Latest Research News
An online study open to anyone, that ended up involving over 100,000 people of all ages from around the world, put participants through 12 cognitive tests, as well as questioning them about their background and lifestyle habits. This, together with a small brain-scan data set, provided an immense data set to investigate the long-running issue: is there such a thing as ‘g’ — i.e. is intelligence accounted for by just a single general factor; is it supported by just one brain network? — or are there multiple systems involved?
Brain scans of 16 healthy young adults who underwent the 12 cognitive tests revealed two main brain networks, with all the tasks that needed to be actively maintained in working memory (e.g., Spatial Working Memory, Digit Span, Visuospatial Working Memory) loading heavily on one, and tasks in which information had to transformed according to logical rules (e.g., Deductive Reasoning, Grammatical Reasoning, Spatial Rotation, Color-Word Remapping) loading heavily on the other.
The first of these networks involved the insula/frontal operculum, the superior frontal sulcus, and the ventral part of the anterior cingulate cortex/pre-supplementary motor area. The second involved the inferior frontal sulcus, inferior parietal lobule, and the dorsal part of the ACC/pre-SMA.
Just a reminder of individual differences, however — when analyzed by individual, this pattern was observed in 13 of the 16 participants (who are not a very heterogeneous bunch — I strongly suspect they are college students).
Still, it seems reasonable to conclude, as the researchers do, that at least two functional networks are involved in ‘intelligence’, with all 12 cognitive tasks using both networks but to highly variable extents.
Behavioral data from some 60,000 participants in the internet study who completed all tasks and questionnaires revealed that there was no positive correlation between performance on the working memory tasks and the reasoning tasks. In other words, these two factors are largely independent.
Analysis of this data revealed three, rather than two, broad components to overall cognitive performance: working memory; reasoning; and verbal processing. Re-analysis of the imaging data in search of the substrate underlying this verbal component revealed that the left inferior frontal gyrus and temporal lobes were significantly more active on tasks that loaded on the verbal component.
These three components could also be distinguished when looking at other factors. For example, while age was the most significant predictor of cognitive performance, its effect on the verbal component was much later and milder than it was for the other two components. Level of education was more important for the verbal component than the other two, while the playing of computer games had an effect on working memory and reasoning but not verbal. Chronic anxiety affected working memory but not reasoning or verbal. Smoking affected working memory more than the others. Unsurprisingly, geographical location affected verbal more than the other two components.
A further test, involving 35 healthy young adults, compared performance on the 12 tasks and score on the Cattell Culture Fair test (a classic pen and paper IQ test). The working memory component correlated most with the Cattell score, followed by the reasoning component, with the Verbal component (unsurprisingly, given that this is designed to be a ‘culture-fair’ test) showing the smallest correlation.
All of this is to say that this is decided evidence that what is generally considered ‘intelligence’ is based on the functioning of multiple brain networks rather than a single ‘g’, and that these networks are largely independent. Thus, the need to focus on and maintain task-relevant information maps onto one particular brain network, and is one strand. Another network specializes in transforming information, regardless of source or type. These, it would seem, are the main processes involved in fluid intelligence, while the Verbal component most likely reflects crystallized intelligence. There are also likely to be other networks which are not perhaps typically included in ‘general intelligence’, but are nevertheless critical for task performance (the researchers suggest the ability to adapt plans based on outcomes might be one such function).
The obvious corollary of all this is that similar IQ scores can reflect different abilities for these strands — e.g., even if your working memory capacity is not brilliant, you can develop your reasoning and verbal abilities. All this is consistent with the growing evidence that, although fundamental WMC might be fixed (and I use the word ‘fundamental’ deliberately, because WMC can be measured in a number of different ways, and I do think you can, at the least, effectively increase your WMC), intelligence (because some of its components are trainable) is not.
If you want to participate in this research, a new version of the tests is available at http://www.cambridgebrainsciences.com/theIQchallenge
 . Fractionating Human Intelligence. Neuron [Internet]. 2012 ;76(6):1225 - 1237. Available from: http://www.cell.com/neuron/abstract/S0896-6273(12)00584-3
My recent reports on brain training for older adults (see, e.g., Review of working memory training programs finds no broader benefit; Cognitive training shown to help healthy older adults; Video game training benefits cognition in some older adults) converge on the idea that cognitive training can indeed be beneficial for older adults’ cognition, but there’s little wider transfer beyond the skills being practiced. That in itself can be valuable, but it does reinforce the idea that the best cognitive training covers a number of different domains or skill-sets. A new study adds little to this evidence, but does perhaps emphasize the importance of persistence and regularity in training.
The study involved 59 older adults (average age 84), of whom 33 used a brain fitness program 5 days a week for 30 minutes a day for at least 8 weeks, while the other group of 26 were put on a waiting list for the program. After two months, both groups were given access to the program, and both were encouraged to use it as much or as little as they wanted. Cognitive testing occurred before the program started, at two months, and at six months.
The first group to use the program used the program on average for 80 sessions, compared to an average 44 sessions for the wait-list group.
The higher use group showed significantly higher cognitive scores (delayed memory test; Boston Naming test) at both two and six months, while the lower (and later) use group showed improvement at the end of the six month period, but not as much as the higher use group.
I’m afraid I don’t have any more details (some details of the training program would be nice) because it was a conference presentation, so I only have access to the press release and the abstract. Because we don’t know exactly what the training entailed, we don’t know the extent to which it practiced the same skills that were tested. But we may at least add it to the evidence that you can improve cognitive skills by regular training, and that the length/amount of training (and perhaps regularity, since the average number of sessions for the wait-list group implies an average engagement of some three times a week, while the high-use group seem to have maintained their five-times-a-week habit) matters.
Another interesting presentation at the conference was an investigation into mental stimulating activities and brain activity in older adults.
In this study, 151 older adults (average age 82) from the Rush Memory and Aging Project answered questions about present and past cognitive activities, before undergoing brain scans. The questions concerned how frequently they engaged in mentally stimulating activities (such as reading books, writing letters, visiting a library, playing games) and the availability of cognitive resources (such as books, dictionaries, encyclopedias) in their home, during their lifetime (specifically, at ages 6, 12, 18, 40, and now).
Higher levels of cognitive activity and cognitive resources were also associated with better cognitive performance. Moreover, after controlling for education and total brain size, it was found that frequent cognitive activity in late life was associated with greater functional connectivity between the posterior cingulate cortex and several other regions (right orbital and middle frontal gyrus, left inferior frontal gyrus, hippocampus, right cerebellum, left inferior parietal cortex). More cognitive resources throughout life was associated with greater functional connectivity between the posterior cingulate cortex and several other regions (left superior occipital gyrus, left precuneus, left cuneus, right anterior cingulate, right middle frontal gyrus, and left inferior frontal gyrus).
Previous research has implicated a decline in connectivity with the posterior cingulate cortex in mild cognitive impairment and Alzheimer’s disease.
Cognitive activity earlier in life was not associated with differences in connectivity.
The findings provide further support for the idea “Use it or lose it!”, and suggests that mental activity protects against cognitive decline by maintaining functional connectivity in important neural networks.
Miller, K.J. et al. 2012. Memory Improves With Extended Use of Computerized Brain Fitness Program Among Older Adults. Presented August 3 at the 2012 convention of the American Psychological Association.
Han, S.D. et al. 2012. Cognitive Activity and Resources Are Associated With PCC Functional Connectivity in Older Adults. Presented August 3 at the 2012 convention of the American Psychological Association.
http://www.sciencedaily.com/releases/2012/08/120803193555.htm (first study only)
I’ve reported before on how London taxi drivers increase the size of their posterior hippocampus by acquiring and practicing ‘the Knowledge’ (but perhaps at the expense of other functions). A new study in similar vein has looked at the effects of piano tuning expertise on the brain.
The study looked at the brains of 19 professional piano tuners (aged 25-78, average age 51.5 years; 3 female; 6 left-handed) and 19 age-matched controls. Piano tuning requires comparison of two notes that are close in pitch, meaning that the tuner has to accurately perceive the particular frequency difference. Exactly how that is achieved, in terms of brain function, has not been investigated until now.
The brain scans showed that piano tuners had increased grey matter in a number of brain regions. In some areas, the difference between tuners and controls was categorical — that is, tuners as a group showed increased gray matter in right hemisphere regions of the frontal operculum, the planum polare, superior frontal gyrus, and posterior cingulate gyrus, and reduced gray matter in the left hippocampus, parahippocampal gyrus, and superior temporal lobe. Differences in these areas didn’t vary systematically between individual tuners.
However, tuners also showed a marked increase in gray matter volume in several areas that was dose-dependent (that is, varied with years of tuning experience) — the anterior hippocampus, parahippocampal gyrus, right middle temporal and superior temporal gyrus, insula, precuneus, and inferior parietal lobe — as well as an increase in white matter in the posterior hippocampus.
These differences were not affected by actual chronological age, or, interestingly, level of musicality. However, they were affected by starting age, as well as years of tuning experience.
What these findings suggest is that achieving expertise in this area requires an initial development of active listening skills that is underpinned by categorical brain changes in the auditory cortex. These superior active listening skills then set the scene for the development of further skills that involve what the researchers call “expert navigation through a complex soundscape”. This process may, it seems, involve the encoding and consolidating of precise sound “templates” — hence the development of the hippocampal network, and hence the dependence on experience.
The hippocampus, apart from its general role in encoding and consolidating, has a special role in spatial navigation (as shown, for example, in the London cab driver studies, and the ‘parahippocampal place area’). The present findings extend that navigation in physical space to the more metaphoric one of relational organization in conceptual space.
The more general message from this study, of course, is confirmation for the role of expertise in developing specific brain regions, and a reminder that this comes at the expense of other regions. So choose your area of expertise wisely!
 . Navigating the Auditory Scene: An Expert Role for the Hippocampus. The Journal of Neuroscience [Internet]. 2012 ;32(35):12251 - 12257. Available from: http://www.jneurosci.org/content/32/35/12251
Full text available at http://www.jneurosci.org/content/32/35/12251.abstract?sid=2d788914-e53c-...
Math-anxiety can greatly lower performance on math problems, but just because you suffer from math-anxiety doesn’t mean you’re necessarily going to perform badly. A study involving 28 college students has found that some of the students anxious about math performed better than other math-anxious students, and such performance differences were associated with differences in brain activity.
Math-anxious students who performed well showed increased activity in fronto-parietal regions of the brain prior to doing math problems — that is, in preparation for it. Those students who activated these regions got an average 83% of the problems correct, compared to 88% for students with low math anxiety, and 68% for math-anxious students who didn’t activate these regions. (Students with low anxiety didn’t activate them either.)
The fronto-parietal regions activated included the inferior frontal junction, inferior parietal lobule, and left anterior inferior frontal gyrus — regions involved in cognitive control and reappraisal of negative emotional responses (e.g. task-shifting and inhibiting inappropriate responses). Such anticipatory activity in the fronto-parietal region correlated with activity in the dorsomedial caudate, nucleus accumbens, and left hippocampus during math activity. These sub-cortical regions (regions deep within the brain, beneath the cortex) are important for coordinating task demands and motivational factors during the execution of a task. In particular, the dorsomedial caudate and hippocampus are highly interconnected and thought to form a circuit important for flexible, on-line processing. In contrast, performance was not affected by activity in ‘emotional’ regions, such as the amygdala, insula, and hypothalamus.
In other words, what’s important is not your level of anxiety, but your ability to prepare yourself for it, and control your responses. What this suggests is that the best way of dealing with math anxiety is to learn how to control negative emotional responses to math, rather than trying to get rid of them.
Given that cognitive control and emotional regulation are slow to mature, it also suggests that these effects are greater among younger students.
The findings are consistent with a theory that anxiety hinders cognitive performance by limiting the ability to shift attention and inhibit irrelevant/distracting information.
Note that students in the two groups (high and low anxiety) did not differ in working memory capacity or in general levels of anxiety.
 . Mathematics Anxiety: Separating the Math from the Anxiety. Cerebral Cortex [Internet]. 2011 . Available from: http://cercor.oxfordjournals.org/content/early/2011/10/19/cercor.bhr289.abstract
You can download a copy of the study here: Math anxiety.pdf
We know active learning is better than passive learning, but for the first time a study gives us some idea of how that works. Participants in the imaging study were asked to memorize an array of objects and their exact locations in a grid on a computer screen. Only one object was visible at a time. Those in the "active study” group used a computer mouse to guide the window revealing the objects, while those in the “passive study” group watched a replay of the window movements recorded in a previous trial by an active subject. They were then tested by having to place the items in their correct positions. After a trial, the active and passive subjects switched roles and repeated the task with a new array of objects.
The active learners learned the task significantly better than the passive learners. Better spatial recall correlated with higher and better coordinated activity in the hippocampus, dorsolateral prefrontal cortex, and cerebellum, while better item recognition correlated with higher activity in the inferior parietal lobe, parahippocampal cortex and hippocampus.
The critical role of the hippocampus was supported when the experiment was replicated with those who had damage to this region — for them, there was no benefit in actively controlling the viewing window.
This is something of a surprise to researchers. Although the hippocampus plays a crucial role in memory, it has been thought of as a passive participant in the learning process. This finding suggests that it is actually part of an active network that controls behavior dynamically.
 . Hippocampal brain-network coordination during volitional exploratory behavior enhances learning. Nat Neurosci [Internet]. 2011 ;14(1):115 - 120. Available from: http://dx.doi.org/10.1038/nn.2693
Previous research has indicated that obesity in middle-age is linked to higher risk of cognitive decline and dementia in old age. Now a study of 32 middle-aged adults (40-60) has revealed that although obese, overweight and normal-weight participants all performed equally well on a difficult cognitive task (a working memory task called the 2-Back task), obese individuals displayed significantly lower activation in the right inferior parietal cortex. They also had lower insulin sensitivity than their normal weight and overweight peers (poor insulin sensitivity may ultimately lead to diabetes). Analysis pointed to the impaired insulin sensitivity mediating the relationship between task-related activation in that region and BMI.
This suggests that it is insulin sensitivity that is responsible for the higher risk of cognitive impairment later in life. The good news is that insulin sensitivity is able to be modified through exercise and diet.
A follow-up study to determine if a 12-week exercise intervention can reverse the differences is planned.
 . Insulin Sensitivity as a Mediator of the Relationship Between BMI and Working Memory-Related Brain Activation. Obesity [Internet]. 2010 ;18(11):2131 - 2137. Available from: http://dx.doi.org/10.1038/oby.2010.183
Perhaps we should start thinking of language less as some specialized process and more as one approach to thought. A study involving native signers of American Sign Language (which has the helpful characteristic that subject-object relationships can be expressed in either of the two ways languages usually use: word order or inflection) has revealed that there are distinct regions of the brain that are used to process the two types of sentences: those in which word order determined the relationships between the sentence elements, and those in which inflection was providing the information. These brain regions are the ones designed to accomplish tasks that relate to the type of sentence they are trying to interpret. Word order sentences activated areas involved in working memory and lexical access, including the dorsolateral prefrontal cortex, the inferior frontal gyrus, the inferior parietal lobe, and the middle temporal gyrus. Inflectional sentences activated areas involved in building and analyzing combinatorial structure, including bilateral inferior frontal and anterior temporal regions as well as the basal ganglia and medial temporal/limbic areas. In other words, as an increasing body of evidence tells us, we process words in the same way as we do the concepts represented by the words; speaking (or reading) is, neutrally speaking, the same as doing.
 . Dissociating neural subsystems for grammar by contrasting word order and inflection. Proceedings of the National Academy of Sciences [Internet]. 2010 ;107(16):7539 - 7544. Available from: http://www.pnas.org/content/107/16/7539.abstract
Older news items (pre-2010) brought over from the old website
Passive learning imprints on the brain just like active learning
New research adds to other recent studies showing that observation can act like actual practice in acquiring new motor skills. In a study where participants played a video game in which they had to move in a particular sequence to match the position of arrows on the screen (similar to the popular Dance Dance Revolution game), it was found that brain activity in the Action Observance Network (mostly in the inferior parietal and premotor cortices) was similar for dance sequences that were actively rehearsed daily for five days, and a different set of sequences that were passively observed for an equivalent amount of time, but declined for unfamiliar sequences.
Cross, E.S. et al. 2008. Sensitivity of the Action Observation Network to Physical and Observational Learning. Cerebral Cortex, Advance Access published on May 30, 2008. doi:10.1093/cercor/bhn083
Language affects how math is done?
A comparison of activity in the brains of Chinese and English participants doing simple arithmetic using Arabic numbers has found that, although both groups utilised the inferior parietal cortex (an area connected to quantity representation and reading), English speakers displayed more activity in the language processing area of the brain, while Chinese speakers used the area of the brain that deals with processing visual information. There was no significant difference in the reaction time and accuracy of the Chinese and English-speaking volunteers. However, an earlier study comparing Canadian and Chinese students found that the latter were better at complex maths. The findings suggest that our native language, or different teaching methods, may influence the way we solve equations.
Tang, Y. et al. 2006. Arithmetic processing in the brain shaped by cultures. Proc. Natl. Acad. Sci. USA, Published online before print June 30, 2006.
Campbell, J.I.D. & Xue, Q. 2001. Cognitive arithmetic across cultures. Journal of Experimental Psychology: General, 130(2), 299-315.
Learning languages increases gray matter density
An imaging study of 25 Britons who did not speak a second language, 25 people who had learned another European language before the age of five and 33 bilinguals who had learned a second language between 10 and 15 years old found that the density of the gray matter in the left inferior parietal cortex of the brain was greater in bilinguals than in those without a second language. The effect was particularly noticeable in the "early" bilinguals. The findings were replicated in a study of 22 native Italian speakers who had learned English as a second language between the ages of two and 34.
Mechelli, A., Crinion, J.T., Noppeney, U., O'doherty, J., Ashburner, J., Frackowiak, R.S. & Price, C.J. 2004. Neurolinguistics: Structural plasticity in the bilingual brain. Nature, 431, 757.
Learning a sequence with explicit knowledge of that sequence involves same
Imaging studies have found that sequence learning accompanied with awareness of the sequence activates entirely different brain regions than learning without awareness of the sequence. It has not been clear to what extent these two forms of learning (declarative vs procedural) are independent. A new imaging study devised a situation where subjects were simultaneously learning different sequences under implicit or explicit instructions, in order to establish whether, as many have thought, declarative learning prevents learning in procedural memory systems. It was found that procedural learning activated the left prefrontal cortex, left inferior parietal cortex, and right putamen. These same regions were also active during declarative learning. It appears that, in a well-controlled situation where procedural and declarative learning are occurring simultaneously, the same neural network for procedural learning is active whether that learning is or is not accompanied by declarative knowledge. Declarative learning, however, activates many additional brain regions.
Willingham, D.B., Salidis, J. & Gabrieli, J.D.E. 2003. Direct Comparison of Neural Systems Mediating Conscious and Unconscious Skill Learning. Journal of Neurophysiology, 88, 1451-1460.