Developing Intelligence

New Optogenetic Tool for Neural Inhibition

Thu, 07 Jan 2010 12:00:00 -0500

A fascinating paper from Gradinaru et al describes a genetically engineered mouse model of Parkinson's disease that expresses a photoreceptor in the neurons of a particular part of the brain - the subthalamic nucleus (STN). This area is widely thought to be the central target of the immensely therapeutic technique for Parkinson's known as deep brain stimulation. With this photoreceptor in place, the authors could direct laser light to that area of the brain and direactly affect neural activity - in particular, whether the behavioral symptoms of Parkinson's would disappear following absolutely precise targeting of the STN. The surprising result: Parkinson's symptoms were completely unaffected by this - instead, they were resolved only by targeting closely related structures.

This result is groundbreaking because it potentially refutes the most commonly held view of the STN's function. However, the photoreceptor used by Gradinaru et al was not 100% effective. Close inspection of their paper's figures reveals that they only inhibited the bursting activity usually found in STN - in contrast, the more tonic or baseline firing rate seemed unaffected. Observe:

Enter today's Nature paper by Chow et al, from Ed Boyden's group (btw, Ed runs a blog over at MIT's Tech Review). They demonstrate the near-total inhibition of mouse neurons (up to 100% reductions in firing rate with 200pA in vitro) using some new receptor types not previously explored for this purpose. Behold:

So, now we're left wondering whether this much more effective optogenetic technique might have yielded different results in the Gradinaru experiment. Here's hoping that Boyden's group plans to find out (and tell us!)...

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fMRI of a dead salmon: Why dead fish have almost nothing to do with "voodoo correlations" in neuroimaging

Fri, 25 Sep 2009 10:10:15 -0500

A number of very smart people (and smart communities) seem like they might be under the impression that the "voodoo correlations" scandal in the neuroimaging community is somehow related to recent work by Bennett et al, who used fMRI to show task-related neural activity in a dead fish.

These two things have almost nothing to do with one another.

1) The Bennett work is, in the words of a friend, "a cute way to make a point" that every fMRI paper I've ever read has failed to explicitly acknowledge. The reason they've failed to acknowledge it is that it's standard to run equivalent statistical tests to the ones that Bennett et al recommend (of course, that doesn't keep a "sizable minority" of studies from failing to do so - in Bennett's estimation between 25-35%; I suppose I'm not reading those studies). Anyway, the Bennett point is simple: when you run a large number of statistical tests simultaneously, even on a random dataset, you're bound to find some percentage of tests that turn up "significant" just as a result of chance, and with some probability those significant results will randomly cluster together in 3D space. If one fails to correct the significance threshold for the large number of statistical tests performed, then you get unreliable results, even if you only consider those significant results that cluster in 3D space. (it's this latter point that makes the study interesting, worthwhile, and worthy of publication in a high profile journal, in my opinion). Regardless, the potential issue was already well known, perhaps explaining the difficulty the authors reportedly have in publishing their work. The problem they identified is why virtually everyone everywhere uses, and for a long time has used, both multiple comparisons correction and cluster-based correction when reporting fMRI results. As Bennett et al noted in their poster, such corrections are widely available in all the major neuroimaging analysis packages and are the default in one major package, FSL.

2) The "Voodoo correlations" work, on the other hand, is principally about the non-independence of multiple tests. Simply put, even when you do both types of the corrections discussed above in point #1, it's not OK to take the results of that analysis (clusters in 3D space) and then run additional analyses of the same clusters in the same dataset because the data is now biased by the first analysis.

An example from the original Vul paper should make this problem clear:

We (the authors of this paper) have identified a weather station whose temperature readings predict daily changes in the value of a specific set of stocks with a correlation of r=-0.87. For $50.00, we will provide the list of stocks to any interested reader. That way, you can buy the stocks every morning when the weather station posts a drop in temperature, and sell when the temperature goes up. Obviously, your potential profits here are enormous. But you may wonder: how did we find this correlation? The figure of -.87 was arrived at by separately computing the correlation between the readings of the weather station in Adak Island, Alaska, with each of the 3315 financial instruments available for the New York Stock Exchange (through the Mathematica function FinancialData) over the 10 days that the market was open between November 18th and December 3rd, 2008. We then averaged the correlation values of the stocks whose correlation exceeded a high threshold of our choosing, thus yielding the figure of -.87. Should you pay us for this investment strategy? Probably not: Of the 3,315 stocks assessed, some were sure to be correlated with the Adak Island temperature measurements simply by chance - and if we select just those (as our selection process would do), there was no doubt we would find a high average correlation. Thus, the final measure (the average correlation of a subset of stocks) was not independent of the selection criteria (how stocks were chosen): this, in essence, is the non-independence error.

To summarize, the dead fish study is a point about first-pass analysis, which almost every paper I've ever seen does correctly. The papers that don't always note that the result failed to pass multiple comparisons or cluster correction, and typicallly discuss those results with caution. On the other hand, "voodoo correlations" is a point about nonindependence in statistical tests. This has not always been done correctly, and has not always been reported clearly. Moreover it primarily affects only a subset of correlations between brain and behavior - and not the vast majority of work in fMRI, which has to do with task-brain relationships.

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Monitoring in the Psychological Refractory Period (of a sort)

Fri, 25 Sep 2009 09:32:27 -0500

Something's afoot when a massively parallel and distributed system shows a bottleneck in performance. We've known that numerous bottlenecks plague cognition since the 1940's, but only with recent advances in neuroimaging have we been able to say whether these bottlenecks reflect the intrusion of executive operations (for managing goals and organizing cognitive processing) or a more passive "queueing" processes inherent to the selection of responses. Thanks to a number of very helpful (and interesting) reviews on a recent paper of mine, I've been pointed towards a fascinating study (by Jiang, Saxe and Kanwisher) suggesting that a queueing process might actually be to blame.

The particular "bottleneck" in cognitive performance investigated by Jiang et al is known as the "psychological refractory period," which can be observed with a very simple manipulation: subjects must simply give separate responses to two different stimuli, presented in rapid succession (<500ms). Reaction times to the second stimulus are longer than when the stimuli are presented at a more leisurely pace (>500ms), as though the selection of responses is subject to a bottleneck in information processing. This bottleneck, or refractory period, persists even when the tasks require responses in different modalities (i.e., verbal and manual), suggesting that a relatively "central" source is to blame for the refractory period.

Jiang et al present two possible explanations for this effect: according to the passive queueing account, the second task "is held in a passive queue until the bottleneck is freed" from processing the first task. Alternatively, the active monitoring account holds that a number of executive operations must occur: the tasks must be ordered for processing, the first task must be checked for completion while the second task is "halted" (inhibited?), and the second task must be triggered when the first has passed through the bottleneck. Jiang et al assume that this active monitoring involves "significantly increased executive functions" and as such that they should be more invoked when the refractory period is observed.

Under the reasonable assumption that executive functioning of this kind would engage the prefrontal cortex, Jiang et al used neuroimaging during a refractory period paradigm. Their results show that the only part of the prefrontal cortex to show increased activity during fast as opposed to short stimulus presentation was theright inferior frontal gyrus. From this, one might conclude that the right inferior frontal gyrus actually instantiates Jiang et al's "active monitoring" process. Maybe.

Instead, Jiang et al hold the right inferior frontal gyrus to a higher standard: they predict that a region involved in active monitoring should show also greater BOLD response according to individual differences in the refractory period. Specifically, the region responsible for the refractory period should be greatest among those individuals showing the largest refractory period.

In a way, the prediction seems natural (particularly if you're coming from an inhibitory perspective on this brain region). But what if the right inferior frontal gyrus actually reduces the psychological refractory period - say, if greater executive functioning actually helps you manage the "refractoriness"? After all, greater use of a putative executive function should probably improve task performance. Moreover, the refractoriness itself (i.e., the braking of motor output) might actually be the direct but involuntary result of a completely different, though connected region (say, the subthalamic nucleus). And the greater recruitment of "active monitoring" might help one detect the stimuli earlier, ultimately streamlining performance in the task. Of course, under this redefinition of monitoring, we'd expect the opposite effect: greater use of monitoring should be associated with a reduced bottleneck. Jiang et al observed this exact effect, but counter-intuitively interpreted it to mean that the activity in the right inferior frontal gyrus was not due to executive functions!

In a second experiment, they suggest that while the right inferior frontal gyrus does not implement an executive function, it may show greater activity due to effort. To manipulate effort, Jiang et al instructed subjects to employ a conservative strategy (i.e., "take your time") in some trials and a "daring" strategy in others (i.e., "respond as quickly as possible!").

A number of regions in the prefrontal cortex showed increased activation as a result of the effort manipulation (including the anterior cingulate, the pre-supplementary motor area, and bilateral middle frontal gyrus...) but the right inferior frontal gyrus was the only gray matter showing more activity both when the refractoriness was induced and when subjects were instructed to effortfully minimize it.

From this, Jiang et al conclude that "the effort to reduce the postponement is active" but suggest that the activity in the right inferior frontal gyrus is not related to executive functioning. I'd like to propose an alternative:

1) activity in the right inferior frontal gyrus is related to executive functions: it's under conscious control, it's engaged in a task-appropriate fashion, and this engagement predicts better task performance

2) activity in the right inferior frontal gyrus is related to an active monitoring process, and this is its executive function, but it's of a different kind than considered by Jiang et al: right inferior frontal activity doesn't reflect the monitoring of working memory (consistent with what Jiang et al seem to be arguing, and with previous work in primates (thanks, Reviewer #2 ;)), but does reflect active monitoring for the occurrence of stimuli in the environment. Detection of these stimuli provoke an involuntary "refractoriness" - perhaps by triggering activity in the subthalamic nucleus - but this itself is not the role of the right IFG (as reflected in the negative correlation between rIFG activity and the refractoriness observed here).

Of course, I don't expect this account of right IFG function to be commonly accepted until there's more neuroimaging data directly supporting it. Now, back to writing up that data of ours...

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Robots in the Classroom: Sejnowski on Machine Learning and Education

Wed, 02 Sep 2009 16:05:31 -0500

I've been busy writing up a new paper, and expect the reviews back on another soon, so ... sorry for the lack of posts. But this should be of interest:

The Dana Foundation has just posted an interview with Terrence Sejnowki about his recent Science paper, "Foundations for a New Science of Learning" (with coauthors Meltzoff, Kuhl & Movellan). Sejnowski is a kind of legendary figure in computational neuroscience, having founded the journal Neural Computation, developed the primary algorithm in independent components analysis (infomax), contrastive hebbian learning, and played an early role in linking the mathematical concept of "prediction error" to dopamine function.

One snippet from the interview:

Q: In what ways has the study of how children learn been used to solve engineering problems?

A: Children's brains are still developing and we need to understand how that helps them to learn. One example is imitation learning, which has been studied by Andrew Meltzoff, Ph.D., at the University of Washington in Seattle, who is trying to understand what makes children such effective learners. Babies and children are really good at imitation. Right out of the womb, babies can imitate facial expressions. If you stick out your tongue, a baby who can barely see will repeat your action. Children have fantastic abilities to mimic actions and behaviors. They learn a lot simply by observing and mimicking, and they will try to repeat not only the action itself - say, reaching out with the arm - but the purpose of the action - say, picking up a ball. This is something humans do much more effectively than any other animal.

Engineers, having seen that imitation is highly effective in humans, combined imitation learning with reinforcement learning to boost the performance of control systems. In apprenticeship learning, for example, a powerful computer tracks the actions of an expert human controlling a complex system, and then programs the reinforcement system to imitate and learn the very complex motor commands that the human makes. Engineers are now able to reproduce human skills that were previously thought beyond the reach of machines. For example, Andrew Ng, Ph.D., at Stanford has used apprenticeship learning with reinforcement to automatically control helicopters that do stunts like flying upside down.

Maximizing Mastication: Chewing Gum To Enhance Cognition

Tue, 28 Jul 2009 09:41:58 -0500

Children assigned to chew sugar-free gum purportedly score 3% higher on standardized tests of math skills (as widely reported in the press). But is this just one of the 5% of all possible untrue hypotheses statistically guaranteed to have some significant result in its favor (in fact, it's worse than that)? Is the effect due to some other aspect of gum chewing (as Michael Posner asks)? Or might there be a real effect here of chewing (i.e., "mastication"), and if so, how can you use it to your maximum advantage?

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Live Webcast of Neuroimaging Summer School @ UCLA

Mon, 20 Jul 2009 10:36:57 -0500

The UCLA Neuroimaging Summer Education Program starts today at 8:30 am Pacific. Standard Time - and is going to be streaming live at this address (video embedded below). The schedule is quite impressive, including talks from Rick Buxton, Mark Cohen, Russ Poldrack, Vince Calhoun, and Jose Hanson among others. Topics include everything from causal modeling to network analysis and multivariate pattern recognition.

Monday, July 20
08:30 Intro & overview (Russ Poldrack & Mark Cohen)
09:30 MRI acquisition: basics (Mark Cohen)
11:00 Ethical issues in cognitive neuroscience (Russ Poldrack)
12:00 lunch
13:15 MRI acquisition: advanced (Mark Cohen)
15:00 Laboratory: Neuroanatomy (Susan Bookheimer)
Tuesday July 21
08:30 Spikes and BOLD: Can they get along? (Dario Ringach, UCLA)
09:30 Hemodynamics and fMRI signals (Rick Buxton, UCSD)
11:00 Neural basis of imaging signals (Rick Buxton, UCSD)
12:00 lunch
13:15 Basic experimental design (Susan Bookheimer)
14:30 Advanced experimental design (Russ Poldrack)
15:30 Lab: Introduction to Mac and FSL
Wednesday July 22
08:30 Preprocessing: Image registration and motion correction (Russ Poldrack)
09:30 Preprocessing: EPI unwarping, intra/intersubject registration (Russ Poldrack)
11:00 Statistics I (Jeanette Mumford, UCLA)
12:00 lunch
13:15 Statistics II (Jeanette Mumford, UCLA)
14:15 Lab: Statistics by hand in MATLAB
15:30 Lab: FSL Preprocessing
Thursday, July 23
08:30 First-level fMRI modeling (Russ Poldrack & Jeanette Mumford, UCLA)
09:30 First-level modeling, continued (Russ Poldrack & Jeanette Mumford, UCLA)
11:00 Group fMRI modeling (Jeanette Mumford, UCLA)
12:00 lunch
13:15 EEG/MEG (Charan Ranganath, UC Davis)
14:15 Lab: First-level statistical analysis
Friday July 24
Software package comparisons (Russ Poldrack)
08:30 Multiple testing problems (Jeanette Mumford, UCLA)
09:30 Data quality control (Mark Cohen)
11:00 Q&A session
12:00 lunch
13:15
14:15 Lab: Group modeling and Multiple testing
Saturday July 25
all day EEG lab, or MRI physics lab
WEEK 2
Monday July 27
08:30 Advanced fMRI modeling: percent change and power analysis (Jeanette Mumford)
09:30 TBD
11:00 fMRI Design Optimization (Tom Liu, UCSD)
12:00 lunch
13:15 Lab: working with datasets, and power analysis Tuesday July 28
08:30 Improving reliability (Gary Glover, Stanford)
09:30 Reporting fMRI data (Russ Poldrack)
11:00 Connectivity analysis (Russ Poldrack)
12:00 lunch
13:15 Computational anatomy (David Shattuck, UCLA)
14:15 Diffusion tensor imaging (Nathan Hageman, UCLA)
15:30 Lab: working with datasets
17:30 EEG/fMRI demo (Brain Mapping Center)
Wednesday July 29
08:30 Network analysis (Steve Petersen, Washington University)
09:30 Dynamic causal modeling (Marta Garrido, UCLA)
11:00 ICA (Vince Calhoun, University of New Mexico)
12:00 lunch
13:15 Graphical causal modeling (Clark Glymour, CMU)
14:15 Lab: Connectivity exercises
18:30 Group Dinner (Napa Valley Grill)
Thursday July 30
08:30 Pattern classification (Steve Hanson, Rutgers)
11:00 Pattern-information and representational similarity analysis (Niko Kriegeskorte, NIMH)
12:00 lunch
13:15 Avoiding statistical circularities in brain-activity analysis (Niko Kriegeskorte, NIMH)
14:15 Lab: working with datasets
Friday July 31
08:30 Psychophysics for fMRI (Don Kalar, UCLA)
09:30 Setting up an imaging lab (Mark Cohen)
11:00 Imaging difficult populations (Susan Bookheimer)
12:00 lunch
13:15 Q&A session
15:30 Presentation of results from data analysis projects

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Pavlov's Dogs: Proving the Null With Bayesianism

Tue, 30 Jun 2009 11:00:34 -0500

How many times did Pavlov ring the bell before his dogs' meals until the dogs began to salivate? Surely, the number of experiences must make a difference, as anyone who's trained a dog would attest. As described in a brilliant article by C.R. Gallistel (in Psych. Review; preprint here), this has been thought so self-evident "as to not require experimental demonstration" - yet information theoretic analysis suggest the idea is incorrect, at least when the time from the bell to the food is constant. More problematic is the fact that the whole issue is ill-formed for experimental verification: technically speaking, one can never actually accept the (null) hypothesis that some experimental manipulation has no effect. But as Gallistel says, while "conventional statistical analysis cannot support [the null hypothesis]; Bayesian analysis can."

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Inhibitory decline with age: The influence of failed strategy.

Mon, 29 Jun 2009 12:35:28 -0500

Don't think of a white bear. Doesn't work so well, does it? Yet under some circumstances, people appear to be able to do precisely this: as described last week, young adults are thought (by some) to actually suppress the neural activity related to to-be-ignored stimuli, and even delay the peak of this neural activity, relative to a situation in which stimuli are to be just passively-viewed. In a follow-up paper at Nature Neuroscience, Gazzaley et al report that cognitively-intact older adults (60-77 years of age) show an impairment in this ability, without concomitant impairments in the enhancement effects normally observed to to-be-attended stimuli.

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Mind Wars: Jonathan Moreno, Neuroscience and the Military

Fri, 26 Jun 2009 12:52:56 -0500

An interesting video interview with the author of (the excellent) Mind Wars.

Here are direct links to the videos.

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Gamma: Insight and Consciousness... Or just Microsaccades?

Fri, 26 Jun 2009 11:28:25 -0500

The cognitive neurosciences have had high frequency oscillations on the brain: so called "gamma-waves", as recorded on the scalp, have been linked to working memory processes (via their interaction with slower "theta waves"), to cognitive insight, and even to consciousness. (I think there's an unwritten rule that whenever someone mentions consciousness, they'll be made to look foolish by a subsequent paper). In the midst of these "inflationary accounts" of the role of gamma oscillations, a debate has emerged: could these oscillations (at least, as recorded on the scalp) reflect simply the movements of the eyes, as now appears to largely be the case? More interestingly, is that necessarily incompatible with the more lofty interpretations of gamma? And what is the relationship between these movement-related artifacts on scalp recordings, and the well-established importance of gamma waves as recorded on or inside the brain?

(UPDATE: first para edited to emphasize scalp- vs. i-EEG distinction; thanks Alex & farraway).

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Enhancing The Magnitude and Speed of Neural Activity - And Suppressing It?

Thu, 25 Jun 2009 11:18:58 -0500

By many current theories, we accomplish control over behavior by using the prefrontal cortex to "bias" the competitive dynamics playing out in the rest of the brain. By some models, this bias is positive - it helps the goal-relevant representations win the competition. By other models, the bias is also negative - it can help the goal-irrelevant representations lose the competition. Regardless, this "prefrontal biasing" is usually considered in terms of the amount of activity in a particular area (higher when that area is under a positive bias, and lower when under a negative bias). But Gazzaley et al's excellent 2005 JoCN article demonstrates that this kind of top-down modulation can also have effects on the speed of neural processing, where positive and negative biasing actually have opposite effects on the latency of some well known ERP components.

To demonstrate this, Gazzaley et al asked 11 subjects to participate in a two session experiment (one in the fMRI scanner and one with electrodes pasted to their heads) in which they saw sequences of pictures of faces and places, in random order. These randomized sequences were preceded by instructions to pay attention to one or the other type of picture while ignoring the other, to pay attention to both, or to just passively view the pictures. A subsequent memory test showed that people were better able to recognize the pictures they'd been told to remember than those they'd passively viewed, and that this advantage was reduced when subjects had been asked to remember both types of stimuli relative to just one. This pattern is consistent with the idea that enhancement of task-relevant information is a resource-limited process that is less effective when it needs to be used more. However, the authors didn't report any reduction in recognizing the to-be-ignored stimuli - perhaps indicating that negative biasing either didn't occur or wasn't effective.

On the other hand, the fMRI results told a different story - one that includes some putative suppression effects. Although fMRI has poor temporal resolution, its excellent spatial resolution allowed the authors to show that neural activity in face- and place-selective regions of the cortex (the FFA and PPA, respectively) was increased when the respective stimulus type was to-be-attended, and decreased when the respective stimulus type was to-be-ignored, relative to the case where both stimulus types were to be passively viewed (the latter "suppression" effect was significant only for the place-selective region, but was in the right direction for the face-stimuli too). Similarly, when subjects had been instructed to remember both stimulus types, this increase in neural activity was reduced (at least for the place-selective region; they didn't report results from the face-selective region), again indicating that top-down enhancement is resource demanding. These effects demonstrate that there is a robust top-down enhancement effect, and also suggest some suppression effect, on the magnitude of neural activity as measured by fMRI.

Gazzaley et al's ERP results take this a step farther: the authors found that the peak latency of a electrical potential characteristic of face processing was earlier when face stimuli were to-be-attended, and later when face stimuli were to-be-ignored, relative to cases where face stimuli were to be passively viewed. In addition, this apparent enhancement of the speed of face processing was reduced when subjects had been instructed to remember both stimulus types, again suggesting that top-down enhancement is resource-limited.

In their discussion, the authors note that such latency shifts have not been observed in single-cell recording studies, suggesting that the top-down effects may be visible only at the level of the local field potential or the resulting ERP. They also note that the latency shifts have not been observed in other selective attention studies, and suggest that this may have to do with task difficulty or that the effect is specific to memory encoding. I found this a little speculative, given that face-selective can show up in either latency or amplitude, and observing one instead of the other is not necessarily informative (some have suggested that these two measures might trade-off).

In a concluding paragraph, the authors note that their effects pertain to the sites of control (i.e., face- and place-selective neural phenomena) and not to the source of that control (i.e., the prefrontal or parietal cortex). Gazzaley et al suggest that multivariate pattern recognition techniques could be used to identify such sources, which might be taken to imply that traditional averaging techniques used in this paper failed to identify those sources.

In summary, Gazzaley et al have collected compelling evidence that goals can be used to positively bias both the magnitude of activity in posterior cortex and the speed with which that processing reaches its peak amplitude. They've also shown some negative effects, such that the magnitude and speed of activity in regions selective for the to-be-ignored items can be reduced relative to passively viewing them, but this is a little less clear. While the enhancement effect is solid and robust, the suppression effect was a little less robust (not significant in the FFA), and did not result in any behavioral suppression effect (at least, not one that was reported).

One possibility here is that passive viewing and ignoring yield identical behavior because in both cases subjects are simply thinking about other things as they see the to-be-ignored or to-be-viewed stimuli. One might get a reduction in visual processing of to-be-ignored stimuli if subjects simply closed their eyes or focused on a different part of the display, and this need not reflect true top-down suppression. If something like this had occurred, it would be visible in the ERP data, because ERP's are exquisitely sensitive to blinking and saccades. Indeed, a relatively large proportion of subjects were excluded from ERP analysis due to excessive oculomotor activity (4/18), perhaps suggesting that just such a strategy was used by some subjects. Even those subjects who were not excluded for that reason nonetheless had very few included trials, due to the detection of oculomotor artifacts (or alpha waves, sometimes thought to reflect a cognitively "idle" state) on 66% of trials (on average; 160/240). It seems to me that this strategy is an alternative explanation not only for the neural "suppression" phenomena observed here but also for the apparent lack of a behavioral suppression effect in this particular study.

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Cognitive Control Is Improved By Taking A Step Back - Literally

Tue, 05 May 2009 17:09:44 -0500

A new study suggests that physically stepping backwards may be associated with gains in the ability to deal with problematic situations. As newly reported in Psychological Science (hat tip to Hannah) by Koch, Holland, Hengstler & Knippenberg, people were better able to resolve interference in laboratory "Stroop" task after stepping backwards, relative to stepping to the side or forwards. The authors argue that stepping backwards is typically associated with problematic situations, which characteristically require cognitive control (the set of capacities which enable us to control our behavior and focus on important features of the environment). Koch et al conclude that stepping backwards allows one to more strongly engage these control processes!

The authors demonstrated this fascinating effect by testing 38 subjects on laptop-based Stroop task (in which subjects must name the colors of words while NOT reading the words themselves - e.g., RED). The laptop was mounted on a mobile cart, and subjects were asked to take a step in one of four possible directions (backwards, forwards, left or right) before 12 words were presented in sequence. These "blocks" of trials consisted of equal numbers of incongruent (RED), congruent (RED), and "neutral" trials in which non-color words were used (LOT). Each subject saw a total of 8 blocks, and verbal reaction times were measured by the onset time of the voice on trials that were both correct and within 2.5 standard deviations of their median reaction times.

The results showed a clear effect of stepping backwards - subjects were remarkably faster to name the ink color of incongruent color-words (RED) when they had stepped backwards, relative to forwards or sideways! Moreover, there were no differences on the neutral or congruent trial types, although there was perhaps a trend towards longer reaction times on congruent trials when subjects had stepped backwards. Both effects are consistent with the idea that stepping backwards allowed subjects to better attend to color (or to better suppress word-reading, depending on your interpretation of what's involved in this task).

This work is remarkable not only for demonstrating how a very concrete and simple bodily experience can influence even the highest levels of cognitive processing (in this case, the so-called "cognitive control" processes that enable focused attention), but also because performance on the Stroop task is notoriously difficult to improve. Previous work indicates that meditation might improve performance on this task, but it requires months of training and yields only small or inconsistent effects. In contrast, more targeted "cognitive" training has shown no or very inconsistent effects on Stroop performance, even when that training is successful at improving performance on other tasks.

There's always the possibility that findings like this just reflect a very (un?)lucky set of researchers (that is, a Type I error), but I find this a little unlikely in this particular case. In particular, the trend towards increased reaction times for congruent trials when subjects had stepped backwards is very suggestive - and very consistent with the significant results found for the incongruent trials. Focusing on the important color features helps in incongruent trials, but could hurt you in congruent trials (where reading the word would actually give you the correct answer). If the influence of stepping backwards were actually random, and the significant improvement in reaction times on incongruent trials just a result of random chance, one wouldn't expect to see any evidence of the opposite effect on congruent trials. On the other hand...

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The Fate of Forgotten Memories: Sudden Death, Not Gradual Decay

Wed, 29 Apr 2009 12:25:40 -0500

Every now and then, I read some science from some other dimension. That is, the methods are so unusual, the relevant theories so fringe, or the conclusions so startling that I feel like the authors must be building on work from a completely separate science, with its own theories and orthodoxy. This can be good or bad, and is usually the latter. But in the case of Zhang & Luck's recent papers, it's very, very good.

To appreciate what they've done, here's a little background from this dimension's science - specifically, the science of forgetting. The phenomenon of "forgetting" has been the subject of much study, and a number of questions remain controversial:

- Is forgetting a process in which items completely vanish from memory, or are these items merely inaccessible to the way people search their memory?
- Does forgetting occur because memories simply decay over time, or because memories get overwritten? (either process could occur completely or partially, depending on the answer to the first question)
- Can forgetting occur intentionally (a la Freudian suppression) or does forgetting only emerge from secondary causes (for example, by practicing the retrieval of other competing items?)

These are just some of the questions addressed in decades of memory research, and clear answers continue to elude the field. But in the midst of these heated and long-standing debates, Zhang and Luck did the following:

1) developed two new theories based on a new question,
2) tested these theories with a new method to mathematically model behavior, and
3) were able to conclusively rule out one of these theories

Hopefully this gives you some appreciation for the sheer creativity required for this work to be done. Now, on to their question:

Is the precision of memory "analogue," such that memories differ in resolution, or is it more digital, such that memories are either present or absent, with fixed resolution?

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"AH-HAH!" Insights And The Right Frontal Lobe

Fri, 24 Apr 2009 14:17:00 -0500

There are three on-off light switches on the wall of the first floor of a building. One of the switches is initially off and controls an incandescent bulb in a lamp on the third floor of the building. The other two switches do not control the bulb or anything else (they are disconnected). How can you find out which one of the three switches turns the light bulb on and off? You can toggle the switches as many times as you want and for as long as you want, but you can walk only once to the third floor to check on the light bulb.

While you're working on that, I'll show you what you look like (no offense!):

(A hint and the solution to the problem are at the bottom of this post.)

It's thrilling when it happens, but what actually causes insight? New research in the Journal of Cognitive Neuroscience takes us one step closer to an answer: up to 8 seconds before people solve problems thought to require insight, a particular set of very fast oscillations are observable above the right frontal lobe.

Sheth, Sandkuehler & Bhattacharya gave 18 subjects a series of "insight problems" like the one at the start of this post, while the electrical activity on subjects' scalp was recorded via a sensor net with 32 electrodes. All the problems shared a number of features:

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The Limits To Memory: Balancing Inhibition and Excitation in the Parietal Cortex

Thu, 23 Apr 2009 11:26:56 -0500

Most computational models of working memory do not explicitly specify the role of the parietal cortex, despite an increasing number of observations that the parietal cortex is particularly important for working memory. A new paper in PNAS by Edin et al remedies this state of affairs by developing a spiking neural network model that accounts for a number of behavioral and physiological phenomena related to working memory.

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