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2020 – today
- 2024
[i2]Valerio Biscione, Dong Yin, Gaurav Malhotra, Marin Dujmovic, Milton Llera Montero, Guillermo Puebla, Federico Adolfi, Rachel F. Heaton, John E. Hummel, Benjamin D. Evans, Karim Habashy, Jeffrey S. Bowers:
MindSet: Vision. A toolbox for testing DNNs on key psychological experiments. CoRR abs/2404.05290 (2024)- 2023
- [j10]Jeffrey S. Bowers
, Gaurav Malhotra, Federico Adolfi, Marin Dujmovic, Milton Llera Montero, Valerio Biscione, Guillermo Puebla, John E. Hummel, Rachel F. Heaton:
On the importance of severely testing deep learning models of cognition. Cogn. Syst. Res. 82: 101158 (2023) - 2022
- [c10]Yuhui Du, John E. Hummel, Alexander A. Petrov:
Exaggeration of Stimulus Attributes in the Representation of Relational Categories. CogSci 2022 - 2021
- [c9]Yuhui Du, John E. Hummel, Alexander A. Petrov:
Probing the Mental Representation of Relation-Defined Categories. CogSci 2021 - 2020
- [c8]Leonidas A. A. Doumas, Guillermo Puebla, Andrea E. Martin, John E. Hummel:
Relation learning in a neurocomputational architecture supports cross-domain transfer. CogSci 2020
2010 – 2019
- 2019
- [i1]Leonidas A. A. Doumas, Guillermo Puebla, Andrea E. Martin, John E. Hummel:
Relation learning in a neurocomputational architecture supports cross-domain transfer. CoRR abs/1910.05065 (2019) - 2017
- [c7]Sean Wilner, John E. Hummel:
Automating the Encoding for the LISA Model of Analogy from Raw Text. MAICS 2017: 115-122 - 2015
- [j9]Wookyoung Jung, John E. Hummel:
Making Probabilistic Relational Categories Learnable. Cogn. Sci. 39(6): 1259-1291 (2015) - 2013
- [c6]Wookyoung Jung, John E. Hummel:
The effects of dual verbal and visual tasks on featural vs. relational category learning. CogSci 2013 - 2012
- [c5]Wookyoung Jung, John E. Hummel:
Hierarchical Category Structures Facilitate Acquisition of Probabilistic Relational Categories. CogSci 2012 - 2011
- [j8]John E. Hummel:
Getting symbols out of a neural architecture. Connect. Sci. 23(2): 109-118 (2011) - [c4]Wookyoung Jung, John E. Hummel:
Progressive Alignment Facilitates Learning of Deterministic But Not Probabilistic Relational Categories. CogSci 2011 - 2010
- [j7]Leonidas A. A. Doumas, John E. Hummel:
A Computational Account of the Development of the Generalization of Shape Information. Cogn. Sci. 34(4): 698-712 (2010) - [j6]John E. Hummel:
Symbolic Versus Associative Learning. Cogn. Sci. 34(6): 958-965 (2010) - [j5]Eric G. Taylor, John E. Hummel:
Corrigendum to "Finding similarity in a model of relational reasoning" [Cognitive Systems Research 10 (2009) 229-239]. Cogn. Syst. Res. 11(2): 210 (2010)
2000 – 2009
- 2009
- [j4]Eric G. Taylor, John E. Hummel:
Finding similarity in a model of relational reasoning. Cogn. Syst. Res. 10(3): 229-239 (2009) - 2008
- [c3]John E. Hummel, David H. Landy, Derek Devnich:
Toward a Process Model of Explanation with Implications for the Type-Token Problem. AAAI Fall Symposium: Naturally-Inspired Artificial Intelligence 2008: 79-86 - 2005
- [j3]Daniel C. Krawczyk, Keith J. Holyoak, John E. Hummel:
The One-to-One Constraint in Analogical Mapping and Inference. Cogn. Sci. 29(5): 797-806 (2005) - 2004
- [j2]James K. Kroger, Keith J. Holyoak, John E. Hummel:
Varieties of sameness: the impact of relational complexity on perceptual comparisons. Cogn. Sci. 28(3): 335-358 (2004) - [c2]John E. Hummel, Keith J. Holyoak, Collin B. Green, Leonidas A. A. Doumas, Derek Devnich, Aniket Kittur, Donald J. Kalar:
A Solution to the Binding Problem for Compositional Connectionism. AAAI Technical Report (3) 2004: 31-34 - 2003
- [j1]John E. Hummel:
"Effective systematicity" in, "effective systematicity" out: a reply to Edelman and Intrator (2003). Cogn. Sci. 27(2): 327-329 (2003)
1990 – 1999
- 1993
- [c1]Irving Biederman, Eric Cooper, John E. Hummel, József Fiser:
Geon Theory as an Account of Shape Recognition in Mind, Brain and Machine. BMVC 1993: 1-12
Coauthor Index
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