Search

Studies have indicated that video gaming is positively associated with cognitive performance in select cognitive domains, but the magnitudes of these associations have been called into question, as they have frequently been based on extreme groups analyses that have compared video gamers with non-gamers. When including the whole range of participants, and not just extreme cases, these effects were observed to reduce markedly (Unsworth et al., 2015). To further study this issue, we compared the associations between video gaming and aspects of working memory (WM) performance in an extreme groups design to those of a design that includes the full range of participants in a large adult sample (n = 503). WM was measured with three composite scores (verbal WM, visuospatial WM, n-back). The extreme groups analyses showed that video gamers performed better than non-gamers on all three WM measures, while the whole sample analyses indicated weak positive associations between the time spent playing video games and visuospatial WM and n-back performance. Thus, study design modulated the effects, but two of the three associations between WM and video gaming were consistent across both analysis techniques. A separate study confirmed that our questionnaire-based estimate of gaming hours was reliable when compared with one-week diaries of videogame playing. While the present cross-sectional results preclude causal inferences, possible mechanisms of WM - videogame playing associations and future research directions are discussed. Overall, our results indicate that cognition - videogame playing relationships, albeit weak, are not solely due to recently discussed methodological artefacts concerning the particular analytical approach and survey reliability.

Working memory (WM) is a key cognitive system that is strongly related to other cognitive domains and relevant for everyday life. However, the structure of WM is yet to be determined. A number of WM models have been put forth especially by factor analytical studies. In broad terms, these models vary by their emphasis on WM contents (e.g., visuospatial, verbal) vs. WM processes (e.g., maintenance, updating) as critical, dissociable elements. Here we conducted confirmatory and exploratory factor analyses on a broad set of WM tasks, half of them numerical-verbal and half of them visuospatial, representing four commonly used task paradigms: simple span, complex span, running memory, and n-back. The tasks were selected to allow the detection of both content-based (visuospatial, numerical-verbal) and process-based (maintenance, updating) divisions. The data were collected online which allowed the recruitment of a large and demographically diverse sample of adults (n = 711). Both factor analytical methods pointed to a clear division according to task content for all paradigms except n-back, while there was no indication for a process-based division. Besides the content-based division, confirmatory factor analyses supported a model that also included a general WM factor. The n-back tasks had the highest loadings on the general factor, suggesting that this factor reflected high-level cognitive resources such as executive functioning and fluid intelligence that are engaged with all WM tasks, and possibly even more so with the n-back. Together with earlier findings that indicate high variability of process-based WM divisions, we conclude that the most robust division of WM is along its contents (visuospatial vs. numerical-verbal), rather than along its hypothetical subprocesses.

Psychological assessment scales need to be psychometrically sound, but previous research on the factorial structure of one of the most common measures of trait reactance, the Hong Psychological Reactance Scale (HPRS), has provided multiple latent structures and contradicting findings, and the statistical methodology of previous research can be criticized. These issues raise questions regarding the interpretability and usefulness of the HPRS as a measure of trait reactance. In this two-part study, we followed contemporary statistical recommendations and tested the fit of 12 previously observed factor solutions of the HPRS using confirmatory factor analysis in two relatively large Finnish samples (n = 624 & 518). Furthermore, we tested what factor structure was supported by a data-driven exploratory factor analysis approach. Both our confirmatory and exploratory factor analyses supported a one-factor solution for the HPRS. However, in the exploratory analyses, the single factor accounted for only approximately 40% of the total variance. Our results also indicated that a reduced scale provided the best fit in our confirmatory analyses. Combined, these findings imply that the HPRS could use some improvement. Finally, to shed more light on the reactance construct and the construct validity of the HPRS, we replicated previous research showing negative associations between trait reactance and the Big Five Agreeableness and Conscientiousness traits. [ABSTRACT FROM AUTHOR]

Background: Studies have shown a high prevalence of depression during pregnancy, and there is also evidence that cognitive behavioral therapy (CBT) is one of the most effective psychosocial interventions. Emerging evidence from randomized controlled trials (RCTs) has shown that technology has been successfully harnessed to provide CBT interventions for other populations. However, very few studies have focused on their use during pregnancy. This approach has become increasingly important in many clinical areas due to the COVID-19 pandemic, and our study aimed to expand the knowledge in this particular clinical area.Objective: Our systematic review aimed to bring together the available research-based evidence on digitalized CBT interventions for depression symptoms during pregnancy.Methods: A systematic review of the Web of Science, Cochrane Central Register of Controlled Trials, CINAHL, MEDLINE, Embase, PsycINFO, Scopus, ClinicalTrials.gov, and EBSCO Open Dissertations databases was carried out from the earliest available evidence to October 27, 2021. Only RCT studies published in English were considered. The PRISMA (Preferred Reporting Items of Systematic Reviews and Meta-analyses) guidelines were followed, and the protocol was registered on the Prospective Register of Systematic Reviews. The risk of bias was assessed using the revised Cochrane risk-of-bias tool for randomized trials.Results: The review identified 7 studies from 5 countries (the United States, China, Australia, Norway, and Sweden) published from 2015 to 2021. The sample sizes ranged from 25 to 1342 participants. The interventions used various technological elements, including text, images, videos, games, interactive features, and peer group discussions. They comprised 2 guided and 5 unguided approaches. Using digitalized CBT interventions for depression during pregnancy showed promising efficacy, with guided intervention showing higher effect sizes (Hedges g=1.21) than the unguided interventions (Hedges g=0.14-0.99). The acceptability of the digitalized CBT interventions was highly encouraging, based on user feedback. Attrition rates were low for the guided intervention (4.5%) but high for the unguided interventions (22.1%-46.5%). A high overall risk of bias was present for 6 of the 7 studies.Conclusions: Our search only identified a small number of digitalized CBT interventions for pregnant women, despite the potential of this approach. These showed promising evidence when it came to efficacy and positive outcomes for depression symptoms, and user feedback was positive. However, the overall risk of bias suggests that the efficacy of the interventions needs to be interpreted with caution. Future studies need to consider how to mitigate these sources of biases. Digitalized CBT interventions can provide prompt, effective, evidence-based interventions for pregnant women. This review increases our understanding of the importance of digitalized interventions during pregnancy, including during the COVID-19 pandemic.Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42020216159; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=216159. [ABSTRACT FROM AUTHOR]

Measurement of cognitive functions is typically based on the implicit assumption that the mental architecture underlying cognitive task performance is constant throughout the task. In contrast, skill learning theory implies that cognitively demanding task performance is an adaptive process that progresses from initial heavy engagement of effortful and task-general metacognitive and executive control processes towards more automatic and task-specific performance. However, this hypothesis is rarely applied to the short time spans of traditional cognitive tasks such as working memory (WM) tasks. We utilized longitudinal structural equation models on two well-powered data sets to test the hypothesis that the initial stages of WM task performances load heavily on a task-general g-factor and then start to diverge towards factors specific to task structure. In line with the hypothesis, data from the first experiment (N = 296) was successfully fitted in a model with task-initial unity of the WM paradigm-specific latent factors, after which their intercorrelations started to diverge. The second experiment (N = 201) replicated this pattern except for one paradigm-specific latent factor. These preliminary results suggest that the processes underlying working memory task performance tend to progress rapidly from more task-general towards task-specific, in line with the cognitive skill learning framework. Such task-internal dynamics has important implications for the measurement of complex cognitive functions.