D. Navarro

Michael D. Lee, Douglas Vickers, Daniel J. Navarro, 2000, KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516).

How do people learn from negative evidence? Non-monotonic generalizations and sampling assumptions in inductive reasoning

Amy Perfors, D. Navarro, Wouter Voorspoels, 2015, Cognitive Psychology.

Fast and accurate calculations for first-passage times in Wiener diffusion models

D. Navarro, I. Fuss, 2009 .

Categorization as nonparametric Bayesian density estimation

Adam N. Sanborn, T. Griffiths, K. Canini, 2008 .

Confirmation bias is rational when hypotheses are sparse

Amy Perfors, D. Navarro, A. Perfors, 2009 .

On the interaction between exemplar-based concepts and a response scaling process

D. Navarro, 2007 .

D. Navarro, 2007, Psychonomic bulletin & review.

The “Small World of Words” English word association norms for over 12,000 cue words

Amy Perfors, M. Brysbaert, D. Navarro, 2018, Behavior Research Methods.

Visual and Affective Grounding in Language and Mind

Amy Perfors, Guillem Collell, Simon De Deyne, 2018 .

The case for formal methodology in scientific reform

D. Navarro, J. Vandekerckhove, E. O. Buzbas, 2020, bioRxiv.

A Qualitative Approach to the Study of Causal Reasoning in Natural Language

D. Navarro, K. O’Doherty, Shona H. Crabb, 2009 .

Modeling Individual Differences with Dirichlet Processes

T. Griffiths, M. Lee, M. Steyvers, 2005 .

Regarding the complexity of additive clustering models: Comment on Lee (2001)

D. Navarro, 2003 .

NML, Bayes and true distributions: A comment on Karabatsos and Walker (2006)

D. Navarro, P. Grünwald, 2009 .

Between the Devil and the Deep Blue Sea: Tensions Between Scientific Judgement and Statistical Model Selection

D. Navarro, 2018, Computational Brain & Behavior.

Information Matrix

Jay I. Myung, D. Navarro, 2004 .

1 Model Evaluation and Selection

Jay I. Myung, I. J. Myung, Daniel R. Cavagnaro, 2014 .

Negative Evidence and Inductive Reasoning in Generalization of Associative Learning

Jessica C. Lee, B. Hayes, D. Navarro, 2019, Journal of experimental psychology. General.

If mathematical psychology did not exist we would need to invent it: A case study in cumulative theoretical development

D. Navarro, 2020 .

Adding Types, But Not Tokens, Affects Property Induction.

B. Hayes, D. Navarro, Belinda Xie, 2020, Cognitive science.

Selective sampling and inductive inference: Drawing inferences based on observed and missing evidence

B. Hayes, D. Navarro, S. Banner, 2019, Cognitive Psychology.

The helpfulness of category labels in semi-supervised learning depends on category structure

Wai Keen Vong, Amy Perfors, D. Navarro, 2016, Psychonomic bulletin & review.

When extremists win : Cultural transmission via iterated learning when priors are heterogeneous

D. Navarro, 2017 .

Bayesian Models of Cognition Revisited: Setting Optimality Aside and Letting Data Drive Psychological Theory

Amy Perfors, Mark Steyvers, Daniel J Navarro, 2017, Psychological review.

Spatial Visualisation of Document Similarity

Michael D. Lee, Daniel J. Navarro, M. Lee, 2001 .

If mathematical psychology did not exist we might need to invent it: A comment on theory building in psychology

D. Navarro, 2020 .

Parameter Space Partitioning 1 Global Model Analysis by Parameter Space Partitioning

Jay I. Myung, M. Pitt, D. Navarro, 2005 .

The structure of sequential effects.

Amy Perfors, D. Navarro, Anna Ma-Wyatt, 2016, Journal of experimental psychology. General.

Encapsulating all uncertainty On the likelihood of “ encapsulating all uncertainty ”

B. Newell, D. Navarro, G. Edmond, 2019 .

Where the truth lies: how sampling implications drive deception without lying

Amy Perfors, D. Navarro, Wouter Voorspoels, 2019 .

How does the presence of a label affect attention to other features

Amy Perfors, D. Navarro, A. Perfors, 2010 .

A Test of the Decision-Time Predictions of the `Take the Best' Model

D. Navarro, B. Schultz, 2008 .

Clustering Using the Contrast Model

M. Lee, D. Navarro, 2001 .

Does memory mediate susceptibility to cognitive biases? Implications of the decision-by-sampling theory

D. Navarro, Matthew Welsh, Belinda Bruza, 2008 .

Categorisation , Bounded Rationality and Bayesian Inference

D. Navarro, 2022 .

Exploring juror evaluations of expert opinions using the Expert Persuasion Expectancy framework

D. Navarro, G. Edmond, K. Martire, 2020 .

The diversity effect in inductive reasoning depends on sampling assumptions

Rachel G. Stephens, B. Hayes, D. Navarro, 2019, Psychonomic Bulletin & Review.

Statistics in the Service of Science: Don't let the Tail Wag the Dog

Gregory E. Cox, Sara D McMullin, R. Shiffrin, 2021, Computational Brain & Behavior.

Aversion to Option Loss in a Restless Bandit Task

D. Navarro, Peter Tran, Nicole Baz, 2018, Computational Brain & Behavior.

Formal Approaches in Categorization: Nonparametric Bayesian models of categorization

Thomas L. Griffiths, Joshua B. Tenenbaum, Kevin Robert Canini, 2011 .

Title of Paper : Towards a Transformational Approach to Perceptual Organization Names of Authors : ( 1 )

M. Lee, D. Vickers, D. Navarro, 2001 .

If Mathematical Psychology Did Not Exist We Might Need to Invent It: A Comment on Theory Building in Psychology

D. Navarro, 2020, Perspectives on psychological science : a journal of the Association for Psychological Science.

A test of the decision-time predictions of the the Best' model the

D. Navarro, Benjamin Schultz, 2013 .

Social context effects on the impact of category labels

Rachel G. Stephens, Amy Perfors, D. Navarro, 2010 .

Between the Devil and the Deep Blue Sea: Tensions Between Scientific Judgement and Statistical Model Selection

D. Navarro, 2018, Computational Brain & Behavior.

Comparing Anticipation and Uncertainty-Penalty Accounts of Non-Instrumental Information Seeking

Shi Xian Liew, B. Newell, D. Navarro, 2021, Decision.

Sensitivity to hypothesis size during information search.

Amy Perfors, Daniel J. Navarro, Andrew T. Hendrickson, 2016 .

Seeing is believing: Priors, trust, and base rate neglect

Daniel J. Navarro, Matthew Welsh, D. Navarro, 2012 .

Minimal Paths in the City Block: Human Performance on Euclidean and Non-Euclidean Traveling Salesperson Problems

Daniel J. Navarro, Amy L. Walwyn, D. Navarro, 2010, J. Probl. Solving.

Model selection in cognitive science as an inverse problem

Mark A. Pitt, Jay I. Myung, Daniel J. Navarro, 2005, IS&T/SPIE Electronic Imaging.

Statistics in the Service of Science: Don’t Let the Tail Wag the Dog

Henrik Singmann, David Kellen, Gregory E. Cox, 2021, Computational Brain & Behavior.

Protection from uncertainty in the exploration/exploitation trade-off.

B. Newell, D. Navarro, T. Beesley, 2021, Journal of experimental psychology. Learning, memory, and cognition.

Protection from uncertainty in the exploration/exploitation trade-off

B. Newell, D. Navarro, T. Beesley, 2020 .

Do Additional Features Help or Hurt Category Learning? The Curse of Dimensionality in Human Learners

Danielle J. Navarro, Amy Perfors, Andrew T. Hendrickson, 2018, Cogn. Sci..

What do the experts know? Calibration, precision, and the wisdom of crowds among forensic handwriting experts

Kristy A Martire, Bethany Growns, D. Navarro, 2018, Psychonomic bulletin & review.

Not every credible interval is credible: Evaluating robustness in the presence of contamination in Bayesian data analysis

D. Navarro, Andrew Perfors, Nancy Briggs, 2017, Behavior Research Methods.

Not every credible interval is credible: Evaluating robustness in the presence of contamination in Bayesian data analysis

Amy Perfors, Lauren A Kennedy, Daniel J Navarro, 2017, Behavior research methods.

Leaping to Conclusions: Why Premise Relevance Affects Argument Strength

Amy Perfors, Daniel J. Navarro, Keith Ransom, 2016, Cogn. Sci..

People are sensitive to hypothesis sparsity during category discrimination

Amy Perfors, Daniel J. Navarro, Andrew Hendrickson, 2014, CogSci.

Sampling Assumptions in Inductive Generalization

Michael D. Lee, Daniel J. Navarro, Matthew J. Dry, 2012, Cogn. Sci..

A Note on the Applied Use of MDL Approximations

Daniel J. Navarro, D. Navarro, 2004, Neural Computation.

Number Preference, Precision and Implicit Confidence

Daniel J. Navarro, Steve Begg, Matthew Welsh, 2011, CogSci.

Learning and choosing in an uncertain world: An investigation of the explore–exploit dilemma in static and dynamic environments

Ben R. Newell, Christin Schulze, Daniel J. Navarro, 2016, Cognitive Psychology.

Combining Dimensions and Features in Similarity-Based Representations

Michael D. Lee, Daniel J. Navarro, M. Lee, 2002, NIPS.

Information versus reward in a changing world

Ben R. Newell, Daniel J. Navarro, B. Newell, 2014, CogSci.

Large-Scale Network Representations of Semantics in the Mental Lexicon

Daniel J. Navarro, Yoed N. Kenett, Simon De Deyne, 2017 .

Evidence for widespread thematic structure in the mental lexicon

Amy Perfors, Daniel J. Navarro, Steven Verheyen, 2015, CogSci.

Quantifying sentence acceptability measures : Reliability, bias, and variability

Amy Perfors, Andrew T. Hendrickson, Steven Langsford, 2018 .

The diversity effect in inductive reasoning depends on sampling assumptions

Rachel G. Stephens, B. Hayes, D. Navarro, 2019, Psychonomic bulletin & review.

Rational approximations to rational models: alternative algorithms for category learning.

Adam N Sanborn, Thomas L Griffiths, Daniel J Navarro, 2010, Psychological review.

The perceptual organization of point constellations

Michael D. Lee, Daniel J. Navarro, Matthew J. Dry, 2009 .

Visual and Affective Multimodal Models of Word Meaning in Language and Mind

Danielle J. Navarro, Simon De Deyne, Guillem Collell, 2021, Cogn. Sci..

Predicting human similarity judgments with distributional models: The value of word associations.

Amy Perfors, Daniel J. Navarro, Simon De Deyne, 2016, COLING.

Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations

Danielle J. Navarro, Simon De Deyne, Gert Storms, 2012, Behavior Research Methods.

Daniel J Navarro, Amy F Perfors, D. Navarro, 2010, Acta psychologica.

One of these greebles is not like the others: Semi-supervised models for similarity structures

Daniel J. Navarro, Rachel G. Stephens, D. Navarro, 2008 .

A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments

Thomas L. Griffiths, Daniel J. Navarro, T. Griffiths, 2006, NIPS.

Sample size, number of categories and sampling assumptions: Exploring some differences between categorization and generalization

Danielle J. Navarro, Amy Perfors, Keith Ransom, 2019, Cognitive Psychology.

Structure at every scale: A semantic network account of the similarities between unrelated concepts.

Amy Perfors, Daniel J Navarro, Gert Storms, 2016, Journal of experimental psychology. General.

Analyzing the RULEX model of category learning

Daniel J. Navarro, D. Navarro, 2005 .

The “Small World of Words” English word association norms for over 12,000 cue words

M. Brysbaert, D. Navarro, G. Storms, 2018, Behavior Research Methods.

An MCMC-Based Method of Comparing Connectionist Models in Cognitive Science

Mark A. Pitt, Daniel J. Navarro, Woojae Kim, 2003, NIPS.

ACCEPTED VERSION

D. Navarro, S. Crabb, Kieran C. O’Doherty, 2008 .

Using MCMC to Compare Network Models in Cognitive Science

I. J. Myung, M. Pitt, D. Navarro, 2003 .

In What Sense is P(A|B) P(B) = P(A,B)? The Relationship between Distributional Format and Subjective Probability Estimates

Daniel J. Navarro, Matthew Welsh, Belinda Bruza, 2010 .

Learning the context of a category

Daniel J. Navarro, D. Navarro, 2010, NIPS.

A more rational model of categorization

Thomas L. Griffiths, Daniel J. Navarro, Adam N. Sanborn, 2006 .

Graded structure in adjective categories

Daniel J. Navarro, Wouter Voorspoels, Gerrit Storms, 2011, CogSci.

An assessment of email and spontaneous dialog visualizations

Marcus A. Butavicius, Kathryn Parsons, Agata McCormac, 2012, Int. J. Hum. Comput. Stud..

Extending and testing the Bayesian theory of generalization

Michael D. Lee, Daniel J. Navarro, Matthew J. Dry, 2008 .

Do additional features help or harm during category learning? An exploration of the curse of dimensionality in human learners

Amy Perfors, Daniel J. Navarro, Wai Keen Vong, 2016, CogSci.

Language Evolution Can Be Shaped by the Structure of the World

Amy Perfors, Daniel J. Navarro, D. Navarro, 2014, Cogn. Sci..

What Bayesian modelling can tell us about statistical learning: What it requires and why it works

Amy Perfors, Daniel J. Navarro, D. Navarro, 2012 .

Language evolution is shaped by the structure of the world: An iterated learning analysis

Amy Perfors, Daniel J. Navarro, D. Navarro, 2011, CogSci.

Skilled Bandits: Learning to Choose in a Reactive World

Ben R. Newell, Danielle J. Navarro, Jared M. Hotaling, 2018, CogSci.

Is Preregistration Worthwhile?

Iris van Rooij, David Kellen, Chris Donkin, 2019, Trends in Cognitive Sciences.

Erroneous Gambling-Related Beliefs as Illusions of Primary and Secondary Control: A Confirmatory Factor Analysis

D. Navarro, A. Ejova, P. Delfabbro, 2015, Journal of Gambling Studies.

When Extremists Win: Cultural Transmission Via Iterated Learning When Populations Are Heterogeneous

Danielle J. Navarro, Amy Perfors, Scott D. Brown, 2018, Cogn. Sci..

When extremists win: On the behavior of iterated learning chains when priors are heterogeneous

Amy Perfors, Daniel J. Navarro, Scott Brown, 2017, CogSci.

Do you want to know a secret? The role of valence and delay in early information preference

Shi Xian Liew, B. Newell, D. Navarro, 2020, CogSci.

Stronger evidence isn't always better: A role for social inference in evidence selection and interpretation

Patrick Shafto, Danielle J. Navarro, Amy Perfors, 2018, CogSci.

Why do echo chambers form? The role of trust, population heterogeneity, and objective truth

Danielle J. Navarro, Amy Perfors, D. Navarro, 2019, CogSci.

A Bayesian Approach to Diffusion Models of Decision-Making and Response Time

Michael D. Lee, Daniel J. Navarro, Ian G. Fuss, 2006, NIPS.

Success-slope effects on the illusion of control and on remembered success-frequency

D. Navarro, A. Ejova, P. Delfabbro, 2013, Judgment and Decision Making.

When to walk away: The effect of variability on keeping options viable

Anastasia Ejova, Amy Perfors, Daniel J. Navarro, 2009 .

The Exploration Advantage: Children’s instinct to explore allows them to find information that adults miss.

Amy Perfors, Emily Sumner, Barbara Wiseheart Sarnecka, 2019 .

A Tale of Two Disasters: Biases in Risk Communication

Daniel J. Navarro, Steve Begg, Matthew Welsh, 2016, CogSci.

A cognitive analysis of deception without lying

Amy Perfors, Daniel J. Navarro, Keith Ransom, 2017, CogSci.

Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations

D. Navarro, G. Storms, S. De Deyne, 2012, Behavior Research Methods.

On the likelihood of "encapsulating all uncertainty".

Daniel J Navarro, Gary Edmond, Ben R Newell, 2017, Science & justice : journal of the Forensic Science Society.

Humans use different statistics for sequence analysis depending on the task

Amy Perfors, Daniel J. Navarro, Anna Ma-Wyatt, 2011, CogSci.

Why are some word orders more common than others? A uniform information density account

Amy Perfors, Daniel J. Navarro, Luke Maurits, 2010, NIPS.

Adding Types, But Not Tokens, Affects Property Induction

Danielle J. Navarro, Brett K. Hayes, Belinda Xie, 2020, Cogn. Sci..

Enlightenment grows from fundamentals

Amy Perfors, Daniel J. Navarro, D. Navarro, 2011, Behavioral and Brain Sciences.

Introduction to the special issue on formal modeling of semantic concepts.

Daniel J Navarro, Gert Storms, Michael D Lee, 2010, Acta psychologica.

Representational and sampling assumptions drive individual differences in single category generalisation

Danielle J. Navarro, Amy Perfors, Keith Ransom, 2018, CogSci.

Sampling frames, Bayesian inference and inductive reasoning

Brett K. Hayes, Daniel J. Navarro, Stephanie Banner, 2017, CogSci.

People ignore token frequency when deciding how widely to generalize

Amy Perfors, Daniel J. Navarro, Keith Ransom, 2014, CogSci.

The role of sampling assumptions in generalization with multiple categories

Amy Perfors, Daniel J. Navarro, Wai Keen Vong, 2013, CogSci.

From natural kinds to complex categories

Daniel J. Navarro, D. Navarro, 2006 .

Strong structure in weak semantic similarity: A graph based account

Amy Perfors, Daniel J. Navarro, Gerrit Storms, 2012, CogSci.

The Illusion of control: structure, measurement and dependence on reinforcement frequency in the context of a laboratory gambling task

D. Navarro, A. Ejova, P. Delfabbro, 2010 .

Using sequential structure to improve visuomotor control

Daniel J. Navarro, Anna Ma-Wyatt, D. Navarro, 2009 .

Understanding User Behavior Using Cognitive Models

Daniel J. Navarro, Marisa Maio Mackay, Kieran O'Doherty, 2007 .

Not every credible interval is credible: Evaluating robustness in the presence of contamination in Bayesian data analysis

D. Navarro, Nancy Briggs, A. Perfors, 2017, Behavior Research Methods.

Joint acquisition of word order and word reference

Amy Perfors, Luke Maurits, Daniel J. Navarro, 2009 .

Open Parallel Cooperative and Competitive Decision Processes: A Potential Provenance for Quantum Probability Decision Models

Daniel J. Navarro, Ian G. Fuss, D. Navarro, 2013, Top. Cogn. Sci..

Merced Proceedings of the Annual Meeting of the Cognitive Science Society Title Learned Categorical Perception for Natural Faces Permalink

D. Navarro, 2022 .