroindustrial Naranjillo obtained on average a profitability of 143 EUR per campaign higher than the control group made up of 20 producers of conventional cocoa that did not belong to the cooperative who obtained a loss of −642 EUR per campaign, even with the same purchase price of 1.92 EUR per kg for both cases during the 2011 campaign. It is concluded that Hypothesis 1 is met, it shows that the cooperative innovation methodology of prospecting for fair trade organic niche encourages the productivity of producers of the experimental group with respect to the control group. Full article

Financial bubbles are a result of aggregate irrational behavior and cannot be explained by standard economic pricing theory. Research in neuroeconomics can improve our understanding of their causes. We conducted an experiment in which 28 healthy subjects traded in a simulated market bubble, while scalp EEG was recorded using a low-cost, BCI-friendly desktop device with 14 electrodes. Independent component (IC) analysis was performed to decompose brain signals and the obtained scalp topography was used to cluster the ICs. We computed single-trial time-frequency power relative to the onset of stock price display and estimated the correlation between EEG power and stock price across trials using a general linear model. We found that delta band (1–4 Hz) EEG power within the left frontal region negatively correlated with the trial-by-trial stock prices including the financial bubble. We interpreted the result as stimulus-preceding negativity (SPN) occurring as a dis-inhibition of the resting state network. We conclude that the combination between the desktop-BCI-friendly EEG, the simulated financial bubble and advanced signal processing and statistical approaches could successfully identify the neural correlate of the financial bubble. We add to the neuroeconomics literature a complementary EEG neurometric as a bubble predictor, which can further be explored in future decision-making experiments. Full article

It is claimed that investment decision-making should rely on rational analyses based on facts and not emotions. However, trying to make money out of market forecasts can trigger all types of emotional responses. As the question on how investors decide remains controversial, we carried out an activation likelihood estimation (ALE) meta-analysis using functional magnetic resonance imaging (fMRI) studies that have reported whole-brain analyses on subjects performing an investment task. We identified the ventral striatum, anterior insula, amygdala and anterior cingulate cortex as being involved in this decision-making process. These regions are limbic-related structures which respond to reward, risk and emotional conflict. Our findings support the notion that investment choices are emotional decisions that take into account market information, individual preferences and beliefs. Full article

Disagreement is an essential element of science and life in general. The language of probabilities and statistics is often used to describe disagreements quantitatively. In practice, however, we want much more than that. We want disagreements to be resolved. This leaves us with a substantial knowledge gap, which is often perceived as a lack of practical intuition regarding probabilistic and statistical concepts. Here, we propose to address disagreements using the methods of financial economics. In particular, we show how a large class of disagreements can be transformed into investment opportunities. The expected financial performance of such investments quantifies the amount of disagreement in a tangible way. This provides intuition for statistical concepts such as the Rényi divergence, which becomes connected to the financial performance of optimized investments. Investment optimization takes into account individual opinions as well as attitudes towards risk. The result is a market-like social mechanism by which funds flow naturally to support a more accurate view. Such social mechanisms can help us with difficult disagreements (e.g., financial arguments concerning the future climate). In terms of scientific validation, we used the findings of independent neurophysiological experiments as well as our own research on the equity premium. Full article

Aquaponics is an innovative food production method that combines the production of aquatic organisms with plant production. This can have environmental advantages such as reducing land conversion and resource input and waste output through nutrient cycling. To support the dissemination of aquaponics, key stakeholders need to be appropriately informed about this production method, an aspect that has received little attention so far. In this pilot study, visual perception of information about aquaponics was explored using eye tracking combined with a questionnaire. The results show that people distinguish between aquaponics variants when evaluating aquaponics. A production system with a more natural appearance is preferred. Allocation of visual attention is linked to the specific information content and to the assessment of the naturalness of aquaponics production. The results of the present study could form a basis for further research, not only to make information about food production systems more appropriate but also to develop food production systems in a way that people become more aware of the sustainability aspects of production methods and its products. Full article

This paper develops a new methodology in order to study the role of dynamic expectations. Neither reference-point theories nor feedback models are sufficient to describe human expectations in a dynamic market environment. We use an interdisciplinary approach and demonstrate that expectations of non-learning agents are time-invariant and isotropic. On the contrary, learning enhances expectations. We uncover the “yardstick of expectations” in order to assess the impact of market developments on expectations. For the first time in the literature, we reveal new insights about the motion of dynamic expectations. Finally, the model is suitable for an AI approach and has major implications on the behaviour of market participants. Full article

Social neuroscience aims to understand the biological systems that underlie people’s thoughts, feelings and actions in light of the social context in which they operate. Over the past few decades, social neuroscience has captured the interest of scholars, practitioners, and experts in other disciplines, as well as the general public who more and more draw upon the insights and methods of social neuroscience to explain, predict and change behavior. With the popularity of the field growing, it has become increasingly important to consider the validity of social neuroscience findings as well as what questions it can and cannot address. In the present review article, we examine the contribution of social neuroscience to economics, health, and law, three domains with clear societal relevance. We address the concerns that the extrapolation of neuroscientific results to applied social issues raises within each of these domains, and we suggest guidelines and good practices to circumvent these concerns. Full article

This study experimentally examined why subjective probability for delayed reward decays non-exponentially (“hyperbolically”, i.e., q ˂ 1 in the q-exponential discount function) in humans. Our results indicate that nonlinear psychophysical time causes hyperbolic time-decay of subjective probability for delayed reward. Implications for econophysics and neuroeconomics are discussed. Full article