FIRST IMPRESSIONS FROM FACES: IDEAL PARTNER PREFERENCES DOMINATED BY ATTRACTIVENESS-RELATED CONCERNS

When people first encounter a potential partner, they derive a wealth of objective and subjective impressions simply from their faces (e.g., age, gender, attractiveness, trustworthiness). Facial first impressions are consequential, for instance, impacting on decisions to approach a potential partner. Hence, it is relevant to have a solid theoretical understanding of how first impressions relate to ideal partner preferences, particularly as romantic relationship researchers primarily use verbal measures. The current research revealed that individuals can perceive traits and factors related to their ideal partner preferences in highly variable everyday face images, and these factors overlapped largely (although not completely) with those identified by face perception researchers. Partner preferences for face images were dominated by attractiveness-related concerns in both sexes. Further, a minimum-exposure paradigm revealed that, even in some non-romantic contexts, attractiveness is particularly salient in face images. Yet, these findings could not be attributed to an attractiveness halo effect, given that attractiveness did not dominate all non-romantic first impressions of face images (e.g., evaluations of faces in terms of occupations). There are multiple potential reasons why individuals might prioritise facial attractiveness (e.g., from an evolutionary perspective, attractiveness is a cue to fertility and resistance to environmental and genetic stressors). Of note, though, a verbal measure of partner preferences revealed that individuals prioritised warmth-trustworthiness, suggesting that face images and verbal measures may capture different elements of preferences. Therefore, these findings attest the relevance of using face images to complement verbal measures of partner preferences

Towards Monitoring Parkinson's Disease Following Drug Treatment: CGP Classification of rs-MRI Data

Background and Objective: It is commonly accepted that accurate monitoring of neurodegenerative diseases is crucial for effective disease management and delivery of medication and treatment. This research develops automatic clinical monitoring techniques for PD, following treatment, using the novel application of EAs. Specifically, the research question addressed was: Can accurate monitoring of PD be achieved using EAs on rs-fMRI data for patients prescribed Modafinil (typically prescribed for PD patients to relieve physical fatigue)? Methods: This research develops novel clinical monitoring tools using data from a controlled experiment where participants were administered Modafinil versus placebo, examining the novel application of EAs to both map and predict the functional connectivity in participants using rs-fMRI data. Specifically, CGP was used to classify DCM analysis and timeseries data. Results were validated with two other commonly used classification methods (ANN and SVM) and via k-fold cross-validation. Results: Findings revealed a maximum accuracy of 74.57% for CGP. Furthermore, CGP provided comparable performance accuracy relative to ANN and SVM. Nevertheless, EAs enable us to decode the classifier, in terms of understanding the data inputs that are used, more easily than in ANN and SVM. Conclusions: These findings underscore the applicability of both DCM analyses for classification and CGP as a novel classification technique for brain imaging data with medical implications for medication monitoring. Furthermore, classification of fMRI data for research typically involves statistical modelling techniques being often hypothesis driven, whereas EAs use data-driven explanatory modelling methods resulting in numerous benefits. DCM analysis is novel for classification and advantageous as it provides information on the causal links between different brain regions.Comment: arXiv admin note: substantial text overlap with arXiv:1910.0537