Cereal Grain Classification by Optimal Features and Intelligent Classifiers

The present paper focused on the classification of cereal grains using different classifiers combined to morphological, colour and wavelet features. The grain types used in this study were Hard Wheat, Tender Wheat and Barley. Different types of features (morphological, colour and wavelet) were extracted from colour images using different approaches. They were applied to different classification methods

Risk attitudes in medical decisions for others: an experimental approach

The aim of this paper is to investigate how risk attitudes in medical decisions for others vary across health contexts. A lab experiment was designed to elicit the risk attitudes of 257 medical and nonmedical students by assigning them the role of a physician who must decide between treatments for patients. An interval regression model was used to estimate individual coefficients of relative risk aversion, and an estimation model was used to test for the effect of type of medical decision and experimental design characteristics on elicited risk aversion. We find that (a) risk attitudes vary across different health contexts, but risk aversion prevails in all of them; (b) students enrolled in health‐related degrees show a higher degree of risk aversion; and (c) real rewards for third parties (patients) make subjects less risk‐averse. The results underline the importance of accounting for attitudes towards risk in medical decision making.Ministerio de Ciencia y Tecnología and FEDER, Grant numbers: ECO2012‐3648, ECO2013‐43526‐R, ECO2015‐65031‐R and ECO2015‐65408‐R; Junta de Andalucía, Grant number: SEJ‐0499

Can Sophie's Choice Be Adequately Captured by Cold Computation of Minimizing Losses? An fMRI Study of Vital Loss Decisions

The vast majority of decision-making research is performed under the assumption of the value maximizing principle. This principle implies that when making decisions, individuals try to optimize outcomes on the basis of cold mathematical equations. However, decisions are emotion-laden rather than cool and analytic when they tap into life-threatening considerations. Using functional magnetic resonance imaging (fMRI), this study investigated the neural mechanisms underlying vital loss decisions. Participants were asked to make a forced choice between two losses across three conditions: both losses are trivial (trivial-trivial), both losses are vital (vital-vital), or one loss is trivial and the other is vital (vital-trivial). Our results revealed that the amygdala was more active and correlated positively with self-reported negative emotion associated with choice during vital-vital loss decisions, when compared to trivial-trivial loss decisions. The rostral anterior cingulate cortex was also more active and correlated positively with self-reported difficulty of choice during vital-vital loss decisions. Compared to the activity observed during trivial-trivial loss decisions, the orbitofrontal cortex and ventral striatum were more active and correlated positively with self-reported positive emotion of choice during vital-trivial loss decisions. Our findings suggest that vital loss decisions involve emotions and cannot be adequately captured by cold computation of minimizing losses. This research will shed light on how people make vital loss decisions