Metabolic state alters economic decision making under risk in humans

Background: Animals' attitudes to risk are profoundly influenced by metabolic state (hunger and baseline energy stores). Specifically, animals often express a preference for risky (more variable) food sources when below a metabolic reference point (hungry), and safe (less variable) food sources when sated. Circulating hormones report the status of energy reserves and acute nutrient intake to widespread targets in the central nervous system that regulate feeding behaviour, including brain regions strongly implicated in risk and reward based decision-making in humans. Despite this, physiological influences per se have not been considered previously to influence economic decisions in humans. We hypothesised that baseline metabolic reserves and alterations in metabolic state would systematically modulate decision-making and financial risk-taking in humans. Methodology/Principal Findings: We used a controlled feeding manipulation and assayed decision-making preferences across different metabolic states following a meal. To elicit risk-preference, we presented a sequence of 200 paired lotteries, subjects' task being to select their preferred option from each pair. We also measured prandial suppression of circulating acyl-ghrelin (a centrally-acting orexigenic hormone signalling acute nutrient intake), and circulating leptin levels (providing an assay of energy reserves). We show both immediate and delayed effects on risky decision-making following a meal, and that these changes correlate with an individual's baseline leptin and changes in acyl-ghrelin levels respectively. Conclusions/Significance: We show that human risk preferences are exquisitely sensitive to current metabolic state, in a direction consistent with ecological models of feeding behaviour but not predicted by normative economic theory. These substantive effects of state changes on economic decisions perhaps reflect shared evolutionarily conserved neurobiological mechanisms. We suggest that this sensitivity in human risk-preference to current metabolic state has significant implications for both real-world economic transactions and for aberrant decision-making in eating disorders and obesity

E-commerce adoption by SMEs in developing countries: evidence from Indonesia

This study aims to provide an overview of e-commerce adoption by SMEs in developing countries and, in particular, the extent of the adoption of e-commerce by Indonesian SMEs. It identifies the e-commerce benefits realized by these SMEs and investigates the relationship between the levels of e-commerce adoption and the benefits thus realized. The study was motivated by the limited studies related to e-commerce adoption by SMEs, especially in developing countries. In addition, it seems that most e-commerce studies are focused more on upstream issues: to see the factors that facilitate, or barriers faced regarding e-commerce adoption, rather than downstream issues: to see post-adoption benefits. This certainly limits our understanding about e-commerce adoption by SMEs in developing countries, as well as the post-adoption benefits of e-commerce. Indonesia was chosen as the place in which to conduct the study. A survey of 292 SMEs shows that the majority of them are still at an early stage in their adoption of e-commerce. Their use of e-commerce is dominated by marketing and purchasing and procurement activities. “Extending market reach”, “increased sales”, “improved external communication”, “improved company image”, “improved speed of processing”, and “increased employee productivity” are reported as the top six e-commerce benefits perceived by these SMEs. This study also shows that SMEs at the higher level of e-commerce adoption experience greater e-commerce benefits than those at other levels of adoption

Computer-Aided Evaluation of Breast MRI for the Residual Tumor Extent and Response Monitoring in Breast Cancer Patients Receiving Neoadjuvant Chemotherapy

Objective: To evaluate the accuracy of a computer-aided evaluation program (CAE) of breast MRI for the assessment of residual tumor extent and response monitoring in breast cancer patients receiving neoadjuvant chemotherapy. Materials and Methods: Fifty-seven patients with breast cancers who underwent neoadjuvant chemotherapy before surgery and dynamic contrast enhanced MRI before and after chemotherapy were included as part of this study. For the assessment of residual tumor extent after completion of chemotherapy, the mean tumor diameters measured by radiologists and CAE were compared to those on histopathology using a paired student t-test. Moreover, the agreement between unidimensional (1D) measurement by radiologist and histopathological size or 1D measurement by CAE and histopathological size was assessed using the Bland-Altman method. For chemotherapy monitoring, we evaluated tumor response through the change in the 1D diameter by a radiologist and CAE and three-dimensional (3D) volumetric change by CAE based on Response Evaluation Criteria in Solid Tumors (RECIST). Agreement between the 1D response by the radiologist versus the 1D response by CAE as well as by the 3D response by CAE were evaluated using weighted kappa (k) statistics. Results: For the assessment of residual tumor extent after chemotherapy, the mean tumor diameter measured by radiologists (2.0 ± 1.7 cm) was significantly smaller than the mean histological diameter (2.6 ± 2.3 cm) (p = 0.01), whereas, no significant difference was found between the CAE measurements (mean = 2.2 ± 2.0 cm) and histological diameter (p = 0.19). The mean difference between the 1D measurement by the radiologist and histopathology was 0.6 cm (95% confidence interval: -3.0, 4.3), whereas the difference between CAE and histopathology was 0.4 cm (95% confidence interval: -3.9, 4.7). For the monitoring of response to chemotherapy, the 1D measurement by the radiologist and CAE showed a fair agreement (k = 0.358), while the 1D measurement by the radiologist and 3D measurement by CAE showed poor agreement (k = 0.106). Conclusion: CAE for breast MRI is sufficiently accurate for the assessment of residual tumor extent in breast cancer patients receiving neoadjuvant chemotherapy. However, for the assessment of response to chemotherapy, the assessment by the radiologist and CAE showed a fair to poor agreement

Optimal Compensation for Temporal Uncertainty in Movement Planning

Motor control requires the generation of a precise temporal sequence of control signals sent to the skeletal musculature. We describe an experiment that, for good performance, requires human subjects to plan movements taking into account uncertainty in their movement duration and the increase in that uncertainty with increasing movement duration. We do this by rewarding movements performed within a specified time window, and penalizing slower movements in some conditions and faster movements in others. Our results indicate that subjects compensated for their natural duration-dependent temporal uncertainty as well as an overall increase in temporal uncertainty that was imposed experimentally. Their compensation for temporal uncertainty, both the natural duration-dependent and imposed overall components, was nearly optimal in the sense of maximizing expected gain in the task. The motor system is able to model its temporal uncertainty and compensate for that uncertainty so as to optimize the consequences of movement

A Rescorla-Wagner Drift-Diffusion Model of Conditioning and Timing

Computational models of classical conditioning have made significant contributions to the theoretic understanding of associative learning, yet they still struggle when the temporal aspects of conditioning are taken into account. Interval timing models have contributed a rich variety of time representations and provided accurate predictions for the timing of responses, but they usually have little to say about associative learning. In this article we present a unified model of conditioning and timing that is based on the influential Rescorla-Wagner conditioning model and the more recently developed Timing Drift-Diffusion model. We test the model by simulating 10 experimental phenomena and show that it can provide an adequate account for 8, and a partial account for the other 2. We argue that the model can account for more phenomena in the chosen set than these other similar in scope models: CSC-TD, MS-TD, Learning to Time and Modular Theory. A comparison and analysis of the mechanisms in these models is provided, with a focus on the types of time representation and associative learning rule used

Observational Constraints on the Common Envelope Phase

The common envelope phase was first proposed more than forty years ago to explain the origins of evolved, close binaries like cataclysmic variables. It is now believed that the phase plays a critical role in the formation of a wide variety of other phenomena ranging from type Ia supernovae through to binary black holes, while common envelope mergers are likely responsible for a range of enigmatic transients and supernova imposters. Yet, despite its clear importance, the common envelope phase is still rather poorly understood. Here, we outline some of the basic principles involved, the remaining questions as well as some of the recent observational hints from common envelope phenomena - namely planetary nebulae and luminous red novae - which may lead to answering these open questions.Comment: 29 pages, 8 figures. To appear in the book "Reviews in Frontiers of Modern Astrophysics: From Space Debris to Cosmology" (eds. Kabath, Jones and Skarka; publisher Springer Nature) funded by the European Union Erasmus+ Strategic Partnership grant "Per Aspera Ad Astra Simul" 2017-1-CZ01-KA203-03556