A network model of affective odor perception.

The affective appraisal of odors is known to depend on their intensity (I), familiarity (F), detection threshold (T), and on the baseline affective state of the observer. However, the exact nature of these relations is still largely unknown. We therefore performed an observer experiment in which participants (N = 52) smelled 40 different odors (varying widely in hedonic valence) and reported the intensity, familiarity and their affective appraisal (valence and arousal: V and A) for each odor. Also, we measured the baseline affective state (valence and arousal: BV and BA) and odor detection threshold of the participants. Analyzing the results for pleasant and unpleasant odors separately, we obtained two models through network analysis. Several relations that have previously been reported in the literature also emerge in both models (the relations between F and I, F and V, I and A; I and V, BV and T). However, there are also relations that do not emerge (between BA and V, BV and I, and T and I) or that appear with a different polarity (the relation between F and A for pleasant odors). Intensity (I) has the largest impact on the affective appraisal of unpleasant odors, while F significantly contributes to the appraisal of pleasant odors. T is only affected by BV and has no effect on other variables. This study is a first step towards an integral study of the affective appraisal of odors through network analysis. Future studies should also include other factors that are known to influence odor appraisal, such as age, gender, personality, and culture

The Influence of Sexual Arousal on Self-Reported Sexual Willingness and Automatic Approach to Models of Low, Medium, and High Prior Attractiveness

Anecdotal evidence suggests that sexual attraction is flexible, and that high levels of sexual arousal can promote sexual willingness and approach tendencies toward a priori low attractive mates. This experimental study tested whether heightened sexual arousal can lower the threshold for sexual willingness and automatic approach tendencies toward potential sex partners of low and medium attractiveness. Heterosexual male (n =54) and female (n =61) participants were randomly assigned to a sexual arousal or control condition. Approach tendencies were indexed using a reaction time task. Sexual willingness was indexed using participant ratings of willingness to kiss and to consider having sex with same- and other-sex models of low, medium, and high attractiveness. Overall, participants showed stronger approach to models of high and medium than of low attractiveness. Sexual arousal weakened this differential responding but did not result in a robust increase of approach toward less attractive other-sex or same-sex models. Sexual willingness toward less attractive models was not affected by sexual arousal. Independent of condition, women reported greater sexual willingness toward same-sex models. The current pattern of findings does not support the notion that sexual arousal promotes automatic approach and sexual willingness to a broader array of sex partners

The possibilities of the use of N-of-1 and do-it-yourself trials in nutritional research.

BACKGROUND:N-of-1 designs gain popularity in nutritional research because of the improving technological possibilities, practical applicability and promise of increased accuracy and sensitivity, especially in the field of personalized nutrition. This move asks for a search of applicable statistical methods. OBJECTIVE:To demonstrate the differences of three popular statistical methods in analyzing treatment effects of data obtained in N-of-1 designs. METHOD:We compare Individual-participant data meta-analysis, frequentist and Bayesian linear mixed effect models using a simulation experiment. Furthermore, we demonstrate the merits of the Bayesian model including prior information by analyzing data of an empirical study on weight loss. RESULTS:The linear mixed effect models are to be preferred over the meta-analysis method, since the individual effects are estimated more accurately as evidenced by the lower errors, especially with lower sample sizes. Differences between Bayesian and frequentist mixed models were found to be small, indicating that they will lead to the same results without including an informative prior. CONCLUSION:For empirical data, the Bayesian mixed model allows the inclusion of prior knowledge and gives potential for population based and personalized inference