Kim Meier

kim [underscore] meier (at) sfu.ca

Hi!

I joined the Cognitive Science Lab in 2007 after deciding research was much more fun than working in the real world. I really encourage anyone interested in pursuing graduate school to volunteer in a research lab – not simply to run participants, but to be aggressive about learning. Nowhere else will you, as a social sciences student, learn how to program in Matlab (or in E-Prime, for that matter)! I love the research process because it’s fun to see how creative people can get when trying to come up with clever solutions to tricky problems. I think category learning is great for studying cognitive processes, because it encompasses so many important skills – perception, attention, decision-making, memory, and even motor behaviour – exercised by people in real, complex situations. I’m also interested in how people approach reasoning tasks (including the category learning we subject first-year psychology students to every semester). I like learning about the evolutionary mechanisms that help (and hurt) human reasoning, and the neural mechanisms behind pattern perception (in all forms – visual categories, music, speech, and the like).

Now, I’m a graduate student at the University of British Columbia. Here, I do research on motion perception and stereopsis. We use a number of tools, including psychophysics and functional magnetic resonance imaging, to study perceptual processes. We conduct studies with children and adults, and people with atypical perception, to understand how these processes develop. One of the many fun parts of working here is taking all the usual psychophysical procedures we use with adults, and applying them to children — it turns out all you need to do is turn it into a video game!

With the CogsLab, I am continuing my collaboration on a line of research that looks at how people adopt their strategies for getting at information according to the environment. One thing that matters is exactly what information is out there to uncover: that is, the relative frequency of the categories you encounter. We’re curious about how different factors of the environment influence these strategies, depending on these frequencies. These include the cost of getting at feature information (do I pick a feature that’s really informative but really expensive, or a feature that’s cheap but only useful some of the time?) and the constraints that interact with our decisions (what do I look at when I know I don’t have time to check everything?).