9 research outputs found
Self-reported nature exposure and its association with well-being as measured with affect and cognition
Utilizing the publicly available MIDUS II Refresher datasets (Ryff et al., 2017) with hundreds of respondents across the United States, the authors attempted to (1) replicate and (2) extend their previous findings with the original MIDUS II data on the relationship between self-reported frequency of nature exposure and well-being, the latter holistically measured by emotional, physiological and cognitive variables (Craig, Menon, & Klein, 2015; Craig, Neilson, & Overbeek, 2016).In the original published research, Craig and colleagues (2015) first observed an association between a 3-pt scale in which middle-aged participants reported the frequency that they appreciated nature and other reported questionnaire measures. These measures included subscales of the Mood Affective Symptoms Questionnaire (MASQ), the Perceived Stress Scale, and scales measuring well-being constructs such as life satisfaction and gratitude. This was followed up with a second study, which found an observed relationship between reported nature exposure with a 7-pt scale and measured physiological variables relevant to emotion and cognition, specifically asymmetrical EEG and eye-blink startle response (Craig et al., 2016). However, the prior research was exploratory and correlational in nature, which many would argue necessitates replication.The original MIDUS II datasets used in the previous investigations (Ryff & Davidson, 2010; Ryff, Seeman, & Weinstein, 2010) were recollected by the original team with a new cohort (Ryff et al., 2017), allowing for a nearly direct replication of the previous analyses (Craig et al., 2015; 2016). Because positive effects associated with nature exposure may be a function of both exposure frequency and degree of appreciation, the first set of analyses replicating and extending the results of Craig and colleagues (2015) used an averaged composite score of two 3-pt scale questions measuring both frequency and degree of nature appreciation, instead of only frequency of nature appreciation as conducted in the original analysis. The second set of analyses that attempted to replicate Craig and colleagues (2016) used the original 7-pt nature exposure scale.For the replication (goal 1), controlling for factors such as age, gender, exercise, and education, multiple regression analyses with the new datasets replicated the association between nature exposure and positive emotions, perceived stress, and metrics such as gratitude and perception of work value. However, there were mixed results for depressive affect, and the previously observed correspondence between nature exposure and emotional reactivity measures, such as eyeblink startle response and epinephrine, did not replicate.For extending the original research (goal 2), exploratory analyses were conducted to explore (1) previously unanalyzed variables related to well-being, and (2) previously unanalyzed cognitive variables. There was an observed and potentially beneficial relationship between self-reported nature exposure, sleep quality, self-control, and low-frequency (.04 - .15 Hz) heart-rate variability. A follow-up analysis focusing on cognitive test batteries including the CANTAB and BTACT mostly did not observe any associations between self-reported frequency of nature exposure and cognitive performance. However, a tentative relationship was noted between nature exposure and category fluency, which should be tested with future research.To clearly demonstrate the effects of nature on general well-being, an exploratory principle components analysis was conducted on 18 measures presently observed to be significantly associated with nature exposure, with a varimax rotation and the extraction based on the Kaiser criterion. Of five identified factors, one appeared to capture a construct akin to well-being (e.g., positive affect, reduced stress, gratitude, cognitive control, anger management, sleep score, work value). Therefore, a single well-being composite variable was computed (regression-weighted) based on the observed factor loadings after standardizing the component variables. A regression of the well-being composite score on the standardized nature exposure composite score (n = 788) was found to be significant, R2 = .095, F(1,786) = 82.81, p < .001.One of the limitations of this study is nature exposure was measured with a single variable, and the type of exposure (nature trails, window scenery) and type of nature (green vs. blue nature) was not explored. Also, the current analysis looked at a large set of survey data and produced relatively small effect sizes, which is understandable given the large number of potentially intervening variables and the imprecision of the survey measurements. Further, the findings here are correlational and precise design recommendations are not warranted, but there may be several avenues to implement nature in and around built spaces. With careful design, even urban scenery designed with components akin to nature could be helpful in improving well-being. Future research could assess whether the amount of time in nature may lead to greater improvement
A review of the limitations of Attention Restoration Theory and the importance of its future research for the improvement of well-being in urban living
While there are benefits to urbanization, deviations from a rural lifestyle can pose an issue for psychological well-being, as there is limited access to restorative environments (e.g., nature; van den Berg, Hartig, & Staats, 2007). Given these concerns associated with increased urbanization, how can we implement components of restorative environments into urban settings? Towards that end, an understanding of the attributes of restorative environments is needed. Attention Restoration Theory (ART; Kaplan, 1995) is the predominant theory identifying characteristics of nature that are thought to make it restorative. Albeit, these characteristics lack operational definitions, thus generating several methodological challenges in critically assessing ART. For example, a major component of restoration within the ART framework is soft fascination, which is an involuntary capturing of attention, but not in a dramatic fashion. However, there is no empirical support of nature’s ability to innately hold attention, and this poor understanding contributes to the challenges in developing an operational definition of soft fascination. We describe attributes of stimuli that are known to capture visual attention (e.g., salience; Ruz & Lupiáñez, 2002) and consider whether such attributes are consistent with the notion of soft fascination. Since ART evolved from literature on aesthetics and environmental preferences (e.g., Kaplan, 1987), a review of this literature may inspire new ways to define restorative characteristics of nature, and thereby, promote the implementation of these characteristics into built environments. Thus, the purpose of this paper is to integrate relevant literature from multiple subfields of psychology to inspire research that can employ new methodology and ultimately better our understanding of the mechanisms underlying restorative environments
A Factor-Analytical Perspective of Sopite Syndrome Assessment in Aerospace Systems
Aerospace systems require pilots to perform complex tasks under demanding conditions. There is an unrecognized component, which has deleterious effects on human performance, called sopite syndrome. Sopite syndrome is characterized by intense drowsiness despite receiving an adequate night’s rest, difficulty concentrating, and lack of motivation. Currently, sopite syndrome is measured exclusively by a 39-item self-report questionnaire called the Mild Motion Questionnaire (MMQ). The purpose of the present research is to develop a shortform for the MMQ that can be used for quick assessments in applied settings, while maintaining internal consistency. Participants (N = 422) completed the MMQ by indicating how they feel following exposure to mild, non-sickening motion. Principal-axis factor analysis with oblimin rotation identified a twofactor solution comprised of 25-items with 2 dimensions: adverse effects and positive affect. Internal consistency was .86. Discussion of efforts to validate the short-form MMQ and the multidimensionality of sopite syndrome is included
Presentation_1_An investigation of cardiac vagal tone over time and its relation to vigilance performance: a growth curve modeling approach.zip
IntroductionResearch over the last couple of decades has demonstrated a relationship between psychophysiological measures, specifically cardiac functions, and cognitive performance. Regulation of the cardiac system under parasympathetic control is commonly referred to as cardiac vagal tone and is associated with the regulation of cognitive and socioemotional states. The goal of the current study was to capture the dynamic relationship between cardiac vagal tone and performance in a vigilance task.Method/ResultsWe implemented a longitudinal growth curve modeling approach which unveiled a relationship between cardiac vagal tone and vigilance that was non-monotonic and dependent upon each person.DiscussionThe findings suggest that cardiac vagal tone may be a process-based physiological measure that further explains how the vigilance decrement manifests over time and differs across individuals. This contributes to our understanding of vigilance by modeling individual differences in cardiac vagal tone changes that occur over the course of the vigilance task.</p
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Proceedings from the 9th annual conference on the science of dissemination and implementation: Washington, DC, USA. 14-15 December 2016
Search for intermediate-mass black hole binaries in the third observing run of Advanced LIGO and Advanced Virgo
International audienceIntermediate-mass black holes (IMBHs) span the approximate mass range 100−105 M⊙, between black holes (BHs) that formed by stellar collapse and the supermassive BHs at the centers of galaxies. Mergers of IMBH binaries are the most energetic gravitational-wave sources accessible by the terrestrial detector network. Searches of the first two observing runs of Advanced LIGO and Advanced Virgo did not yield any significant IMBH binary signals. In the third observing run (O3), the increased network sensitivity enabled the detection of GW190521, a signal consistent with a binary merger of mass ∼150 M⊙ providing direct evidence of IMBH formation. Here, we report on a dedicated search of O3 data for further IMBH binary mergers, combining both modeled (matched filter) and model-independent search methods. We find some marginal candidates, but none are sufficiently significant to indicate detection of further IMBH mergers. We quantify the sensitivity of the individual search methods and of the combined search using a suite of IMBH binary signals obtained via numerical relativity, including the effects of spins misaligned with the binary orbital axis, and present the resulting upper limits on astrophysical merger rates. Our most stringent limit is for equal mass and aligned spin BH binary of total mass 200 M⊙ and effective aligned spin 0.8 at 0.056 Gpc−3 yr−1 (90% confidence), a factor of 3.5 more constraining than previous LIGO-Virgo limits. We also update the estimated rate of mergers similar to GW190521 to 0.08 Gpc−3 yr−1.Key words: gravitational waves / stars: black holes / black hole physicsCorresponding author: W. Del Pozzo, e-mail: [email protected]† Deceased, August 2020
Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo
Advanced LIGO and Advanced Virgo are monitoring the sky and collecting gravitational-wave strain data with sufficient sensitivity to detect signals routinely. In this paper we describe the data recorded by these instruments during their first and second observing runs. The main data products are gravitational-wave strain time series sampled at 16384 Hz. The datasets that include this strain measurement can be freely accessed through the Gravitational Wave Open Science Center at http://gw-openscience.org, together with data-quality information essential for the analysis of LIGO and Virgo data, documentation, tutorials, and supporting software