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Daniel J. Navarro [15]Daniel Joseph Navarro [3]
  1.  17
    Rational Approximations to Rational Models: Alternative Algorithms for Category Learning.Adam N. Sanborn, Thomas L. Griffiths & Daniel J. Navarro - 2010 - Psychological Review 117 (4):1144-1167.
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  2. Open Parallel Cooperative and Competitive Decision Processes: A Potential Provenance for Quantum Probability Decision Models.Ian G. Fuss & Daniel J. Navarro - 2013 - Topics in Cognitive Science 5 (4):818-843.
    In recent years quantum probability models have been used to explain many aspects of human decision making, and as such quantum models have been considered a viable alternative to Bayesian models based on classical probability. One criticism that is often leveled at both kinds of models is that they lack a clear interpretation in terms of psychological mechanisms. In this paper we discuss the mechanistic underpinnings of a quantum walk model of human decision making and response time. The quantum walk (...)
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  3.  40
    Language Evolution Can Be Shaped by the Structure of the World.Amy Perfors & Daniel J. Navarro - 2014 - Cognitive Science 38 (4):775-793.
    Human languages vary in many ways but also show striking cross-linguistic universals. Why do these universals exist? Recent theoretical results demonstrate that Bayesian learners transmitting language to each other through iterated learning will converge on a distribution of languages that depends only on their prior biases about language and the quantity of data transmitted at each point; the structure of the world being communicated about plays no role (Griffiths & Kalish, , ). We revisit these findings and show that when (...)
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  4.  76
    Sampling Assumptions in Inductive Generalization.Daniel J. Navarro, Matthew J. Dry & Michael D. Lee - 2012 - Cognitive Science 36 (2):187-223.
    Inductive generalization, where people go beyond the data provided, is a basic cognitive capability, and it underpins theoretical accounts of learning, categorization, and decision making. To complete the inductive leap needed for generalization, people must make a key ‘‘sampling’’ assumption about how the available data were generated. Previous models have considered two extreme possibilities, known as strong and weak sampling. In strong sampling, data are assumed to have been deliberately generated as positive examples of a concept, whereas in weak sampling, (...)
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  5.  2
    Hypothesis Generation, Sparse Categories, and the Positive Test Strategy.Daniel J. Navarro & Amy F. Perfors - 2011 - Psychological Review 118 (1):120-134.
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  6.  7
    Global Model Analysis by Parameter Space Partitioning.Mark A. Pitt, Woojae Kim, Daniel J. Navarro & Jay I. Myung - 2006 - Psychological Review 113 (1):57-83.
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  7.  13
    The Structure of Sequential Effects.Dinis Gökaydin, Daniel J. Navarro, Anna Ma-Wyatt & Amy Perfors - 2016 - Journal of Experimental Psychology: General 145 (1):110-123.
  8.  13
    Categorization as Nonparametric Bayesian Density Estimation.Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini & Daniel J. Navarro - 2008 - In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press.
  9. Learned Categorical Perception for Natural Faces.Daniel Joseph Navarro, Michael David Lee & H. C. Nikkerud - manuscript
     
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  10.  11
    Leaping to Conclusions: Why Premise Relevance Affects Argument Strength.Keith J. Ransom, Amy Perfors & Daniel J. Navarro - 2016 - Cognitive Science 40 (7):1775-1796.
    Everyday reasoning requires more evidence than raw data alone can provide. We explore the idea that people can go beyond this data by reasoning about how the data was sampled. This idea is investigated through an examination of premise non-monotonicity, in which adding premises to a category-based argument weakens rather than strengthens it. Relevance theories explain this phenomenon in terms of people's sensitivity to the relationships among premise items. We show that a Bayesian model of category-based induction taking premise sampling (...)
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  11.  26
    The Perceptual Organization of Point Constellations.Matthew J. Dry, Daniel J. Navarro, Kym Preiss & Michael D. Lee - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
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  12. Joint Acquisition of Word Order and Word Reference.Luke Maurits, Amy F. Perfors & Daniel J. Navarro - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 36.
     
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  13.  14
    Extending and Testing the Bayesian Theory of Generalization.Daniel J. Navarro, Michael D. Lee, Matthew J. Dry & Benjamin Schultz - 2008 - In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society.
  14.  1
    Structure at Every Scale: A Semantic Network Account of the Similarities Between Unrelated Concepts.Simon De Deyne, Daniel J. Navarro, Amy Perfors & Gert Storms - 2016 - Journal of Experimental Psychology: General 145 (9):1228-1254.
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  15.  16
    Enlightenment Grows From Fundamentals.Daniel Joseph Navarro & Amy Francesca Perfors - 2011 - Behavioral and Brain Sciences 34 (4):207-208.
    Jones & Love (J&L) contend that the Bayesian approach should integrate process constraints with abstract computational analysis. We agree, but argue that the fundamentalist/enlightened dichotomy is a false one: Enlightened research is deeply intertwined with the basic, fundamental work upon which it is based.
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  16.  13
    What Are the Mechanics of Quantum Cognition?Daniel Joseph Navarro & Ian Fuss - 2013 - Behavioral and Brain Sciences 36 (3):299-300.
    Pothos & Busemeyer (P&B) argue that quantum probability (QP) provides a descriptive model of behavior and can also provide a rational analysis of a task. We discuss QP models using Marr's levels of analysis, arguing that they make most sense as algorithmic level theories. We also highlight the importance of having clear interpretations for basic mechanisms such as interference.
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  17.  12
    One of These Greebles is Not Like the Others: Semi-Supervised Models for Similarity Structures.Rachel G. Stephens & Daniel J. Navarro - 2008 - In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 1996--2001.
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  18. Bayesian Models of Cognition Revisited: Setting Optimality Aside and Letting Data Drive Psychological Theory.Sean Tauber, Daniel J. Navarro, Amy Perfors & Mark Steyvers - 2017 - Psychological Review 124 (4):410-441.
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