Working memory training does not improve executive functioning or fluid intelligence

Cognitive influences on biosecurity measure compliance during a global pandemic

Introduction

During the first years of the pandemic, COVID-19 forced governments worldwide to take drastic measures to reduce the spread of the virus. Some of these measures included mandatory confinements, constant use of masks, and social distancing. Despite these measures being mandatory in many countries and the abundance of evidence on their effectiveness at slowing the spread of the virus, many people failed to comply with them.

Methods

This research explored the role of cognitive factors in predicting compliance with COVID-19 safety measures across two separate studies. Building on earlier work demonstrating the relevance of cognitive processes in health behaviour, this study aimed to identify key predictors of adherence to safety guidelines during the pandemic. Utilising hierarchical regression models, we investigated the influence of age, sex, cognitive control, cognitive flexibility (Study 1), working memory, psychological health, and beliefs about COVID-19 (Study 2) on compliance to biosafety measures.

Results

Demographic variables and cognitive control were significant predictors of compliance in both studies. However, cognitive flexibility and working memory did not improve the models' predictive capacities. In Study 2, integrating measures of psychological health and beliefs regarding COVID-19 severity significantly improved the model. Further, interaction effects between age and other variables also enhanced the predictive value.

Discussion

The findings emphasise the significant role cognitive control, age, psychological health, and perceptions about COVID-19 play in shaping compliance behaviour, highlighting avenues for targeted interventions to improve public health outcomes during a pandemic.

Can we enhance working memory? Bias and effectiveness in cognitive training studies

Meta-analyses have found that working memory (WM) can be improved with cognitive training; however, some authors have suggested that these improvements are mostly driven by biases in the measurement of WM, especially the use of similar tasks for assessment and training. In the present meta-analysis, we investigated whether WM, fluid intelligence, executive functions, and short-term memory can be improved by cognitive training and evaluated the impact of possible sources of bias. We performed a risk of bias assessment of the included studies and took special care in controlling for practice effects. Data from 52 independent comparisons were analyzed, including cognitive training aimed at different cognitive functions. Our results show small improvements in WM after training (SMD = 0.18). Much larger effects were observed when the analysis was restricted to assessment tasks similar to those used for training (SMD = 1.15). Fluid intelligence was not found to improve as a result of training, and improvements in WM were not related to changes in fluid intelligence. Our analyses did however indicate that cognitive training can improve specific executive functions. Contrary to expectations, a set of meta-regressions indicated that characteristics of the training programme, such as dosage and type of training, do not have an impact on the effectiveness of training. The risk of bias assessment revealed some concerns in the randomization process and possible selective reporting among studies. Overall, our results identified various potential sources of bias, with the most significant being the choice of assessment tasks.

Asymmetric negative transfer effects of working memory training

Gathercole et al. (Journal of Memory and Language, 105, 19-42, 2019) presented a cognitive routine framework for explaining the underlying mechanisms of working memory (WM) training and transfer. This framework conceptualizes training-induced changes as the acquisition of novel cognitive routines similar to learning a new skill. We further infer that WM training might not always generate positive outcomes because previously acquired routines may affect subsequent task performance in various ways. Thus, the present study aimed to demonstrate the negative effects of WM training via two experiments. We conducted Experiment 1 online using a two-phase training paradigm with only three training sessions per phase and replicated the key findings of Gathercole and Norris (in prep.) that training on a backward circle span task (a spatial task) transferred negatively to subsequent training on a backward letter span task (a verbal task). We conducted Experiment 2 using a reversed task order design corresponding to Experiment 1. The results indicated that the transfer from backward letter training to backward circle training was not negative, but rather weakly positive, suggesting that the direction of the negative transfer effect is asymmetric. The present study therefore found that a negative transfer effect can indeed occur under certain WM training designs. The presence of this asymmetric effect indicates that backward circle and backward letter tasks require different optimal routines and that the locus of negative transfer might be the acquisition process of such optimal routines. Hence, the routines already established for backward circle might hinder the development of optimal routines for backward letter, but not vice versa.

The role of attention control in complex real-world tasks

Working memory capacity is an important psychological construct, and many real-world phenomena are strongly associated with individual differences in working memory functioning. Although working memory and attention are intertwined, several studies have recently shown that individual differences in the general ability to control attention is more strongly predictive of human behavior than working memory capacity. In this review, we argue that researchers would therefore generally be better suited to studying the role of attention control rather than memory-based abilities in explaining real-world behavior and performance in humans. The review begins with a discussion of relevant literature on the nature and measurement of both working memory capacity and attention control, including recent developments in the study of individual differences of attention control. We then selectively review existing literature on the role of both working memory and attention in various applied settings and explain, in each case, why a switch in emphasis to attention control is warranted. Topics covered include psychological testing, cognitive training, education, sports, police decision-making, human factors, and disorders within clinical psychology. The review concludes with general recommendations and best practices for researchers interested in conducting studies of individual differences in attention control.

Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach

Wearable technologies for measuring digital and chemical physiology are pervading the consumer market and hold potential to reliably classify states of relevance to human performance including stress, sleep deprivation, and physical exertion. The ability to efficiently and accurately classify physiological states based on wearable devices is improving. However, the inherent variability of human behavior within and across individuals makes it challenging to predict how identified states influence human performance outcomes of relevance to military operations and other high-stakes domains. We describe a computational modeling approach to address this challenge, seeking to translate user states obtained from a variety of sources including wearable devices into relevant and actionable insights across the cognitive and physical domains. Three status predictors were considered: stress level, sleep status, and extent of physical exertion; these independent variables were used to predict three human performance outcomes: reaction time, executive function, and perceptuo-motor control. The approach provides a complete, conditional probabilistic model of the performance variables given the status predictors. Construction of the model leverages diverse raw data sources to estimate marginal probability density functions for each of six independent and dependent variables of interest using parametric modeling and maximum likelihood estimation. The joint distributions among variables were optimized using an adaptive LASSO approach based on the strength and directionality of conditional relationships (effect sizes) derived from meta-analyses of extant research. The model optimization process converged on solutions that maintain the integrity of the original marginal distributions and the directionality and robustness of conditional relationships. The modeling framework described provides a flexible and extensible solution for human performance prediction, affording efficient expansion with additional independent and dependent variables of interest, ingestion of new raw data, and extension to two- and three-way interactions among independent variables. Continuing work includes model expansion to multiple independent and dependent variables, real-time model stimulation by wearable devices, individualized and small-group prediction, and laboratory and field validation.