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ORCIDDanielle J. Navarro
Person information
- affiliation: University of New South Wales, School of Psychology, Australia
- affiliation (former): University of Adelaide, School of Psychology, Australia
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Journal Articles
- 2021
[j12]Simon De Deyne
, Danielle J. Navarro, Guillem Collell, Andrew Perfors:
Visual and Affective Multimodal Models of Word Meaning in Language and Mind. Cogn. Sci. 45(1) (2021)- 2020
- [j11]Belinda Xie, Danielle J. Navarro, Brett K. Hayes:
Adding Types, But Not Tokens, Affects Property Induction. Cogn. Sci. 44(9) (2020) - 2019
- [j10]Wai Keen Vong, Andrew T. Hendrickson, Danielle J. Navarro, Amy Perfors:
Do Additional Features Help or Hurt Category Learning? The Curse of Dimensionality in Human Learners. Cogn. Sci. 43(3) (2019) - 2018
- [j9]Danielle J. Navarro, Amy Perfors, Arthur Kary, Scott D. Brown, Christopher Donkin:
When Extremists Win: Cultural Transmission Via Iterated Learning When Populations Are Heterogeneous. Cogn. Sci. 42(7): 2108-2149 (2018) - 2016
- [j8]Keith Ransom, Amy Perfors, Daniel J. Navarro:
Leaping to Conclusions: Why Premise Relevance Affects Argument Strength. Cogn. Sci. 40(7): 1775-1796 (2016) - 2014
- [j7]Amy Perfors, Daniel J. Navarro:
Language Evolution Can Be Shaped by the Structure of the World. Cogn. Sci. 38(4): 775-793 (2014) - 2013
- [j6]Ian G. Fuss, Daniel J. Navarro:
Open Parallel Cooperative and Competitive Decision Processes: A Potential Provenance for Quantum Probability Decision Models. Top. Cogn. Sci. 5(4): 818-843 (2013) - 2012
- [j5]Daniel J. Navarro, Matthew J. Dry, Michael D. Lee:
Sampling Assumptions in Inductive Generalization. Cogn. Sci. 36(2): 187-223 (2012) - [j4]Marcus A. Butavicius, Michael D. Lee, Brandon M. Pincombe, Louise G. Mullen, Daniel J. Navarro, Kathryn Parsons, Agata McCormac:
An assessment of email and spontaneous dialog visualizations. Int. J. Hum. Comput. Stud. 70(6): 432-449 (2012) - 2010
- [j3]Amy L. Walwyn, Daniel J. Navarro:
Minimal Paths in the City Block: Human Performance on Euclidean and Non-Euclidean Traveling Salesperson Problems. J. Probl. Solving 3(1) (2010) - 2008
- [j2]Daniel J. Navarro, Thomas L. Griffiths:
Latent Features in Similarity Judgments: A Nonparametric Bayesian Approach. Neural Comput. 20(11): 2597-2628 (2008) - 2004
- [j1]Daniel J. Navarro:
A Note on the Applied Use of MDL Approximations. Neural Comput. 16(9): 1763-1768 (2004)
Conference and Workshop Papers
- 2020
- [c46]Jake Embrey, Shi Xian Liew, Danielle J. Navarro, Ben R. Newell:
Do you want to know a secret? The role of valence and delay in early information preference. CogSci 2020 - 2019
- [c45]Amy Perfors, Danielle J. Navarro:
Why do echo chambers form? The role of trust, population heterogeneity, and objective truth. CogSci 2019: 918-923 - 2018
- [c44]Simon De Deyne, Amy Perfors, Danielle J. Navarro:
Learning word meaning with little means: An investigation into the inferential capacity of paradigmatic information. CogSci 2018 - [c43]Jared M. Hotaling, Danielle J. Navarro, Ben R. Newell:
Skilled Bandits: Learning to Choose in a Reactive World. CogSci 2018 - [c42]Amy Perfors, Danielle J. Navarro, Patrick Shafto:
Stronger evidence isn't always better: A role for social inference in evidence selection and interpretation. CogSci 2018 - [c41]Keith Ransom, Andrew Hendrickson, Amy Perfors, Danielle J. Navarro:
Representational and sampling assumptions drive individual differences in single category generalisation. CogSci 2018 - [c40]Belinda Xie, Brett K. Hayes, Danielle J. Navarro:
Adding types, but not tokens, affects the breadth of property induction. CogSci 2018 - 2017
- [c39]Brett K. Hayes, Stephanie Banner, Daniel J. Navarro:
Sampling frames, Bayesian inference and inductive reasoning. CogSci 2017 - [c38]Lauren Kennedy, Amy Perfors, Daniel J. Navarro:
Priors, informative cues and ambiguity aversion. CogSci 2017 - [c37]Steven Langsford, Andrew Hendrickson, Amy Perfors, Daniel J. Navarro:
When do learned transformations influence similarity and categorization? CogSci 2017 - [c36]Daniel J. Navarro, Amy Perfors, Arthur Kary, Scott Brown, Christopher Donkin:
When extremists win: On the behavior of iterated learning chains when priors are heterogeneous. CogSci 2017 - [c35]Keith Ransom, Wouter Voorspoels, Amy Perfors, Daniel J. Navarro:
A cognitive analysis of deception without lying. CogSci 2017 - [c34]Simon De Deyne, Amy Perfors, Daniel J. Navarro:
Predicting Human Similarity Judgments with Distributional Models: The Value of Word Associations. IJCAI 2017: 4806-4810 - 2016
- [c33]Wai Keen Vong, Andrew Hendrickson, Amy Perfors, Daniel J. Navarro:
Do additional features help or harm during category learning? An exploration of the curse of dimensionality in human learners. CogSci 2016 - [c32]Matthew Welsh, Sandy Steacy, Steve Begg, Daniel J. Navarro:
A Tale of Two Disasters: Biases in Risk Communication. CogSci 2016 - [c31]Simon De Deyne, Amy Perfors, Daniel J. Navarro:
Predicting human similarity judgments with distributional models: The value of word associations. COLING 2016: 1861-1870 - 2015
- [c30]Simon De Deyne, Steven Verheyen, Amy Perfors, Daniel J. Navarro:
Evidence for widespread thematic structure in the mental lexicon. CogSci 2015 - [c29]Andrew Hendrickson, Daniel J. Navarro, Christopher Donkin:
Quantifying the time course of similarity. CogSci 2015 - [c28]Keith Ransom, Wouter Voorspoels, Amy Perfors, Daniel J. Navarro:
Sensitivity to communicative norms when deceiving without lying. CogSci 2015 - [c27]Wouter Voorspoels, Daniel J. Navarro, Amy Perfors, Keith Ransom:
Gricean maxims influence inductive inference with negative observations. CogSci 2015 - 2014
- [c26]Andrew Hendrickson, Amy Perfors, Daniel J. Navarro:
Adaptive information source selection during hypothesis testing. CogSci 2014 - [c25]Steven Langsford, Andrew Hendrickson, Amy Perfors, Daniel J. Navarro:
People are sensitive to hypothesis sparsity during category discrimination. CogSci 2014 - [c24]Daniel J. Navarro, Ben R. Newell:
Information versus reward in a changing world. CogSci 2014 - [c23]Amy Perfors, Keith Ransom, Daniel J. Navarro:
People ignore token frequency when deciding how widely to generalize. CogSci 2014 - [c22]Sean Tauber, Daniel J. Navarro, Amy Perfors, Michael D. Lee:
Inferring the hypothesis spaces underlying inductive generalization. CogSci 2014 - [c21]Wai Keen Vong, Amy Perfors, Daniel J. Navarro:
The relevance of labels in semi-supervised learning depends on category structure. CogSci 2014 - 2013
- [c20]Simon De Deyne, Daniel J. Navarro, Gert Storms:
Associative strength and semantic activation in the mental lexicon: evidence from continued word associations. CogSci 2013 - [c19]Daniel J. Navarro:
Finding hidden types: Inductive inference in long-tailed environments. CogSci 2013 - [c18]Wai Keen Vong, Andrew Hendrickson, Amy Perfors, Daniel J. Navarro:
The role of sampling assumptions in generalization with multiple categories. CogSci 2013 - 2012
- [c17]Simon De Deyne, Daniel J. Navarro, Amy Perfors, Gert Storms:
Strong structure in weak semantic similarity: A graph based account. CogSci 2012 - [c16]Daniel J. Navarro, Amy Perfors:
Anticipating changes: Adaptation and extrapolation in category learning. CogSci 2012 - 2011
- [c15]Belinda Bruza, Matthew Welsh, Daniel J. Navarro, Steve Begg:
Does anchoring cause overconfidence only in experts? CogSci 2011 - [c14]Simon De Deyne, Wouter Voorspoels, Steven Verheyen, Daniel J. Navarro, Gert Storms:
Graded structure in adjective categories. CogSci 2011 - [c13]Dinis Gökaydin, Anna Ma-Wyatt, Daniel J. Navarro, Amy Perfors:
Humans use different statistics for sequence analysis depending on the task. CogSci 2011 - [c12]Robert Montague, Daniel J. Navarro, Amy Perfors, Russell Warner, Patrick Shafto:
To catch a liar: The effects of truthful and deceptive testimony on inferential learning. CogSci 2011 - [c11]Amy Perfors, Daniel J. Navarro:
Language evolution is shaped by the structure of the world: An iterated learning analysis. CogSci 2011 - [c10]Matthew Welsh, Daniel J. Navarro, Steve Begg:
Number Preference, Precision and Implicit Confidence. CogSci 2011 - 2010
- [c9]Luke Maurits, Daniel J. Navarro, Amy Perfors:
Why are some word orders more common than others? A uniform information density account. NIPS 2010: 1585-1593 - [c8]Daniel J. Navarro:
Learning the context of a category. NIPS 2010: 1795-1803 - 2006
- [c7]Michael D. Lee, Ian G. Fuss, Daniel J. Navarro:
A Bayesian Approach to Diffusion Models of Decision-Making and Response Time. NIPS 2006: 809-816 - [c6]Daniel J. Navarro, Thomas L. Griffiths:
A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments. NIPS 2006: 1033-1040 - 2005
- [c5]Jay I. Myung, Mark A. Pitt, Daniel J. Navarro:
Model selection in cognitive science as an inverse problem. Computational Imaging 2005: 219-228 - [c4]Daniel J. Navarro, Michael D. Lee:
An application of minimum description length clustering to partitioning learning curves. ISIT 2005: 587-591 - 2003
- [c3]Woojae Kim, Daniel J. Navarro, Mark A. Pitt, In Jae Myung:
An MCMC-Based Method of Comparing Connectionist Models in Cognitive Science. NIPS 2003: 937-944 - 2002
- [c2]Daniel J. Navarro, Michael D. Lee:
Combining Dimensions and Features in Similarity-Based Representations. NIPS 2002: 59-66 - 2000
- [c1]Douglas Vickers, Daniel J. Navarro, Michael D. Lee:
Towards a transformational approach to perceptual organization. KES 2000: 325-328
Coauthor Index
aka: Amy Perfors
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