f music be the food of love, play on!
- Willy Shakespeare

A lot of ink has been spilled over the question of what effect music has on the brain. Oliver Sacks' book Musicophilia ("Music, Ophelia?") tackles the topic in great detail.

But three recent studies on the effects of music on learning should also have us tapping our collective toes.

First up: a paper in the Journal of Music Therapy that looks at the effects of music therapy on social and participatory behaviors within various learning disabled populations (ASD, developmental disabilities such as Downs and communication disorders such as stuttering). Music therapy was found in over 70 studies to promote a variety of "social and participation outcomes, such as frequency of responses, initiation of communication, turn-taking, joint attention, and group participation." The evidence was not overwhelming, however, and the outcomes varied with the known deficits of each population's condition.

Still, music therapy was the key factor in improving behaviors characterized by increased focus and active participation.

And what learner behaviors are associated with poor training outcomes? How about: lack of focus and non-participation?

Hold that thought, and let's move on to study #2, this one from the journal, Educational Technology Research and Development. The authors put together the results of 30 studies over a 10 year period, ending in 2018, examining the role of background music (BM) on learning outcomes. And they started out hopeful, noting that "instrumental BM may positively affect attention in the workplace. Similarly, college students use music while studying to increase concentration on academic tasks. Individuals use self-selected BM to regulate their mood, to be calmer and more relaxed while driving, and to enhance their emotional state while traveling."

However, after a rigorous study selection process, the results were disappointing. Inconsistencies between the studies made conclusions difficult. The impact of BM on things like memory, recall, reading comprehension and writing skills were contradictory. Moreover, all of the qualifying studies focused only on narrow, simple tasks; none addressed the top Bloom levels of applying, analyzing, synthesizing or evaluating knowledge.

They did, however, find uniformly positive effects on student motivation, enhanced recall of facts and improved foreign language learning when it came to the three studies that looked at multimedia modalities such as video games, VR and interactive lessons--the sorts of things we do in eLearning. This, despite some researchers' concerns about distraction and cognitive overload.

The authors end with an exhortation to their peers to smarten up and use more rigor in future studies.

Enter study #3,  not from a music or training journal but from the European Journal of Pediatric Surgery, which answers the concerns of study #2 by using a more rigorous methodology including TWO distinct genres of music AND a control group AND a six month follow-up.

The result? The medical students listening to Bach (classical) while they learned a complex fine motor skill (tying a surgical knot) showed significantly better speed and  quality than those who studied without any music at all or who listened to Bushido (a German rapper). However, scores on knot-tying performance, accuracy and knot strength were no better than in the other groups.

The authors' conclusion? 2 out of 5 ain't bad. They confidently claim that given a fine motor skill task, Med students exposed to classical music during their training improved in speed and quality more so than those who were not. It is not surprising that performance, accuracy and strength were the same for everyone, really, so this is a significant finding.

But another question the authors don't ask is, what is it about the classical music that is having this effect? They posit that it is the "non-disturbing" nature of the genre as opposed to rap music, which is less melodic, that was the main factor. However, they are clearly unaware of the great work that has been done in the field of neurotherapy regarding how different sound frequencies affect brain function, based on the fact that different groups of neurons vibrate at different frequencies and that can be targeted to produce predictable results. So it may be that classical has a greater range of frequencies than rap, rather than it's easy-listening qualities. There is also a lot of research supporting the idea that the complexification of our neural systems are what puts the oil in our mental chassey, so perhaps the complex harmonics have something to do with it.

So what are the implications for instructional design, you ask?

Well, we can say that if you are designing training for a fine motor skill, adding classical music to the experience  will likely improve performance, and not much else. Not everyone likes classical music, so it may not be that useful even there. Also, exposure to music that is interactive, as in music therapy, can promote prosocial behavior, which is good for encouraging attentiveness and participation. That's something.

HOWEVER, THESE AREN'T THE ONLY 3 STUDIES OUT THERE. My point is, we should be discussing these things. As learning professionals I find we often get stuck in a rut, discussing the same strategies over and over without looking at new research and its implications.

Maybe we should consider the soundscape of a course to be as important as its visual design, rather than seeing music as just a frill. If background music benefits speed and accuracy in one domain, maybe that extends to others. And if music encourages focus and active participation in general, I'm down with that, too.

Of course, this is extrapolating, more research needs to be done. But I think it's high time we paid attention to such research if we really want to call ourselves scientific in our efforts to improve our clients' learning--and business--outcomes.

Mitch (whose son is a beatmaker--check him out here)

P.S. If you like this blog, do me a favor and retweet or share it so more people can read it. Thanks muchly!
P.P.S. And now, for your listening pleasure: Bushido!

lan Turing (the subject of the rightfully award-winning movie, The Imitation Game, on Netflix, which basically credits him with winning WWII for the British by leading the development of the first computer to crack an unsolvable German code and then being outcast because he was gay) is mainly known as the creator of the Turing Test, the first Artificial Intelligence test.

The basic premise was that an individual conversing with a person and a computer, both hidden by screens, would not be able to tell which was which.

The test was the basis for the "Voight-Kampff Test" used in the 1992 movie Blade Runner, an entertaining clip of which is provided below (I wonder how they came up with that name?).

So a question to ask is: How does the computer or robot go about fooling the observer? And the answer is, By guessing.

The computer must be able to listen to and parse the language spoken and develop a response based on the words used in the way that they are used without a degree of hesitation that would tip the listener off. To do this, it needs a s***load of data. Now the logical way to do this would be to provide a databank of words and rules as to how they were used in the language and some algorithms to direct it in composing a reply. However, this is not the way it is done. It is done rather buy providing program with millions of sentences in context from different sorts of printed matter and letting it make its best guess.

Which is exactly what the human behind the screen is doing.

Take the Blade Runner interview. The detective's objective is to find out whether the person opposite is a robot or a human. The robot has to answer biographical questions based on implanted memories, and the interviewer has to determine whether these guesses at human-like responses are real enough. If a human is being interviewed, the truth is they are also guessing at the answers based on their own faulty memories. These memories, whether real or implanted, may be considered "top down" knowledge, the interview questions "bottom up" inputs.

People develop top down systems their whole lives to deal with new bottom up inputs.

The type of training AI systems go through builds top-down knowledge systems on massive inputs rather than years-long experience.

What I'm saying is, we don't know things any more than the computer knows things. Our knowledge is based on years of input gathering and pattern recognition, figuring things out by guessing and verifying over and over again. But because our conclusions are based on our unique experience profiles they are not necessarily the same as our neighbor's, which is why you have Liberals and Conservatives.

So if this is how people naturally learn, i.e., by experiencing things and figuring it out through guesses and verification, how can we L&D folk harness it?

A simplistic reading would lead to rules of thumb like teach by doing or teach by example. Going a little farther, we would include things like teach by making mistakes and teach by bad examples.

But digging deeper, we have to recognize that the human capacity for finding order in chaos, of finding patterns within disorganized material, for making sense of nonsense, may not be entirely practical as an instructional approach. It takes too long. Life experience teaches this way, which is why some people insist that apprenticeship is the only way to learn certain jobs; and they may not be wrong.

However, it should make us question our main technique of spoon feeding content and checking for comprehension or application. This clearly does not jive with natural learning. Even the average simulation is not deep or wide enough to take advantage.

So what to do?

I don't know, I'm must bringing it up as something worth thinking about.

                                             ; )

But while I'm on the subject of AI, we should be thinking as a community about how AI can be used to improve our products. The challenge is to think of problems that have been extremely resistant to past IF-THEN approaches that may be accessible using an AI lens.

What comes to mind for me is that I have always been overly hopeful and therefore overly disappointed in the lack of our ability to screen students for preexisting knowledge and use that to customize the learning for them alone. Bespoke learning is an area that elearning has always seemed to me to be perfect for, and yet we are still providing standard lessons to everyone, with some branching, perhaps, based on their job description or things like that.

If any of you have some barrier busting hunches as to how AI might be applied, I'd love to see them in the comments.

Peace, and get vaccinated whenever you can.
Mitch (Not a robot.)

don't usually recommend TV shows, but there's this one on Netflix I think everyone in L&D should watch.

"ATYPICAL" is about a family with a son on the autism spectrum and how they learn how to respect his difference.
It stars Jennifer Jason Leigh, who somehow didn't become a legend although she was truly one of the best actors of her generation.

The boy, Sam, has a sister who has her own teenage crosses to bear, but who also kind of looks out for him at school. And he has an odd but still neurotypical girlfriend. (Making the point, I think, that neurotypical is a way broad category on its own.)

There's an early episode in which Sam wants to take his girlfriend to a high school dance, but can't. Why? Because he can't stand the loudness of the music, it overwhelms him and gives him uncontrollable anxiety. Sam tells his girlfriend (or his sister, I forget which) that what he would like would be for the dance to be held in silence. Sam thinks this is not an unreasonable ask (which is played for laughs, part of the humor of the show), but unbeknownst to him, his sister (or girlfriend, I forget which) takes him seriously.

The episode ends with a silent high school auditorium filled with dancing kids all wearing bluetooth headphones.

Which is to say that unreasonable is in the eye of the beholder.

Which is to say, when it comes to the current drive for accessibility in eLearning (maybe all training, but my experience is with eLearning), it occurs to me that I've never seen guidelines that take into account different neuroprofiles within the population.

What do people on the spectrum think of it? Stephanie Bethany youtubed her thoughts, below.

Discuss.

Mitch, the ID Fanatic

"Have Laptop, Won't Travel" Mitch Moldofsky is a remote contract ID for hire. Sign up here to be notified about new podcasts or blog posts.

n our last blog, we talked about the 20th Century conceptualization of learning which was based on algorithms:

Learning = Acquiring rules

And of course these rules had to be logical, because:

Rationality = Logic

​​We then did the Wason Sorting Task. In case you didn't read the earlier blog, here it is again.
​

Solve the following
To test the proposition, “If one of these cards has an even number on one side
then its other side is green,” which cards would you turn over without turning
​over any cards unnecessarily?

I'll give you a second to think about it. Answer below.











Answer: 8 and Blue, because no matter what's on the other side of 3 or Green, it doesn't disprove the statement.

Only 30% of people get this right on the first try, which goes to prove that either

• A) people aren't rational or
• ​B) rationality isn't logic after all.

​Finally, we asked the begging question:

If the acquisition of logical rules isn’t learning, what is? 

And the answer, according to cognitive science, is....
​

​Simple right?  Only the contents of THE BOX keep CHANGING. Why?

Because the contents of THE CIRCLE keep CHANGING!

So really, it’s a two way street.  ​

​This is what we call a Dynamical System, where both our internal, embodied experience and our external, sense-driven experiences keep changing and influencing one another on an ongoing basis.  

I suppose you're wondering,  "How does this apply to AI?" 

To get there, let’s try the Wason Selection Task one more time, this time with a twist. 

Again, we borrow from Puzzlewocky.

Solve the following
You work in a bar. Your job is to enforce this rule:​
“If someone is drinking alcohol, then that person must be age 18 or older.”

From where you are standing, you can observe four people:

1. a person drinking soda (you can’t see how old they are);
2. a person drinking beer (you can’t see how old they are);
3. a 30-year-old person (you can’t see what they’re drinking) and
4. a 16-year-old person (you can’t see what they’re drinking).

​Which of these four items must be checked, at a minimum,
​in order to make sure the rule is being followed? 
​

Answer below.



​



The beer and the girl, right? You need to check that the beer is being drunk by an adult, and that the girl isn't drinking alcohol.

​Surprise! This is exactly the same as the previous question, only people answer close to 100% rather than 30%.

Why?

​Because it gives context, something to which we can bring—that’s right--our experience.  

You see the problem? If we really want computers to think like we do, we can’t just tell them what to do. We have to make it so they can figure it out themselves. 

How the heck can we do that? We'll cover that in the next blog.

Life is not a box of chocolates.
​Mitch

​P.S. As mentioned in the first blog in this series, I'll be speaking about AI in L&D at the Learning Guild's LEARN2021 conference in Orlando FL, November 8. If you're there, say hi and we can talk further.