Geon Theory as an Account of Shape Recognition in Mind, Brain and Machine. - Irving Biederman; Eric Cooper; John E. Hummel; József Fiser (BMVC, 1993)

"Effective systematicity" in, "effective systematicity" out: a reply to Edelman and Intrator (2003). - John E. Hummel (Cogn. Sci., 2003)

A Solution to the Binding Problem for Compositional Connectionism. - John E. Hummel; Keith J. Holyoak; Collin B. Green; Leonidas A. A. Doumas; Derek Devnich; Aniket Kittur; Donald J. Kalar (AAAI Technical Report (3), 2004)

Varieties of sameness: the impact of relational complexity on perceptual comparisons. - James K. Kroger; Keith J. Holyoak; John E. Hummel (Cogn. Sci., 2004)

The One-to-One Constraint in Analogical Mapping and Inference. - Daniel C. Krawczyk; Keith J. Holyoak; John E. Hummel (Cogn. Sci., 2005)

Toward a Process Model of Explanation with Implications for the Type-Token Problem. - John E. Hummel; David H. Landy; Derek Devnich (AAAI Fall Symposium: Naturally-Inspired Artificial Intelligence, 2008)

Finding similarity in a model of relational reasoning. - Eric G. Taylor; John E. Hummel (Cogn. Syst. Res., 2009)

A Computational Account of the Development of the Generalization of Shape Information. - Leonidas A. A. Doumas; John E. Hummel (Cogn. Sci., 2010)

Symbolic Versus Associative Learning. - John E. Hummel (Cogn. Sci., 2010)

Corrigendum to "Finding similarity in a model of relational reasoning" [Cognitive Systems Research 10 (2009) 229-239]. - Eric G. Taylor; John E. Hummel (Cogn. Syst. Res., 2010)

Progressive Alignment Facilitates Learning of Deterministic But Not Probabilistic Relational Categories. - Wookyoung Jung; John E. Hummel (CogSci, 2011)

Getting symbols out of a neural architecture. - John E. Hummel (Connect. Sci., 2011)

Hierarchical Category Structures Facilitate Acquisition of Probabilistic Relational Categories. - Wookyoung Jung; John E. Hummel (CogSci, 2012)

The effects of dual verbal and visual tasks on featural vs. relational category learning. - Wookyoung Jung; John E. Hummel (CogSci, 2013)

Making Probabilistic Relational Categories Learnable. - Wookyoung Jung; John E. Hummel (Cogn. Sci., 2015)

Automating the Encoding for the LISA Model of Analogy from Raw Text. - Sean Wilner; John E. Hummel (MAICS, 2017)

Relation learning in a neurocomputational architecture supports cross-domain transfer. - Leonidas A. A. Doumas; Guillermo Puebla; Andrea E. Martin; John E. Hummel (CogSci, 2020)

Probing the Mental Representation of Relation-Defined Categories. - Yuhui Du; John E. Hummel; Alexander A. Petrov (CogSci, 2021)

Exaggeration of Stimulus Attributes in the Representation of Relational Categories. - Yuhui Du; John E. Hummel; Alexander A. Petrov (CogSci, 2022)

On the importance of severely testing deep learning models of cognition. - Jeffrey S. Bowers; Gaurav Malhotra; Federico Adolfi; Marin Dujmovic; Milton Llera Montero; Valerio Biscione; Guillermo Puebla; John E. Hummel; Rachel F. Heaton (Cogn. Syst. Res., 2023)

MindSet: Vision. A toolbox for testing DNNs on key psychological experiments. - 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 (CoRR, 2024)