Research on the Cognitive Neuroscience of Memory
- How and where memory occurs in the brain, particularly memory acquired through practice
- How experience shapes action, perception and thought through pervasive mechanisms of plasticity throughout the human brain
- Implicit and explicit memory contributions to perceptual-motor skill learning
- Implicit and explicit memory in visual category learning
- How general cognitive ability can be improved through cognitive practice
- Repetitive training of working memory span to improve cognition
- For both younger adults and to remediate age-related cognitive decline
- Perceptual-motor skill learning using the SISL task
- Working memory training using the SeVi-WM task
- Using computational modeling and functional neuroimaging to study interactions among the brain’s memory systems
Check the Presentations link on the right side bar to see the most recent ideas and reports as presented as posters and talks at recent conferences.
309 Cresap Laboratory
Department of Psychology
Phone: (847) 467-5779
2029 Sheridan Road
Evanston, IL 60201
This is a very interesting piece on the philosophy of science and popular understandings of science:
As an exercise to the reader, explain what is wrong with his complaint that what most people think of science is actually the opposite of science.
Seems like a topic we should be discussing in 205. I think it’s the right level of ‘meta’ for a class on experimental design.
For some reason, I’ve been getting a lot of requests lately to explain why we are bad at remembering people’s names lately. An email exchange on this with an Atlantic reporter got summarized online here:
Curiously, it then also got picked up on another site, Lifehacker:
And then I was contacted earlier this week and did a short conversation on the phone with a radio show, Newstalk, in Ireland with host Sean Moncrieff.
All the conversations went well, although I’m not sure I had much to say beyond the basics that names are hard and arbitrary, unlike other facts you tend to learn about people you meet.
A more interesting idea is that I suspect there is a “reverse Dunning-Kruger” effect for name memory. Dunning-Kruger effects are cases where everybody thinks they are above average. For names, my sense is that most people think they are below average. I would guess they aren’t, but just that most of us are bad at names. In theory, it wouldn’t be very hard to test this, but I don’t think anybody has even run a real experiment.
I’m a big fan of Jerry, who posts to YouTube as ChessNetwork his videos of playing chess online. One of the things he does regularly is playing online speed chess — ultra-rapid, “bullet” chess where each player has ~1m for the whole game.
Chess is a different game when you have 60 seconds to make every move in a whole game. I find it compelling because it exposes the absence of calculation in very high level chess play. At 1-2 seconds/move, it is almost purely pattern matching, habit and processes we would have to call intuition. There is no time for anything but the most rudimentary of calculation. And yet the level of play is pretty sharp.
Jerry is particularly entertaining because he keeps up a verbal stream of consciousness patter while playing. He notes positional principles that guide some move selection and his voice gives away his excitement audibly when he senses a tactical play coming.
Understanding how this type of cognitive process is accomplished would tell us a lot about human cognitive function. What he is doing here is not really hard for any chess player with decent playing experience (I am decent at bullet chess — nothing like Jerry, but I can play). And relevant to the old post about AI & Hofstadter, the fact that computers are unequivocally dominant at chess has nothing to do with understanding how humans play bullet chess.
I’ve spoken with chess professionals about speed chess in the past and the general sense is that playing speed will not make you better at chess. But studying and playing chess slow will make you better at speed chess. Perhaps a principle of training intuition in complex tasks can be derived from that.
Our article on our “cortical cyptography” project is out in the Communications of the ACM:
The focus is on how implicit knowledge of a password provides resistance to coercion attacks were you might be asked/forced to give up your password. While true, we frequently see people raising concerns that our method is too slow/cumbersome in its current implementation for regular use — also true! Probably the useful practical application would be things like replacing the current system of personal questions secure websites ask you for when you need to reset your password. If we were really to build an app for that, I think we’d still need to improve the learning rate (shorten time) and the knowledge detection methods.
Fortunately, doing those things requires learning more about how the brain system that does this kind of learning works — which is what we do here every day.
We may have discovered a way to use this method to do secure transmission of arbitrary messages as well. However, to get a reasonably secure amount of entropy, it might be far too cumbersome for actual practical use. I like the idea conceptually, though, so maybe we’ll run a low-entropy proof of concept anyway just because I think it’s cool.
Enhancing Intuitive Decision Making through Implicit Learning
We are looking for a post-doctoral researcher to contribute to a new ONR funded project that will use computational modeling and fMRI to examine intuitive decision making. Using our PINNACLE framework, we will build computational simulation models of cognitive processing that depends on interactions between implicit and explicit knowledge. These will be used in conjunction with fMRI data collection to test and expand the cognitive models. The overarching goal is to use the cognitive neuroscience of memory systems to identify conditions in which both types of memory can be optimally applied in support of decision processes.
Requirements: expertise in either cognitive modeling or fMRI design and analysis. Experience in both a plus.
Funding is available through at least June 2017.
Posted March 17, 2014. Applications will be reviewed until the position is filled.