Multisensory Facilitation of Working Memory Training

Pahor, Anja; Collins, Curtis; Smith-Peirce, Rachel N.; Moon, Austin; Stavropoulos, Trevor; Silva, Ilse; Peng, Elaine; Jaeggi, Susanne M.; Seitz, Aaron R.

doi:10.1007/s41465-020-00196-y

Cited by 12 publications

(9 citation statements)

References 44 publications

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“…We also collected participants' cognitive assessments after training, however, they are not included for the purpose of the present paper. These data are reported elsewhere (e.g Pahor et al, 2021;2022)…”

supporting

confidence: 70%

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

Feng,

Pahor,

Seitz

et al. 2023

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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.

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confidence: 70%

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

Feng,

Pahor,

Seitz

et al. 2023

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“…Our study also contributes to decades of research confirming benefits when multisensory information is available in domains as varied as memory [ 72 ], learning [ 73 ], and reaction time [ 74 ]. In validating the use of VR to study multisensory processes, this new technique provides the capacity to expand on these traditional paradigms to evaluate participants as they move interactively with their environment.…”

Section: Discussionmentioning

confidence: 78%

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

et al. 2022

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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.

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“…Interestingly, untrained N-back performance only mediated transfer to the WM composite and not the IC composite. Transfer to sequencing and/or complex WM tasks has been reported by some [53][54][55][56][57][58][59] , but not other N-back training studies 54,[60][61][62][63][64][65][66] . Likewise, there is no consensus regarding transfer to inhibitory control measures, with some WM training studies reporting training-related benefits 55,56,67,68 , yet others failing to find evidence of transfer 51,69 .…”

Section: Discussionmentioning

confidence: 88%

“…To provide context, in gamified N-back training, participants played a platform game called "Recollect the Study" (video can be found here: https://www.youtube.com/watch?v=zhgL8Oe42Yk) in which they were tasked with collecting stimuli that match those presented N items earlier. Stimuli consisted of simple colored objects and were paired with unique sounds to support multisensory processing of said objects 59 .…”

Section: Methodsmentioning

confidence: 99%

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Getting at Underlying Mechanisms of Far Transfer - the Mediating Role of Near Transfer

Pahor¹,

Seitz²,

Jaeggi³

2021

Preprint

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The extent that 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 upon near transfer using mediation models in three separate randomized controlled trials (RCTs). In all three RCTs, totaling 460 individuals, performance on untrained N-back tasks (near transfer) mediated transfer to Matrix Reasoning (a measure of fluid intelligence, representing far transfer). Untrained N-back performance also mediated transfer to a working memory composite (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.

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Multisensory Facilitation of Working Memory Training

Cited by 12 publications

References 44 publications

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

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

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

Getting at Underlying Mechanisms of Far Transfer - the Mediating Role of Near Transfer

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