UNSW Computational Cognitive Science

This page lists the academic publications associated with our lab. We hope you find the work interesting!

2019

De Deyne, S., Navarro, D. J., Perfors, A., Brysbaert, M. and Storms, G. (2019). The Small World of Words: English word association norms for over 12,000 cue words. Behavior Research Methods, 51, 987-1006 [pdf, source, doi, psyarxiv]
Hayes, B. K., Banner, S., Forrester, S. and Navarro, D. J. (2019). Selective sampling and inductive inference: Drawing inferences based on observed and missing evidence. Cognitive Psychology, 113. [pdf, source, doi, psyarxiv, github]
Hayes, B. K., Navarro, D. J., Stephens, R., Ransom, K. and Dilevski, N. (2019). The diversity effect in inductive reasoning depends on sampling assumptions. Psychonomic Bulletin & Review, 26, 1043-1050. [pdf, source, doi, psyarxiv, osf, github]
Hendrickson, A., Perfors, A., Navarro, D. J. and Ransom, K. (2019). Sample size, number of categories and sampling assumptions: Exploring some differences between categorization and generalization. Cognitive Psychology, 111, 80-102 [pdf, source, doi, psyarxiv]
Lee, J. C., Lovibond, P. F., Hayes, B. K. and Navarro, D. J. (2019). Negative evidence and inductive reasoning in generalization of associative learning. Journal of Experimental Psychology: General, 148, 289-303. [pdf, doi, psyarxiv]
Navarro, D. J. (2019). Between the devil and the deep blue sea: Tensions between scientific judgement and statistical model selection. Computational Brain and Behavior, 2, 28-34 [pdf, source, doi, osf, psyarxiv]
Vong, W. K., Hendrickson, A., Navarro, D. J. and Perfors, A. (2019). Do additional features help or hurt category learning? The curse of dimensionality in human learners. Cognitive Science, 43, e12724 [pdf, source, doi, psyarxiv]

2018

Langsford, S., Perfors, A., Hendrickson, A., Kennedy, L. and Navarro, D. J. (2018). A systematic comparison and reliability analysis of formal measures of sentence acceptability. Glossa: A Journal of General Linguistics, 3, 37. [pdf, source, doi, psyarxiv]
Martire, K. A., Growns, B. and Navarro, D. J. (2018). What do the experts know? Calibration, precision, and the wisdom of crowds among forensic handwriting experts. Psychonomic Bulletin and Review, 25, 2346–2355. [pdf, source, doi, psyarxiv, osf, github]
Navarro, D. J., Perfors, A., Kary, A., Brown, S. and Donkin, C. (2018). When extremists win: Cultural transmission via iterated learning when priors are heterogeneous. Cognitive Science, 42, 2108-2149. [pdf, source, doi, psyarxiv, osf, github]
Navarro, D. J., Tran, P. and Baz, N. (2018). Aversion to option loss in a restless bandit task. Computational Brain and Behavior, 1, 151-164. [pdf, source, doi, psyarxiv, osf]

2017

Kennedy, L., Navarro, D. J., Perfors, A. and Briggs, N. (2017). Not every credible interval is credible: Evaluating robustness in the presence of contamination in Bayesian data analysis. Behavior Research Methods, 49, 2219–2234. [pdf, source, doi, psyarxiv]
Martire, K. A., Edmond, G., Navarro, D. J, and Newell, B. R. (2017). On the likelihood of ‘encapsulating all uncertainty’. Science and Justice, 57, 76-79 [pdf, source, doi, psyarxiv]
Navarro, D. J. and Kemp, C. (2017). None of the above: A Bayesian account of the detection of novel categories. Psychological Review, 124, 643-677 [pdf, source, doi, psyarxiv]
Tauber, S., Navarro, D. J., Perfors A. and Steyvers, M. (2017). Bayesian models of cognition revisited: Setting optimality aside and letting data drive psychological theory. Psychological Review, 124, 410-441. [pdf, source, doi, psyarxiv]

2016

De Deyne, S., Kenett, Y. N., Anaki, D., Faust, M. and Navarro, D. J. (2016). Large-scale network representations of semantics in the mental lexicon. In M. N. Jones (Ed.) Big Data in Cognitive Science: From Methods to Insights (pp. 174-202)
De Deyne, S., Navarro D. J., Perfors, A. and Storms, G. (2016). Structure at every scale: A semantic network account of the similarities between very unrelated concepts. Journal of Experimental Psychology: General, 145, 1228-1254 [pdf, source, doi, psyarxiv]
Gokaydin, D. Navarro, D. J., Ma-Wyatt, A. and Perfors, A. (2016). The structure of sequential effects. Journal of Experimental Psychology: General, 145, 110-123 [pdf, source, doi, psyarxiv]
Hendrickson, A., Navarro D. J. and Perfors, A. (2016). Sensitivity to hypothesis size during information search. Decision, 3, 62-80. [pdf, doi, psyarxiv]
Navarro, D. J., Newell, B. and Schulze, C. (2016). Learning and choosing in an uncertain world: An investigation of the explore-exploit dilemma in static and dynamic environments. Cognitive Psychology, 85, 43-77. [pdf, source, doi, psyarxiv, github]
Ransom, K., Perfors, A. and Navarro, D. J. (2016). Leaping to conclusions: Why premise relevance affects argument strength. Cognitive Science, 40, 1775-1796 [pdf, source, doi, psyarxiv]
Vong, W. K., Navarro, D. J. and Perfors, A. (2016). The helpfulness of category labels in semi-supervised learning depends on category structure. Psychonomic Bulletin and Review, 23, 230-238 [pdf, source, doi, psyarxiv]

2015

Voorspoels, W., Navarro, D. J., Perfors, A., Ransom K. and Storms, G. (2015). How do people learn from negative evidence? Non-monotonic generalizations and sampling assumptions in inductive reasoning. Cognitive Psychology, 81, 1-25. [pdf, source, doi, psyarxiv]

2014

De Deyne, S., Voorspoels, W., Verheyen, S., Navarro, D. J. and Storms, G. (2014). Accounting for graded structure in adjective categories with valence-based opposition relationships. Language and Cognitive Processes, 29, 568-583. [pdf, source, doi, psyarxiv]
Perfors, A. and Navarro, D. J. (2014). Language evolution can be shaped by the structure of the world. Cognitive Science, 38, 775-793 [pdf, source, doi, psyarxiv]

2013

De Deyne, S., Navarro, D. J. and Storms, G. (2013). Better explanations of lexical and semantic cognition using networks derived from continued rather than single word associations. Behavior Research Methods, 45, 480-498. [pdf, source, doi, psyarxiv]

2012

Navarro, D. J., Dry, M. J. and Lee, M D. (2012). Sampling assumptions in inductive generalization. Cognitive Science, 36, 187-223 [pdf, source, doi, psyarxiv]
Shafto, P., Eaves, B., Navarro, D. J. and Perfors, A. (2012). Epistemic trust: Modeling children’s reasoning about others’ knowledge and intent. Developmental Science, 15, 436-447. [pdf, doi, psyarxiv]

2011

Navarro, D. J. and Perfors, A. (2011). Hypothesis generation, the positive test strategy, and sparse categories. Psychological Review, 118, 120-34 [pdf, source, doi, psyarxiv]

2010

Navarro, D. J. and Perfors, A. (2010). Similarity, feature discovery, and the size principle. Acta Psychologica, 133, 256-268 [pdf, source, doi, psyarxiv]
Sanborn, A. N., Griffiths, T. L. and Navarro, D. J. (2010). Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review, 117, 1144-1167. [pdf, doi, psyarxiv]

2009

Navarro, D. J. and Fuss, I. (2009). Fast and accurate calculations for first-passage times in Wiener diffusion models. Journal of Mathematical Psychology, 53, 222-230. [pdf, doi, psyarxiv]
Grunwald, P. and Navarro, D. J. (2009). NML, Bayes and true distributions: A comment on Karabatsos and Walker (2006). Journal of Mathematical Psychology, 53, 43-51. [pdf, source, doi, psyarxiv]

2008

Navarro, D. J. and Griffiths, T. L. (2008). Latent features in similarity judgments: A nonparametric Bayesian approach. Neural Computation, 20, 2597-2628. [pdf, source, doi, psyarxiv]

2007

Griffiths, T. L., Canini, K. R., Sanborn, A. N. and Navarro, D. J. (2007). Unifying rational models of categorization via the hierarchical Dirichlet process. In D. S. McNamara and J. G. Trafton (Eds) Proceedings of the 29th Annual Conference of the Cognitive Science Society (pp. 323-328). [pdf, psyarxiv]
Myung, J. I., Pitt, M. A. and Navarro, D. J. (2007). Does response scaling cause the Generalized Context Model to mimic a prototype model? Psychonomic Bulletin and Review, 14, 1043-1050. [pdf, source, doi, psyarxiv]

2006

Myung, J. I., Navarro, D. J. and Pitt, M. A. (2006). Model selection by Normalized Maximum Likelihood. Journal of Mathematical Psychology, 50, 167-179. [pdf, source, doi, psyarxiv]
Navarro, D. J., Griffiths, T. L., Steyvers, M. and Lee, M. D. (2006). Modeling individual differences using Dirichlet processes. Journal of Mathematical Psychology, 50, 101-122. [pdf, source, doi, psyarxiv]
Pitt, M. A., Kim, W., Navarro, D. J. and Myung, J. I. (2006). Global model analysis by parameter space partitioning. Psychological Review, 113, 57-83. [pdf, doi, psyarxiv]

2005

Lee, M. D. and Navarro, D. J. (2005). Minimum description length and psychological clustering models. In P. Grunwald, I. J. Myung and M. A. Pitt (Eds) Advances in Minimum Description Length: Theory and Applications (pp. 355-384). [pdf, psyarxiv]

2004

Navarro, D. J. (2004). A note on the applied use of MDL approximations. Neural Computation, 16, 1763-1768. [pdf, source, doi, psyarxiv]
Navarro, D. J. and Lee, M. D. (2004). Common and distinctive features in stimulus representation: A modified version of the contrast model. Psychonomic Bulletin and Review, 11, 961-974. [pdf, doi, psyarxiv]
Navarro, D. J., Pitt, M. A. and Myung, I. J. (2004). Assessing the distinguishability of models and the informativeness of data. Cognitive Psychology, 49, 47-84 [pdf, source, doi, psyarxiv]

2003

Navarro, D. J. (2003). Regarding the complexity of additive clustering models: Comment on Lee (2001). Journal of Mathematical Psychology, 47, 241-243. [pdf, source, doi, psyarxiv]
Navarro, D. J. and Lee, M. D. (2003). Combining dimensions and features in similarity-based representations. In S. Becker, S. Thrun and K. Obermayer (Ed.) Advances in Neural Information Processing Systems (pp. 67-74). [pdf, psyarxiv]

2002

Lee, M. D. and Navarro, D. J. (2002). Extending the ALCOVE model of category learning to featural stimulus domains. Psychonomic Bulletin and Review, 9, 43-58. [pdf, doi, psyarxiv]

Books

I’m not really sure how all the Learning Statistics with {{Software}} books fit into an academic publication list. They seem to sit in their own world rather distinct from traditional publication. Anyway, here they are:

Navarro, D. J. and Foxcroft D. R. (2019). Learning statistics with jamovi: A tutorial for psychology students and other beginners. [site]
Navarro, D. J., Foxcroft D. R. and Faulkenberry, T. J. (2019). Learning statistics with JASP: A tutorial for psychology students and other beginners. [site]
Navarro, D. J. (2015). Learning statistics with R: A tutorial for psychology students and other beginners. [site]

Publications