I stumbled across the “cup song” by Anna Kendrick (from the movie Pitch Perfect, also performed by her on Letterman and originally learned from a “viral video” which sources to a homemade youtube video by Lulu and the Lampshades).  The trick is singing a short song while tapping out a short percussion sequence using a cup on a table.  For good singers, it sounds pretty good.

The sequence has 11 or 14 elements depending how you count (is “clap twice” one thing or two?).  Either way, it’s pretty short.  It’s also quick to execute. The sequence is repeated a number of times during the song, which is only ~1m long total.

This video, which is a 5m tutorial on how to do the cup-tapping sequence is interesting from a learning and memory perspective.  It is taught by explicit description, then repeated demonstration, in very short chunks 3-4 steps at a time.

http://www.youtube.com/watch?v=qa4BUtsATsg

I think it’s a good example of how we learn sequences, but raises some interesting questions.  Why is it so hard to memorize?  It’s just 14 steps.  Why not use bigger chunks?  Why so much repetition even by the tutor?  Is remembering a timed motor sequence really that hard?  And it’s pretty obvious that verbally memorizing the steps isn’t going to let you perform it fluidly.  Something very important is happening in repeated practice.

Maybe the difficulty of learning a relatively short sequence this way is why it is so surprising that we can see 30-80 item sequence learning in SISL.  If we depend on going through explicit memory to guide performance for initial practice, we’re going to mainly learn pretty short sequences.  The implicit skill learning system isn’t apparently so constrained, though, and pulls structure out of those sequences pretty quickly (~50 reps, regardless of length) if you can get the motor system to go through the steps.

I did a short interview with an Australian radio show called Ghosts of Oz, hosted by Danni Remali, on Saturday night.  The show focuses on paranormal topics and they wanted to talk about deja vu — they found me by the Scientific American AskTheBrains column.  They assured me that they wanted a real scientific perspective and some information about the brain.  Of course, I was a little worried about pseudoscience, etc., but decided to give it a shot anyway.

I figured I could make the case for the experience of deja vu as related to a benign misfiring in the brain of a feeling of familiarity — creating a sudden feeling of familiarity even during a new experience.  The idea is based on neuropsychological research by Martin Conway with patients who experience this phenomenon constantly (due to FTD), That seemed to go over ok, I just needed to be careful to say it was a normal experience for healthy participants and did not imply anything wrong with the brain.  It’s more prevalent in younger people as well, for what it’s worth.

On the fly I realized that there is a nice point to make about implicit learning and awareness.  The fact that we have knowledge and some processing happening outside of awareness, that could very likely create the sense that we have some additional mysterious cognitive capacity.  A flash of intuition based on implicitly learned environmental patterns is functionally indistinguishable from a precognitive event.  From this, I tried to make the case that some experiences that get called “paranormal” may simply reflect the fact that the brain is more complex than we realize — we really are smarter than we know.

It felt like a nice way to make contact with the more positive aspects of the show/host’s approach to “spirituality” without endorsing any pseudoscience.  Overall, I think the conversation went well.

Sanchez, D. J., Yarnik, E. N., & Reber, P. J. (submitted). Specificity of transfer in perceptual-motor sequence learning.

Examining the transfer of skilled knowledge can tell us about both the underlying representation of what has been learned and how this information is applied in contexts that differ from training. A common model of transfer is that performance in the new context will be based on the overlap of component processes between training and test conditions. However, we recently reported (Gobel, Sanchez & Reber, 2011) that a selective change to one aspect of performance (timing) apparently disrupted application of any learned information in an implicit skill learning task. This implies an important role for integration of information that limits transfer across context. Two experiments tested the degree to which integration of information across separable information components limited transfer from training to test using a perceptual-motor sequence learning task. In Experiment 1, selective disruption of timing or order information was re-examined using a novel response manipulandum that allowed for analysis of each response component and more sensitive detection of transfer. In Experiment 2, transfer was examined after selective disruption of perceptual information that left the motor response sequence intact. Both experiments found evidence of partial transfer that was less than would be predicted by simple overlap of information from training to test. The relatively weak transfer implies that implicit skill learning also occurs in the integration of information, even across modalities, meaning that learning will generally be specific to training context and full transfer of knowledge will be difficult to achieve.

So apparently a new hastag, #overlyhonestmethods, is burning up the twitterverse.  It appears to be driven by students, technicians, post-docs in science labs blowing off steam about the challenges of doing research.  It’s funny and probably a good thing in the overall sociology of science — I think.  It is a good thing if it helps people find the appropriate level of “healthy skepticism” to approach science reporting with.  Sometimes people are a bit too quick to accept statements like “Science found X to be true” without consideration of how the conclusion was drawn.  On the other hand, it isn’t a good thing to further strengthen the anti-science crowd too much (e.g., climate change).

Fans of neuroimaging may find this blog post on that topic particularly entertaining:

http://blog.ketyov.com/2013/01/overlyhonestmethods-for-neuroimaging.html

Amusing excerpt:

Data were preprocessed 8 different times, tweaking lots of settings because Author One kept screwing up or the data kept “looking weird” with significant activation in the ventricles and stuff, which can’t be right.

and one that we hope won’t hit too close to home:

The methods section is difficult to write because the data on which this paper is based were collected 4 years ago by a roton (Author Two) and dumped on Author One because he didn’t have any other idea what to do for his PhD and this is, quite frankly, his last hope.

http://www.ted.com/talks/daphne_bavelier_your_brain_on_video_games.html

Here’s a recent talk by Daphne Bauvelier (University of Rochester) on brain plasticity & video game playing. I’m not usually a huge TED fan, but this is actually a decent neuroscience one, it seems. Positive press from the online neuroscience community, too, which is neat. Hopefully such discussion fuels further research talk on the topic!