The Sweet Spot of Plasticity in Perceptual Learning Depends on Training Variability

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Abstract

SUMMARY Perceptual learning improves sensory discrimination, yet the brain must balance specificity and generalization, especially in variable environments. To investigate how variability shapes perceptual learning, we used functional magnetic resonance imaging while human subjects performed an orientation discrimination task with low or high task-irrelevant variability in spatial frequency. Behaviorally, both training regimes improved performance, but only high variability training enabled transfer to a novel location. Retinotopic orientation representations in early visual areas changed similarly across groups, indicating that variability does not alter local sensory representations. In contrast, functional connectivity differed: low variability training enhanced task-dependent coupling between the intraparietal sulcus and V2, while high variability training differentially affected connectivity with hV4. These results support a “sweet spot” model of perceptual learning, in which the locus of learning flexibly depends on the training environment. But rather than modifying sensory representations, variability reconfigures network-level readout, enabling specific or generalizable improvements in perception.
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SUMMARY Perceptual learning improves sensory discrimination, yet the brain must balance specificity and generalization, especially in variable environments. To investigate how variability shapes perceptual learning, we used functional magnetic resonance imaging while human subjects performed an orientation discrimination task with low or high task-irrelevant variability in spatial frequency. Behaviorally, both training regimes improved performance, but only high variability training enabled transfer to a novel location. Retinotopic orientation representations in early visual areas changed similarly across groups, indicating that variability does not alter local sensory representations. In contrast, functional connectivity differed: low variability training enhanced task-dependent coupling between the intraparietal sulcus and V2, while high variability training differentially affected connectivity with hV4. These results support a “sweet spot” model of perceptual learning, in which the locus of learning flexibly depends on the training environment. But rather than modifying sensory representations, variability reconfigures network-level readout, enabling specific or generalizable improvements in perception. Competing Interest Statement The authors have declared no competing interest.

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last seen: 2026-05-20T01:45:00.602351+00:00