Getting aligned on representational alignment
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arXiv
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| Natura: | Preprint |
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2023
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| author | Sucholutsky, Ilia Muttenthaler, Lukas Weller, Adrian Peng, Andi Bobu, Andreea Kim, Been Love, Bradley C. Cueva, Christopher J. Grant, Erin Groen, Iris Achterberg, Jascha Tenenbaum, Joshua B. Collins, Katherine M. Hermann, Katherine L. Oktar, Kerem Greff, Klaus Hebart, Martin N. Cloos, Nathan Kriegeskorte, Nikolaus Jacoby, Nori Zhang, Qiuyi Marjieh, Raja Geirhos, Robert Chen, Sherol Kornblith, Simon Rane, Sunayana Konkle, Talia O'Connell, Thomas P. Unterthiner, Thomas Lampinen, Andrew K. Müller, Klaus-Robert Toneva, Mariya Griffiths, Thomas L. |
| author_facet | Sucholutsky, Ilia Muttenthaler, Lukas Weller, Adrian Peng, Andi Bobu, Andreea Kim, Been Love, Bradley C. Cueva, Christopher J. Grant, Erin Groen, Iris Achterberg, Jascha Tenenbaum, Joshua B. Collins, Katherine M. Hermann, Katherine L. Oktar, Kerem Greff, Klaus Hebart, Martin N. Cloos, Nathan Kriegeskorte, Nikolaus Jacoby, Nori Zhang, Qiuyi Marjieh, Raja Geirhos, Robert Chen, Sherol Kornblith, Simon Rane, Sunayana Konkle, Talia O'Connell, Thomas P. Unterthiner, Thomas Lampinen, Andrew K. Müller, Klaus-Robert Toneva, Mariya Griffiths, Thomas L. |
| contents | Biological and artificial information processing systems form representations of the world that they can use to categorize, reason, plan, navigate, and make decisions. How can we measure the similarity between the representations formed by these diverse systems? Do similarities in representations then translate into similar behavior? If so, then how can a system's representations be modified to better match those of another system? These questions pertaining to the study of representational alignment are at the heart of some of the most promising research areas in contemporary cognitive science, neuroscience, and machine learning. In this Perspective, we survey the exciting recent developments in representational alignment research in the fields of cognitive science, neuroscience, and machine learning. Despite their overlapping interests, there is limited knowledge transfer between these fields, so work in one field ends up duplicated in another, and useful innovations are not shared effectively. To improve communication, we propose a unifying framework that can serve as a common language for research on representational alignment, and map several streams of existing work across fields within our framework. We also lay out open problems in representational alignment where progress can benefit all three of these fields. We hope that this paper will catalyze cross-disciplinary collaboration and accelerate progress for all communities studying and developing information processing systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_13018 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Getting aligned on representational alignment Sucholutsky, Ilia Muttenthaler, Lukas Weller, Adrian Peng, Andi Bobu, Andreea Kim, Been Love, Bradley C. Cueva, Christopher J. Grant, Erin Groen, Iris Achterberg, Jascha Tenenbaum, Joshua B. Collins, Katherine M. Hermann, Katherine L. Oktar, Kerem Greff, Klaus Hebart, Martin N. Cloos, Nathan Kriegeskorte, Nikolaus Jacoby, Nori Zhang, Qiuyi Marjieh, Raja Geirhos, Robert Chen, Sherol Kornblith, Simon Rane, Sunayana Konkle, Talia O'Connell, Thomas P. Unterthiner, Thomas Lampinen, Andrew K. Müller, Klaus-Robert Toneva, Mariya Griffiths, Thomas L. Neurons and Cognition Artificial Intelligence Machine Learning Neural and Evolutionary Computing Biological and artificial information processing systems form representations of the world that they can use to categorize, reason, plan, navigate, and make decisions. How can we measure the similarity between the representations formed by these diverse systems? Do similarities in representations then translate into similar behavior? If so, then how can a system's representations be modified to better match those of another system? These questions pertaining to the study of representational alignment are at the heart of some of the most promising research areas in contemporary cognitive science, neuroscience, and machine learning. In this Perspective, we survey the exciting recent developments in representational alignment research in the fields of cognitive science, neuroscience, and machine learning. Despite their overlapping interests, there is limited knowledge transfer between these fields, so work in one field ends up duplicated in another, and useful innovations are not shared effectively. To improve communication, we propose a unifying framework that can serve as a common language for research on representational alignment, and map several streams of existing work across fields within our framework. We also lay out open problems in representational alignment where progress can benefit all three of these fields. We hope that this paper will catalyze cross-disciplinary collaboration and accelerate progress for all communities studying and developing information processing systems. |
| title | Getting aligned on representational alignment |
| topic | Neurons and Cognition Artificial Intelligence Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2310.13018 |