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ObjectiveStimulus-selective response modulation (SRM) of sensory evoked potentials represents a well-established non-invasive index of long-term potentiation-like (LTP-like) synaptic plasticity in the human sensory cortices. Although our understanding of the mechanisms underlying stimulus-SRM has increased over the past two decades, it remains unclear how this form of LTP-like synaptic plasticity is related to other basic learning mechanisms, such as perceptual learning. The aim of the current study was twofold; firstly, we aimed to corroborate former stimulus-SRM studies, demonstrating modulation of visual evoked potential (VEP) components following high-frequency visual stimulation. Secondly, we aimed to investigate the association between the magnitudes of LTP-like plasticity and visual perceptual learning (VPL).Methods42 healthy adults participated in the study. EEG data was recorded during a standard high-frequency stimulus-SRM paradigm. Amplitude values were measured from the peaks of visual components C1, P1, and N1. Embedded in the same experimental session, the VPL task required the participants to discriminate between a masked checkerboard pattern and a visual “noise” stimulus before, during and after the stimulus-SRM probes.ResultsWe demonstrated significant amplitude modulations of VEPs components C1 and N1 from baseline to both post-stimulation probes. In the VPL task, we observed a significant change in the average threshold levels from the first to the second round. No significant association between the magnitudes of LTP-like plasticity and performance on the VPL task was evident.ConclusionTo the extent of our knowledge, this study is the first to examine the relationship between the visual stimulus-RM phenomenon and VPL in humans. In accordance with previous studies, we demonstrated robust amplitude modulations of the C1 and N1 components of the VEP waveform. However, we did not observe any significant correlations between modulation magnitude of VEP components and VPL task performance, suggesting that these phenomena rely on separate learning mechanisms implemented by different neural mechanisms.