Stimulus preexposure speeds or slows subsequent acquisition of associative learning depending on learning test procedures and response measure

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.

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.

Facilitation and retardation of flavor preference conditioning following prior exposure to the flavor conditioned stimulus

In two experiments, rats received pairings of an almond flavor (Experiments 1 and 2B) or a vanilla flavor (Experiment 2A) with sucrose. In each experiment, half of the rats received prior exposure to the flavor and half were exposed to water. Conditioned preference was then assessed through two-bottle, flavor versus water, choice tests. Latent inhibition (indicated by a weaker preference in pre-exposed subjects) was observed in the experiment using the vanilla flavor. However, facilitation (a stronger preference in pre-exposed subjects) instead of latent inhibition was evident with the almond flavor, both across acquisition trials and in the final choice test. These results indicate that, unlike most other paradigms of Pavlovian conditioning, conditioned stimulus pre-exposure in flavor preference learning may either facilitate or retard the acquisition (or the expression) of a conditioned flavor preference. We explore the proposal that the critical difference between the flavors lies in their hedonic values, with facilitation being more likely in a flavor that is initially disliked.