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Intelligent Insights on Intelligence Theories and Tests
Monday, October 22, 2012
Free access to highly cited journals in Neuroscience
Sunday, October 21, 2012
Saturday, October 20, 2012
The Brain Clock “Times”
http://paper.li/iqmobile/1316026988?utm_source=subscription&utm_medium=email&utm_campaign=paper_sub
Friday, October 19, 2012
IQs Corner Recent Literature of Interest: Three weeks worth
Posted with Blogsy
Wednesday, October 17, 2012
Article: Researchers release first data from human connectome project —
Researchers release first data from human connectome project —
http://sfari.org/news-and-opinion/conference-news/2012/society-for-neuroscience-2012/researchers-release-first-data-from-human-connectome-project
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Tuesday, October 16, 2012
Article: When does anxiety help or hinder cognitive test performance? The role of working memory capacity - Owens - 2012 - British Journal of Psychology
When does anxiety help or hinder cognitive test performance? The role of working memory capacity - Owens - 2012 - British Journal of Psychology
http://onlinelibrary.wiley.com/doi/10.1111/bjop.12009/abstract
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Article: Will Neuroscience Radically Transform the Legal System?
Will Neuroscience Radically Transform the Legal System?
http://www.slate.com/articles/technology/future_tense/2012/10/fmri_in_court_neuroscience_may_change_the_legal_system.html
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Journal of Experimental Psychology: Human Perception and Performance - Online First Publications
APA Journal alert for:
Journal of Experimental Psychology: Human Perception and Performance
The following articles have been published online this week before they appear in a final print and online issue of Journal of Experimental Psychology: Human Perception and Performance:
Monday, October 15, 2012
Research byte: Cognitive-neuro models of reading: A meta-analysis
Excellent research synthesis study that relates cognitive models of reading ability/disability to brain regions. Awesome use of colors and figures to demonstrate research results Click on images to enlarge.
Article: Childhood stimulation key to brain development, study finds
Childhood stimulation key to brain development, study finds
http://www.guardian.co.uk/science/2012/oct/14/childhood-stimulation-key-brain-development?newsfeed=true
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Friday, October 12, 2012
Implications of 20 years of CHC COG-ACH research: Back-to-the-future and Beyond CHC
10-15-12 Update. Some people, due to agency filters or whatever, have been unable to view the slide show. I have made a PDF copy of the slides that can be downloaded by clicking here. It is a 4+ MB file so be sure your agency/organization does not have some kind of filter set to not allow for files that may be this large.
Another study demonstrates positive impact of Interactive Metronome on reading achievement
I just learned that the following article is soon to be
published (click
here for journal info)
[Click on image to enlarge]
This is the second peer-reviewed article to demonstrate a significant positive impact of Interactive
Metronome (IM) training on certain reading behaviors in a study with both
experimental and control groups. The
other study was one I was involved with (Taub, McGrew, & Keith, 2007; the abstract
is presented below). You can access that
complete 2007 manuscript at the Brain
Clock blog.
[Click image to enlarge]
In the new Ritter et al. study, IM was combined with reading
and language interventions in school-age children that had language and reading
impairments. This will be called the
IM+language/reading intervention experimental group (IM+). Half of the subjects
were randomly assigned to this experimental group (n=21). The other subjects (n=28) were randomly
assigned to the same language/reading intervention, but without IM. So, this study
is not a pure investigation of the isolated benefits of IM. Instead, it should be viewed as a study that
investigated whether IM training could be a good “add on” component to other
interventions focused on language and reading.
The outcome domain assessed was various components of reading
achievement.
Both groups demonstrated statistically
significant gains in reading rate/fluency and comprehension. However, the IM+ demonstrated statistically significant
stronger gains than the language/reading intervention only (control) group. This suggests that IM may be a useful adjunct intervention to be used with other more
traditional academic related treatments directed at reading improvement.
Similar to the Taub et al. (2007) study, the IM+ students showed more improvement
(over the control students) in reading
fluency/rate. This consistent finding
across both studies has been hypothesized to be due to either (a) improvements in speed of cognitive processing, which results in greater efficiency
and automaticity in reading words, (b) greater controlled attention (focus) which improves working memory functioning, or (c) a combination of both.
The new study differed from the earlier study in that IM+ group displayed greater reading
comprehension gains than the academic only intervention group. Taub et al. (2007) found no improvement in reading
comprehension. Given that both groups
received the same language and reading comprehension treatment, it is
hypothesized that the addition of IM may be impacting some cognitive processes
that facilitate reading comprehension. I
agree with Ritter et al. (2012) that a viable hypothesis is that by increasing
focus (attentional control) the students working memory’s were more efficient. Working memory is the minds limited capacity
“mental workbench” (just think of trying to recall a new phone number you just
looked up in the phone book). Increased
attentional control (focus) increases the ability to actively maintain
information just read in working memory long enough for it to be associated
with material retrieved from long-term memory—thus “hooking” newly read
information into the person’s store of acquired knowledge. Click here for a recent brief video (I
think…therefore IM) where I explain the role of focus and working memory
and how it may facilitate higher level cognitive processing, comprehension,
etc.
Of course, the small total sample (n=49) suggests some
degree of caution. But when combined
with the Taub et al. (2007) study with larger samples, this form of replication
in a new sample provides more support for the academic benefits (especially
ease and rate of reading words) of IM interventions in school-age children. Independent replication is a cornerstone of
scientific research.
Wednesday, October 10, 2012
Research Byte: Gf, Gc and and creativity (metaphors)
.Articles suggests that creative metaphors related to Gf and Glr abilities while conventional metaphors related to Gc
Click on images to enlarge
Saturday, October 06, 2012
Obama the unemployment trend bender? A quant's analysis and teachable stat lesson
I sat in disbelief as I watched Chris Mathews interview with Jack Welch on MSNBC's Hardball yesterday. The new unemployment figure released this week of 7.8 set off a fire storm of charges that somehow the Obama administration had "cooked the books." My disbelief in Jack Welch's charges, which he admitted were not based on any evidence, clanged with the unemployment graphs that one constantly see's on the news. As I looked at the graphs they showed clear systematic up and down trends...trends that are so obvious that anyone should be able to see the trends and easily describe them as going up, flattening out, and then going down. I decided that I would take this "teachable moment" to provide a brief short course on some applied quantitative statistical methods we quant's in educational psychology use with regularity. So this is a teaching post, although in the spirit of full disclosure, I should disclose that I voted for Obama in 2008 and plan to vote for him in 2012.
First, if you want to see the video of Mathew's and Welch you can click on the video below:
Below is a version of the graph I am referring to. It is free and available via Google's public use feature
One can go to the Google data visualization page and move a slide button to view as many years of this data as your heart desires. I decided to extract just the unemployment figures from Jan, 2008, when Obama took office. I could have gone back farther into the Bush presidency, but I did not want to turn this into a partisan post.
I believe that any reasonably intelligent and objective person who views the above chart would describe it as showing clear systematic trends. If you start at 2009, the inescapable conclusion is that upon taking office in 2009, given what he was presented with, the unemployment figures demonstrate a clear systematic upward (more unemployment) trend. Then, just after 2009 there appears to be a plateau. Then, there is a clear systematic downward trend. These are objective data points. These are multiple data points over time. These multiple data points should inform any reasonably intelligent and objective person that there has been a systematic increase in unemployment during the first 1+ years of Obama's presidency, followed by a plateau, and then a downward trend. It is so obvious that any third-grader could probably describe this trend.
As an applied quantoid (educational psychologist with expertise in applied statistics, measurement and psychometrics--click here for my bio and vita and other professional information), these set of data are beautiful. The graph shows clear systematic trends. When we applied quant's see data like this, we often want to play with such data...which is what I did. I followed the following steps
1. I extracted the by-month unemployment percents from Jan 2008 thru October 2012.
2. I entered these values into standard statistical software for evaluating trends (for those curious, I used the TableCurve2D program that helps scientists find the best set of equations to describe a set of data points).
3. I ran the software which generated all possible equations that would explain the data. I then selected the one that was the "best fit" in terms of being systematic--an equation that produced a curve that did not have lots of bumps and squiggles.
4. The result is below.
As you can see, a very complicated equation (Eqn 7509) can be used to describe the trends in the unemployment data. (Aren't you wishing you could take back your "but when will I ever use this in real life after I leave high school" comment to your junior and senior high math teachers?) In reality, you can just ignore the complicated equation. What is crucial, and which should be obvious to any reasonably intelligent and objective person, is that the curved line (which represents the complex equation) does a nice job of capturing the general up, plateau, and down trends of the unemployment stats from 2008 to 2012. It is so obvious. It is so clear.
Just how good is the best fitting equation? If you look at the second line of numbers at the top of the graph, you will see an r2 value of 0.98913471. To make things easier, lets round to two digits. R2 = .99. What does this mean? This means that the above equation (represented by the curve over the raw unemployment figures) explains 99% of the trends. In quant speak, we say that it "accounts for 99% of the variance"). This is HUGE. One can only explain 100% of anything, and we are just under that perfect value.
This means that there is a clear systematic pattern to the unemployment data from 2008 thru October 2012. Now, to address Jack Welch's charges (with his own admission of "no evidence") that it is magical that post-debate (and only weeks before the election) the unemployment value magically dips below the magical threshold value of 8.0 % (a threshold set by the republican's as the bar Obama has been unable to reach--and thus a sign he is not improving the economy), and this is evidence of "cooking of the books," I offer the following scientific-based conclusions.
1. If one uses the 99% explanatory equation and uses it to predict what the October value would be prior to it being announced, the predicted value would have been 7.91 (see above figure). Business folks, economists and financial investors use similar statistical tools to monitor trends and make predictions, and this is often how such executives and financial folks make their money--finding a good prediction equation and then investing or moving money around (or selling or buying stock) given predicted trends over the next few months. So, prior to the announcement of the 7.8% value, the best prediction of what the October value might have been was still below 8.0 %. It was 7.91. Now, that is just under 8.0%, but nevertheless, it is under 8.0% and and not far from the actual reported value of 7.8%.
2. Given the conclusions and stats in point #1, the 7.8% value is very consistent with the 99% explained trend and should not be viewed as some kind of magical "outlier" number that is evidence of "book cooking." The 7.8% value is in complete agreement with the systematic statistical modeled trend based on 57 different data points (57 months). To claim that 7.8 is a sign of something completely unexpected baffles my trained statistical mind.
3. As can be seen in the above figure, I then used the equation to predict the unemployment values for the next two months...7.83 and 7.74.
4. It appears the unemployment trends have been bent during the past 3.5 years...and this is positive bending...not trickery. The above figure shows obvious "unemployment trend bending"-- a very negative upward trend was halted and has now been reversed.
In summary, the unemployment statistics since 2012 show clear systematic trends that can be mathematically modeled and used for predictive purposes. The 7.8 % value is very much in line with the trends. The 7.8% value is not some kind of "outlier" or "anomalous" data point. More important, and something that escapes my scientific quantoid mind, is how intelligent people (and especially a successful CEO and business person) can not see the obvious downward trend in unemployment since approximately the middle of 2009. Am I missing something?
We in the social behavioral sciences hardly ever see such systematic data over time as we deal with phenomena that typically one can only account for 40% to 60% of the trends. Being able to account for 99% of the trends via a mathematical objective equation has touched my quantitative soul. Finally, I hope this post demonstrates the importance of using applied statistical methods to explain and model systematic trends in data that allow for subsequent prediction.
Getting back to my field, applied quant's in psychology use statistical curve fitting methods to develop norms for tests, find the best prediction of outcomes from a combination of variables, etc. Some examples are in one module of my applied test development series of PPT modules.
First, if you want to see the video of Mathew's and Welch you can click on the video below:
Below is a version of the graph I am referring to. It is free and available via Google's public use feature
One can go to the Google data visualization page and move a slide button to view as many years of this data as your heart desires. I decided to extract just the unemployment figures from Jan, 2008, when Obama took office. I could have gone back farther into the Bush presidency, but I did not want to turn this into a partisan post.
[Click on image to enlarge]
As an applied quantoid (educational psychologist with expertise in applied statistics, measurement and psychometrics--click here for my bio and vita and other professional information), these set of data are beautiful. The graph shows clear systematic trends. When we applied quant's see data like this, we often want to play with such data...which is what I did. I followed the following steps
1. I extracted the by-month unemployment percents from Jan 2008 thru October 2012.
2. I entered these values into standard statistical software for evaluating trends (for those curious, I used the TableCurve2D program that helps scientists find the best set of equations to describe a set of data points).
3. I ran the software which generated all possible equations that would explain the data. I then selected the one that was the "best fit" in terms of being systematic--an equation that produced a curve that did not have lots of bumps and squiggles.
4. The result is below.
[Click on image to enlarge]
As you can see, a very complicated equation (Eqn 7509) can be used to describe the trends in the unemployment data. (Aren't you wishing you could take back your "but when will I ever use this in real life after I leave high school" comment to your junior and senior high math teachers?) In reality, you can just ignore the complicated equation. What is crucial, and which should be obvious to any reasonably intelligent and objective person, is that the curved line (which represents the complex equation) does a nice job of capturing the general up, plateau, and down trends of the unemployment stats from 2008 to 2012. It is so obvious. It is so clear.
This means that there is a clear systematic pattern to the unemployment data from 2008 thru October 2012. Now, to address Jack Welch's charges (with his own admission of "no evidence") that it is magical that post-debate (and only weeks before the election) the unemployment value magically dips below the magical threshold value of 8.0 % (a threshold set by the republican's as the bar Obama has been unable to reach--and thus a sign he is not improving the economy), and this is evidence of "cooking of the books," I offer the following scientific-based conclusions.
1. If one uses the 99% explanatory equation and uses it to predict what the October value would be prior to it being announced, the predicted value would have been 7.91 (see above figure). Business folks, economists and financial investors use similar statistical tools to monitor trends and make predictions, and this is often how such executives and financial folks make their money--finding a good prediction equation and then investing or moving money around (or selling or buying stock) given predicted trends over the next few months. So, prior to the announcement of the 7.8% value, the best prediction of what the October value might have been was still below 8.0 %. It was 7.91. Now, that is just under 8.0%, but nevertheless, it is under 8.0% and and not far from the actual reported value of 7.8%.
2. Given the conclusions and stats in point #1, the 7.8% value is very consistent with the 99% explained trend and should not be viewed as some kind of magical "outlier" number that is evidence of "book cooking." The 7.8% value is in complete agreement with the systematic statistical modeled trend based on 57 different data points (57 months). To claim that 7.8 is a sign of something completely unexpected baffles my trained statistical mind.
3. As can be seen in the above figure, I then used the equation to predict the unemployment values for the next two months...7.83 and 7.74.
4. It appears the unemployment trends have been bent during the past 3.5 years...and this is positive bending...not trickery. The above figure shows obvious "unemployment trend bending"-- a very negative upward trend was halted and has now been reversed.
In summary, the unemployment statistics since 2012 show clear systematic trends that can be mathematically modeled and used for predictive purposes. The 7.8 % value is very much in line with the trends. The 7.8% value is not some kind of "outlier" or "anomalous" data point. More important, and something that escapes my scientific quantoid mind, is how intelligent people (and especially a successful CEO and business person) can not see the obvious downward trend in unemployment since approximately the middle of 2009. Am I missing something?
We in the social behavioral sciences hardly ever see such systematic data over time as we deal with phenomena that typically one can only account for 40% to 60% of the trends. Being able to account for 99% of the trends via a mathematical objective equation has touched my quantitative soul. Finally, I hope this post demonstrates the importance of using applied statistical methods to explain and model systematic trends in data that allow for subsequent prediction.
Getting back to my field, applied quant's in psychology use statistical curve fitting methods to develop norms for tests, find the best prediction of outcomes from a combination of variables, etc. Some examples are in one module of my applied test development series of PPT modules.
Article: Believing is Seeing: The Pygmalion Effect and Creativity
Believing is Seeing: The Pygmalion Effect and Creativity
http://www.creativitypost.com/psychology/believing_is_seeing_the_pygmalion_effect_and_creativity#.UHBKQLqhN-I.twitter
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Friday, October 05, 2012
Article: Psychometrics from the Ground Up 10: Covariance
Another excellent quant tutorial from Joel Schneider
Psychometrics from the Ground Up 10: Covariance
http://assessingpsyche.wordpress.com/2012/10/05/psychometrics-from-the-ground-up-10-covariance/
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Article: Maternal Hypertension Linked to Lower IQ
Maternal Hypertension Linked to Lower IQ
http://abcnews.go.com/Health/Wellness/maternal-hypertension-linked-lower-iq/story?id=17387782
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Tuesday, October 02, 2012
How accurate are teacher judgements of student achievement? New meta-analysis
An average correlation of .63 indicates that although statistically significant, teacher judgements of student achievement account for only 40 % of actual student achievement. Authors present nice explanatory model to explain findings.
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