The Psychological Science Accelerator: Advancing Psychology Through a Distributed Collaborative Network

Source at https://doi.org/10.1177/2515245918797607.Concerns about the veracity of psychological research have been growing. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions or replicate prior research in large, diverse samples. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time limited), efficient (in that structures and principles are reused for different projects), decentralized, diverse (in both subjects and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside the network). The PSA and other approaches to crowdsourced psychological science will advance understanding of mental processes and behaviors by enabling rigorous research and systematic examination of its generalizability

To which world regions does the valence–dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy, the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution.C.L. was supported by the Vienna Science and Technology Fund (WWTF VRG13-007); L.M.D. was supported by ERC 647910 (KINSHIP); D.I.B. and N.I. received funding from CONICET, Argentina; L.K., F.K. and Á. Putz were supported by the European Social Fund (EFOP-3.6.1.-16-2016-00004; ‘Comprehensive Development for Implementing Smart Specialization Strategies at the University of Pécs’). K.U. and E. Vergauwe were supported by a grant from the Swiss National Science Foundation (PZ00P1_154911 to E. Vergauwe). T.G. is supported by the Social Sciences and Humanities Research Council of Canada (SSHRC). M.A.V. was supported by grants 2016-T1/SOC-1395 (Comunidad de Madrid) and PSI2017-85159-P (AEI/FEDER UE). K.B. was supported by a grant from the National Science Centre, Poland (number 2015/19/D/HS6/00641). J. Bonick and J.W.L. were supported by the Joep Lange Institute. G.B. was supported by the Slovak Research and Development Agency (APVV-17-0418). H.I.J. and E.S. were supported by a French National Research Agency ‘Investissements d’Avenir’ programme grant (ANR-15-IDEX-02). T.D.G. was supported by an Australian Government Research Training Program Scholarship. The Raipur Group is thankful to: (1) the University Grants Commission, New Delhi, India for the research grants received through its SAP-DRS (Phase-III) scheme sanctioned to the School of Studies in Life Science; and (2) the Center for Translational Chronobiology at the School of Studies in Life Science, PRSU, Raipur, India for providing logistical support. K. Ask was supported by a small grant from the Department of Psychology, University of Gothenburg. Y.Q. was supported by grants from the Beijing Natural Science Foundation (5184035) and CAS Key Laboratory of Behavioral Science, Institute of Psychology. N.A.C. was supported by the National Science Foundation Graduate Research Fellowship (R010138018). We acknowledge the following research assistants: J. Muriithi and J. Ngugi (United States International University Africa); E. Adamo, D. Cafaro, V. Ciambrone, F. Dolce and E. Tolomeo (Magna Græcia University of Catanzaro); E. De Stefano (University of Padova); S. A. Escobar Abadia (University of Lincoln); L. E. Grimstad (Norwegian School of Economics (NHH)); L. C. Zamora (Franklin and Marshall College); R. E. Liang and R. C. Lo (Universiti Tunku Abdul Rahman); A. Short and L. Allen (Massey University, New Zealand), A. Ateş, E. Güneş and S. Can Özdemir (Boğaziçi University); I. Pedersen and T. Roos (Åbo Akademi University); N. Paetz (Escuela de Comunicación Mónica Herrera); J. Green (University of Gothenburg); M. Krainz (University of Vienna, Austria); and B. Todorova (University of Vienna, Austria). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.https://www.nature.com/nathumbehav/am2023BiochemistryGeneticsMicrobiology and Plant Patholog

The difference between in bed and out of bed activity as a behavioral marker of cancer patients: A comparative actigraphic study

The current study was conducted to provide normative data on actigraphic dichotomy index (I5O) (the percentage of in bed activity counts that are less than the median of out of bed counts) in healthy population and to assess whether the I5O could be an effective index in discriminating the circadian motor activity of cancer patients from healthy controls. In this retrospective study, we recovered 408 actigraphic records from two databases: healthy controls (n¼182; 79 males; mean age 38.7 ± 12.6) and patients with metastatic colorectal cancer (n¼226; 149 males; mean age 58.4 ± 11.4). Beside the usual actigraphic sleep parameters (time in bed, sleep onset latency, total sleep time, wake after sleep onset, sleep efficiency, number of awakenings, and mean motor activity), we also computed the dichotomy index and number of actigraphic wake parameters, namely, diurnal motor activity, diurnal total sleep time, number of sleep episodes, and the mean duration of the longest diurnal sleep episode. Using the Youden index, we calculated the cut off value that performed the best for I5O and actigraphic wake parameters. Finally, we created Receiver Operator Characteristic curves to test the efficacy of each actigraphic parameter to discriminate cancer patient from healthy controls. Mean I5O was 99.5% (SD, 0.48%) in the healthy group, as compared to 96.6% (SD, 3.6%) in the cancer group (p50.0001). Important age-related effects appeared unlikely after performing both the main analysis with age as a covariate, and a subset analysis in 104 subjects matched for age and sex. In the main analysis, all actigraphic parameters, except total sleep time, significantly differentiated the two groups of participants. However, the I5O was the one that clearly performed best. Here, we provide the first large dataset on I5O in healthy subjects, we confirm the relevance of this circadian index for discriminating advanced stage colorectal cancer patients from healthy subjects, and we lay the grounds for further investigations of this circadian index in patients with other chronic diseases

The difference between in bed and out of bed activity as a behavioral marker of cancer patients : a comparative actigraphic study

The current study was conducted to provide normative data on actigraphic dichotomy index (I < O) (the percentage of in bed activity counts that are less than the median of out of bed counts) in healthy population and to assess whether the I < O could be an effective index in discriminating the circadian motor activity of cancer patients from healthy controls. In this retrospective study, we recovered 408 actigraphic records from two databases: healthy controls (n = 182; 79 males; mean age 38.7 ± 12.6) and patients with metastatic colorectal cancer (n = 226; 149 males; mean age 58.4 ± 11.4). Beside the usual actigraphic sleep parameters (time in bed, sleep onset latency, total sleep time, wake after sleep onset, sleep efficiency, number of awakenings, and mean motor activity), we also computed the dichotomy index and number of actigraphic wake parameters, namely, diurnal motor activity, diurnal total sleep time, number of sleep episodes, and the mean duration of the longest diurnal sleep episode. Using the Youden index, we calculated the cut off value that performed the best for I < O and actigraphic wake parameters. Finally, we created Receiver Operator Characteristic curves to test the efficacy of each actigraphic parameter to discriminate cancer patient from healthy controls. Mean I < O was 99.5% (SD, 0.48%) in the healthy group, as compared to 96.6% (SD, 3.6%) in the cancer group (p < 0.0001). Important age-related effects appeared unlikely after performing both the main analysis with age as a covariate, and a subset analysis in 104 subjects matched for age and sex. In the main analysis, all actigraphic parameters, except total sleep time, significantly differentiated the two groups of participants. However, the I < O was the one that clearly performed best. Here, we provide the first large dataset on I < O in healthy subjects, we confirm the relevance of this circadian index for discriminating advanced stage colorectal cancer patients from healthy subjects, and we lay the grounds for further investigations of this circadian index in patients with other chronic diseases

An Ethnoveterinary Important Plant Terminalia Arjuna

The phytochemical, antioxidant, and antibacterial properties of Terminalia arjuna were investigated using a standard methodology. It's high in secondary metabolites such as alkaloids, glycosides, tannins, saponins, proteins, and steroid hormones. As well as carbs, The methanol extract had a DPPH value of 208.6. Terminalia On Aspergillus fumigatus (20 mm), arjuna at 100 mg/ml demonstrated the most action. More research is required to identify the active principle from the phytopharmaceutical study on several extracts