Does distance from the equator predict self-control? Lessons from the Human Penguin Project

IJzerman, Hans; Čolić, Marija; Hennecke, Marie; Hong, Youngki; Chuan-Peng, Hu; Joy-Gaba, Jennifer A.; Lazarević, Dušanka; Lazarević, Ljiljana B.; Parzuchowski, Michał; Ratner, Kyle G.; Schubert, Thomas W.; Schütz, Astrid; Stojilović, Darko; Weißgerber, Sophia Christin; Zickfeld, Janis; Lindenberg, Siegwart

doi:10.1017/s0140525x16001035

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…In Figure 3, we display the variable importance provided by the conditional random forests, where the order for the first three variables to predict total conspiracy beliefs is similar to the Random Forest: the Government Response (gov_score), Perceived Stress (pss_score), and ECR Partner Avoidance (ecr_partner_avoidance) were the three most important variables. Age and ECR Partner Anxiety (ecr_partner_anxiety) crossed the threshold of random noise (the dotted red line; see also IJzerman et al, 2017), but barely so.…”

Section: Results Exploratory Analysesmentioning

confidence: 96%

“…The outcome in the case of random forests and conditional random forests is a variable importance list. The importance list allows us to identify which are the best predictors of the variable of interest and which of the computed variables differ from random noise when predicting the variable of interest (see also IJzerman et al, 2016IJzerman et al, , 2017IJzerman et al, , 2018Szabelska et al, 2021;Wittmann et al, 2022).…”

Section: Discussion Confirmatory Analysesmentioning

confidence: 99%

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COVID-19-related conspiracy beliefs and their relationship with perceived stress and pre-existing conspiracy beliefs in a Prolific Academic sample: A replication and extension of Georgiou et al. (2020)

Braud¹,

Gaboriaud²,

Ferry³

et al. 2021

Preprint

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The authors conducted a close replication of a study by Georgiou et al (2020), who found amongst 660 (reported in abstract) or 640 (reported in participant section) participants that 1) Covid-19 related conspiracy theory beliefs were strongly related to broader conspiracy theory beliefs, that 2) Covid-19 related conspiracy beliefs were higher in those with lower levels of education, and that 3) Covid-19 related conspiracy beliefs were positively (although weakly) correlated with more negative attitudes towards different individual items measuring the government’s response. Finally, they find that 4) Covid-19 beliefs were unrelated to self-reported stress. In a pre-registered replication and extension in a study sufficiently well-powered to detect f2 = 0.05, at an alpha level of .05, with an a priori power of .95, and with 5 Predictors in a multiple regression analysis, we do not find the same results. First, we find that education level is unrelated to Covid-19 related conspiracy beliefs, that stress is related to Covid-19 related conspiracy beliefs, but that the government’s response is indeed related to Covid-19 related conspiracy beliefs. We point out measurement problems in measuring conspiracy beliefs, extend the study through supervised machine learning by finding that attachment avoidance and anxiety are important predictors of conspiracy beliefs (Covid-19-related and beyond). Part of the differences between their and our study are likely due to differences in analysis approach; others may be due to the errors in Georgiou et al.’s (2020 reporting.

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Section: Results Exploratory Analysesmentioning

confidence: 96%

Section: Discussion Confirmatory Analysesmentioning

confidence: 99%

COVID-19-related conspiracy beliefs and their relationship with perceived stress and pre-existing conspiracy beliefs in a Prolific Academic sample: A replication and extension of Georgiou et al. (2020)

Braud¹,

Gaboriaud²,

Ferry³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…RF also enables the calculation of feature importance by counting the number of times each variable is selected by all individual trees in the ensemble termed feature importance. Unlike other nonlinear classifiers, RF ML is robust to over‐fitting (working perfectly well on a small dataset and poorly on a more general dataset) and yields good classification results even without extensive tuning of the algorithm parameters (Breiman, 2001 ; IJzerman et al, 2016 ; Shen et al, 2007 ; Zhou et al, 2019 ). RF ML was also used to estimate variable importance.…”

Section: Methodsmentioning

confidence: 99%

Relationship between electrocardiogram‐based features and personality traits: Machine learning approach

Boljanić

Miljković

Lazarević

et al. 2021

Noninvasive Electrocardiol

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Background: Based on the known relationship between the human emotion and standard surface electrocardiogram (ECG), we explored the relationship between features extracted from standard ECG recorded during relaxation and seven personality traits (Honesty/humility, Emotionality, eXtraversion, Agreeableness, Conscientiousness, Openness, and Disintegration) by using the machine learning (ML) approach which learns from the ECG-based features and predicts the appropriate personality trait by adopting an automated software algorithm.Methods: A total of 71 healthy university students participated in the study. For quantification of 62 ECG-based parameters (heart rate variability, as well as temporal and amplitude-based parameters) for each ECG record, we used computation procedures together with publicly available data and code. Among 62 parameters, 34 were segregated into separate features according to their diagnostic relevance in clinical practice. To examine the feature influence on personality trait classification and to perform classification, we used random forest ML algorithm.Results: Classification accuracy when clinically relevant ECG features were employed was high for Disintegration (81.3%) and Honesty/humility (75.0%) and moderate to high for Openness (73.3%) and Conscientiousness (70%), while it was low for Agreeableness (56.3%), eXtraversion (47.1%), and Emotionality (43.8%). When all calculated features were used, the classification accuracies were the same or lower, except for the eXtraversion (52.9%). Correlation analysis for selected features is presented. Conclusions:Results indicate that clinically relevant features might be applicable for personality traits prediction, although no remarkable differences were found among selected groups of parameters. Physiological associations of established relationships should be further explored.

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“…While pathological cry in newborns has been extensively studied, little is known about how crying early in life relates to potential maladaptive behaviors later in life. Large samples of crying recordings, combined with novel machine learning and deep learning techniques can help identify possible connections (for examples in psychology, see [62,63]). One of the difficulties with research in the early stages of life is that it is often too intrusive.…”

Section: The Origin and Role Of Infant Crymentioning

confidence: 99%

Development and Analysis of Deep Learning Architectures

2020

Studies in Computational Intelligence

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In this chapter, we compare deep learning and classical approaches for detection of baby cry sounds in various domestic environments under challenging signal-to-noise ratio conditions. Automatic cry detection has applications in commercial products (such as baby remote monitors) as well as in medical and psycho-social research. We design and evaluate several convolutional neural network (CNN) architectures for baby cry detection, and compare their performance to that of classical machine-learning approaches, such as logistic regression and support vector machines. In addition to feedforward CNNs, we analyze the performance of recurrent neural network (RNN) architectures, which are able to capture temporal behavior of acoustic events. We show that by carefully designing CNN architectures with specialized nonsymmetric kernels, better results are obtained compared to common CNN architectures.

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Does distance from the equator predict self-control? Lessons from the Human Penguin Project

Cited by 5 publications

References 14 publications

COVID-19-related conspiracy beliefs and their relationship with perceived stress and pre-existing conspiracy beliefs in a Prolific Academic sample: A replication and extension of Georgiou et al. (2020)

COVID-19-related conspiracy beliefs and their relationship with perceived stress and pre-existing conspiracy beliefs in a Prolific Academic sample: A replication and extension of Georgiou et al. (2020)

Relationship between electrocardiogram‐based features and personality traits: Machine learning approach

Development and Analysis of Deep Learning Architectures

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