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Search results (11)

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Humans and neural networks show similar patterns of transfer and interference during continual learning.

Holton E. et al, (2026), Nat Hum Behav, 10, 111 - 125

I Surrogacy in Psychological Research

D'Alessandro W. and Thompson JAF., (2025)

Reward Model Interpretability via Optimal and Pessimal Tokens

Christian B. et al, (2025)

Humans and neural networks show similar patterns of transfer and interference during continual learning

Holton E. et al, (2025)

Zero-shot counting with a dual-stream neural network model.

Thompson JAF. et al, (2024), Neuron, 112, 4147 - 4158.e5

Thinking beyond the ventral stream: Comment on Bowers et al.

Summerfield C. and Thompson JAF., (2023), Behav Brain Sci, 46

Learning to count visual objects by combining “what” and “where” in recurrent memory

Thompson JAF. et al, (2023), Proceedings of Machine Learning Research, 210, 199 - 218

Forms of explanation and understanding for neuroscience and artificial intelligence.

Thompson JAF., (2021), J Neurophysiol, 126, 1860 - 1874

Noise increases the correspondence between artificial and human vision.

Thompson JAF., (2021), PLoS Biol, 19

Forms of explanation and understanding for neuroscience and artificial intelligence

Thompson JAF., (2021)

Training neural networks to recognize speech increased their correspondence to the human auditory pathway but did not yield a shared hierarchy of acoustic features

Thompson JAF. et al, (2021)