On the sufficiency of the velocity field for perception of heading. - William H. Warren Jr.; A. W. Blackwell; Kenneth J. Kurtz; Nicholas G. Hatsopoulos; M. L. Kalish (Biol. Cybern., 1991)

Interaction between Transparency and Structure from Motion. - Daniel J. Kersten; Heinrich H. Bülthoff; Bennett L. Schwartz; Kenneth J. Kurtz (Neural Comput., 1992)

Re-representation in comparison: building an empirical case. - Kenneth J. Kurtz (J. Exp. Theor. Artif. Intell., 2005)

Relations, Objects, and the Composition of Analogies. - Dedre Gentner; Kenneth J. Kurtz (Cogn. Sci., 2006)

Evaluating the Divergent Auto-Encoder (DIVA) as a Machine Learning Algorithm. - Kenneth J. Kurtz; Xavier Oyarzabal (CogSci, 2011)

Observational Category Learning as a Path to More Robust Generative Knowledge. - Kimery R. Levering; Kenneth J. Kurtz (CogSci, 2011)

Types of Cognitive Content and the Role of Relational Processing in the Illusion of Explanatory Depth. - Graham Silk-Eglit; Kenneth J. Kurtz (CogSci, 2011)

Category learning in the context of co-presented items. - Janet K. Andrews; Kenneth R. Livingston; Kenneth J. Kurtz (Cogn. Process., 2011)

Observational category learning increases sensitivity to prototypical and correlational information. - Kimery R. Levering; Kenneth J. Kurtz (CogSci, 2012)

Evolving data sets to highlight the performance differences between machine learning classifiers. - Thomas Raway; J. David Schaffer; Kenneth J. Kurtz; Hiroki Sayama (GECCO (Companion), 2012)

Models of Human Category Learning: Do they Generalize? - Nolan Conaway; Kenneth J. Kurtz (CogSci, 2013)

Using Relational Encoding to Promote Creative Problem Solving. - Kenneth J. Kurtz; Nuoya Zhang; Tamar Skolnick (CogSci, 2013)

Evaluating case-based decision theory: Predicting empirical patterns of human classification learning. - Andreas D. Pape; Kenneth J. Kurtz (Games Econ. Behav., 2013)

Now you know it, now you don't: Asking the right question about category knowledge. - Nolan Conaway; Kenneth J. Kurtz (CogSci, 2014)

Optimizing the category construction task to promote learning and transfer of knowledge in classroom instruction. - Kenneth J. Kurtz; Andy Cavagnetto; Garrett Honke; Nolan Conaway; John D. Patterson; James C. Marr; Yan Tao (CogSci, 2014)

Engaging the comparison engine: Implications for relational category learning and transfer. - John D. Patterson; Kenneth J. Kurtz (CogSci, 2014)

Brainprint: Identifying Unique Features of Neural Activity with Machine Learning. - Maria V. Ruiz-Blondet; Negin Khalifian; Blair C. Armstrong; Zhanpeng Jin; Kenneth J. Kurtz; Sarah Laszlo (CogSci, 2014)

Complexity Measures and Concept Learning. - Andreas D. Pape; Kenneth J. Kurtz; Hiroki Sayama (CoRR, 2014)

A Dissociation between Categorization and Similarity to Exemplars. - Nolan Conaway; Kenneth J. Kurtz (CogSci, 2015)

Exemplar models can't see the forest for the trees. - Nolan Conaway; Kenneth J. Kurtz (CogSci, 2015)

Learning mode and comparison in relational category learning. - John D. Patterson; Kenneth J. Kurtz (CogSci, 2015)

Brainprint: Assessing the uniqueness, collectability, and permanence of a novel method for ERP biometrics. - Blair C. Armstrong; Maria V. Ruiz-Blondet; Negin Khalifian; Kenneth J. Kurtz; Zhanpeng Jin; Sarah Laszlo (Neurocomputing, 2015)

Does Contrast or Comparison Help More? The Role of Learning Mode and Category Type. - Jan Andrews; Kenneth R. Livingston; Calais Larson; Kenneth J. Kurtz (CogSci, 2016)

Generalization of within-category feature correlations. - Nolan Conaway; Kenneth J. Kurtz (CogSci, 2016)

Switch it up: Learning Categories via Feature Switching. - Garrett Honke; Nolan Conaway; Kenneth J. Kurtz (CogSci, 2016)

Linear separability and human category learning: Revisiting a classic study. - Kimery R. Levering; Nolan Conaway; Kenneth J. Kurtz (CogSci, 2016)

Performance Pressure and Comparison in Relational Category Learning. - John D. Patterson; Kenneth J. Kurtz (CogSci, 2016)

Effects of Analogical Processing: Evidence for Re-representation. - Daniel Silliman; Kenneth J. Kurtz (CogSci, 2016)

The role of higher order relational structure in relational category label extension. - Sean Snoddy; Kenneth J. Kurtz (CogSci, 2016)

Object Understanding: Exploring the Path from Percept to Meaning. - Kenneth J. Kurtz; Daniel Silliman (CogSci, 2017)

Relational Concept Learning via Guided Interactive Discovery. - John D. Patterson; David Landy; Kenneth J. Kurtz (CogSci, 2017)

Promoting Spontaneous Analogical Transfer: The Role of Category Status. - Sean Snoddy; Kenneth J. Kurtz (CogSci, 2017)

Solving Nonlinearly Separable Classifications in a Single-Layer Neural Network. - Nolan Conaway; Kenneth J. Kurtz (Neural Comput., 2017)

Relational Categories: Why they're Important and How they are Learned. - Dedre Gentner; Nina Simms; Kenneth J. Kurtz; Garrett Honke; Sean Snoddy; Kenneth D. Forbus; Lindsey E. Richland; Bryan J. Matlen; Emily McLaughlin Lyons; Ellen C. Klostermann (CogSci, 2018)

Semi-supervised learning: A role for similarity in generalization-based learning of relational categories. - John D. Patterson; Kenneth J. Kurtz (CogSci, 2018)

What does a dimension that predicts nothing do to human classification learning? - Sean Snoddy; Kenneth J. Kurtz (CogSci, 2018)

Human generalization of an alternating category structure. - Matt Wetzel; Kenneth J. Kurtz (CogSci, 2018)

Warning: The Exemplars in Your Category Representation May Not Be the Ones Experienced During Learning. - Kenneth J. Kurtz; Daniel Silliman (CogSci, 2019)

Semi-supervised Learning with 2D Categories. - John D. Patterson; Kenneth J. Kurtz (CogSci, 2019)

Family Resemblance in Unsupervised Categorization: A Dissociation Between Production and Evaluation. - John D. Patterson; Sean Snoddy; Kenneth J. Kurtz (CogSci, 2019)

Introducing Recursive Linear Classification (RELIC) for Machine Learning. - Sean Snoddy; Kenneth J. Kurtz (CogSci, 2019)

Promoting relational responding by varying presentation conditions. - Mercury K. Mason; Kenneth J. Kurtz (CogSci, 2020)

Costly Exceptions: Deviant Exemplars Reduce Category Compression. - Daniel Silliman; Sean Snoddy; Matt Wetzel; Kenneth J. Kurtz (CogSci, 2020)

Analogical Transfer and Recognition Memory in Relational Classification Learning. - Sean Snoddy; Kenneth J. Kurtz (CogSci, 2020)

Promoting Relational Responding: The Role of Prior Exposure to the Sample. - Mercury K. Mason; Kenneth J. Kurtz (CogSci, 2021)

Extrapolation Under Caricatured Representations. - Daniel Silliman; Kenneth J. Kurtz (CogSci, 2021)

Comparison Promotes the Spontaneous Transfer of Relational Categories. - Sean Snoddy; Kenneth J. Kurtz (CogSci, 2021)

On the Generalization of Simple Alternating Category Structures. - Kenneth J. Kurtz; Matthew T. Wetzel (Cogn. Sci., 2021)

Local versus global coherence in the generalization of category training. - Josh C. Glass; Kenneth J. Kurtz (CogSci, 2022)

Investigating the impacts of an immersive learning mode and graded feedback on category learning. - Alexus S. Longo; Mercury K. Mason; Kenneth J. Kurtz (CogSci, 2022)

Examining Strategy Differences on the Relational Match-to-Sample Task (RMTS). - Mercury K. Mason; Kenneth J. Kurtz (CogSci, 2022)

Paths to Learning in Traditional Artificial Classification Tasks. - Keith L. Sherman; Kenneth J. Kurtz (CogSci, 2022)

Alternation as a Relational Category. - Josh C. Glass; Alexus S. Longo; Kenneth J. Kurtz (CogSci, 2023)

Modeling Human Performance on SHJ Category Structures with a Divergent Autoencoder. - Josh C. Glass; Mercury K. Mason; Kenneth J. Kurtz (CogSci, 2023)

Release from Proactive Interference with Relational Categories Versus Traditional Entity Categories. - Alexus S. Longo; Kenneth J. Kurtz (CogSci, 2023)

Sustaining Relational Preference in a Repeated Relational Match-to-Sample Task in the Absence of Task Support. - Mercury K. Mason; Kenneth J. Kurtz (CogSci, 2023)

Conceptual Integration and Semantic Relational Processing as Study Tasks to Promote Cued-Recall of Word Pairs. - Keith L. Sherman; Kenneth J. Kurtz (CogSci, 2023)

Abstracted Gaussian Prototypes for One-Shot Concept Learning. - Chelsea Zou; Kenneth J. Kurtz (CoRR, 2024)

Simple Auto-Associative Networks Succeed at Universal Generalization of the Identity Function and Reduplication Rule. - Kenneth J. Kurtz (Cogn. Sci., 2025)