Modeling Unsupervised Learning with SUSTAIN. - Todd M. Gureckis; Bradley C. Love (FLAIRS, 2002)

Human Unsupervised and Supervised Learning as a Quantitative Distinction. - Todd M. Gureckis; Bradley C. Love (Int. J. Pattern Recognit. Artif. Intell., 2003)

Towards a unified account of supervised and unsupervised category learning. - Todd M. Gureckis; Bradley C. Love (J. Exp. Theor. Artif. Intell., 2003)

Collective Behavior. - Robert L. Goldstone; Todd M. Gureckis (Top. Cogn. Sci., 2009)

How You Named Your Child: Understanding the Relationship Between Individual Decision Making and Collective Outcomes. - Todd M. Gureckis; Robert L. Goldstone (Top. Cogn. Sci., 2009)

Direct Associations or Internal Transformations? Exploring the Mechanisms Underlying Sequential Learning Behavior. - Todd M. Gureckis; Bradley C. Love (Cogn. Sci., 2010)

Grow your own representations: Computational constructivism. - Joseph L. Austerweil; Thomas L. Griffiths 001; Todd M. Gureckis; Robert L. Goldstone; Kevin Robert Canini; Matt Jones 002 (CogSci, 2011)

Don't Stop 'Til You Get Enough: Adaptive Information Sampling in a Visuomotor Estimation Task. - Mordechai Juni; Todd M. Gureckis; Laurence T. Maloney (CogSci, 2011)

Modeling information sampling over the course of learning. - Douglas Markant; Todd M. Gureckis (CogSci, 2011)

Learning categories from an intermittent teacher. - John V. McDonnell; Todd M. Gureckis (CogSci, 2011)

Re-evaluating Dissociations between Implicit and Explicit Category Learning: An Event-related fMRI Study. - Todd M. Gureckis; Thomas W. James; Robert M. Nosofsky (J. Cogn. Neurosci., 2011)

One-shot lotteries in the park. - Mordechai Juni; Todd M. Gureckis; Laurence T. Maloney (CogSci, 2012)

The role of exploratory decision-making in enhancing episodic memory. - Douglas Markant; Sarah Dubrow; Lila Davachi; Todd M. Gureckis (CogSci, 2012)

Does the utility of information influence sampling behavior? - Douglas Markant; Todd M. Gureckis (CogSci, 2012)

One piece at a time: Learning complex rules through self-directed sampling. - Douglas Markant; Todd M. Gureckis (CogSci, 2012)

Self-directed information selection aids learning of logical rules. - John V. McDonnell; Devin Domingo; Todd M. Gureckis (CogSci, 2012)

Sparse category labels obstruct generalization of category membership. - John V. McDonnell; Carol A. Jew; Todd M. Gureckis (CogSci, 2012)

How does this thing work? Evaluating computational models of intervention-based causal learning. - Anna Coenen; Bob Rehder; Todd M. Gureckis (CogSci, 2013)

Changes in information search strategy under "dense" hypothesis spaces. - Douglas Markant; Todd M. Gureckis (CogSci, 2013)

Informavores: Active information foraging and human cognition. - Douglas Markant; Todd M. Gureckis; Björn Meder; Jonathan D. Nelson; Peter Pirolli; Chen Yu 001 (CogSci, 2013)

Does category labeling lead to forgetting? - Nathaniel Blanco; Todd M. Gureckis (Cogn. Process., 2013)

Adaptive teaching: Improving the efficiency of learning through hypothesis-dependent selection of training data. - Patricia Angie Chan; Douglas Markant; Brenden M. Lake; Todd M. Gureckis (CogSci, 2014)

Decisions to intervene on causal systems are adaptively selected. - Anna Coenen; Bob Rehder; Todd M. Gureckis (CogSci, 2014)

Online Experiments using jsPsych, psiTurk, and Amazon Mechanical Turk. - Josh de Leeuw; Anna Coenen; Douglas Markant; Jay B. Martin; John V. McDonnell; Alexander Rich 002; Todd M. Gureckis (CogSci, 2014)

A preference for the unpredictable over the informative during self-directed learning. - Douglas Markant; Todd M. Gureckis (CogSci, 2014)

The value of approaching bad things. - Alexander Rich 002; Todd M. Gureckis (CogSci, 2014)

Are Biases When Making Causal Interventions Related to Biases in Belief Updating? - Anna Coenen; Todd M. Gureckis (CogSci, 2015)

Optimal stopping in a natural sampling task. - Anna Coenen; Todd M. Gureckis (CogSci, 2015)

Understanding developmental bottlenecks in active inquiry. - George Kachergis; Marjorie Rhodes; Todd M. Gureckis (CogSci, 2015)

Deep Neural Networks Predict Category Typicality Ratings for Images. - Brenden M. Lake; Wojciech Zaremba; Rob Fergus; Todd M. Gureckis (CogSci, 2015)

The Attentional Learning Trap and How to Avoid It. - Alexander Rich 002; Todd M. Gureckis (CogSci, 2015)

Asking useful questions: Active learning with rich queries. - Anselm Rothe; Brenden M. Lake; Todd M. Gureckis (CogSci, 2015)

The distorting effect of deciding to stop sampling. - Anna Coenen; Todd M. Gureckis (CogSci, 2016)

Desirable difficulties in the development of active inquiry skills. - George Kachergis; Marjorie Rhodes; Todd M. Gureckis (CogSci, 2016)

Predictable stimulus onsets improve memory. - George Kachergis; Shannon Tubridy; Todd M. Gureckis (CogSci, 2016)

Asking and evaluating natural language questions. - Anselm Rothe; Brenden M. Lake; Todd M. Gureckis (CogSci, 2016)

Active control of study leads to improved episodic memory in children. - Azzurra Ruggeri; Douglas B. Markant; Todd M. Gureckis; Fei Xu (CogSci, 2016)

Self-Directed Learning Favors Local, Rather Than Global, Uncertainty. - Douglas Markant; Burr Settles; Todd M. Gureckis (Cogn. Sci., 2016)

Beliefs about sparsity affect causal experimentation. - Anna Coenen; Neil Bramley; Azzurra Ruggeri; Todd M. Gureckis (CogSci, 2017)

How does of initial inaccuracy benefit cross-situational word learning? - Chris Grimmick; George Kachergis; Todd M. Gureckis (CogSci, 2017)

Categorization, Information Selection and Stimulus Uncertainty. - David Halpern; Todd M. Gureckis (CogSci, 2017)

Does a present bias influence exploratory choice? - Alexander S. Rich; Todd M. Gureckis (CogSci, 2017)

Progress in building a machine that can ask interesting and informative questions. - Anselm Rothe; Brenden M. Lake; Todd M. Gureckis (CogSci, 2017)

Question Asking as Program Generation. - Anselm Rothe; Brenden M. Lake; Todd M. Gureckis (NIPS, 2017)

Modeling Second-Language Learning from a Psychological Perspective. - Alexander Rich 002; Pamela Osborn Popp; David Halpern; Anselm Rothe; Todd M. Gureckis (BEA@NAACL-HLT, 2018)

Contemporary Cognitive Approaches to Decision-Making. - Daniel M. Bartels; Oleg Urminsky; Todd M. Gureckis; Jennifer Trueblood (CogSci, 2018)

Grounding Compositional Hypothesis Generation in Specific Instances. - Neil Bramley; Anselm Rothe; Josh Tenenbaum 001; Fei Xu; Todd M. Gureckis (CogSci, 2018)

A Causal Model Approach to Dynamic Control. - Zachary Davis 001; Neil Bramley; Bob Rehder; Todd M. Gureckis (CogSci, 2018)

Modeling dynamics of suspense and surprise. - Zhiwei Li; Neil Bramley; Todd M. Gureckis (CogSci, 2018)

A neurocognitive model for predicting the fate of individual memories. - Shannon Tubridy; David Halpern; Lila Davachi; Todd M. Gureckis (CogSci, 2018)

Knowledge Tracing Using the Brain. - David Halpern; Shannon Tubridy; Hong Yu Wang; Camille Gasser; Pamela Osborn Popp; Lila Davachi; Todd M. Gureckis (EDM, 2018)

Evidence of error-driven cross-situational word learning. - Chris Grimmick; Todd M. Gureckis; George Kachergis (CogSci, 2019)

Exploring informal science interventions to promote children's understanding of natural categories. - George Kachergis; Todd M. Gureckis; Marjorie Rhodes (CogSci, 2019)

The critical moment is coming: Modeling the dynamics of suspense. - Zhiwei Li; Neil Bramley; Todd M. Gureckis (CogSci, 2019)

Active physical inference via reinforcement learning. - Shuaiji Li; Yu Sun; Sijia Liu; Tianyu Wang; Todd M. Gureckis; Neil Bramley (CogSci, 2019)

Limits on the Use of Simulation in Physical Reasoning. - Ethan Ludwin-Peery; Neil Bramley; Ernest Davis; Todd M. Gureckis (CogSci, 2019)

Causal intervention strategies change across adolescence. - Kate Nussenbaum; Alexandra Cohen; Zachary Davis 001; David Halpern; Todd M. Gureckis; Catherine Hartley (CogSci, 2019)

Modeling Intuitive Teaching as Sequential Decision Making Under Uncertainty. - Pamela Osborn Popp; Todd M. Gureckis (CogSci, 2019)

Asking goal-oriented questions and learning from answers. - Anselm Rothe; Brenden M. Lake; Todd M. Gureckis (CogSci, 2019)

Lessons for artificial intelligence from the study of natural stupidity. - Alexander S. Rich; Todd M. Gureckis (Nat. Mach. Intell., 2019)

Dynamic Control Under Changing Goals. - Zachary Davis 001; Neil Bramley; Bob Rehder; Todd M. Gureckis (CogSci, 2020)

Extending the Rogers and McClelland Model of Semantic Cognition (2003) to work with Raw Pixel Information. - Arihant Jain; Brenden M. Lake; Todd M. Gureckis (CogSci, 2020)

A Generalization Test of Conjunction Errors in Physical Reasoning. - Ethan Ludwin-Peery; Neil Bramley; Ernest Davis; Todd M. Gureckis (CogSci, 2020)

Pictorial Depth Cues in Young Children's Drawings of Layouts and Objects. - Théo Morfoisse; Todd M. Gureckis; Moira R. Dillon (CogSci, 2020)

Ask or Tell: Balancing questions and instructions in intuitive teaching. - Pamela Osborn Popp; Todd M. Gureckis (CogSci, 2020)

Causal Information-Seeking Strategies Change Across Childhood and Adolescence. - Kate Nussenbaum; Alexandra Cohen; Zachary Davis 001; David Halpern; Todd M. Gureckis; Catherine Hartley (Cogn. Sci., 2020)

Fast and Flexible: Human program induction in abstract reasoning tasks. - Aysja Johnson; Wai Keen Vong; Brenden M. Lake; Todd M. Gureckis (CogSci, 2021)

Can losses help attenuate learning traps? - Amy X. Li; Todd M. Gureckis; Brett K. Hayes (CogSci, 2021)

Information sampling for contingency planning. - Ili Ma; Wei Ji Ma; Todd M. Gureckis (CogSci, 2021)

Creativity, Compositionality, and Common Sense in Human Goal Generation. - Guy Davidson; Todd M. Gureckis; Brenden M. Lake (CogSci, 2022)

Where Questions Come From: Reusing Old Questions in New Situations. - Emily Liquin; Todd M. Gureckis (CogSci, 2022)

Rule discovery performance unchanged by incentives. - Pamela Osborn Popp; Ben R. Newell; Daniel M. Bartels; Todd M. Gureckis (CogSci, 2022)

Name that state: How language affects human reinforcement learning. - Angela Radulescu; Wai Keen Vong; Todd M. Gureckis (CogSci, 2022)

Teaching and Learning Through Pedagogical Environment Design. - Emily Liquin; Nicole Luzuriaga; Todd M. Gureckis (CogSci, 2023)

How does the primate brain combine generative and discriminative computations in vision? - Benjamin Peters; James J. DiCarlo; Todd M. Gureckis; Ralf Haefner; Leyla Isik; Joshua B. Tenenbaum; Talia Konkle; Thomas Naselaris; Kimberly L. Stachenfeld; Zenna Tavares; Doris Tsao; Ilker Yildirim; et al. (CoRR, 2024)

H-ARC: A Robust Estimate of Human Performance on the Abstraction and Reasoning Corpus Benchmark. - Solim LeGris; Wai Keen Vong; Brenden M. Lake; Todd M. Gureckis (CoRR, 2024)

Goals as reward-producing programs. - Guy Davidson; Graham Todd; Julian Togelius; Todd M. Gureckis; Brenden M. Lake (Nat. Mac. Intell., 2025)

Do different prompting methods yield a common task representation in language models? - Guy Davidson; Todd M. Gureckis; Brenden M. Lake; Adina Williams (CoRR, 2025)