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A variety of factors contribute to the degree to which a person feels lonely and socially isolated. These factors may be particularly relevant in contexts requiring social distancing, e.g., during the COVID-19 pandemic or in states of immunodeficiency. We present the Loneliness and Isolation during Social Distancing (LISD) Scale. Extending existing measures, the LISD scale measures both state and trait aspects of loneliness and isolation, including indicators of social connectedness and support. In addition, it reliably predicts individual differences in anxiety and depression. Data were collected online from two independent samples in a social distancing context (the COVID-19 pandemic). Factorial validation was based on exploratory factor analysis (EFA; Sample 1, N = 244) and confirmatory factor analysis (CFA; Sample 2, N = 304). Multiple regression analyses were used to assess how the LISD scale predicts state anxiety and depression. The LISD scale showed satisfactory fit in both samples. Its two state factors indicate being lonely and isolated as well as connected and supported, while its three trait factors reflect general loneliness and isolation, sociability and sense of belonging, and social closeness and support. Our results imply strong predictive power of the LISD scale for state anxiety and depression, explaining 33 and 51% of variance, respectively. Anxiety and depression scores were particularly predicted by low dispositional sociability and sense of belonging and by currently being more lonely and isolated. In turn, being lonely and isolated was related to being less connected and supported (state) as well as having lower social closeness and support in general (trait). We provide a novel scale which distinguishes between acute and general dimensions of loneliness and social isolation while also predicting mental health. The LISD scale could be a valuable and economic addition to the assessment of mental health factors impacted by social distancing.
Objective
Alzheimer’s disease (AD) is a growing challenge worldwide, which is why the search for early-onset predictors must be focused as soon as possible. Longitudinal studies that investigate courses of neuropsychological and other variables screen for such predictors correlated to mild cognitive impairment (MCI). However, one often neglected issue in analyses of such studies is measurement invariance (MI), which is often assumed but not tested for. This study uses the absence of MI (non-MI) and latent factor scores instead of composite variables to assess properties of cognitive domains, compensation mechanisms, and their predictability to establish a method for a more comprehensive understanding of pathological cognitive decline.
Methods
An exploratory factor analysis (EFA) and a set of increasingly restricted confirmatory factor analyses (CFAs) were conducted to find latent factors, compared them with the composite approach, and to test for longitudinal (partial-)MI in a neuropsychiatric test battery, consisting of 14 test variables. A total of 330 elderly (mean age: 73.78 ± 1.52 years at baseline) were analyzed two times (3 years apart).
Results
EFA revealed a four-factor model representing declarative memory, attention, working memory, and visual–spatial processing. Based on CFA, an accurate model was estimated across both measurement timepoints. Partial non-MI was found for parameters such as loadings, test- and latent factor intercepts as well as latent factor variances. The latent factor approach was preferable to the composite approach.
Conclusion
The overall assessment of non-MI latent factors may pose a possible target for this field of research. Hence, the non-MI of variances indicated variables that are especially suited for the prediction of pathological cognitive decline, while non-MI of intercepts indicated general aging-related decline. As a result, the sole assessment of MI may help distinguish pathological from normative aging processes and additionally may reveal compensatory neuropsychological mechanisms.
In social interaction, the facial expression of an opponent contains information that may influence the interaction. We asked whether facial expression affects decision-making in the ultimatum game. In this two-person game, the proposer divides a sum of money into two parts, one for each player, and then the responder decides whether to accept the offer or reject it. Rejection means that neither player gets any money. Results of a large-sample study support our hypothesis that offers from proposers with a smiling facial expression are more often accepted, compared to a neutral facial expression. Moreover, we found lower acceptance rates for offers from proposers with an angry facial expression.
Theta oscillations in the EEG have been shown to reflect ongoing cognitive processes related to mental effort. Here, we show that the pattern of theta oscillation in response to varying cognitive demands reflects stable individual differences in the personality trait epistemic motivation: Individuals with high levels of epistemic motivation recruit relatively more cognitive resources in response to situations possessing high, compared to low, cognitive demand; individuals with low levels do not show such a specific response. Our results provide direct evidence for the theory of the construct need for cognition and add to our understanding of the neural processes underlying theta oscillations. More generally, we provide an explanation how individual differences in personality traits might be represented on a neural level.
The present study investigates how different emotions can alter social bargaining behavior. An important paradigm to study social bargaining is the Ultimatum Game. There, a proposer gets a pot of money and has to offer part of it to a responder. If the responder accepts, both players get the money as proposed by the proposer. If he rejects, none of the players gets anything. Rational choice models would predict that responders accept all offers above 0. However, evidence shows that responders typically reject a large proportion of all unfair offers. We analyzed participants’ behavior when they played the Ultimatum Game as responders and simultaneously collected electroencephalogram data in order to quantify the feedback-related negativity and P3b components. We induced state affect (momentarily emotions unrelated to the task) via short movie clips and measured trait affect (longer-lasting emotional dispositions) via questionnaires. State happiness led to increased acceptance rates of very unfair offers. Regarding neurophysiology, we found that unfair offers elicited larger feedback-related negativity amplitudes than fair offers. Additionally, an interaction of state and trait affect occurred: high trait negative affect (subsuming a variety of aversive mood states) led to increased feedback-related negativity amplitudes when participants were in an angry mood, but not if they currently experienced fear or happiness. We discuss that increased rumination might be responsible for this result, which might not occur, however, when people experience happiness or fear. Apart from that, we found that fair offers elicited larger P3b components than unfair offers, which might reflect increased pleasure in response to fair offers. Moreover, high trait negative affect was associated with decreased P3b amplitudes, potentially reflecting decreased motivation to engage in activities. We discuss implications of our results in the light of theories and research on depression and anxiety.
It's costly punishment, not altruistic: Low midfrontal theta and state anger predict punishment
(2020)
Punishment in economic games has been interpreted as “altruistic.” However, it was shown that punishment is related to trait anger instead of trait altruism in a third‐party dictator game if compensation is also available. Here, we investigated the influence of state anger on punishment and compensation in the third‐party dictator game. Therefore, we used movie sequences for emotional priming, including the target states anger, happy, and neutral. We measured the Feedback‐Related Negativity (FRN) and midfrontal theta band activation, to investigate an electro‐cortical correlate of the processing of fair and unfair offers. Also, we assessed single‐trial FRN and midfrontal theta band activation as a predictor for punishment and compensation. We found that punishment was linked to state anger. Midfrontal theta band activation, which has previously been linked to altruistic acts and cognitive control, predicted less punishment. Additionally, trait anger led to enhanced FRN for unfair offers. This led to the interpretation that the FRN depicts the evaluation of fairness, while midfrontal theta band activation captures an aspect of cognitive control and altruistic motivation. We conclude that we need to redefine “altruistic punishment” into “costly punishment,” as no direct link of altruism and punishment is given. Additionally, midfrontal theta band activation complements the FRN and offers additional insights into complex responses and decision processes, especially as a single trial predictor.
We investigated the influence of mental imagery expertise in 15 pen and paper role-players as an expert group compared to the gender-matched control group of computer role-players in the difficult Vandenberg and Kuse mental rotation task. In this task, the participants have to decide which two of four rotated figures match the target figure. The dependent measures were performance speed and accuracy. In our exploratory investigation, we further examined midline frontal theta band activation, parietal alpha band activation, and parietal alpha band asymmetry in EEG as indicator for the chosen rotation strategy. Additionally, we explored the gender influence on performance and EEG activation, although a very small female sample section was given. The expected gender difference concerning performance accuracy was negated by expertise in pen and paper role-playing women, while the gender-specific difference in performance speed was preserved. Moreover, gender differences concerning electro-cortical measures revealed differences in rotation strategy, with women using top-down strategies compared to men, who were using top-down strategies and active inhibition of associative cortical areas. These strategy uses were further moderated by expertise, with higher expertise leading to more pronounced activation patters, especially during successful performance. However, due to the very limited sample size, the findings of this explorative study have to be interpreted cautiously.
Altruistic punishment is connected to trait anger, not trait altruism, if compensation is available
(2018)
Altruistic punishment and altruistic compensation are important concepts that are used to investigate altruism. However, altruistic punishment has been found to be correlated with anger. We were interested whether altruistic punishment and altruistic compensation are both driven by trait altruism and trait anger or whether the influence of those two traits is more specific to one of the behavioral options. We found that if the participants were able to apply altruistic compensation and altruistic punishment together in one paradigm, trait anger only predicts altruistic punishment and trait altruism only predicts altruistic compensation. Interestingly, these relations are disguised in classical altruistic punishment and altruistic compensation paradigms where participants can either only punish or compensate. Hence altruistic punishment and altruistic compensation paradigms should be merged together if one is interested in trait altruism without the confounding influence of trait anger.
The prosocial tendencies measure (PTM; Carlo and Randall, 2002) is a widely used measurement for prosocial tendencies in English speaking participants. This instrument distinguishes between six different types of prosocial tendencies that partly share some common basis, but also can be opposed to each other. To examine these constructs in Germany, a study with 1067 participants was conducted. The study investigated the structure of this German version of the PTM-R via exploratory factor analysis, confirmatory factor analysis, correlations with similar constructs in subsamples as well as via measurement invariance test concerning the original English version. The German translation showed a similar factor structure to the English version in exploratory factor analysis and in confirmatory factor analysis. Measurement invariance was found between the English and German language versions of the PTM and support for the proposed six-factor structure (altruistic, anonymous, compliant, dire, emotional and public prosocial behavior) was also found in confirmatory factor analysis. Furthermore, the expected interrelations of these factors of prosocial behavior tendencies were obtained. Finally, correlations of the prosocial behavior tendencies with validating constructs and behaviors were found. Thus, the findings stress the importance of seeing prosocial behavior not as a single dimension construct, but as a factored construct which now can also be assessed in German speaking participants.
Background: Since the replication crisis, standardization has become even more important in psychological science and neuroscience. As a result, many methods are being reconsidered, and researchers’ degrees of freedom in these methods are being discussed as a potential source of inconsistencies across studies.
New Method: With the aim of addressing these subjectivity issues, we have been working on a tutorial-like EEG (pre-)processing pipeline to achieve an automated method based on the semi-automated analysis proposed by Delorme and Makeig.
Results: Two scripts are presented and explained step-by-step to perform basic, informed ERP and frequency-domain analyses, including data export to statistical programs and visual representations of the data. The open-source software EEGlab in MATLAB is used as the data handling platform, but scripts based on code provided by Mike Cohen (2014) are also included.
Comparison with existing methods: This accompanying tutorial-like article explains and shows how the processing of our automated pipeline affects the data and addresses, especially beginners in EEG-analysis, as other (pre)-processing chains are mostly targeting rather informed users in specialized areas or only parts of a complete procedure. In this context, we compared our pipeline with a selection of existing approaches.
Conclusion: The need for standardization and replication is evident, yet it is equally important to control the plausibility of the suggested solution by data exploration. Here, we provide the community with a tool to enhance the understanding and capability of EEG-analysis. We aim to contribute to comprehensive and reliable analyses for neuro-scientific research.