I am a computational cognitive scientist at the University of New South Wales. My research focuses on human concept learning, reasoning and decision making.
The devil and the deep blue sea: Tensions between statistical and scientific reasoning
Slides for my talk to the UNSW business school (work in progress!)
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
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.
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/)