Using MI Theory to understand Torah

This article in the San Diego Jewish World describes education resources offered by a multimedia company that creates animated lessons on the Torah used by thousands of children around the world, of all denominations of Judaism. The company has also developed a gaming website.

The appeal of the offerings is the ability of users to engage their multiple intelligences. As the author writes,

“Whether Rabbi Roth and his crew realize it or not, they are implementing Professor Gardner’s theory of multiple intelligences through their multifaceted Torah Live programs, enabling children, parents and their teachers to find their own pathway to Torah.” 

Through the new gaming site, students can create their own content, and upload photos and videos. The gaming element is based on the earning of coins which can be spent in meaningful ways, for example, to provide food for a poor family for Shabbat, or to send flowers to an elderly person. The hope is that eventually, as the children become adults, they will help real people in real ways. 

Link to full article here.

Photo credit: Levi Meir Clancy on Unsplash

A Brain Basis for Intra-personal Intelligence?

Introduction by Howard Gardner

When I originally developed the theory of multiple intelligences, one important criterion I searched for was evidence supporting the relative independence of each posited intelligence—the demonstration that particular portions of the cerebral cortex are associated with processing information relevant to a specific intelligence.

 As we contemplated linguistic, musical, and spatial intelligences, studies of individuals who had suffered damage to the cortex provided us with such evidence. There was also evidence of neural tissue dedicated to logical-mathematical, naturalist, bodily-kinesthetic, and even interpersonal/social forms of information.

 In contrast, when it came to the posited “intra-personal” intelligence, I had to throw up my hands. To be sure, I believe that understanding of oneself is important (especially in a complex and unpredictable society), and quite different from understanding other persons…but I could not conceive of how one could find the “brain basis” of self-knowledge. There’s even an evolutionary basis for this skeptical stance: primates and perhaps many other species need to have an understanding of other members of their species (so-called con-specifics), but it’s less plausible that these non-humans have an analogous understanding of self.

But now, some new lines of study in neurobiology open up the possibility that there might actually be a “brain basis” for knowledge of self. My own attention was caught by studies of what is termed “interoception”: researchers examine how the body senses and responds to changes in the state of one’s body. 

 I then had the idea of contacting Dr. Dan Dillon, an excellent neuroscientist who, I am proud to say, was once a student of mine. Dan kindly consented to give me a tutorial on the various lines of research that appear to be relevant to the human capacity to understand oneself. I now have the pleasure—indeed, the privilege—of sharing that tutorial with you. And, since I am now a grateful student of his, I add a few concluding comments.

The “Systems Neuroscience” of Intra-personal Intelligence

By Dan Dillon

Humans have neural networks for apprehending and interacting with the external world, and these networks have all been carefully studied in various species. The visual, auditory, olfactory, gustatory, and motor systems are all reasonably well-understood, and we can now characterize each system in at least two ways: within individuals, by tracing information flow from the first input to the last output, and across species, by documenting the development of each system across the great span of evolutionary time. In short, although there is much left to do, we now have a good grasp on how we know the world around us. 

 But what about the worlds inside us? Many of us seek to live up to the ancient Greek maxim, “Know thyself”. Is there a neural network for self-knowledge, for intra-personal intelligence?

 Indeed, there is—it is called the default mode network (DMN), and it has been a major topic of neuroscientific investigation over the last 20 years. As detailed in an important early review (Buckner et al., 2008), the DMN was discovered by accident. When neuroscientists use methods like functional magnetic resonance imaging (fMRI) to investigate particular cognitive or emotional functions, they commonly present participants with several trials of tasks that probe those functions; periods of rest are interspersed between the trials to give participants a break. In the 1990s, this type of task-based neuroimaging study formed the foundation of large literatures on attention, language, and memory, and these literatures continue to grow today. 

 An intriguing finding emerged, however, when researchers decided to “flip the script” by looking for brain areas consistently more active during the rest periods versus when the tasks were being performed. This approach yielded a consistent pattern across early fMRI and positron emission tomography (PET) studies (Gusnard & Raichle, 2001) that has since been replicated many times, including in non-human primates (Vincent et al., 2007): in contrast to sensory and motor networks, which are highly active during task performance, aspects of the ventral medial prefrontal cortex (mPFC), posterior cingulate cortex, inferior parietal lobes, and medial temporal lobes—prominently including the hippocampus—are typically more active when the organism is apparently at rest. The fact that this distributed neural network was consistently engaged when participants were not supposed to be doing anything particular is what earned it its name: it is the network that comes on by default, when you’re not doing anything special or at least nothing dictated by the current situation.

 It is important not to confuse “default” with “simple”, however, because even though the rest periods in most experiments do not feature stimuli or response requirements, cognition does not stop during these periods. Ancient wisdom and recent research (Killingsworth & Gilbert, 2010; Vago & Zeidan, 2016) both emphasize that minds “at rest” are often highly active—in other words, our minds wander. What do people think about when they’re not asked to think about something specific? They tend to think about themselves, of course—about what they’ve done in the past, about their ongoing experience, and about their plans for the future (e.g., Andreasen et al., 1995). And while the precise relationships between spontaneous cognition and DMN activity remain a highly active area of research, our tendency to recall our past and envision our future helps explain why the DMN prominently includes the mPFC and hippocampus—it’s because these two brain regions are well-known for supporting self-referential thinking (Mitchell et al., 2005) and mental time travel (Schacter et al., 2008), respectively. And, crucially, although thinking about yourself can disrupt task performance (Weissman et al., 2006), this sort of self-referential cognition can be highly adaptive (Buckner et al., 2008): by drawing on prior experience we can envision ourselves taking more adaptive actions in the future and thus increase our chances for happiness and success going forward.

 Because it allows us to draw on past experience and envision the future, the DMN is a network that certainly seems to support self-knowledge, which I think is either synonymous with or critical to intra-personal intelligence. It’s not the only relevant network, however, and it does not always act alone. 

 For example, I conducted an fMRI study of emotion regulation in which I asked depressed and non-depressed adults to manipulate their responses to emotionally negative and neutral pictures (Dillon & Pizzagalli, 2013). Because the task required participants to think about themselves, it activated many components of the DMN directly. Critically, however, the amygdala—a brain region engaged by emotionally arousing material—was also activated, and its activation increased when participants used self-referential thinking to engage with the pictures more fully (for a meta-analysis of studies using this approach, see Buhle et al., 2014). This study thus showed that although the amygdala is not classically considered part of the DMN, it can be coactivated with the DMN if a task directs self-referential cognition towards emotional material, and presumably when spontaneous cognition involves emotionally arousing concepts.

 As another example, interoception has emerged as a central topic in recent work on anxiety and depression (Khalsa et al., 2016; Paulus & Stein, 2010). Interoception refers to detection of bodily sensations, and dysregulated interoception is critical to certain forms of psychopathology. Panic disorder, for instance, may be triggered by a pounding heart and sweaty palms, but it is sustained by the fact that sufferers become exquisitely sensitive to those sensations and the fear of bringing them on can be greatly constraining (Ehlers, 1993). Specifically, individuals with panic disorder can become preoccupied with self-monitoring for physiological symptoms of anxiety, and consequently they often sharply restrict their lives to avoid bringing those sensations on; this is why panic disorder and agoraphobia often co-occur—the anxious adult avoids engaging with the world for fear of the terrifying sensations that might result. To treat panic disorder and related conditions, it would help to have a better understanding of interoception. 

 With this in mind, researchers have focused their attention on the anterior insula, a brain region that lies below the frontal operculum and that plays a key role in detection of bodily sensations. A review of this work is beyond the scope of this brief essay, but there is an important point to convey. Although not considered part of the DMN, the insula may contribute to our intra-personal  intelligence by enabling us to have a sense of our physiological responses. I have highlighted how this internal sense goes awry in panic disorder, but imagine that I know that I tend to react to the slightest disapproval with a pounding heart. Armed with this self-knowledge, I may be less thrown when a critical remark sets my heart racing, and so may be better able to persevere when faced with scrutiny.

 To summarize, the DMN is probably the core neural network that underpins intra-personal intelligence, and it works in concert with other brain regions, including the amygdala and insula, when those regions convey useful self-relevant information. Is any of this work relevant to understanding individual differences in intra-personal intelligence? 

 One reason for guarded optimism is that the field has recognized that while short fMRI scans (~ 6 minutes) are fine for identifying the broad contours of the DMN and other networks, longer scans (~20-30 minutes) are necessary for characterizing individual differences in network details (Gordon et al., 2017; Laumann et al., 2015). This approach to deep phenotyping has revealed interesting individual differences in the DMN and other networks (Braga & Buckner, 2017)’ and it may ultimately be possible to relate those differences to between-participant variability in intra-personal intelligence—for instance, the differences seen between adults with alexithymia, who make only broad distinctions between feeling good or bad, as opposed to those who make fine distinctions among the wide variety of emotions they experience (Preece et al., 2022). If such a synthesis can be forged, then neuroimaging will have made an important contribution to the study of multiple intelligences.

 © Dan Dillon, 2022

References

Andreasen, N. C., O'Leary, D. S., Cizadlo, T., Arndt, S., Rezai, K., Watkins, G. L., ... & Hichwa, R. D. (1995). Remembering the past: two facets of episodic memory explored with positron emission tomography. American Journal of Psychiatry152(11), 1576-1585.

Braga, R. M., & Buckner, R. L. (2017). Parallel interdigitated distributed networks within the individual estimated by intrinsic functional connectivity. Neuron95(2), 457-471.

Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain's default network: anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences1124, 1-38.

Buhle, J. T., Silvers, J. A., Wager, T. D., Lopez, R., Onyemekwu, C., Kober, H., ... & Ochsner, K. N. (2014). Cognitive reappraisal of emotion: a meta-analysis of human neuroimaging studies. Cerebral Cortex24(11), 2981-2990.

Dillon, D. G., & Pizzagalli, D. A. (2013). Evidence of successful modulation of brain activation and subjective experience during reappraisal of negative emotion in unmedicated depression. Psychiatry Research: Neuroimaging212(2), 99-107.

Ehlers, A. (1993). Interoception and panic disorder. Advances in Behaviour Research and Therapy15(1), 3-21.

Gordon, E. M., Laumann, T. O., Gilmore, A. W., Newbold, D. J., Greene, D. J., Berg, J. J., ... & Dosenbach, N. U. (2017). Precision functional mapping of individual human brains. Neuron95(4), 791-807.

Gusnard, D. A., & Raichle, M. E. (2001). Searching for a baseline: functional imaging and the resting human brain. Nature Reviews Neuroscience2(10), 685-694.

Khalsa, S. S., Adolphs, R., Cameron, O. G., Critchley, H. D., Davenport, P. W., Feinstein, J. S., ... & Zucker, N. (2018). Interoception and mental health: a roadmap. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging3(6), 501-513.

Killingsworth, M. A., & Gilbert, D. T. (2010). A wandering mind is an unhappy mind. Science330(6006), 932-932.

Laumann, T. O., Gordon, E. M., Adeyemo, B., Snyder, A. Z., Joo, S. J., Chen, M. Y., ... & Petersen, S. E. (2015). Functional system and areal organization of a highly sampled individual human brain. Neuron87(3), 657-670.

 Mitchell, J. P., Banaji, M. R., & Macrae, C. N. (2005). The link between social cognition and self-referential thought in the medial prefrontal cortex. Journal of cognitive neuroscience17(8), 1306-1315.

 Paulus, M. P., & Stein, M. B. (2010). Interoception in anxiety and depression. Brain Structure and Function214(5), 451-463.

Preece, D. A., Mehta, A., Becerra, R., Chen, W., Allan, A., Robinson, K., ... & Gross, J. J. (2022). Why is alexithymia a risk factor for affective disorder symptoms? The role of emotion regulation. Journal of Affective Disorders296, 337-341.

Schacter, D. L., Addis, D. R., & Buckner, R. L. (2008). Episodic simulation of future events: concepts, data, and applications. Annals of the New York Academy of Sciences1124, 39-60.

Vago, D. R., & Zeidan, F. (2016). The brain on silent: mind wandering, mindful awareness, and states of mental tranquility. Annals of the New York Academy of Sciences1373(1), 96-113.

Vincent, J. L., Patel, G. H., Fox, M. D., Snyder, A. Z., Baker, J. T., Van Essen, D. C., ... & Raichle, M. E. (2007). Intrinsic functional architecture in the anaesthetized monkey brain. Nature447(7140), 83-86.

Weissman, D. H., Roberts, K. C., Visscher, K. M., & Woldorff, M. G. (2006). The neural bases of momentary lapses in attention. Nature Neuroscience9(7), 971-978.

Comment by Howard Gardner

 This excellent tutorial is a state-of-the-art report on those brain structures and regions that appear to be crucial to intra-personal intelligence—the capacity and tendency of human beings to think about themselves, and, ultimately more importantly, to know themselves accurately. While the structures and processes identified by Dan are the necessary grounding for any knowledge of self, they do not in themselves reveal whether a person has accurate self-knowledge. After all, a narcissist may think of herself or himself constantly and yet have a quite inaccurate view of self, at least as judged by those who know the person well. Put differently, we may now know far more about the brain basis of intra-personal intelligence than about individual differences in accuracy of intra-personal intelligence.

 As a comparison, think of what it means to have spatial intelligence. We can identify the areas in the parietal lobe that enable spatial thinking; but if we are to compare individuals in terms of their respective spatial intelligences, we need measures of spatial excellence (e.g. maze running or geometry tests) which we can then correlate with neural structures and regions. Clearly, such a comparison is possible—as it is for the other intelligences—but it is particularly challenging for understanding of self, since such understanding is so subjective. 

But I want to raise an additional point. In modern times, and especially in the West (roughly Europe and North America), a great deal of importance is currently placed on thinking about oneself and knowing oneself. It’s not clear that this capacity has always been important, even in the West. Dan refers to the Greek injunction to “know thyself”. Yet, it is entirely possible that this idea was relatively new in the Socratic era. Over forty years ago, the psychologist Julian Jaynes caused quite a stir when he argued that the early Greeks did not have self-consciousness—let alone a developed sense of self. Instead, they heard voices which they attributed to the gods, and followed the injunctions of what they heard. Jaynes posited that only with the advent of extensive writing (as opposed to simple bookkeeping), and the rise of philosophical thinking in the Athenian age did individuals become conscious of themselves as selves in a way that we now take for granted.

At the risk of raising additional controversy, I would add that not all other cultures—even ones highly developed—have an equal obsession with the self. In particular, Confucian societies—and I am thinking here of Japan more than of China—think much more about others, about the “we” than about the “I”. This may also be true of various groups in India—cf. the work of Richard Shweder. This is not to assert, of course, that any humans—even those who lived 10,000 years ago—lack any sense of self. Indeed, with respect to the functions described by Dan, they may well have as much intra-personal intelligence as those of us who live in the 21stcentury. But having a developed, differentiated, and accurate intra-personal intelligence may be a blessing—or a curse—of modern, Freudian times.

References

Jaynes, Julian. The origin of consciousness in the breakdown of the bicameral mind. Boston: Houghton Mifflin, 1976.

Shweder, Richard. Thinking through cultures: Expeditions in cultural psychology. Cambridge: Harvard University Press, 1991.

Are Brain Surgeons and Rocket Scientists Really Smarter?

A recent study in the British Medical Journal has found that brain surgeons and rocket scientists perform no better than laymen on 12 online tasks using the Great British Intelligence Test (GBIT).

Howard Gardner was asked by The Daily Telegraph for his thoughts on this finding. He responded as follows:

"The standard IQ test is a reasonably good predictor of how one will perform in a standard school," says Gardner, now Hobbs research professor of cognition and education at Harvard. 

 "But once you move away from performance in a scholastic environment, other abilities come to the fore. IQ tests tap linguistic and logical-mathematical abilities. But you need assessments of other intelligences to predict who will become an effective therapist or sales person (personal intelligences), athlete or surgeon (bodily-kinesthetic intelligence), composer (musical intelligence), sculptor or architect (spatial intelligence), biologist (naturalist intelligence)." 

 As a result, Gardner believes we "need to talk about intelligences, rather than intelligence, and about personality traits and motivational factors if one wants to understand fully the nature of surgery or rocket science." 

The full article is available here.

Musical Intelligences: Human and Artificial

Recently I happened to read two articles about music back to back—and they generated strands of thought which may inform one another.

It’s long been realized that individuals familiar with a musical idiom have some capacity to anticipate what’s going to happen next. If it’s a surprise, it may be a pleasant one, a harsh one, a boring one, and—though rarely—one that is totally discombobulating. We speak about the amount of information in the signal and how much new information is provided by the next tone or sequence. To use examples familiar to musical aficionados, this phenomenon may explain the delight heard at the first performance of George Gershwin’s Rhapsody in Blue, the anger expressed at Igor Stravinsky’s The Rite of Spring, and the range of reactions to the final movement of Beethoven’s Ninth (Choral) Symphony.

 In the first article I read, I learned about research conducted at Aarhus University in Denmark. The puzzle: Are we more likely to anticipate a change, when a musical fragment ends; or if we are to be surprised, must we await the appearance of the unexpected?  How do we know that a musical phrase has ended and something new is about to begin? Can we recognize an ending before something new begins, so that we can start anticipating something new? Or, to shift metaphors, do we have to see the first scene of the second act to know whether it’s going to be “business as usual”. 

The researchers invoke the concept of entropy. High entropy tones are unexpected; low entropy tones are expected. Their study confirms that in listening to music, the mind (and, of course, the brain) is one step ahead of the musical signal—it anticipates the next signal and notes whether or not it meets expectations. 

 As investigator Niels Chr. Hansen describes it, “We clearly see that people have a tendency to expect high-entropy tones (ones that are relatively unexpected) as musical phrases endings.” In other words, we experience melodies that end in an unexpected way as more complete than those that end in a low entropy expected way. Participants lingered for longer on high entropy tones… as if they were expecting something new to emerge after the perceived end of phrase.

 In terms of “MI theory” one could say that individuals who are especially good at such anticipation are displaying or exhibiting musical intelligence. And we could further probe that capacity by exposing subjects to new styles of music and see how rapidly they can anticipate when a “high entropy” break in the expected pattern is about to occur.

 The second article is far grander, even grandiose. Over the last two years, musical experts have accomplished a feat that eluded the greatest of classical musical composers—they have completed Beethoven’s 10th Symphony—and in fact, a full recording of Beethoven #10 was released on October 9, 2021. If you thought Beethoven had only completed 9 symphonies, you would have been literally correct. Before his death in 1827, Beethoven just left some musical sketches for the commissioned 10thsymphony. But as the article reports “Now, thanks to the work of a team of music historians, musicologists, computers and computer scientists, Beethoven’s vision will come to life”.

In their words: “In June 2019, the group gathered for a two-day workshop at Harvard’s music library. In a large room with a piano, blackboard and a stack of Beethoven sketchbooks, spanning most of his works. We talked about how fragments could be turned into a complete piece of music, and how AI could help solve this puzzle, while still remaining faithful to Beethoven’s processes and vision.”

 How did AI solve this puzzle? The “AI” system needed to “learn” from Beethoven’s entire body of work how he might have approached and completed this final symphony. As described by the leader of the AI team “We would need to use notes and completed compositions from Beethoven’s entire body of work—along with the available sketches from the 10thsymphony—to create something that Beethoven might have written.”

 After many efforts, they did a test—appropriately, in Bonn, Beethoven’s hometown. The team printed some AI-developed scores and played them for an audience on a piano. The audience was challenged to determine where Beethoven’s phrases ended and where the AI extrapolation begins. The audience failed the test… or, as we might quip, “AI passed the test!” This process was repeated several times. Over 18 months, the team constructed two entire movements, each lasting longer than 20 minutes. Now you can listen to this human-AI team’s work and judge for yourself. 

Note that these articles both highlight the capacity to synthesize. In the first instance, listeners put together—synthesize—what they have heard to this point and make their best guess about what is to come. In the second instance, humans and AI programs each review the earlier Beethoven musical corpus and put together—synthesize—a possible new work in the Beethovian style.

As this pair of articles illustrates, our understanding of musical intelligence is being enhanced thanks to research by psychologists, neuroscientists, AI experts, and—of course—musicians and musicologists. Perhaps, thanks to these scholarly undertakings, the musical intelligence of all human beings can be enhanced. We then have to hope that, as with the case of all intelligences, they are mobilized for positive ends… as did happen with Beethoven’s Tenth Symphony.

 

REFERENCES

 The Brain Is a Prediction Machine, and Music Reveals How It Works - Neuroscience News. (2021). Retrieved 13 October 2021, from https://neurosciencenews.com/brain-prediction-music-19364/

Elgammal, A. (2021). How AI helped to complete Beethoven's 10th Symphony. Retrieved 13 October 2021, from https://www.straitstimes.com/opinion/how-ai-helped-to-complete-beethovens-10th-symphony

 Hansen, N., Kragness, H., Vuust, P., Trainor, L., & Pearce, M. (2021). Predictive Uncertainty Underlies Auditory Boundary Perception. Psychological Science, 32(9), 1416-1425.

Photo by benjamin lehman on Unsplash


Are "play personalities" the same as multiple intelligences?

I recently received a question from a person working in the field of education. She wanted to know whether there is any overlap between the eight play personalities described by Stuart Brown and how these might intersect with multiple intelligences. She asked “Are we intelligent in the same way we play?”

My response:

I suspect that there may be some link between personality types and "MI" e.g. personal intelligence strengths lead to different careers and ways of thinking compared to logical-mathematical intelligences, but how to show it, and how to figure out which came first, is a chicken-egg problem.

I DON’T think that personality types and intelligences are the same.

Photo by Fabian Centeno on Unsplash