Abstract
Thomas McCoy, Abigail Tenenbaum, Zhang Enyan, Zhiyu Zhou
Abstract
Authors
Institutions
Provenance
crossref
Confidence 100%
openalex
Confidence 95%
datacite
Confidence 0%
No local reference links have been materialized yet.
No local citing links have been materialized yet.
Physical symbol systems
1980
Unresolved referenced work
2024
Unresolved referenced work
1986
Unresolved referenced work
Kept as external metadata until matched
Mastering the game of Go with deep neural networks and tree search
10.1038/nature16961 · 2016
Unresolved referenced work
Kept as external metadata until matched
A logical calculus of the ideas immanent in nervous activity
10.1007/bf02478259 · 1943
10.1145/130385.130432
10.1145/130385.130432
What formal languages can transformers express? A survey
10.1162/tacl_a_00663 · 2024
Connectionism and cognitive architecture: a critical analysis
10.1016/0010-0277(88)90031-5 · 1988
Rethinking eliminative connectionism
10.1006/cogp.1998.0694 · 1998
10.18653/v1/w18-0102
10.18653/v1/w18-0102
The neural architecture of language: Integrative modeling converges on predictive processing
10.1073/pnas.2105646118 · 2021
How can deep neural networks inform theory in psychological science?
10.1177/09637214241268098 · 2024
On the proper treatment of connectionism
10.1017/s0140525x00052432 · 1988
Neurocompositional computing: from the central paradox of cognition to a new generation of AI systems
2022
Why concepts are (probably) vectors
10.1016/j.tics.2024.06.011 · 2024
Unresolved referenced work
1982
Whither symbols in the era of advanced neural networks?
2025
Tensor product variable binding and the representation of symbolic structures in connectionist systems
10.1016/0004-3702(90)90007-m · 1990
Unresolved referenced work
Kept as external metadata until matched
Unresolved referenced work
Kept as external metadata until matched
Discovering the Compositional Structure of Vector Representations with Role Learning Networks
2020
10.18653/v1/w16-2524
10.18653/v1/w16-2524
Unresolved referenced work
Kept as external metadata until matched
Unresolved referenced work
Kept as external metadata until matched
10.18653/v1/2024.naacl-long.281
10.18653/v1/2024.naacl-long.281
Unresolved referenced work
Kept as external metadata until matched
Unresolved referenced work
Kept as external metadata until matched
10.18653/v1/w16-2503
10.18653/v1/w16-2503
Unresolved referenced work
Kept as external metadata until matched
Unresolved referenced work
Kept as external metadata until matched
Unresolved referenced work
Kept as external metadata until matched
Unresolved referenced work
Kept as external metadata until matched
Under the hood: using diagnostic classifiers to investigate and improve how language models track agreement information
2018
10.18653/v1/2021.conll-1.15
10.18653/v1/2021.conll-1.15
Unresolved referenced work
Kept as external metadata until matched
Interventionist methods for interpreting deep neural networks
2024
Break it down: evidence for structural compositionality in neural networks
10.52202/075280-1848 · 2023
On the biology of a large language model
2025
Encoding interference effects support self-organized sentence processing
10.1016/j.cogpsych.2020.101356 · doi-reference
Language models, like humans, show content effects on reasoning tasks
10.1093/pnasnexus/pgae233 · doi-reference
10.18653/v1/2020.emnlp-main.731
10.18653/v1/2020.emnlp-main.731 · doi-reference
Modeling rapid language learning by distilling Bayesian priors into artificial neural networks
10.1038/s41467-025-59957-y · doi-reference
Human-like systematic generalization through a meta-learning neural network
10.1038/s41586-023-06668-3 · doi-reference
Learning transformer programs
10.52202/075280-2131 · doi-reference
Break it down: evidence for structural compositionality in neural networks
10.52202/075280-1848 · doi-reference
10.18653/v1/2021.conll-1.15
10.18653/v1/2021.conll-1.15 · doi-reference
10.18653/v1/w16-2503
10.18653/v1/w16-2503 · doi-reference
10.18653/v1/2024.naacl-long.281
10.18653/v1/2024.naacl-long.281 · doi-reference
10.18653/v1/w16-2524
10.18653/v1/w16-2524 · doi-reference
Tensor product variable binding and the representation of symbolic structures in connectionist systems
10.1016/0004-3702(90)90007-m · doi-reference
Why concepts are (probably) vectors
10.1016/j.tics.2024.06.011 · doi-reference
On the proper treatment of connectionism
10.1017/s0140525x00052432 · doi-reference
How can deep neural networks inform theory in psychological science?
10.1177/09637214241268098 · doi-reference
The neural architecture of language: Integrative modeling converges on predictive processing
10.1073/pnas.2105646118 · doi-reference
10.18653/v1/w18-0102
10.18653/v1/w18-0102 · doi-reference
Rethinking eliminative connectionism
10.1006/cogp.1998.0694 · doi-reference
Connectionism and cognitive architecture: a critical analysis
10.1016/0010-0277(88)90031-5 · doi-reference
What formal languages can transformers express? A survey
10.1162/tacl_a_00663 · doi-reference
10.1145/130385.130432
10.1145/130385.130432 · doi-reference
A logical calculus of the ideas immanent in nervous activity
10.1007/bf02478259 · doi-reference
Mastering the game of Go with deep neural networks and tree search
10.1038/nature16961 · doi-reference