Multisensory Facilitation of Working Memory Training

Multisensory training based on an APP for enhanced verbal working memory in older adults

With the increasing aging population, contemporary society faces the imperative to develop approaches that efficiently delay the age-related decline in working memory capacity, which is a critical area within cognitive aging research. Nevertheless, there is insufficient evidence to support the efficacy of verbal working memory training across various sensory modalities (visual, auditory, and audiovisual) in enhancing the verbal working memory capacity of older adults. In this study, 60 healthy older adults (mean age = 67.07 ± 3.79 years, comprising 34 women and 26 men, mean education = 15.55 ± 2.53 years) were randomly assigned to one of four groups: visual verbal working memory (V-VWM) group, auditory verbal working memory (A-VWM) group, visual-auditory verbal working memory (VA-VWM) group, and a control group. The training duration spanned 12 days. We also investigated whether baseline level and education predicted the outcomes. Findings indicated that V-VWM training had a large effect on improving V-VWM task performance (Cohen's d = 1.765), A-VWM training showed a substantial effect on A-VWM task performance (Cohen's d = 1.904), and VA-VWM training demonstrated a significant effect on VA-VWM task performance (Cohen's d = 2.319) over pretest scores in older adults. Enhancements achieved through V-VWM training exhibited near transfer effects, improving performance in both A-VWM and VA-VWM tasks. In contrast, gains from A-VWM training were selectively transferred to the VA-VWM task. Furthermore, VA-VWM training led to improvements not only in V-VWM and A-VWM tasks but also extended to verbal operation span task with a significant 29.7 % increase. However, no significant transfer effects were observed for the DSF and DSB tasks across the three training groups. The maintenance effect of VA-VWM training persisted for two weeks across tasks involving VA-VWM, V-VWM, and A-VWM. The baseline of VWM span score influence the effect of V-VWM training and transfer effect of VA-VWM training. Education level did not predict the training effects of V-VWM, A-VWM, and VA-VWM. These findings highlight the nuanced effects of sensory-specific verbal working memory training in older adults, emphasizing the potential of tailored interventions to enhance specific aspects of cognitive function, while also highlighting the promising applications of mobile device training in enhancing cognitive skills among the elderly.

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.

Unicorn, Hare, or Tortoise? Using Machine Learning to Predict Working Memory Training Performance

People differ considerably in the extent to which they benefit from working memory (WM) training. Although there is increasing research focusing on individual differences associated with WM training outcomes, we still lack an understanding of which specific individual differences, and in what combination, contribute to inter-individual variations in training trajectories. In the current study, 568 undergraduates completed one of several N-back intervention variants over the course of two weeks. Participants' training trajectories were clustered into three distinct training patterns (high performers, intermediate performers, and low performers). We applied machine-learning algorithms to train a binary tree model to predict individuals' training patterns relying on several individual difference variables that have been identified as relevant in previous literature. These individual difference variables included pre-existing cognitive abilities, personality characteristics, motivational factors, video game experience, health status, bilingualism, and socioeconomic status. We found that our classification model showed good predictive power in distinguishing between high performers and relatively lower performers. Furthermore, we found that openness and pre-existing WM capacity to be the two most important factors in distinguishing between high and low performers. However, among low performers, openness and video game background were the most significant predictors of their learning persistence. In conclusion, it is possible to predict individual training performance using participant characteristics before training, which could inform the development of personalized interventions.

Audiovisual n-Back Training Alters the Neural Processes of Working Memory and Audiovisual Integration: Evidence of Changes in ERPs

(1) Background: This study investigates whether audiovisual n-back training leads to training effects on working memory and transfer effects on perceptual processing. (2) Methods: Before and after training, the participants were tested using the audiovisual n-back task (1-, 2-, or 3-back), to detect training effects, and the audiovisual discrimination task, to detect transfer effects. (3) Results: For the training effect, the behavioral results show that training leads to greater accuracy and faster response times. Stronger training gains in accuracy and response time using 3- and 2-back tasks, compared to 1-back, were observed in the training group. Event-related potentials (ERPs) data revealed an enhancement of P300 in the frontal and central regions across all working memory levels after training. Training also led to the enhancement of N200 in the central region in the 3-back condition. For the transfer effect, greater audiovisual integration in the frontal and central regions during the post-test rather than pre-test was observed at an early stage (80-120 ms) in the training group. (4) Conclusion: Our findings provide evidence that audiovisual n-back training enhances neural processes underlying a working memory and demonstrate a positive influence of higher cognitive functions on lower cognitive functions.

Enriched learning: behavior, brain, and computation

The presence of complementary information across multiple sensory or motor modalities during learning, referred to as multimodal enrichment, can markedly benefit learning outcomes. Why is this? Here, we integrate cognitive, neuroscientific, and computational approaches to understanding the effectiveness of enrichment and discuss recent neuroscience findings indicating that crossmodal responses in sensory and motor brain regions causally contribute to the behavioral benefits of enrichment. The findings provide novel evidence for multimodal theories of enriched learning, challenge assumptions of longstanding cognitive theories, and provide counterevidence to unimodal neurobiologically inspired theories. Enriched educational methods are likely effective not only because they may engage greater levels of attention or deeper levels of processing, but also because multimodal interactions in the brain can enhance learning and memory.

Unifying framework for cognitive training interventions in brain aging

Cognitive training is a promising tool for slowing or preventing cognitive decline in older adults at-risk for dementia. Its success, however, has been limited by a lack of evidence showing that it reliably causes broad training effects: improvements in cognition across a range of domains that lead to real-world benefits. Here, we propose a framework for enhancing the effect of cognitive training interventions in brain aging. The focus is on (A) developing cognitive training task paradigms that are informed by population-level cognitive characteristics and pathophysiology, and (B) personalizing how these sets are presented to participants during training via feedback loops that aim to optimize "mismatch" between participant capacity and training demands using both adaptation and random variability. In this way, cognitive training can better alter whole-brain topology in a manner that supports broad training effects in the context of brain aging.

Rapid assessment of hand reaching using virtual reality and application in cerebellar stroke

The acquisition of sensory information about the world is a dynamic and interactive experience, yet the majority of sensory research focuses on perception without action and is conducted with participants who are passive observers with very limited control over their environment. This approach allows for highly controlled, repeatable experiments and has led to major advances in our understanding of basic sensory processing. Typical human perceptual experiences, however, are far more complex than conventional action-perception experiments and often involve bi-directional interactions between perception and action. Innovations in virtual reality (VR) technology offer an approach to close this notable disconnect between perceptual experiences and experiments. VR experiments can be conducted with a high level of empirical control while also allowing for movement and agency as well as controlled naturalistic environments. New VR technology also permits tracking of fine hand movements, allowing for seamless empirical integration of perception and action. Here, we used VR to assess how multisensory information and cognitive demands affect hand movements while reaching for virtual targets. First, we manipulated the visibility of the reaching hand to uncouple vision and proprioception in a task measuring accuracy while reaching toward a virtual target (n = 20, healthy young adults). The results, which as expected revealed multisensory facilitation, provided a rapid and a highly sensitive measure of isolated proprioceptive accuracy. In the second experiment, we presented the virtual target only briefly and showed that VR can be used as an efficient and robust measurement of spatial memory (n = 18, healthy young adults). Finally, to assess the feasibility of using VR to study perception and action in populations with physical disabilities, we showed that the results from the visual-proprioceptive task generalize to two patients with recent cerebellar stroke. Overall, we show that VR coupled with hand-tracking offers an efficient and adaptable way to study human perception and action.

Near transfer to an unrelated N-back task mediates the effect of N-back working memory training on matrix reasoning

The extent to which working memory training improves performance on untrained tasks is highly controversial. Here we address this controversy by testing the hypothesis that far transfer may depend on near transfer using mediation models in three separate randomized controlled trials (RCTs). In all three RCTs, totalling 460 individuals, performance on untrained N-back tasks (near transfer) mediated transfer to Matrix Reasoning (representing far transfer) despite the lack of an intervention effect in RCTs 2 and 3. Untrained N-back performance also mediated transfer to a working memory composite, which showed a significant intervention effect (RCT 3). These findings support a model of N-back training in which transfer to untrained N-back tasks gates further transfer (at least in the case of working memory at the construct level) and Matrix Reasoning. This model can help adjudicate between the many studies and meta-analyses of working memory training that have provided mixed results but have not examined the relationship between near and far transfer on an individual-differences level.