Reactivation-coupled brain stimulation enables complete learning generalization
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eLife Assessment
This valuable study shows that combining reactivation-based training with anodal tDCS yields an unusually broad generalization of visual perceptual learning, while preserving robust learning gains and markedly reducing total training time. Although the empirical evidence is solid, the proposed mechanistic account, i.e., the GABA modulation, disrupted offline consolidation and reduced perceptual overfitting, remains insufficiently substantiated, as these assumptions lack direct neurochemical support, and several alternative behavioral explanations and necessary control comparisons have not been fully addressed. The work will be of broad interest to researchers investigating brain plasticity, perceptual learning, and rehabilitation training.
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Abstract
Generalization of learned knowledge to new contexts is essential for adaptive behavior. Despite extensive research on the brain plasticity mechanisms underlying learning specificity, the mechanisms that facilitate generalization remain poorly understood. Here, we investigate whether using brain stimulation to disrupt offline consolidation in visual cortex promotes learning generalization. Separate groups of participants (N = 144) were trained on visual detection tasks using either a reactivation-based protocol or conventional full-practice, combined with anodal or sham transcranial direct current stimulation (tDCS) over the visual cortex. Strikingly, only combination of reactivation-based learning with anodal tDCS produced complete generalization from trained to untrained stimuli, an effect consistently replicated across features (orientation, motion direction). In contrast, reactivation-based learning alone and conventional full-practice – whether with or without brain stimulation – yielded stimulus-specific learning. Importantly, reactivation-coupled brain stimulation achieved generalization with an 80% reduction in training trials while maintaining learning gains comparable to full-practice. These findings demonstrate that reactivation and neuromodulation interact to unlock learning generalization, revealing a key brain plasticity mechanism and offering a rapid, translatable strategy for sensory rehabilitation.
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eLife Assessment
This valuable study shows that combining reactivation-based training with anodal tDCS yields an unusually broad generalization of visual perceptual learning, while preserving robust learning gains and markedly reducing total training time. Although the empirical evidence is solid, the proposed mechanistic account, i.e., the GABA modulation, disrupted offline consolidation and reduced perceptual overfitting, remains insufficiently substantiated, as these assumptions lack direct neurochemical support, and several alternative behavioral explanations and necessary control comparisons have not been fully addressed. The work will be of broad interest to researchers investigating brain plasticity, perceptual learning, and rehabilitation training.
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Reviewer #1 (Public review):
Summary:
This manuscript by Xie and colleagues presents an intriguing behavioral finding for the field of perceptual learning (PL): combining the reactivation-based training paradigm with anodal tDCS induces complete generalization of the learning effect. Notably, this generalization is achieved without compromising the magnitude of learning effects and with an 80% reduction in total training time. The experimental design is well-structured, and the observed complete generalization is robustly replicated across two stimulus dimensions (orientation and motion direction).
However, while the empirical results are methodologically valid and scientifically surprising, the theoretical framework proposed to explain them appears underdeveloped and, in some cases, difficult to reconcile with the existing literature. …
Reviewer #1 (Public review):
Summary:
This manuscript by Xie and colleagues presents an intriguing behavioral finding for the field of perceptual learning (PL): combining the reactivation-based training paradigm with anodal tDCS induces complete generalization of the learning effect. Notably, this generalization is achieved without compromising the magnitude of learning effects and with an 80% reduction in total training time. The experimental design is well-structured, and the observed complete generalization is robustly replicated across two stimulus dimensions (orientation and motion direction).
However, while the empirical results are methodologically valid and scientifically surprising, the theoretical framework proposed to explain them appears underdeveloped and, in some cases, difficult to reconcile with the existing literature. Several arguments are insufficiently justified. In addition, the introduction of a non-standard metric (NGI: normalized learning gain index) raises concerns about the interpretability and comparability with existing PL literature.
Strengths:
(1) Rigorous experimental design
In this study, Xie and colleagues employed a 2×2 factorial design (Training paradigm: Reactivation vs. Full-Practice × tDCS protocols: Anodal vs. Sham), which allowed clear dissociation of the main and interaction effects.
(2) High statistical credibility
Sample sizes were predetermined using G*Power, non-significant effects were evaluated using the Bayes factor, and the core behavioral findings were replicated in a second stimulus dimension. These strengthen the credibility of the findings.
(3) Strong translational potential
The observed complete generalization could have useful implications for sensory rehabilitation. The large reduction (80%) in total training time is particularly compelling.
Weaknesses:
(1) NGI (Normalized learning gain index) is a non-standard behavioral metric and may distort interpretability.
NGI (pre - post / ((pre + post) / 2)) is rarely used in PL studies to measure learning effects. Almost all PL studies rely on raw thresholds and percent improvements (pre - post / pre), making it difficult to contextualize the current NGI-based results within the broader field. The current manuscript provides no justification for adopting NGI.
A more critical issue is the NGI's nonlinearity: by normalizing to the mean of pre- and post-test thresholds, it disproportionately inflates learning effects for participants with lower post-test thresholds. Notably, the "complete generalization" claims are illustrated mainly with NGI plots. Although the authors also analyze thresholds directly and the results also support the core claim, the interpretation in the text relies heavily on NGI.
The authors may consider rerunning key analyses using the standard percent improvement metric. If retaining NGI, the authors should provide explicit justification for why NGI is superior to standard measures.
(2) The proposed theoretical framework is sometimes unclear and insufficiently supported.
The authors propose the following mechanistic chain:
(a) reactivation-based learning depends on offline consolidation mediated by GABA (page 4 line 73);
(b) online a-tDCS reduces GABA (page 4, line 76), thereby disrupting offline consolidation (page 11, line 225);
(c) disrupted offline consolidation reduces perceptual overfitting (page 4, line 77; page 11, line 225), thereby enabling generalization;
(d) under full-practice training, a-tDCS increases specificity via a different mechanism (page 11 line 235).
While this framework is plausible in broad terms, several components are speculative at best in the absence of neurochemical or neural measurements.
(3) Several reasoning steps require further clarification.
(a) Mechanisms of Reactivation-based Learning.
The manuscript focuses on the neurochemical basis of reactivation-based learning. However, reactivation-induced neurochemical changes differ across brain regions. In the motor cortex, Eisenstein et al. (2023) reported that after reactivation, increased GABA and decreased E/I ratio were associated with offline gains. In contrast, Bang et al. (2018) demonstrated that, in the visual cortex, reactivation decreased GABA and increased E/I ratio. While both studies are consistent with GABA involvement, the direction of GABA modulation differs. The authors should clarify this discrepancy.
More importantly, Bang et al. (2018) demonstrated that reactivation-based (3 blocks) and full-practice (16 blocks) training produced similar time courses of E/I ratio changes in V1: an initial increase followed by a decrease. Given this similarity, the manuscript would benefit from a more thorough discussion of how the two paradigms diverge mechanistically. For example, behaviorally, Song et al. (2021) reported greater generalization with reactivation-based training than with full-practice training, aligning with Kondat et al. (2025). Neurally, Kondat et al. (2024) showed that reactivation-based training increased activity in higher-order brain regions (e.g., IPS), whereas full practice training reduced connectivity between temporal and parietal regions.(b) tDCS Mechanisms and Protocols.
The effect of a-tDCS on GABA is not consistent across brain regions. While a-tDCS reliably reduces GABA in the motor cortex, recently, a more related work (Abuleli et al., 2025) reports no significant modulation of GABA or Glx in V1, challenging the authors' assumption of tDCS-induced GABA reduction in the visual cortex.
The manuscript proposes that online a-tDCS disrupts offline consolidation is somewhat difficult to interpret conceptually. Online tDCS typically modulates processes occurring during stimulation (e.g., encoding process, attentional state), whereas consolidation occurs afterward. Thus, stating that online tDCS protocols only disrupt offline consolidation without considering the possibility that they first modulate the encoding process is difficult to interpret. Even if tDCS has prolonged effects, the link between online stimulation and disruption of offline consolidation remains unelucidated.
(c) Missing links between GABA modulation and perceptual overfitting.
The proposed chain ("tDCS disrupts consolidation → reduced overfitting → improved generalization") skips a critical step: how GABA modulation translates to changes in neural representational properties (e.g., tuning width, representational overlap between trained/untrained stimuli) that define "perceptual overfitting." The PL literature has not established a link between GABA levels and these representational changes, leaving a key component of the mechanistic explanation underspecified.
(d) Insufficient explanation of the opposite effects.
The manuscript does not fully explain why the same a-tDCS promotes generalization in reactivation-based training but increases specificity in full-practice training. Both paradigms engage offline consolidations, and, as mentioned above, the time courses of E/I ratio changes are similar for 3-block reactivation-based or 16-block training. Thus, if offline consolidation mechanisms (and their associated E/I changes) are comparable across paradigms, it is unclear why identical a-tDCS would produce opposite outcomes in the two paradigms.
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Reviewer #2 (Public review):
Xie et al., combined transcranial direct current brain stimulation (tDCS) and a reactivation-based training protocol to investigate the generalization of learning. Using visual perceptual learning as a model, they found that a reactivation-based training protocol, when combined with anodal tDCS over the visual cortex, can induce learning transfer to untrained visual orientations and motion directions. Interestingly, extending reactivation-based training to a full-training protocol with more training trials did not induce generalization of learning. Furthermore, even when paired with tDCS, extending the training protocol did not provide benefits for generalization of learning. This study provides interesting insights into the mechanisms of brain plasticity and how future training protocols could be designed …
Reviewer #2 (Public review):
Xie et al., combined transcranial direct current brain stimulation (tDCS) and a reactivation-based training protocol to investigate the generalization of learning. Using visual perceptual learning as a model, they found that a reactivation-based training protocol, when combined with anodal tDCS over the visual cortex, can induce learning transfer to untrained visual orientations and motion directions. Interestingly, extending reactivation-based training to a full-training protocol with more training trials did not induce generalization of learning. Furthermore, even when paired with tDCS, extending the training protocol did not provide benefits for generalization of learning. This study provides interesting insights into the mechanisms of brain plasticity and how future training protocols could be designed to achieve robust and generalizable learning outcomes.
The authors supported their arguments with a series of well-constructed experiments. The conclusions are largely supported by the data, although some clarifications about their hypotheses and control analyses could strengthen the work:
(1) The authors hypothesize that tDCS can reduce perceptual overfitting through reduced GABA concentrations in the visual cortex, which leads to learning transfer. However, without a clear description of the role of GABA in perceptual learning and perceptual overfitting, it is difficult for the reader to understand why reduced GABA concentrations would contribute to generalization. Do the authors imply that increased GABA can lead to specificity? Are there studies that can support this argument? The authors also did not describe clearly how reactivation-based visual perceptual learning can modify GABA levels in the visual cortex differently (compared to full-practice) during training and during the offline consolidation phase. In order for the reader to better understand their hypotheses and the motivation of the current study, it is beneficial for the authors to provide a concise but clearer description of the roles of GABA in perceptual learning with a focus on the roles of GABA in generalization and during off-line consolidation for different types of training protocols (see for instance Bang et al., 2018; Frangou et al., 2019; Frank et al., 2022; Jia et al., 2024; Shibata et al., 2011; Tamaki et al., 2020; Yamada et al., 2024).
(2) Based on the results, an alternative explanation is that the amount of transfer to the untrained visual feature might be related to the amount of learning for the trained visual feature, which might be different depending on the training protocol and brain stimulation combination. Is it beneficial to compare the amount of learning gains across different training and stimulation protocols to rule out this possibility? Would more learning gains for the trained visual feature predict less transfer for the untrained visual feature? Are there correlations between learning gains and learning transfer?
(3) The authors argued that a reactivation-based training protocol, rather than the amount of training, was critical for the generalization of learning. The control experiment in the study showed that full-practice training combined with tDCS did not lead to transfer, as in reactivation-based training. However, in order to rule out the confounding effects from the amount of training, it is crucial to examine whether a training protocol in which a similar number of trials as in the reactivation-based training but not separated across training sessions would lead to similar generalization of learning.
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Reviewer #3 (Public review):
Summary:
This research focuses on a long-lasting and interesting phenomenon in human plasticity. When humans learn basic perceptual skills such as judging the orientation of a simple line, the learned abilities are often limited to the trained condition but not generalizable to untrained conditions. The authors hypothesized that this learning specificity was related to GABA, an inhibitory neurotransmitter in the brain. Using a novel training method that combines reactivation and a brain stimulation method (tDCS) that hypothetically inactivates GABA, the authors hypothesized that learned visual perceptual skills would show greater transfer.
Strengths:
The authors conducted a list of well-conceived behavior studies to demonstrate the effectiveness of their proposed method in enabling learning transfer in two …
Reviewer #3 (Public review):
Summary:
This research focuses on a long-lasting and interesting phenomenon in human plasticity. When humans learn basic perceptual skills such as judging the orientation of a simple line, the learned abilities are often limited to the trained condition but not generalizable to untrained conditions. The authors hypothesized that this learning specificity was related to GABA, an inhibitory neurotransmitter in the brain. Using a novel training method that combines reactivation and a brain stimulation method (tDCS) that hypothetically inactivates GABA, the authors hypothesized that learned visual perceptual skills would show greater transfer.
Strengths:
The authors conducted a list of well-conceived behavior studies to demonstrate the effectiveness of their proposed method in enabling learning transfer in two different visual tasks, and carefully conducted comparison studies to elucidate other possible explanations. The sample size was adequate to convey convincing results, and the analyses were thorough.
Weaknesses:
While the authors built their training paradigm on
(1) the hypothetical role GABA plays in inhibiting learning transfer, and
(2) the hypothetical impact tDCS may have on GABA, there was no direct evidence supporting these hypotheses in the current study.
Further, learning specificity takes many formats from features to locations to tasks; it is not yet clear the scope of the observed transfer with the proposed method.
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