Explore the intersection of cognitive science and machine learning in this UC Berkeley lecture by Tom Griffiths on evaluating neural network representations against human cognition. Delve into key concepts like categorization, family resemblance, and psychological spaces. Examine the differences between prototypes and exemplars, and learn about multidimensional scaling techniques. Investigate the limitations of spatial representations and the importance of features in cognitive models. Analyze convolutional neural networks and their performance in predicting human similarity judgments. Discover how hierarchical clustering can provide insights into human representations of categories. Compare vector space models with human word associations and explore the challenges in modeling relational similarity. Gain a deeper understanding of the complexities involved in bridging artificial and human cognition through this comprehensive exploration of representation learning.