Daniel J. Navarro

Affiliations: 
University of Adelaide, Adelaide, South Australia, Australia 
Area:
Higher-order cognition
Google:
"Daniel Navarro"
Mean distance: 24.73 (cluster 23)
 
BETA: Related publications

Publications

You can help our author matching system! If you notice any publications incorrectly attributed to this author, please sign in and mark matches as correct or incorrect.

Navarro DJ, Kemp C. (2017) None of the Above: A Bayesian Account of the Detection of Novel Categories. Psychological Review
Kennedy LA, Navarro DJ, Perfors A, et al. (2017) Not every credible interval is credible: Evaluating robustness in the presence of contamination in Bayesian data analysis. Behavior Research Methods
Tauber S, Navarro DJ, Perfors A, et al. (2017) Bayesian Models of Cognition Revisited: Setting Optimality Aside and Letting Data Drive Psychological Theory. Psychological Review
Martire KA, Edmond G, Navarro DJ, et al. (2017) On the likelihood of "encapsulating all uncertainty". Science & Justice : Journal of the Forensic Science Society. 57: 76-79
De Deyne S, Navarro DJ, Perfors A, et al. (2016) Structure at every scale: A semantic network account of the similarities between unrelated concepts. Journal of Experimental Psychology. General. 145: 1228-54
Navarro DJ, Newell BR, 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
Gökaydin D, Navarro DJ, Ma-Wyatt A, et al. (2015) The Structure of Sequential Effects. Journal of Experimental Psychology. General
Ransom KJ, Perfors A, Navarro DJ. (2015) Leaping to Conclusions: Why Premise Relevance Affects Argument Strength. Cognitive Science
Vong WK, Navarro DJ, Perfors A. (2015) The helpfulness of category labels in semi-supervised learning depends on category structure. Psychonomic Bulletin & Review
Fuss IG, Navarro DJ. (2013) Open parallel cooperative and competitive decision processes: a potential provenance for quantum probability decision models. Topics in Cognitive Science. 5: 818-43
See more...