Poster No:
765
Submission Type:
Abstract Submission
Authors:
Yohan Wards1, Shane Ehrhardt1, Kelly Garner2, Jason Mattingley1, Hannah Filmer1, Paul Dux1
Institutions:
1The University of Queensland, St Lucia, Queensland, 2The University of New South Wales, Sydney, New South Wales
First Author:
Yohan Wards
The University of Queensland
St Lucia, Queensland
Co-Author(s):
Kelly Garner
The University of New South Wales
Sydney, New South Wales
Paul Dux
The University of Queensland
St Lucia, Queensland
Introduction:
Humans excel at learning generalisation-the ability to transfer skills from one task to another-which is essential for adapting to new environments. However, this flexibility often comes at the expense of multitasking performance. Recent theories propose a trade-off between multitasking and generalisation, where shared variability in task representations in frontoparietal and subcortical regions facilitates learning transfer but limits multitasking efficiency (Musslick, 2017; Garner, 2023). Training improves multitasking performance by minimising representational overlap in these regions (Garner, 2015). While effective for the trained task, this reduction may constrain transfer to untrained tasks. Non-invasive brain stimulation, such as transcranial direct current stimulation (tDCS), offers a promising avenue to address this limitation. tDCS has been shown to enhance performance on untrained tasks when paired with cognitive training (Filmer, 2017). This study investigates whether pairing left prefrontal tDCS with multitasking training facilitates transfer to an untrained visual search task by sustaining or enhancing representational overlap, leveraging ultra-high-field fMRI and multivariate pattern analysis to explore the neural mechanisms underlying transfer.
Methods:
In a double-blind, preregistered study, 178 participants were divided into five groups, four that trained on multitasking combined with active or sham tDCS (1mA left PFC, 1mA right PFC, 2mA left PFC, sham) and one that trained on a control task while receiving 1mA left PFC stimulation. Participants completed pre- and post-training behavioural assessments, multitasking training (4 days) paired with tDCS, and ultra-high-field 7T fMRI scans before and after training. Multitasking performance was assessed via a single/dual-task paradigm, and transfer was measured with a visual search task requiring participants to locate a target among distractors. Representational overlap of the single-tasks in task-relevant regions was evaluated using multivariate pattern analysis (MVPA), focusing on changes in decoding accuracy.
Results:
Participants receiving left or right hemisphere 1mA prefrontal tDCS during multitasking training demonstrated significant improvements in visual search transfer compared to sham, with faster response times for set sizes 12 and 16 (BF10 = 11.41–122.78). These effects persisted one month post-training (BF10 = 6.08–54.28). No transfer effects were observed for 2mA left stimulation or the RSVP training group, highlighting the specificity of the 1mA and multitasking training condition. MVPA revealed that increases in decoding accuracy in task-relevant regions, including the anterior cerebellum, left superior parietal lobe, and right orbitofrontal cortex, were negatively correlated with improvements in visual search performance for the 1mA left PFC group (r = −0.45 to −0.58, BF10 = 5.0–64.7). Crucially, these correlations were significantly different from sham (FDR corrected p < 0.05). No significant correlations were observed for the right PFC, 2mA left PFC, or RSVP training groups when compared to sham.
Conclusions:
Our findings demonstrate that coupling left prefrontal tDCS with multitasking training modifies task representation dynamics, facilitating learning generalisation to an untrained visual search task. Specifically, tDCS paired with training counteracts the typical reduction in representational overlap, promoting shared task representations in regions such as the left superior parietal lobe, right orbitofrontal cortex, and cerebellum. These results align with recent theories suggesting that shared task representations support generalisation, and they highlight the role of tDCS in sustaining an early learning state to enhance transfer. This study underscores the potential of combining tDCS with targeted training to optimise skill transfer and provides a robust framework for future exploration of generalisation mechanisms.
Brain Stimulation:
TDCS
Higher Cognitive Functions:
Higher Cognitive Functions Other 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Classification and Predictive Modeling
Multivariate Approaches 2
Keywords:
Basal Ganglia
Cerebellum
FUNCTIONAL MRI
Machine Learning
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Other, Please specify
-
tDCS
Functional MRI
For human MRI, what field strength scanner do you use?
7T
Which processing packages did you use for your study?
Other, Please list
-
fMRIPrep; PRoNTo; Pingouin
SPM
Provide references using APA citation style.
Filmer, H. L. (2017). Anodal tDCS applied during multitasking training leads to transferable performance gains. Scientific Reports, 7(1), 12988-12911.
Garner, K. G. (2015). Training conquers multitasking costs by dividing task representations in the frontoparietal-subcortical system. Proceedings of the National Academy of Sciences, 112(46), 14372–14377.
Garner, K. G. (2023). Multitasking and the brain: Mechanisms, training, and applications. Nature Reviews Neuroscience, 24(2),98-112.
Musslick, S. (2017). Multitasking capability versus learning efficiency in neural network architectures. In Proceedings of the 39th Annual Meeting of the Cognitive Science Society (pp. 829–834).
No