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Found 5 papers, 1 papers with code
SEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction
Sketching is a powerful tool for creating abstract images that are sparse but meaningful.
Semantic Feature Verification in FLAN-T5
This study evaluates the potential of a large language model for aiding in generation of semantic feature norms - a critical tool for evaluating conceptual structure in cognitive science.
Human-machine cooperation for semantic feature listing
Semantic feature norms, lists of features that concepts do and do not possess, have played a central role in characterizing human conceptual knowledge, but require extensive human labor.
Conceptual structure coheres in human cognition but not in large language models
Neural network models of language have long been used as a tool for developing hypotheses about conceptual representation in the mind and brain.
How does task structure shape representations in deep neural networks?
While modern deep convolutional neural networks can be trained to perform at human levels of object recognition and learn visual features in the process, humans use vision for a host of tasks beyond object recognition including — drawing, acting, and making propositional statements.
