A recent Popular Science article discusses the phenomenon of acquired savant syndrome.  Essentially, it discusses the stories of normal everyday people who happen to suffer acute head trauma and then instantaneously acquire astounding musical, creative, or mathematical talents never before exhibited.  Think Rainman (1988), starring Dustin Hoffman who plays a highly austistic savant individual with amazing mathematical abilities.  The autistic character in Rainmain was congenitally autistic savant; acquired savant syndrome can technically happen to anybody.  Implication: should a hard-pressed student need to pass a very difficult upcoming math exam or music recital, there is no need to study or practice hard – just have a partner strike specific head regions.

This is technically neurologically plausible.  Working with Alzheimer’s dementia patients, one can easily trace back patient histories to find previously inhibited, distrustful, and taciturn individuals rapidly growing more outgoing, extroverted, and perhaps happier seeming as the disorder progresses and vice versa.  According to several neuroscientists as mentioned in the article (e.g. Bruce Miller, UCSF), dementia patients and savants share similarities in abnormal activity in their left anterior temporal lobes.  Wong & Gallate (2012)  show that this region appears to process general semantic information biased with social interactive interpretations. 

Figure 1.  Temporal lobe location.  The front, or face, is towards the left.  The anterior region of the temporal lobe is the left-most portion.

While nobody is seriously advocating self-inflicted or assisted-inflicted permanent lesions of precise brain regions – e.g. designer brain lesions – the technology already does exist for temporary lesion-like effects (i.e. Transcranial Magnetic Stimulation).  For research purposes, scientists may use low levels of electric currents and magnetic fields to disrupt targeted brain regions and simulate lesions.  Generating temporary lesion-like effects on the left anterior temporal lobe could technically induce temporary acquired savant syndrome. 

While applying such research technologies for obvious commercial purposes (e.g. “Transcranial Cram Study Centers”) would be anathema to any sane neuroscience researcher, serendipitously exploiting head-trauma accidents is done wherever possible.  Phineas Gage as an early instance – who in 1848 survived having his own tamping iron penetrating through his brain – apparently began to make public appearances afterwards to capitalize on his fame.  The fact that there could conceivably be a market for this sort of brain and behavioral alteration is a social commentary on our culture. 

What does this say about the brain?  Logically, losing a portion of the brain and surviving indicates that not all brain regions are immediately critical to bodily survival.  Logically, losing a portion of the brain and qualitatively acquiring some novel or hidden and latent skill indicates brain either brain regions compete for activity (i.e. left anterior temporal lobe does not do music or math but consistently out-competes the brain region that does) or some regions are inhibitors of others (e.g. left anterior temporal lobe actively shuts down the music and math brain region). 

Applying the anthropic principle and evolution, if the music and math region of the brain – and ensuing music and math abilities – were critically important to bodily survival, then there should be no left anterior temporal lobe.  It would have evolved away.  So why do we have one?  

One can only surmise that since the left anterior temporal lobe is involved with social interpretation and interaction – as implied by Wong & Gallate (2012) and by the Alzheimer’s patient anecdote – then social interpretation and interaction may be more probabilistically critical to bodily survival than the math and music regions.  Therefore, one must be extremely careful not just in technically disabling the precise anterior temporal lobe for that boost of music and math, but also in wisely deciding whether the ability to do so under voluntary control is desirable.

To place this under a more objective model, there are certain text and naming matching mechanical algorithms existing for intent detection.  A simple one uses word presence and word absence differencing kernels.  For example, in a two-word vocabulary consisting of “Uncle” and “Bob” one must distinguish between an Uncle Bob and a Cousin Bob.  In normal English parlance, we would call Uncle Bob, “Uncle Bob” or “Uncle.”  We would call cousin Bob simply “Bob.”  Below are the word vectors for Uncle Bob and Cousin Bob, respectively. 

In a given test phrase, similarity points are added for every word present in common.  In a given test phrase, similarity points are added for every word absent in common.  In machine learning parlance, this is akin to including a feature’s complement.  In neuroscience parlance, this is akin to an inhibitory connection. 

So if we heard the phrase, “Bob,” it matches Uncle Bob once (for the “Bob”) and misses Uncle Bob once (since it is missing “Uncle”).  It matches Cousin Bob twice (once for “Bob” and once for the absence of “Uncle”).  Hearing “Bob,” this algorithm detects the intent to call Cousin Bob rather than Uncle Bob.  If we altered the algorithm to ignore the inhibition – say to correctly fix another instance where the inhibition was non-optimal – then “Bob” would equally match both Uncle Bob and Cousin Bob.  It is a matter of how likely the inhibition is helpful vs. how likely it is harmful in correctly identifying intent.  See below. 

In this table, we compare “Bob” vs. Uncle Bob and Cousin Bob.  There are four word columns: two for the presence of Uncle and Bob, and two for the absence of Uncle and Bob.  If a word is present, it gets a “1” under the presence column and a “0” in the absence vector and vice versa.  The sum across any row is always 2. 

Comparing “Bob” vs. Uncle Bob and Cousin Bob, we can see that it perfectly matches Cousin Bob but poorly matches Uncle Bob.  This is, by the way, the core matching algorithm for a Fuzzy ART or ARTMAP neural network. 

In conclusion, if a hard-pressed student wishes to pass a math or music course for which he is woefully unprepared, he could conceivably choose to visit a future “Transcranial Cram Study Center.”  If the hard-pressed student ever wishes to succeed in perhaps anything else is life, up to and including getting a job, getting a girl-friend, or getting married, or starting a business, then he might be best advised to look elsewhere for answers.