Lecture 7F (2026-04-28): Predictive Coding, Latent Learning, and Self-Supervised Learning

In this lecture, we pivot from our discussion of the autoencoder as an example of unsupervised learning to an introduction to predictive coding, latent learning, and ultimately self-supervised learning (like pre-trained transformers including BERT and GPT). A key historical example described is the case of Tolman's rats and their "latent learning" of a "cognitive map" that allowed them to more quickly learn the location of a reward when presented in a later trial. We connect this with modern pre-training of large language models (LLM's) that gives them the ability to make later inferences that benefit from long-range relationships they learned (by way of complex attention heads) without retraining. We close with some remarks about large multimodal models and their connection with embedding spaces like CLIP (which we introduced earlier as we transitioned from the opening example of the autoencoder). Interactive widgets mentioned/used in this lecture can be found at:

• Autoencoder Explorer: https://tpavlic.github.io/asu-bioinspired-ai-and-optimization/unsupervised_learning/autoencoder_explorer.html
• Transformer Architecture Explorer: https://tpavlic.github.io/asu-bioinspired-ai-and-optimization/transformers/transformer_explorer.html
• Toward Multimodal AI: https://tpavlic.github.io/asu-bioinspired-ai-and-optimization/transformers/toward_multimodal_AI.html

Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/pihkdryix5e4ynqy5zotx/IEE598-Lecture7F-2026-04-28-Predictive_Coding_Latent_Learning_and_Self_Supervised_Learning-Notes.pdf?rlkey=b0ejd4usvqn4fpievga9sbl4m&dl=0