Analisis Preferensi Konsumen terhadap Buah Jeruk Lokal dan Buah Jeruk (Sunkis) Impor Kota Palu

This study aims to determine the attributes of local and imported citrus fruits mostly considered and preferred in Palu. The number of Respondents was 60 people determined using an incidental sampling technique. Data were analyzed using chi square and Fishbein multiattribute analyses. The result of the research showed that the number of respondents aged between 19 – 27 years who bought both local and imported citrus is eight respondents while whose aged between 28 – 36 years is 21 respondents (53%). The respondents whose age > 36 years (18 people, 35%) prefer to buy imported citrus. Based on the results of chi square analysis, there are differences in consumer preferences on all attributes of local and imported citrus except on the fruit color of the imported fruits. The results of the Fishbein multiattribute analysis indicate that the attributes of the local fruits mostly considered by the respondents are flavor, price, size and color whereas that of the imported fruits are flavor, color, size and price

Direct and indirect effects of mood on risk decision making in safety-critical workers

The study aimed to examine the direct influence of specific moods (fatigue, anxiety, happiness) on risk in safety-critical decision making. It further aimed to explore indirect effects, specifically, the potential mediating effects of information processing assessed using a goodness-of-simulation task. Trait fatigue and anxiety were associated with an increase in risk taking on the Safety-Critical Personal Risk Inventory (S-CPRI), however the effect of fatigue was partialled out by anxiety. Trait happiness, in contrast was related to less risky decision making. Findings concerning the ability to simulate suggest that better simulators made less risky decisions. Anxious workers were generally less able to simulate. It is suggested that in this safety-critical environment happiness had a direct effect on risk decision making while the effect of trait anxiety was mediated by goodness-of-simulation

Machine learning to detect brain lesions in focal epilepsy

PURPOSE: Identifying areas of abnormality on MRI brain scans in individuals with focal epilepsy is funda- mental to the diagnosis and treatment of the condition. However, in about a third of patients with focal epilepsy, brain scans appear to be normal (MRI-negative) as human observers cannot detect any abnormality with cur- rent imaging technology. The objective of this paper is to provide a novel approach in presenting localization using machine learning in order to locate areas of abnormality on patients with focal epilepsy on a per-voxel basis by comparing them with healthy controls. As a proof-of-concept, the technique is first applied to patients with visible lesions providing a ground truth (MRI-positive), but future work will extend this to MRI-negative subjects. METHODS: Our data consists of multi-modal brain MR images from 62 healthy control subjects and 44 MRI- positive patients with focal epilepsy. We utilized a support vector machine (SVM) as our probabilistic classifier and train it with two classes of data. We generate probability maps applying our machine learning classifier on all voxels of a test subjects to visualize the predictions. Overlap scores are used to evaluate the classifier performance in MRI-positive patients. RESULTS: Our model reached 83% specificity, 91% sensitivity, and an Area Under the Curve (AUC) of 0.896 for the task of voxel-based classification of normal versus abnormal voxels. In addition, Dice scores of up to 0.66 were achieved for the overlap measure of lesion probability map and the ground truth labels annotated by a neurologist. CONCLUSION: We demonstrated a novel approach in presenting localization using machine learning tech- niques to localize focal epilepsy lesions from multi-modal MR images

Affect-matching music improves cognitive performance in adults and young children for both positive and negative emotions

Three experiments assessed the hypothesis that cognitive benefits associated with exposure to music only occur when the perceived emotion expression of the music and the participant’s affective state match. Experiment 1 revealed an affect-matching pattern modulated by gender when assessing high-arousal states of opposite valence (happy/angry) in an adult sample (n=94) in which mood classification was based on self-report, and affective valence in music was differentiated by mode and other expressive cues whilst keeping tempo constant (139 BPM). The affect-matching hypothesis was then tested in two experiments with children using a mood-induction procedure: Experiment 2 tested happy/angry emotions with, respectively, 3-5- (n=40) and 6-9-year-old (n=40) children, and Experiment 3 compared happy/sad emotions (i.e., states differing both for valence and arousal profiles) with 3-5-year-old children (n=40), using music pieces differentiated also by fast vs. slow tempo. While young children failed to discriminate systematically between fast tempo music conveying different emotions, they did display cognitive benefits from exposure to affect-matching music when both valence (e.g., mode) and arousal level (e.g., tempo) differentiated the musical excerpts, with no gender effects

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

Non-parametric combination of multimodal MRI for lesion detection in focal epilepsy

One third of patients with medically refractory focal epilepsy have normal-appearing MRI scans. This poses a problem as identification of the epileptogenic region is required for surgical treatment. This study performs a multimodal voxel-based analysis (VBA) to identify brain abnormalities in MRI-negative focal epilepsy. Data was collected from 69 focal epilepsy patients (42 with discrete lesions on MRI scans, 27 with no visible findings on scans), and 62 healthy controls. MR images comprised T1-weighted, fluid-attenuated inversion recovery (FLAIR), fractional anisotropy (FA) and mean diffusivity (MD) from diffusion tensor imaging, and neurite density index (NDI) from neurite orientation dispersion and density imaging. These multimodal images were coregistered to T1-weighted scans, normalized to a standard space, and smoothed with 8 mm FWHM. Initial analysis performed voxel-wise one-tailed t-tests separately on grey matter concentration (GMC), FLAIR, FA, MD, and NDI, comparing patients with epilepsy to controls. A multimodal non-parametric combination (NPC) analysis was also performed simultaneously on FLAIR, FA, MD, and NDI. Resulting p-maps were family-wise error rate corrected, threshold-free cluster enhanced, and thresholded at p < 0.05. Sensitivity was established through visual comparison of results to manually drawn lesion masks or seizure onset zone (SOZ) from stereoelectroencephalography. A leave-one-out cross-validation with the same analysis protocols was performed on controls to determine specificity. NDI was the best performing individual modality, detecting focal abnormalities in 38% of patients with normal MRI and conclusive SOZ. GMC demonstrated the lowest sensitivity at 19%. NPC provided superior performance to univariate analyses with 50% sensitivity. Specificity in controls ranged between 96 and 100% for all analyses. This study demonstrated the utility of a multimodal VBA utilizing NPC for detecting epileptogenic lesions in MRI-negative focal epilepsy. Future work will apply this approach to datasets from other centres and will experiment with different combinations of MR sequences

Affective state influences retrieval-induced forgetting for integrated knowledge

Selectively testing parts of learned materials can impair later memory for nontested materials. Research has shown that such retrieval-induced forgetting occurs for low-integrated materials but may be prevented for high-integrated materials. However, previous research has neglected one factor that is ubiquitous in real-life testing: affective stat

Self-Affirmation Improves Problem-Solving under Stress

High levels of acute and chronic stress are known to impair problem-solving and creativity on a broad range of tasks. Despite this evidence, we know little about protective factors for mitigating the deleterious effects of stress on problem-solving. Building on previous research showing that self-affirmation can buffer stress, we tested whether an experimental manipulation of self-affirmation improves problem-solving performance in chronically stressed participants. Eighty undergraduates indicated their perceived chronic stress over the previous month and were randomly assigned to either a self-affirmation or control condition. They then completed 30 difficult remote associate problem-solving items under time pressure in front of an evaluator. Results showed that self-affirmation improved problem-solving performance in underperforming chronically stressed individuals. This research suggests a novel means for boosting problem-solving under stress and may have important implications for understanding how self-affirmation boosts academic achievement in school settings. © 2013 Creswell et al

Walking the walk: a phenomenological study of long distance walking

Evidence suggests that regular walking can elicit significant psychological benefits although little evidence exists concerning long distance walking. The purpose of this study was to provide detailed accounts of the experiences of long distance walkers. Phenomenological interviews were conducted with six long distance walkers. Data were transcribed verbatim before researchers independently analyzed the transcripts. Participants reported a cumulative effect with positive feelings increasing throughout the duration of the walk. Long distance walking elicited positive emotions, reduced the effects of life-stress, and promoted an increased sense of well-being and personal growth. Results are aligned to theories and concepts from positive psychology