Exploring Individual Differences in Visuo-Tactile Preferences: Bottom-up versus Predefined Categories

BackgroundResearch evidence suggests that physiological state of hunger might affect preference for female body weight, such that hungrier, compared to satiate, men prefer heavier body weight and rate as more attractive heavier female figures. Here, we seek to extend these findings by comparing the effects of fasting and snack on aesthetics judgements of the bodies and faces of conspecifics and of objects in a sample of female and male participants.MethodsForty-four participants (women: n = 21, mean age = 23.70 yrs ± 0.62) provided aesthetic liking judgments of round and slim human bodies, faces and objects, under at least 12 h of overnight fasting and immediately after having eaten a snack (i.e., bananas). An anthropometric measure of adiposity (i.e., Body Mass Index, BMI) was also collected from each observer.ResultsOverall, we found that participants’ aesthetic judgements were higher for slim stimuli compared to round ones. However, after fasting, participants rated round stimuli as more attractive compared to when they had a snack. This hunger-based shift in ratings not only was apparent when stimuli depicted a human body or face, but also when they depicted an object, thus suggesting a general modification of observers’ aesthetic preference related to hunger. Importantly, this effect was modulated by participants’ BMI so that only participants with a high BMI provided higher aesthetic judgements for round stimuli after fasting than after a snack.ConclusionsOur results demonstrated that both the modification of the physiological state and the individual differences in adiposity level of the observers might affect the aesthetic appreciation of the external world.

Decision letter: Characterizing a psychiatric symptom dimension related to deficits in goal-directed control

AimPositive relationships in compositional similarity between consumer and resource assemblages are widely known in free‐living taxa, but less is known about parasites and their hosts. We investigated whether congruent patterns of assemblage similarity across diverse taxa of hosts and parasites exist at a continental scale and quantified the relative importance of host assemblages and environmental variables in shaping these relationships.LocationEuropean freshwaters.Major taxa studiedThe hosts were fishes, birds and mammals. The parasites were monogeneans, trematodes and copepods.MethodsWe extracted distribution data from the Limnofauna Europaea for three aquatic parasite taxa and for three vertebrate taxa functioning as their definitive hosts across 25 biogeographical regions in Europe. First, we investigated β‐diversity congruence patterns between parasite and host assemblages, corrected for the distance between regions using partial Mantel tests. Second, we assessed the relative importance of host assemblages and environmental variables in shaping parasite β‐diversity patterns using generalized dissimilarity models (GDMs).ResultsSpatial community dissimilarities of regional parasite assemblages were positively correlated with those of their respective host assemblages in all five parasite–host groups studied. The GDMs highlighted the equal importance of both host assemblages and environmental variables in shaping parasite assemblages. However, the direct effect of host assemblages was relatively small compared with the effect of environmental factors mediated by host assemblages. Climatic parameters (precipitation and temperature) contributed most to the variance explained by environmental variables.Main conclusionsOur analyses indicate that spatially congruent patterns of assemblage similarity exist between parasites and their hosts at a continental scale. They also suggest that this congruence is driven not only by host assemblages but also by environmental (climatic) variables, either directly or indirectly via their effects on host assemblages. Thus, environmental variables are important for mapping, forecasting and management of parasites at a geographical scale.

The dynamics of the mountain vegetation is governed by multiple climatic drivers including temperature, precipitation, radiation and snow cover variability. However, in the Mediterranean environment, little is known about the relative importance of each variable. In this study we assess how different snowpack indices (the maximum annual accumulation, the length of the snow season, and the melt-out date) and key climate variables (precipitation, temperature and shortwave solar radiation) control the interannual variability of the maximum Normalized Difference Vegetation Index (peak NDVI) in the Pyrenees. We use a 33 year long remote sensing dataset (1981–2014) to build a statistical model relating the annual peak NDVI with snow and climate variables. In elevated areas characterized by a well developed seasonal snowpack the melt-out date was the most important climatic variable for predicting the annual peak NDVI. However, at lower elevations where snow presence is ephemeral, shortwave solar radiation was the most important variable. This change in the relative importance of climatic variables occurs around 1300 m a.s.l. The results do not show a significant contribution of maximum snow accumulation, suggesting that indicators of snow presence (i.e. melt-out date or snow season duration), which are significantly easier to obtain than snow mass indicators from remote sensing, could be used to model the influence of the snowpack on peak NDVI at regional scale.

A database was examined using artificial neural network (ANN) models to investigate the efficacy of predicting PCR-identified Norwalk-like virus presence and absence in shellfish. The relative importance of variables in the model and the predictive power obtained by application of ANN modelling methods were compared with previously developed logistic regression models. In addition, two country-specific datasets were analysed separately with ANN models to determine if the relative importance of the input variables was similar for geographically diverse regions. The results of this analysis found that ANN models predicted Norwalk-like virus presence and absence in shellfish with equivalent, and better, precision than logistic regression models. For overall classification performance, ANN modelling had a rate of 93%, vs 75% for the logistic regression. ANN models were able to illuminate the site-specific relationships between indicators and pathogens.

Short-term traffic prediction is vital for intelligent traffic systems and influenced by neighboring traffic condition. Gradient boosting decision trees (GBDT), an ensemble learning method, is proposed to make short-term traffic prediction based on the traffic volume data collected by loop detectors on the freeway. Each new simple decision tree is sequentially added and trained with the error of the previous whole ensemble model at each iteration. The relative importance of variables can be quantified in the training process of GBDT, indicating the interaction between input variables and response. The influence of neighboring traffic condition on prediction performance is identified through combining the traffic volume data collected by different upstream and downstream detectors as the input, which can also improve prediction performance. The relative importance of input variables for 15 GBDT models is different, and the impact of upstream traffic condition is not balanced with that of downstream. The prediction accuracy of GBDT is generally higher than SVM and BPNN for different steps ahead, and the accuracy of multi-step-ahead models is lower than 1-step-ahead models. For 1-step-ahead models, the prediction errors of GBDT are smaller than SVM and BPNN for both peak and nonpeak hours.

Corn yields vary spatially and temporally in the plots as a result of weather, altitude, variety, plant density, available water, nutrients, and planting date; these are the main factors that influence crop yield. In this study, different multispectral and red-green-blue (RGB) vegetation indices were analyzed, as well as the digitally estimated canopy cover and plant density, in order to estimate corn grain yield using a neural network model. The relative importance of the predictor variables was also analyzed. An experiment was established with five levels of nitrogen fertilization (140, 200, 260, 320, and 380 kg/ha) and four replicates, in a completely randomized block design, resulting in 20 experimental polygons. Crop information was captured using two sensors (Parrot Sequoia_4.9, and DJI FC6310_8.8) mounted on an unmanned aerial vehicle (UAV) for two flight dates at 47 and 79 days after sowing (DAS). The correlation coefficient between the plant density, obtained through the digital count of corn plants, and the corn grain yield was 0.94; this variable was the one with the highest relative importance in the yield estimation according to Garson’s algorithm. The canopy cover, digitally estimated, showed a correlation coefficient of 0.77 with respect to the corn grain yield, while the relative importance of this variable in the yield estimation was 0.080 and 0.093 for 47 and 79 DAS, respectively. The wide dynamic range vegetation index (WDRVI), plant density, and canopy cover showed the highest correlation coefficient and the smallest errors (R = 0.99, mean absolute error (MAE) = 0.028 t ha−1, root mean square error (RMSE) = 0.125 t ha−1) in the corn grain yield estimation at 47 DAS, with the WDRVI index and the density being the variables with the highest relative importance for this crop development date. For the 79 DAS flight, the combination of the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), WDRVI, excess green (EXG), triangular greenness index (TGI), and visible atmospherically resistant index (VARI), as well as plant density and canopy cover, generated the highest correlation coefficient and the smallest errors (R = 0.97, MAE = 0.249 t ha−1, RMSE = 0.425 t ha−1) in the corn grain yield estimation, where the density and the NDVI were the variables with the highest relative importance, with values of 0.295 and 0.184, respectively. However, the WDRVI, plant density, and canopy cover estimated the corn grain yield with acceptable precision (R = 0.96, MAE = 0.209 t ha−1, RMSE = 0.449 t ha−1). The generated neural network models provided a high correlation coefficient between the estimated and the observed corn grain yield, and also showed acceptable errors in the yield estimation. The spectral information registered through remote sensors mounted on unmanned aerial vehicles and its processing in vegetation indices, canopy cover, and plant density allowed the characterization and estimation of corn grain yield. Such information is very useful for decision-making and agricultural activities planning.

A variety of demographic, seasonal, and site-specific variables may influence parasitism, but the relative importance of these variables is generally unclear. We measured the relative ability of host characteristics, season, and site to explain louse (Trichodectes octomaculatus) and flea (Orchopeas howardi) infestation across 10 populations of raccoons (Procyon lotor). Lice are highly dependent on specific hosts and are predicted to display a relatively strong relationship with factors intrinsic to the host, when compared to fleas, which can infest multiple species and survive off-host for weeks without feeding. We developed a priori models that represented explicit hypotheses and contrasted their ability to predict infestation patterns. While the abundance of lice was seasonal, models that included solely host age and sex best predicted prevalence and abundance, in part because males were infested with 3 times the number of lice than were females. Conversely, flea prevalence and abundance, which peaks sharply in the spring, was best predicted by season; factors intrinsic to the host were relatively unimportant for predicting abundance. These, and other, recent findings emphasize the need to simultaneously assess the relative importance of multiple ecological variables between parasite species when attempting to describe general trends and constraints of host-parasite associations.

Nanofluids are broadly employed in heat transfer mediums to enhance their efficiency and heat transfer capacity. Thermophysical properties of nanofluids play a crucial role in their thermal behavior. Among various properties, the dynamic viscosity is one of the most crucial ones due to its impact on fluid motion and friction. Applying appropriate models can facilitate the design of nanofluidics thermal devices. In the present study, various machine learning methods including MPR, MARS, ANN-MLP, GMDH, and M5-tree are used for modeling the dynamic viscosity of CuO/water nanofluid based on the temperature, concentration, and size of nanostructures. The input data are extracted from various experimental studies to propose a comprehensive model, applicable in wide ranges of input variables. Moreover, the relative importance of each variable is evaluated to figure out the priority of the variables and their influences on the dynamic viscosity. Finally, the accuracy of the models is compared by employing the statistical criteria such as R-squared value. The models’ outputs disclosed that employing ANN-MLP approach leads to the most precise model. R-square value and average absolute percent relative error (AAPR) value of the model by using ANN-MLP model are 0.9997 and 1.312%, respectively. According to these values, ANN-MLP is a reliable approach for predicting the dynamic viscosity of the studied nanofluid. Additionally, based on the relative importance of the input variables, it is concluded that concentration has the highest relative importance; while the influence of size is the lowest one.

Sensitivity analysis of a mathematical model to simulate aeration of wheat stored in Brazil

We studied the relative importance of local variables and dispersal for the occurrence and colonisation of the epiphytic bryophytes Orthotricum speciosum (spore dispersed), and O. obtusifolium (spore and asexual gemmae) on aspen trees (‘patches’) in two forest landscapes (one old‐growth and one fragmented) using multiple logistic regression. The relative importance of dispersal was quantified as the reduction of residual deviance for a connectivity variable. In modelling dispersal, we assumed that trees with low local abundance were recent colonisations, and that trees with high local abundance were diaspore sources for colonisation. The occurrence of O. speciosum in the fragmented landscape was most affected by shading, but also by connectivity, aspen diameter and vitality. In the old‐growth landscape, connectivity was the single most important variable for recent colonisations, but its effect was lower than the sum of the effects of all local environmental variables. The occurrence of O. obtusifolium in the fragmented landscape was related to similar variables but the relative importance of these variables was different, and connectivity did not affect the probability of a recent colonisation in this species. We describe the epiphyte‐tree system in the patch‐tracking metapopulation model. In this model colonisations are distance dependent, but in contrast to the classical metapopulation model local extinctions are caused by deterministic patch destruction – once the epiphyte has colonised the tree it remains until the tree dies.

Just-In-Time (JIT) research has been limited primarily to descriptive works, case studies, surveys, and a few simulations and analytic models. This paper, by contrast, reports on an empirical field study exploring the relative importance of several JIT-based independent variables to the total level of supplier-linked inventory in an environment where the customer was implementing JIT. The results lend some support (as expected) to classical inventory theory, but also indicate the relative importance of the variables in this JIT environment. Open-ended discussions with customer and supplier managers lend further insights into the results.

In 2006, research on the neurotransmitter serotonin and its transporter protein (5‐HTT) in the synaptic gap celebrated a series of anniversaries. Forty‐five years earlier, presynaptic neurotransmitter uptake was discovered (Hertting & Axelrod, 1961). Two decades later, 5‐HTT was first linked to depression (Langer et al , 1981), shortly after its identification as a target of antidepressant drugs (Raisman et al , 1979). The sequence of the transporter gene from rats was published 10 years after that (Blakely et al , 1991), which initiated an avalanche of molecular genetic studies on the regulation of emotionality. This research culminated in two reports revealing an association between variations of the 5‐HTT gene ( 5‐HTT and SLC6A4 ) and anxiety‐related traits as well as depression (Collier et al , 1996; Lesch et al , 1996). Since then, clinical studies have further supported the link between variants of 5‐HTT and disorders in the regulation of emotion. Although modest effect sizes—typical of non‐Mendelian traits—polygenic patterns of inheritance, epistatic and epigenetic interactions, and heterogeneity between studies confounded the results, 5‐HTT comprises a model molecule for studying gene–environment interactions in cognitive and psychiatric neuroscience. The demonstration in rhesus macaques that stress early in life uniquely reinforces links between variations of 5‐HTT , behaviour and psychopathology seems to herald in a new era of behavioural genetics. Moreover, the discovery that 5‐HTT is a susceptibility gene for depression is a first step towards explaining the molecular dimensions of personality and behaviour, identifying physiological pathways that lead to other disorders of cognitive function and emotion, and analysing the interactive effects of genes and environment in the development of disease. On the heels of these results from behavioural genetics, novel approaches including neurophysiology, neuropsychology and functional neuroimaging, as well as the inclusion of other phenotypes (such as higher cognitive functions, communication skills, social competence and longevity), have …

Moral decisions can be thought of as distinct from other types of decisions in that they involve decisions which affect others in meaningful ways. Specifically, a moral decision is made when it affects the interests (utilities) of others Baron (2000). Moral decision making is a complex process that involves consideration of a number of diverse factors, including those specific to the situation and those related to the person. It is the aim of this thesis to consider several of these influential factors and examine their relative contribution in explaining ethical outcomes -specifically the focus will be on predicting moral judgments. An important and ongoing theoretical debate in psychology concerns the relative importance of situational variables and individual difference (dispositional and cognitive) variables, and whether there exists consistency in terms of individual responses to situations which are predictable not solely in terms of the situation (Mischel & Shoda, 1995; Mischel, 2004). This thesis contributes to this debate in the domain of moral judgments. Three studies are presented which help to address both the situational and individual differ¬ence variables relevant to moral judgments, where the focus in on exploring moral judgments in the domain of medical ethics. The first study is based on Jones’ (1991) Issue-Contingent model of moral decision making and his concept of moral intensity (MI) which was developed in the field of business ethics. This construct (or set of variables) pertains to situational in¬fluences relevant to moral judgments. The previous findings in business ethics are replicated using a business scenario and extended to the domain of medicine using medical ethical content. A scenario and questionnaire based methodology is used with a student sample. Individual difference influences are assessed via the Multidimensional Ethics Scale (Cohen, Pant, & Sharp, 1993) and the Ethics Position Questionnaire (Forsyth, 1980). Moral intensity is shown to be useful for predicting moral judgments in medical as well as business scenarios with the finding that both consequences and social consensus are significant predictors of ethical judgments. The individual difference variables in general are less predictive of moral judgments. Study 2a is an extension and partial replication of Study 1. The concept of moral intensity is refined and further explored solely using medical ethical content. The aim here is to extend the evidence for the applicability of the concept of moral intensity in medical ethics using typ¬ical medical ethical cases. In addition, Study 2a incorporates a number of other dispositional variables which have been previously related to moral judgments including Machiavellianism, altruism, cynicism, and empathy. This is done in order to investigate the relative contribu¬tion of situational and individual difference variables in explaining ethical judgments. The real novelty of Study 2a lies in the assessment of a range of individual difference variables and using these to predict moral judgments in medical ethical cases. Results show that other components of moral intensity beside the consequences and social consensus are impor¬tant predictors in medical ethical cases. Moreover, while some of the individual difference variables are significant, the situational information seems to dominate judgments of ethical issues which lends support to Mischel’s (1990) notion of situationism. Taking the approach to individual differences in moral decision making one step further, a conceptually distinct part of the second study (Study 2b) develops a measure of the medical ethical constructs (derived from normative principles) using the Analytic Hierarchy Process (Saaty, 1980). Principalism is the dominant approach to the teaching and evaluation of ethical issues in medicine. The four principles of Beauchamp and Childress (2001) (autonomy, non-maleficence, beneficence and justice) have been extremely influential in the field of medical ethics, and are fundamental for understanding the current approach to ethical assessment in health care. Therefore a measure of individual differences in medical ethics is designed to assess the principles in a general sense independent of specific situational information. Overall, it is found that individuals can weight the principles when they conflict and they do have a hierarchy for the principles with non-maleficence rated as the most important principle. This study also tests whether this measure of the medical ethical principles predicts ethical judgments in the four medical ethical scenarios which are used in Study 2a. The relationships between the weighting of the principles, ethical judgments and intentions, and the individual difference variables measured in Study 2a are also investigated. Surprisingly, the weights for the principles are not related to ethical outcomes in specific scenarios. Theoretically, this finding also supports situationism (Mischel, 1990). Last, Study 3 compares this new measure of the medical ethical principles with an existing one in the literature (Price, Price, Williams, & Hoffenberg, 1998) in order to assess whether there is convergence between the two types of methods. Surprisingly, the results suggest that there is little overlap between the findings from these two methods except under very clear-cut circumstances of conflict between two principles. In addition, three groups of students are compared on their weightings of the principles for both of these principle measures and on individual difference measures. Several significant differences between the groups are found. For example, medical students give significantly more weight to the principle of beneficence and the principle of confidentiality compared to both psychology and business students. Also, psychology students rate the principle of justice as most important. In addition, medical stu-dents report significantly less distress, being better able to take the perspective of another and greater altruism than the business students. Business students scored higher on Machi¬avellianism. These results are discussed in relation to previous individual difference research in ethics. The empirical and theoretical implications of this set of findings is discussed in relation to Mischel’s situationist theory and the use of the concept of moral intensity in medical ethics. In addition, the results are discussed in relation to recent research in the field of moral psychology which highlights the role of individual difference variables as predictive of ethical outcomes (Hauser, Cushman, Young, Kang-Xing Jin, & Mikhail, 2007; Bartels, 2008; Hardman, 2008).

Individual differences among adults have generally been conceptualized in terms of personality theory and traits. In contrast, individual differences among very young children (birth to kindergarten) have generally been conceptualized in terms of temperament theory and traits. The present study compares and contrasts measures of temperament and personality in a sample of preschool children. Temperament traits were assessed with a well‐established measure (the Rothbart CBQ), and a new preschool rating instrument was used to assess personality traits from the five‐factor framework (M5‐PS). Indeed, a key purpose of this study was to further the development of the M5‐PS. Data were gathered on 122 preschool children who were rated by their teachers. Significant correlations were found between the temperament trait Surgency and the personality trait Extraversion, between the temperament trait Negative Affect and the personality trait Neuroticism, and between the temperament trait Effortful Control and the personality trait Conscientiousness. The overall pattern of correlational data suggests that individual differences in preschool children can be adequately described using the five‐factor theory, and that this framework may effectively subsume traditional theories of temperament. Preliminary support for the reliability and validity of the M5‐PS is offered. Copyright © 2010 John Wiley & Sons, Ltd.