<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Min Susan Li | Virtual Reality Lab</title><link>https://virtualrealitylab.netlify.app/author/min-susan-li/</link><atom:link href="https://virtualrealitylab.netlify.app/author/min-susan-li/index.xml" rel="self" type="application/rss+xml"/><description>Min Susan Li</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><image><url>https://virtualrealitylab.netlify.app/author/min-susan-li/Avatar_hu_b560c41286be1be5.jpg</url><title>Min Susan Li</title><link>https://virtualrealitylab.netlify.app/author/min-susan-li/</link></image><item><title>Min Susan Li</title><link>https://virtualrealitylab.netlify.app/author/min-susan-li/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://virtualrealitylab.netlify.app/author/min-susan-li/</guid><description>&lt;p&gt;My research interest is the computational understanding of expectations and predictions, with a focus on processing spatiotemporal properties of multisensory stimuli. With psychophysical approach and Bayesian modeling, my work investigates how the human brain perceives time and space for sensory estimates and action. I graduated from University of Kent with a BSc Hons in Psychology and took a Research Masters degree at the University of Birmingham, where I completed my PhD with Prof. Alan Wing and Dr. Max Di Luca in 2018. I then joined Prof. Lars Muckli&amp;rsquo;s lab at the University of Glasgow as a postdoctoral researcher to use high-field fMRI to investigate how predictive information and illusory perception are represented in the visual system, in particular the feedforward and feedback connectivity of numerosity perception. I recently returned to Birmingham to the Aging Touch project, where we use behavioural measures and brain-imaging to investigate how multisensory signals about surface textures are processed and perceived by young and elderly participants.&lt;/p&gt;</description></item></channel></rss>