I am a computational cognitive scientist at the University of New South Wales. My research focuses on human concept learning, reasoning and decision making.
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/
Transition your workflow, keep your sanity
Keynote for Sydney ResBaz 2019
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/
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/)