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Found 5 papers, 4 papers with code
Distinguishing representational geometries with controversial stimuli: Bayesian experimental design and its application to face dissimilarity judgments
Comparing representations of complex stimuli in neural network layers to human brain representations or behavioral judgments can guide model development.
Testing the limits of natural language models for predicting human language judgments
Neural network language models can serve as computational hypotheses about how humans process language.
Controversial stimuli: pitting neural networks against each other as models of human recognition
To efficiently compare models' ability to predict human responses, we synthesize controversial stimuli: images for which different models produce distinct responses.
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI
Unfortunately, acquiring sufficient "labeled" pairs of {Image, fMRI} (i. e., images with their corresponding fMRI responses) to span the huge space of natural images is prohibitive for many reasons.
Neural network models and deep learning - a primer for biologists
Originally inspired by neurobiology, deep neural network models have become a powerful tool of machine learning and artificial intelligence, where they are used to approximate functions and dynamics by learning from examples.
