Sex ratios in vocal ensembles affect perceptions of threat and belonging.

People often interact with groups (i.e., ensembles) during social interactions. Given that group-level information is important in navigating social environments, we expect perceptual sensitivity to aspects of groups that are relevant for personal threat as well as social belonging. Most ensemble perception research has focused on visual ensembles, with little research looking at auditory or vocal ensembles. Across four studies, we present evidence that (i) perceivers accurately extract the sex composition of a group from voices alone, (ii) judgments of threat increase concomitantly with the number of men, and (iii) listeners' sense of belonging depends on the number of same-sex others in the group. This work advances our understanding of social cognition, interpersonal communication, and ensemble coding to include auditory information, and reveals people's ability to extract relevant social information from brief exposures to vocalizing groups.

Sex-specific differences in physiological parameters related to SARS-CoV-2 infections among a national cohort (COVI-GAPP study).

Considering sex as a biological variable in modern digital health solutions, we investigated sex-specific differences in the trajectory of four physiological parameters across a COVID-19 infection. A wearable medical device measured breathing rate, heart rate, heart rate variability, and wrist skin temperature in 1163 participants (mean age = 44.1 years, standard deviation [SD] = 5.6; 667 [57%] females). Participants reported daily symptoms and confounders in a complementary app. A machine learning algorithm retrospectively ingested daily biophysical parameters to detect COVID-19 infections. COVID-19 serology samples were collected from all participants at baseline and follow-up. We analysed potential sex-specific differences in physiology and antibody titres using multilevel modelling and t-tests. Over 1.5 million hours of physiological data were recorded. During the symptomatic period of infection, men demonstrated larger increases in skin temperature, breathing rate, and heart rate as well as larger decreases in heart rate variability than women. The COVID-19 infection detection algorithm performed similarly well for men and women. Our study belongs to the first research to provide evidence for differential physiological responses to COVID-19 between females and males, highlighting the potential of wearable technology to inform future precision medicine approaches.

Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective cohort study (COVI-GAPP).

OBJECTIVES: We investigated machinelearningbased identification of presymptomatic COVID-19 and detection of infection-related changes in physiology using a wearable device. DESIGN: Interim analysis of a prospective cohort study. SETTING, PARTICIPANTS AND INTERVENTIONS: Participants from a national cohort study in Liechtenstein were included. Nightly they wore the Ava-bracelet that measured respiratory rate (RR), heart rate (HR), HR variability (HRV), wrist-skin temperature (WST) and skin perfusion. SARS-CoV-2 infection was diagnosed by molecular and/or serological assays. RESULTS: A total of 1.5 million hours of physiological data were recorded from 1163 participants (mean age 44±5.5 years). COVID-19 was confirmed in 127 participants of which, 66 (52%) had worn their device from baseline to symptom onset (SO) and were included in this analysis. Multi-level modelling revealed significant changes in five (RR, HR, HRV, HRV ratio and WST) device-measured physiological parameters during the incubation, presymptomatic, symptomatic and recovery periods of COVID-19 compared with baseline. The training set represented an 8-day long instance extracted from day 10 to day 2 before SO. The training set consisted of 40 days measurements from 66 participants. Based on a random split, the test set included 30% of participants and 70% were selected for the training set. The developed long short-term memory (LSTM) based recurrent neural network (RNN) algorithm had a recall (sensitivity) of 0.73 in the training set and 0.68 in the testing set when detecting COVID-19 up to 2 days prior to SO. CONCLUSION: Wearable sensor technology can enable COVID-19 detection during the presymptomatic period. Our proposed RNN algorithm identified 68% of COVID-19 positive participants 2 days prior to SO and will be further trained and validated in a randomised, single-blinded, two-period, two-sequence crossover trial. Trial registration number ISRCTN51255782; Pre-results.

Development of infants' representation of female and male faces.

We examined development of 5- and 10.5-month-old infants' face representations, focusing on infants' discrimination and categorization of female and male faces. We tested for gender-based preferences and categorization of female and male faces by presenting infants with pairs of faces and then habituating them to a series of majority female or male face ensembles. We then tested for gender preferences with new face pairs (one female and one male; Study 1) or new face ensembles (majority female and majority male; Study 2). We found that both 5- and 10.5-month-old infants discriminated female from male faces in face pairs, and both age groups looked more at female faces during habituation. Neither age group, however, provided evidence of gender-based categorization. We interpret these findings within a theoretical framework that stresses environmental exposure to different social categories, and infants' ability to detect commonalities of features within categories. We conclude that infants' gender-based categorization of faces is constrained by the set of features available in the input.

Groups at a glance: Perceivers infer social belonging in a group based on perceptual summaries of sex ratio.

Human observers extract perceptual summaries for sets of items after brief visual exposure, accurately judging the average size of geometric shapes (Ariely, 2001), walking direction of a crowd (Sweeny, Haroz, & Whitney, 2013), and the eye gaze of groups of faces (Sweeny & Whitney, 2014). In addition to such actuarial summaries, we hypothesize that observers also extract social information about groups that may influence downstream judgments and behavior. In four studies, we first show that humans quickly and accurately perceive the sex ratio of a group after only 500 ms of visual exposure. We then test whether these percepts bias judgments about the group's social attitudes and affect the perceiver's sense of belonging. As the ratio of men to women increased, both male and female perceivers judged the group to harbor more sexist norms, and judgments of belonging changed concomitantly, albeit in opposite directions for men and women. Thus, observers judge a group's sex ratio from a mere glimpse and use it to infer social attitudes and interpersonal affordances. We discuss the implication of these findings for a heretofore overlooked hurtle facing women in male-dominated fields (e.g., science, technology, engineering, or mathematics): how the ratio of men to women provides an early visible cue that signals an individual's potential fit. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Modern fertility awareness methods: wrist wearables capture the changes in temperature associated with the menstrual cycle.

Core and peripheral body temperatures are affected by changes in reproductive hormones during the menstrual cycle. Women worldwide use the basal body temperature (BBT) method to aid and prevent conception. However, prior research suggests that taking one's daily temperature can prove inconvenient and subject to environmental factors. We investigate whether a more automatic, non-invasive temperature measurement system can detect changes in temperature across the menstrual cycle. We examined how wrist skin temperature (WST), measured with wearable sensors, correlates with urinary tests of ovulation and may serve as a new method of fertility tracking. One hundred and thirty-six eumenorrheic, non-pregnant women participated in an observational study. Participants wore WST biosensors during sleep and reported their daily activities. An at-home luteinizing hormone (LH) test was used to confirm ovulation. WST was recorded across 437 cycles (mean cycles/participant = 3.21, S.D. = 2.25). We tested the relationship between the fertile window and WST temperature shifts, using the BBT three-over-six rule. A sustained 3-day temperature shift was observed in 357/437 cycles (82%), with the lowest cycle temperature occurring in the fertile window 41% of the time. Most temporal shifts (307/357, 86%) occurred on ovulation day (OV) or later. The average early-luteal phase temperature was 0.33°C higher than in the fertile window. Menstrual cycle changes in WST were impervious to lifestyle factors, like having sex, alcohol, or eating prior to bed, that, in prior work, have been shown to obfuscate BBT readings. Although currently costlier than BBT, the present study suggests that WST could be a promising, convenient parameter for future multiparameter fertility awareness methods.