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Here, we report on the long-term stability of changes in behavior and brain activity following perceptual learning of conjunctions of simple motion features. Participants were trained for 3 weeks on a visual search task involving the detection of a dot moving in a “v”-shaped target trajectory among inverted “v”-shaped distractor trajectories. The first and last training sessions were carried out during functional magnetic resonance imaging (fMRI). Learning stability was again examined behaviorally and using fMRI 3 years after the end of training. Results show that acquired behavioral improvements were remarkably stable over time and that these changes were specific to trained target and distractor trajectories. A similar pattern was observed on the neuronal level, when the representation of target and distractor stimuli was examined in early retinotopic visual cortex (V1–V3): training enhanced activity for the target relative to the surrounding distractors in the search array and this enhancement persisted after 3 years. However, exchanging target and distractor trajectories abolished both neuronal and behavioral effects, suggesting that training-induced changes in stimulus representation are specific to trained stimulus identities.
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Passive exposure to task-relevant stimuli enhances categorization learning
Learning to perform a perceptual decision task is generally achieved through sessions of effortful practice with feedback. Here, we investigated how passive exposure to task-relevant stimuli, which is relatively effortless and does not require feedback, influences active learning. First, we trained mice in a sound-categorization task with various schedules combining passive exposure and active training. Mice that received passive exposure exhibited faster learning, regardless of whether this exposure occurred entirely before active training or was interleaved between active sessions. We next trained neural-network models with different architectures and learning rules to perform the task. Networks that use the statistical properties of stimuli to enhance separability of the data via unsupervised learning during passive exposure provided the best account of the behavioral observations. We further found that, during interleaved schedules, there is an increased alignment between weight updates from passive exposure and active training, such that a few interleaved sessions can be as effective as schedules with long periods of passive exposure before active training, consistent with our behavioral observations. These results provide key insights for the design of efficient training schedules that combine active learning and passive exposure in both natural and artificial systems.
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- Award ID(s):
- 2024926
- PAR ID:
- 10548392
- Publisher / Repository:
- eLife
- Date Published:
- Journal Name:
- eLife
- Volume:
- 12
- ISSN:
- 2050-084X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.7554/eLife.88406.3
