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Found 5 papers, 2 papers with code
Evaluating (and improving) the correspondence between deep neural networks and human representations
These networks learn representations of real-world stimuli that can potentially be leveraged to capture psychological representations.
Adapting Deep Network Features to Capture Psychological Representations
To remedy this, we develop a method for adapting deep features to align with human similarity judgments, resulting in image representations that can potentially be used to extend the scope of psychological experiments.
Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies
Learning a visual concept from a small number of positive examples is a significant challenge for machine learning algorithms.
Human memory search as a random walk in a semantic network
The human mind has a remarkable ability to store a vast amount of information in memory, and an even more remarkable ability to retrieve these experiences when needed.
Testing a Bayesian Measure of Representativeness Using a Large Image Database
How do people determine which elements of a set are most representative of that set?
