Human Nonindependent Mate Choice: Is Model Female Attractiveness Everything?

Following two decades of research on non-human animals, there has recently been increased interest in human nonindependent mate choice, namely the ways in which choosing women incorporate information about a man's past or present romantic partners ('model females') into their own assessment of the male. Experimental studies using static facial images have generally found that men receive higher desirability ratings from female raters when presented with attractive (compared to unattractive) model females. This phenomenon has a straightforward evolutionary explanation: the fact that female mate value is more dependent on physical attractiveness compared to male mate value. Furthermore, due to assortative mating for attractiveness, men who are paired with attractive women are more likely to be of high mate value themselves. Here, we also examine the possible relevance of model female cues other than attractiveness (personality and behavioral traits) by presenting video recordings of model females to a set of female raters. The results confirm that the model female's attractiveness is the primary cue. Contrary to some earlier findings in the human and nonhuman literature, we found no evidence that female raters prefer partners of slightly older model females. We conclude by suggesting some promising variations on the present experimental design

An exploratory conformational analysis of 3-mercapto-propanamide and 2-methyl-3-mercapto-propanamide as well as their S-deprotonated conjugate basis: An ab initio study

Ab initio conformational analysis has been carried out on 3-mercapto-propanamide, (R)- and (S)-2-methyl-3-mercaptopropanamide as well as their S-deprotonated conjugate basis. They were carried out at the HF/3-21G level of theory. The topology of the conformational potential energy surfaces and hypersurfaces have been analysed.Fil: Torday, László L.. Albert Szent-Györgyi Medical University; HungríaFil: Penke, Botond. Albert Szent-Györgyi Medical University; HungríaFil: Zamarbide, Graciela Nidia. Universidad Nacional de San Luis; ArgentinaFil: Enriz, Ricardo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis; ArgentinaFil: Papp, Julius Gy. Albert Szent-Györgyi Medical University; Hungrí

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology

We predicted residual fluid intelligence scores from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions across the cortex. Individual structural covariance networks (SCN) were abstracted into graph-theory metrics averaged over nodes across the brain and in data-driven communities/modules. Metrics included degree, path length, clustering coefficient, centrality, rich club coefficient, and small-worldness. These features derived from the training set were used to build various regression models for predicting residual fluid intelligence scores, with performance evaluated both using cross-validation within the training set and using the held-out validation set. Our predictions on the test set were generated with a support vector regression model trained on the training set. We found minimal improvement over predicting a zero residual fluid intelligence score across the sample population, implying that structural covariance networks calculated from T1-weighted MR imaging data provide little information about residual fluid intelligence.Comment: 8 pages plus references, 3 figures, 2 tables. Submission to the ABCD Neurocognitive Prediction Challenge at MICCAI 201