Passive exposure to task-relevant stimuli enhances categorization learning

Author:

Schmid Christian1ORCID,Haziq Muhammad1,Baese-Berk Melissa M.2ORCID,Murray James M.1,Jaramillo Santiago1ORCID

Affiliation:

1. Institute of Neuroscience, University of Oregon. Eugene, OR 97403

2. Department of Linguistics, University of Oregon. Eugene, OR 97403

Abstract

Learning to perform a perceptual decision task is generally achieved through sessions of effortful practice with feedback. Here, we investigated how passive exposure to 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.

Publisher

eLife Sciences Publications, Ltd

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