Robot Learning Engineer with Cognitive Neuroscience expertise
X.com, LinkedIn, GitHub, GoogleScholar
Photo Credit: Erielle Bakkum
Currently, I work in robotics, developing software to teach robots. Many aspects of cognitive neuroscience training seem to transfer to robotics - designing evaluation tasks is similar, perception and action “networks” are comparable, and both involve extensive debugging.
During my PhD and postdoc, I used web-based cognitive tasks, brain imaging, eye-tracking, and machine learning to study human decision-making. For example, I developed The Choose-And-Solve Task to show how some individuals with math anxiety choose to avoid math and published my work in Science Advances (Choe et al., 2019). See my Google Scholar page for other cognition works.
These tasks are based on the jsPsych library and have been used in my research with Qualtrics. You can try these right now! NOTE: These tasks are NOT designed to work on mobile phones and tablets.
Have you wondered how to use jsPsych with Qualtrics? Here is the jsPsych in Qualtrics Tutorial Series!
The Choose-And-Solve Task (CAST) is a novel effort-based decision-making task in which participants chose between solving easy, low-reward problems and hard, high-reward problems in both math and nonmath contexts.
Higher levels of math anxiety were associated with a tendency to select easier, low-reward problems over harder, high-reward math (but not word) problems, suggesting that we cannot even pay math-anxious people to do hard math. Addressing math avoidance behaviors can help break the vicious cycle of math anxiety and increase interest and success in STEM fields. Please see the paper (Choe et al., 2019, Science Advances).
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