Posts Tagged 'Functional magnetic resonance imaging'

Mapping and Predicting Fluid Intelligence

British-American psychologist Raymond Cattell

Raymond Cattell – photo Wikipedia

The idea of dividing Spearman‘s theory of general intelligence (g) into two separate concepts; fluid (gF) and crystallized (gC) intelligence, is not particularly new. Psychologist Raymond Cattell, best known for his work on defining and analysing personality types and the Cattell Culture Fair Test, published a paper on his theories on intelligence back in 1941 (Cattell, 1941).  Horn and Cattell (1967) later described fluid intelligence as “…the ability to perceive relationships independent of previous specific practice or instruction concerning those relationships.” That is, it’s the type of intelligence we bring to bear on previously-unseen problems we encounter in daily life that we need to find solutions for, and that may require abstract thinking.  Contrast this with crystallized intelligence, which relies on previous experience and long-term memory, and may also be influenced by cultural factors.

In a previous post from early 2009 I discussed the use of functional Magnetic Resonance Imaging (fMRI) techniques to map human neural activity to visual recognition of  previously-viewed scenes. Back then, researcher John-Dylan Haynes used Blood Oxygenation Level Dependent (BOLD-fMRI) imaging to determine what item a subject might be thinking about.  More recently, researchers at  universities in the US and Slovenia have used similar BOLD scanning techniques to map global connectivity of the prefrontal cortex to predict cognitive control and intelligence. Their research is based on the premise that “individuals with higher intelligence ha[ve] more efficient whole-brain network organization (van den Heuval et. al., 2009)”.  The researchers first tested their 94 young adult subjects for general intelligence using Cattell’s Culture Fair Test. They subsequently mapped the global connectivity of their subjects’ brains between specific regions in the Lateral Prefrontal Cortex (LPFC) and then correlated their findings with the results of the fluid intelligence tests mentioned above. This allowed them to make statistically-significant predictions about their subjects’ levels of intelligence.  The research concluded that “a specific region’s global connectivity [i.e. the relative strength of its neural connections] predicts intelligence” (Cole et. al., 2012).

The implications of this research for learning and teaching are not entirely clear.  Intelligence testing using any methodology can be contentious at the best of times.  Cattell’s attempt to ameliorate the effects of culture on intelligence testing, the Culture Fair Test, has its fair share of both supporters and critics, the latter generally commenting that measures of intelligence should not be based on single assessments, no matter how well designed to counteract the effects of biases such as culture and economically-deprived education.

From my own perspective, an undesirable outcome of the research discussed above would be the use of such quantitative measures of predicted intelligence to stream children into a range of vocational education programs supposedly best suited to their predicted future abilities and consequent needs.  This approach assumes that young adults are likely to be incapable of making wise choices regarding their own futures  (which is, regrettably, already the situation for some children whose parents see them as a projection of themselves and their social status).  The freedom to choose one’s own path, and to make changes along the way is, in my opinion, a fairly basic human right.  Fortunately, the ability to successfully re-invent yourself has some recent research to back it up; neural plasticity studies, as discussed by Norman Doidge (2007) in “The Brain that Changes Itself“, at least offer the hope that most human brains can successfully re-organize, even in late adulthood, according to changes in a person’s circumstances, immediate environment, or due to training specifically designed with that purpose in mind. Stanford’s Carol Dweck (2000) has her own take on how people see their own level of intelligence (as a personal “mindset”) and how they can realise their own potential through approaches based on personal growth.

References
Cattell, R. B. (1941). Some theoretical issues in adult intelligence testing. Psychology Bulletin 38, p.592

Horn J. L. & Cattell R. B. (1967). “Age differences in fluid and crystallized intelligence.” Acta Psychologica, 26, pp. 107-129.

Cole, M. W. , Yarkoni,T., Repovš, G., Anticevic, A. and Braver, T.S. (2012). Global Connectivity of Prefrontal Cortex Predicts Cognitive Control and Intelligence. The Journal of Neuroscience 27 June 2012, 32 (26), pp. 8988-8999

van den Heuvel M. P., Stam C.J. , Kahn R.S., Hulshoff Pol H.E. (2009).  Efficiency of functional brain networks and intellectual performance. The Journal of  Neuroscience 29: pp. 7619–7624.

Doidge, N. (2007). The Brain that Changes Itself. Penguin Books, London.

Dweck, C. (2000). Self-theories: their role in motivation, personality, and development. Hove: Psychology Press

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Some Small Steps for Mind Reading by Machines

Brain and Coke logoI’m not sure exactly why, but 2009 is shaping up to be a breakthrough year for mind reading by machines. A recent CBS News 60 Minutes item, broadcast on January 4th, 2009, looks at current research on using brain scanning (neuroscanning) technologies such as magnetoencephalography  (MEG), functional MRI (fMRI) and powerful computational approaches to determine what a subject is thinking about, whether they have previously been in a particular location, how they really feel about a product, or what their true intentions are.

Shari FinkelsteinCBS Interviewer Shari Finkelstein talked to several researchers in this field about how they are beginning to make sense of brain-scan images by relating them to stimulus images that subjects were asked to think about while being scanned. Carnegie Mellon researcher and psychologist Marcel Just demonstrates the use of fMRI scans and a specific algorithm he developed with co-researcher  Tom Mitchell, head of Carnegie Mellon’s School of Computer Science’s Machine Learning Department, to correctly identify ten items that a subject was asked to think about in random order.  Here’s a video of a “thought reading demonstration” done by Just and Mitchell, and an extended abstract by Tom Mitchell titled “Computational Models of Neural Representations in the Human Brain”, published in Springer’s Lecture Notes in Artificial Intelligence, 2008.

John-Dylan HaynesMs Finkelstein also interviewed John-Dylan Haynes, a researcher at Humboldt University’s  Bernstein Centre for Computational Neuroscience in Berlin about the use of fMRI to scan subjects’ brains as they moved through a virtual reality (VR) setting.  By monitoring the subject’s scans while specific rooms of the VR are replayed, researchers can reliably determine if the subject had visited that room – i.e. they can detect visual recognition of a previously-viewed scene.

Here’s a video lecture titled “Decoding Mental States from Human Brain Activity” given by Professor Haynes at a recent conference in Jerusalem (5th European Conference on Complex Systems – ECSS’08).  He uses Blood Oxygenation Level Dependent (BOLD-fMRI) imaging which can achieve a claimed 85% accuracy in determining what item a person is thinking about.  Interestingly, he mentions that there are only two “specialized”cortical modules in the brain for thinking about visual items – one for faces and one for houses.  All other thoughts are held as “distributed patterns of activity”, that can be decoded and read out, given the correct classification and decoding techniques.

Paul Wolpe Psychiatrist Paul Wolpe, Director of Emory University’s Center for Ethics in Atlanta, Georgia, discusses ethical and legal issues arising from mind-reading research with 60 Minutes in this video extract  The research has spawned a whole new field of legal study, known as “neurolaw”, which looks at subjects such as the admissibility of fMRI scans as lie-detection evidence in court.  Professor Wolpe is concerned that, for the first time in  history, science is able to access data directly from the brain, as opposed to obtaining data from the peripheral nervous system.

Gemma CalvertA new approach to selling, known as “neuromarketing”, makes use of neuroscans to determine subjects’ responses to visual or aural stimuli and the effect that has on their desire to purchase goods. Professor Gemma Calvert, Managing Director of Neurosense Limited, a market research consultancy,  specialises in the use of MEG and fMRI neuroscanning  techniques for marketing purposes, such as predicting consumer behaviour.

Dutch marketing researcher Tjaco Walvis concludes that the brain’s recognition and retrieval of information about brands occurs in the same way that a Google search engine retrieves links related to a search term.  Read a MarketWire article on his research here.

To me, this marketing application of what is essentially exciting science is getting a bit too close to the “dark side” for my liking.  In a previous article I mentioned the psychological and political aspects of applied neuroscience research, where brain monitoring is becoming an increasingly real possibility.  Paul Wolpe alludes to this when discussing  recent research into covert scanning devices that use light beams to scan an unsuspecting subject’s frontal lobe (see my previous post on Hitachi’s use of IR light to perform brain scans).  I suppose we should now add consumer monitoring to the list.

[images sourced from:  here (brain), here (Shari), here (Haynes), here (Wolpe) and here (Calvert)

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