Implications of capacity-limited, generative models for human vision
Although discriminative deep neural networks are currently dominant in cognitive modeling, we suggest that capacity-limited, generative models are a promising avenue for future work. Generative models tend to learn both local and global features of stimuli and, when properly constrained, can learn c...
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| Veröffentlicht in: | The Behavioral and brain sciences 2023-12, Vol.46, p.e391-e391, Article e391 |
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| container_end_page | e391 |
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| container_title | The Behavioral and brain sciences |
| container_volume | 46 |
| creator | German, Joseph Scott Jacobs, Robert A. |
| description | Although discriminative deep neural networks are currently dominant in cognitive modeling, we suggest that capacity-limited, generative models are a promising avenue for future work. Generative models tend to learn both local and global features of stimuli and, when properly constrained, can learn componential representations and response biases found in people's behaviors. |
| doi_str_mv | 10.1017/S0140525X23001772 |
| format | Article |
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| ispartof | The Behavioral and brain sciences, 2023-12, Vol.46, p.e391-e391, Article e391 |
| issn | 0140-525X 1469-1825 |
| language | eng |
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| source | Cambridge University Press Journals Complete |
| subjects | Cognition & reasoning Cognitive ability Decision theory Machine learning Memory Neural networks Open Peer Commentary |
| title | Implications of capacity-limited, generative models for human vision |
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