The proposed schemas, particularly the LEARN FLAVOR-LOCATION and LEARN METRIC REPRESENTATION schemas, can be adapted for one-shot learning in language and object recognition tasks by modifying the input representations and the associative memory mechanisms. For language tasks, the schemas could incorporate linguistic features as sensory cues, allowing the agent to associate specific words or phrases with their meanings or contexts. This could involve using embeddings that capture semantic relationships, enabling the agent to generalize from a single exposure to new vocabulary or grammatical structures.
In object recognition, the LEARN FLAVOR-LOCATION schema could be extended to include visual features extracted from images, allowing the agent to form associations between visual stimuli and their corresponding labels or categories. The neural implementation could leverage convolutional neural networks (CNNs) to process visual inputs, while the associative memory could be structured to store and retrieve object representations based on visual cues. By integrating these modifications, the schemas could facilitate rapid learning in diverse domains, enhancing the agent's ability to adapt to new tasks with minimal training data.