Projects
Depletion: This data is very peculiar. It seems like we do have something happening with the depletion manipulation, but I think there might be too much variance in pre-experimental depletion (i.e. depletion prior to manipulation) to make sense of things.
Single-Quad: Ramping up data collection.
Implicit Explicit: I’ve been ramping up editing and should be shooting this back to you shortly. Since we’re going to submit to cognition next, we can be somewhat liberal with word count. Obviously, concise is good, but at least we can expand on ideas.
Wiitar: Sweet Mary, it’s done. Now it just needs to be analyzed…
mTurk Delay: Draft mode.
mTurk Fragments: What’s happening with this one?
A/B/C: Dave is going to rescore his Sound vs. No-Sound and let us know if he finds anything interesting. This is sitting on the backburner while I hammer out the I/E manuscript – that is definitely at the forefront right now.

Projects
Depletion: Collecting Data
Single-Quad: Collecting Data
Implicit Explicit: I’ve been working on editing this. Few Notes: The in-text results only report means for trained sequence and foil sequences separately, but not the subtraction score. However, the figures only have subtraction scores. I think I’ll edit this to be more consistent (i.e. add in-text subtraction numbers). Also, the correlation between recognition score and SISL score in Experiment 1 dropped from .3 to .22 and is no longer significant. The difference between the high-rec and low-rec post-hoc groups has also dropped so the significance is sitting at p = .09. Upon closer inspection, the correlations between recognition and SISL score in Experiment 1 are hugely driven by the explicit pre-training group (.39) as opposed to the implicit group (.03). I’m thinking we’ll want to reframe this in the manuscript (or possibly just get rid of it?). The motivation for Experiment 2 is still valid even without the post-hoc results (more robust explicit training).
Wiitar: The manual rescoring is going well. I keep finding new categories that my old code didn’t catch. For instance, someone would always press the next button without lifting off the previous button (so, essentially, their button press would look like D, DF) and then lift off the previous button (D) before strumming. Completely legitimate and allowed, but a very different left-hand response profile. However, I think this will essentially collapse into categories as if the person had released the previous key like every other normal person who was in the experiment. Interesting none-the-less (at least to me).
mTurk Delay: Still being drafted.
mTurk Fragments: Totally cool data. Very interested to see the next round. However, I was thinking about the “precognitive” trigrams in the 3-fragment experiment. Essentially, the first two cues are predictive of the third. From a response stand-point, that’s not argued. However, if you take spatial layout into account, that on-screen triplet is unique and viewable prior to responding to any of the cues. It’s possible that the appearance of that learned spatial layout (in this instance, a small triplet of circles) is cuing the knowledge in a way that is allowing for the expression of knowledge across all three items in the 3-item fragment.
A/B/C: I’ve got ABC data AND Sound vs. No Sound data from Dave. If I get tired of editing manuscripts and hand-scoring data, I might write a quick script to analyze these. ABC is clearly on the back-burner; but the sound data might be interesting (especially if it’s only a small time investment on my part).

Fountain of Youth Dept.
Re-Start
by John Seabrook January 30, 2012

Nolan Bushnell stopped by the office the other day, to play an anti-aging video game. “It’s what I call a ‘looking forward by looking backward’ game,” he said, settling in at the keyboard and loosening up his shoulders. “Meaning that you have to be able to solve a problem using information you received before you were distracted by something else. That’s why older people lose their cars in parking lots. They park, then they go in and shop—that’s the distraction—and then they can’t remember where they put their car.”

Tax dollars funding our research is especially touchy in the given economy, so it seems like this would be another bit of negative news to the researchers just wanting to do their jobs.

Please excuse the spam, but I’d be very grateful if people would be kind enough to spread the word to their students and colleagues about an online memory experiment we’ve launched this week in collaboration with the Guardian newspaper.  We’re hoping that thousands of people from all over the world will take part.

See press release: http://www.cam.ac.uk/research/news/test-your-memory/

You can test your memory at the Guardian experiment website: http://www.guardian.co.uk/memorystudy

Thanks so much!

Best wishes,
Jon Simons.

I haven’t clicked the links to see what this is about, but in principle I like the idea of large N studies on memory collected online and given who it is from, it’s likely scientifically sensible.

http://calnewport.com/blog/2011/12/23/flow-is-the-opiate-of-the-medicore-advice-on-getting-better-from-an-accomplished-piano-player/

I think the idea comes from the perspective of “deliberate practice” and the idea that practicing that which is too easy (and therefore you experience a flow state during practice) does not lead to improvement.

Can we reconcile this with our idea that practice should maximize dopamine release by successfully overcoming challenges?  I have described that idea previously as training so that you have as much success as possible while also realistically expecting that you might fail.

Perhaps we’d say that the flow state described by the pianist reflects a state of euphoria associated with performing so successfully that you cannot fail?  That would separate the idea of a “flow state” from our idea of dopamine reward release — which is just a rough hypothesis anyway.  Or perhaps the experience of the truly skilled expert is that they need to be challenged with more difficult training tasks to hit the maximal training reward level.  That would account for the pianist’s experience seeing the “mediocre” practicing things that seem too easy as just reflecting the fact that they are less skilled and simpler training tasks are optimal for them.  I wonder if anybody at the Music School here would have any insight on this kind of phenomenon.

In theory, we could test this with the SISL task by comparing sequence learning in conditions where we set the task speed to adaptively keep people at 75% correct or at ~100% correct (then test them under identical conditions).

Sanchez, D.J., & Reber, P.J. (2011) Society for Neuroscience.

Perceptual-motor sequence learning has often been used as a task for dissociating the neural mechanisms and operating characteristics of the explicit and implicit memory systems. Although neuropsychological and behavioral evidence indicate separable memory systems in the brain, the interaction of knowledge representations across the systems is likely to be vital in everyday activities. For instance, motor skill acquisition typically relies on explicit, declarative knowledge of what to do, which precedes the development of the implicit, procedural knowledge necessary for improving performance. Using the Serial Interception Sequence Learning (SISL) task, the effect of explicit knowledge on skill learning was examined in order to identify how the interaction of knowledge representations may benefit (or inhibit) performance. The SISL task utilizes perceptually-guided responding to intercept moving cues, and mimics real world skill expertise in requiring accurate timing and order between motor responses. A repeating sequence of cues can be covertly embedded in the task, which is then learned implicitly by participants. Across two experiments, adding explicit pre-instruction about the 12-item sequence prior to training produced no benefit in acquisition or performance in this task, even when explicit knowledge was exceptionally robust. In a second set of experiments examining skill learning consolidation, participants trained on two sequences (SeqA and SeqB) consecutively on one day and 48 hours later received training on a third sequence (SeqC), followed by a test of all three sequences. Retroactive interference between the first two trained sequences (from SeqB to SeqA) was affected by whether participants had explicit knowledge of the initial sequence (SeqA). Participants with relatively higher concomitant explicit knowledge of SeqA exhibited better retention at test for all three sequences than those with lower explicit knowledge (measured by sequence recognition). This relationship between recognition and performance was not found for either SeqB or SeqC. Curiously, while the ability to recognize this particular sequence predicted higher levels of sequence-specific performance improvements, it did not predict higher levels of corresponding explicit knowledge for the other sequences. Although explicit sequence knowledge did not directly translate to a benefit in initial acquisition or performance, a positive effect of explicit knowledge on motor skill consolidation may be related to the ability of the medial temporal lobe memory system in sorting and storing information representations without catastrophic interference.

Cryptographic systems often rely on the secrecy of cryptographic keys given to users. However, many schemes cannot resist coercion attacks where the user is forcibly asked by an attacker to reveal the key. These attacks, known as rubber hose cryptanalysis, are often the easiest way to defeat cryptography. We present a new approach to protecting against coercion attacks using the concept of implicit learning from cognitive psychology. Implicit learning refers to learning of patterns without any conscious knowledge of the learned pattern. We use a carefully crafted computer game to plant a secret password in the participant’s brain without the participant having any conscious knowledge of the trained password. While the planted secret can be used for authentication, the participant cannot be coerced into revealing it since he or she has no conscious knowledge of it. We performed a number of user studies using Amazon’s Mechanical Turk to verify that participants can successfully re-authenticate over time and that they are unable to reconstruct or even recognize short fragments of the planted secret.

Although implicit skill learning occurs incidentally and without conscious awareness of what is learned, the rate and effectiveness of learning may still be affected by variation in the cognitive state of the learner during practice. Ego depletion theory states that humans possess a limited store of cognitive resources that, when depleted, results in deficits in self-regulation and cognitive control. While the specific mechanism of ego depletion is not known, its effects on processes associated with dopaminergic function suggest the possibility that ego depletion might affect the dopamine-gated plasticity that is hypothesized to support implicit sequential skill learning. In a first experiment this idea was tested using an assessment of pre-experimental depletion given prior to training on the Serial Interception Skill Learning (SISL) task and relative depletion state was found to predict implicit learning rate. In a second experiment, ego depletion was manipulated by having participants complete a depleting task prior to a standard implicit learning protocol. Depleted participants exhibited less learning than did non-depleted controls. In a third experiment, depletion was administered after training and was not found to interfere with skilled performance, suggesting the effect of ego depletion is specific to learning. These results indicate that in both training and educational contexts, ego depletion should be avoided prior to practice to maximize training gains even from rote or repetitive practice.

Submitted to CABN.

Skills are typically learned by initial explicit instruction followed by repetitive practice to hone and improve performance. Memory systems theory provides a framework for characterizing the basis of this process whereby declarative memory plays a large initial role in instruction and implicit learning subsequently becomes important during practice. However, prior studies dissociating these two types of memory have not indicated how these systems interact during the skill learning process. The types of memory could operate largely independently, with explicit memory merely providing an initial scaffolding to guide future practice. Alternately, there could be cooperative interactions between the systems by which they form a shared representation that drives performance. Using an implicit perceptual-motor sequence learning task, skill learning was compared when explicit instruction was available or under typical incidental learning conditions. In Experiment 1, sequence pre-instruction did not lead to improved skill learning, but explicit memory for the sequence was poor after practice. In Experiment 2, improved instruction led to better explicit knowledge, but as in Experiment 1, sequence learning was robust and equivalent for both conditions. The lack of an instruction benefit suggests that during skill learning, implicit and explicit memory operate independently. Initial instruction appears to mainly serve to guide initial action sequence performance so that it can be made faster, more accurate and more fluid by implicit learning during repetitive practice.

Submitted to Memory & Cognition