Danielle Navarro
I am a computational cognitive scientist at the University of New South Wales. My research focuses on human concept learning, reasoning and decision making.
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Toward a grammar for behavioural experiments
Invited talk for rstudio::conf(2020L)
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Science and statistics (version 2)
Slides for my invited talk for the mathematical psychology satellite day at the 2019 psychonomics conference. This is a variation on an earlier talk I gave at the Aarhus open science workshop. http://mathpsych.org/conferences/psychonomics2019/
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Transition your workflow, keep your sanity
Keynote for Sydney ResBaz 2019
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Science and statistics
Slides for my talk for Open Science and Reproducibility, at Aarhus University. 12 March 2019. http://interactingminds.au.dk/events/single-events/artikel/2-day-workshop-open-science-and-reproducibility/
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Tidyverse for Beginners
Slides for the "Little Miss Tidyverse" workshop hosted by R-Ladies Sydney (August 22, 2018). Blog post here: http://rladiessydney.org/littlemisstidyverse. Github repository here: https://github.com/rladiessydney/littlemisstidyverse
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R for Psychological Science?
Traditionally, R has been viewed as a language for data science and statistics. In the social sciences it has been extremely popular with researchers at the more quantitative end of the spectrum - but uptake has been less widespread outside of the more statistically inclined. I don't think the R language needs to be limited in this way. Since 2011 I've been teaching introductory research methods classes for undergraduates using R, running programming classes for R with postgraduate students, doing my own data analysis with R, implementing cognitive models with R and occasionally even running behavioural experiments in R. In this talk I reflect on some of these experiences - the good, the bad and the ugly - and discuss prospects and challenges for wider adoption R as a tool within the psychological sciences.
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Aversion to option loss in a restless bandit task
We present two experiments using multi- armed bandit tasks in both static and dynamic environments, in situations where options can become unviable and vanish if they are not pursued. A Kalman filter model provides an excellent account of human learning in a standard restless bandit task, but there are systematic departures in the vanishing bandit task. (Talk for the 2018 mid-year meeting of the Australian Learning Group. Preprint: https://psyarxiv.com/3g4p5/)