A generation ago it was only a brave eclectic minority of psychologists and neuroscientists who dared to address the arts. Things have changed considerably since then. “Art and brain” is now a legitimate and respected target of study, and is approached from a variety of viewpoints, from reductionistic neurophysiology to evolutionary approaches.
Things have changed so quickly that late 20th century conversations about how to create stronger art-science collaborations and connections are dated only a decade later – everyone’s already doing it! And the new generation of students being trained are at home in both the arts and sciences in a way that was rare before.
Although we are all now more culturally comfortable bathing in conversations about art and brain, are we making progress? Has looking into the brain helped us make sense of the arts? Here I will briefly explain why I believe we have made little progress. And then I will propose an alternative route to understanding art and its origins.
Perhaps the most common modus operandi in the cognitive and brain sciences approach to art is (i) to point to some known principle of brain science, and then (ii) to provide examples of art showing conformance with that principle. As fun as it may be to read explanations of art of this kind, the approach suffers from two fundamental difficulties – one on the brain side, one on the arts side.
Let’s start with the “brain” difficulty, which is simply this: we don’t understand the brain. Although the field is jam-packed with fantastically clever experiments giving us fascinating and often valid data, there is usually very little agreement (or ought to be little agreement) about how to distill the data into broad principles. And the broader and higher-level the supposed principle, the more controversial and difficult-to-defend it is. Consequently, most of the supposed principles in the brain sciences remotely rich enough to inform us about the arts are deeply questionable.
If we are so ignorant of the brain, why is the modus operandus above sometimes seemingly able to explain art? There is a lot of art out there, and it comes in a wide variety. Consequently, given any supposed principle from neuroscience or psychology, one can nearly always cherry pick art pieces fitting it. What very few scientific studies do is attempt to quantitatively gauge whether the predicted feature is a general tendency across the arts. The fundamental difficulty on the “arts” side is that we often don’t have a good idea what facets of art are universal tendencies that need to be explained.
These difficulties for the brain and arts make the common modus operandus a poor way to make progress comprehending art and brain. What initially looks like neuroscientific principles being used to explain artistic phenomena is, more commonly, suspect brain principles being used to explain artistic phenomena that may not exist. (A second common approach to linking art and the brain sciences goes in the other direction: to begin with a piece of art, and then to cherry-pick principles from the brain sciences to explain it.)
How, then, should we move forward in our quest to understand the arts? Here I will suggest to you a path, one that addresses the brain and art difficulties above.
The “arts” difficulty can be overcome by identifying regularities actually found in the arts, whether universals, near-universals, or statistical tendencies. One reason large-scale measurements across the arts are not commonly carried out may be that any discipline of the arts tends to be vast and tremendously diverse, and it may seem prima facie unlikely that one will find any interesting regularity. With a strong stomach, however, it is often possible to collect enough data to capture a signal through the noise.
The “arts” difficulty, then, can be addressed by good-old-fashioned data collection, and distillation of empirical regularities. But even so, we are left with another big problem to overcome. “Good-old-fashioned data collection” involves more than simply collecting data. Which data should one collect? And which kinds of regularities should be sought after? Although it is well-known that data helps drive theory, it is not as widely appreciated that theory drives data. There’s effectively infinitely many ways of collecting data, and effectively unlimited ways of analyzing any set of data. Without theory as a guide, one is not likely to identify empirical regularities at all, much less ones that are interesting. Good-old-fashioned theory is required in good-old-fashioned data collection. We need predictions about empirical regularities, and then need to gather data in a manner designed to test the prediction.
But this brings us back to our first difficulty, the “brain” one. If we are so ignorant of the principles of the brain, then how can we hope to use it to make predictions about regularities in art?
We are, indeed, woefully ignorant of the brain, but we can make progress in explaining art. Here is the fundamental insight I believe we need: the arts have been culturally selected over time to be a “good fit” for our brain, and our brain has been naturally selected over time to be a good fit to nature …so, perhaps the arts have come to be shaped like nature, exactly the shape our brain came to be highly efficient at processing. For example, perhaps music has been culturally selected to be structured like some natural class of stimuli, a class of stimuli our auditory system evolved via natural selection to process. (See Figure 1.)
If the arts are as I describe just above – selected to harness our brains by mimicking nature – then we can pursue the origins of art without having to crack open the brain. We can, instead, focus our attention on the regularities found in nature, the regularities which our brains evolved to competently process. I’ll suggest in a moment that we can do exactly this, and give examples where I have been successful at doing so. But first let’s deal with a potential problem…
Don’t brains have quirks? And if so, couldn’t the arts tap into our quirks, and then no analysis of nature would help explain the arts? What do I mean by a quirk? Brains possess mechanisms selected to work well when the inputs to the mechanisms are natural. What happens when the inputs are not natural? That is, what happens when the inputs are of a kind the mechanism was not selected to accommodate? The answer is, “Who knows?!” The mechanism never was selected to accommodate non-natural inputs, and so the mechanism may carry out some arbitrary, inane computation.
To grasp what the mechanism does on these non-natural inputs, we may have no choice but to crack open the hardware and figure out how it actually works. If the arts tended to be culturally selected to tap into the brain’s quirks, then nature wouldn’t help us, and we’d be bound to the brain’s enigmatic details in our grasp of the arts.
There is, however, a good reason to suspect that cultural selection won’t try to harness the brain’s quirks, and the reason is this: quirks are stupid. When your brain mechanisms are running as nature “intended,” they are exceedingly sophisticated machines. When they are run on inputs not in their design specs, however, the behavior of the brain’s mechanisms (now quirks) are typically not intelligent at all. For example, the plastic fork in front of me is well-designed for muffin eating, and although I can comb my hair with it, it is a terribly designed comb. The quirks will usually be embarrassing in their lack of sophistication for any task. …because they weren’t designed for any task. And that’s fundamentally why we expect the arts to have culturally been selected to tap into our functional brain mechanisms, running roughly as nature “intended”.
If we can set aside the quirks, then we can side-step the brain in our attempt to grasp the origins of the arts. If I am correct about this, we can remove the most complicated object in the universe from the art equation!
With the brain put on the shelf, the goal is, instead, to analyze nature, and use it to explain the structure of the arts. Is this really possible? And isn’t nature just as complicated as the brain, or, at any rate, sufficiently complicated that we’re headed for despair?
No. Nature is filled with simple regularities, many of them having physics or mathematical foundations. And although it may not be trivial to discover them, our hopes should be far greater than our hopes for unraveling the brain’s mechanisms. Our presumption, then, is that our brains evolved to “know” these regularities of nature, and if we, as scientists, can unravel the regularities, we have thereby unraveled the brain’s competencies. What regularities from nature am I referring to? For the remainder of this piece, I’ll give you three brief examples from my research. Only one is explictly about the arts, but all three concern the cultural evolution of human artifacts, and how they harness our brains via mimicking nature. (See Figure 2.)
The first concerns the origins of writing, and why letters are shaped as they are. Our visual systems evolved for more than a hundred million years to be highly competent at visually processing natural scenes. One of the most central features of these natural scenes was simply this: they are filled with opaque objects strewn about. And that is enough to lead to visual regularities in nature. For example, there are three junction types having two contours – L, T and X. Ls happen at many object corners, Ts when one edge goes behind an object, and these two are accordingly common in natural scenes. X, however, is rare in natural scenes.
Matching nature, letter shapes with L and T topologies are also common across languages, but X topologies rare. More generally, the shapes found more commonly in natural scenes are those found more commonly in writing systems. [See this SB piece for more: http://www.scientificblogging.com/mark_changizi/topography_language ]
The second concerns the origins of speech, and why speech sounds as it does. Our auditory systems evolved for tens of millions of years to be highly efficient at processing natural sounds.
Although nature consists of lots of sounds, one of the most fundamental categories of sound is this: solid-object events. Events among solid objects, it turns out, have rich regularities that one can work out. For starters, there are primarily three kinds of sound among solid objects: hits, slides and rings, the latter occurring as periodic vibrations of objects that have been involved in a physical interaction (namely a hit or a slide). Just as hit, slides and rings are the fundamental atoms of solid-object physical events, speech is built out of hits, slides and rings – called plosives, fricatives and sonorants. For another starter example, just as solid-object events consist of a physical interaction (hit or slide) followed by the resultant ring, the most fundamental simple structure across language is the syllable, most commonly of the CV, or consonant-sonorant form. More generally, and as I describe in my upcoming book, Harnessed: How Language and Music Mimicked Nature and Transformed Ape to Man (2011), spoken languages share a wide variety of solid-object event signatures.
Written and spoken language look and sound like fundamental aspects of nature: opaque objects strewn about and solid-objects interacting with one another, respectively. Writing thereby harnesses our visual object-recognition mechanisms, and speech harnesses our event-recognition mechanisms. Neither opaque objects nor solid objects are especially evocative sources in nature, and that’s why the look of most writing and the sound of most speech is not evocative. [See this SciAm piece for more: http://www.scientificamerican.com/article.cfm?id=why-does-music-make-us-fe ]
Music – the third cultural production I have addressed with a nature-harnessing approach – is astoundingly evocative. What kind of story could I give here? A nature-harnessing theory would have to posit a class of natural auditory stimuli that music has culturally evolved to mimic, but haven’t I already dealt with nature’s sounds in my story for speech? In addition to general event recognition systems, we probably possess auditory mechanisms specifically designed for the recognition of human behavior. Human gait, I have argued, has signature patterns found in the regularities of rhythm. Doppler shifts of movers have regularities that one can work out, and these regularities are found in music’s melodic contours. And loudness modulations due to proximity predict how loudness is used in music.
These results are described in my upcoming book, Harnessed. For example, just as faster movers have a greater range of pitches from their directed-toward-you high pitch to their directed-away-from-you low pitch, faster tempo music tends to use a wider range of pitches for its melody. [See this SB piece for more: http://www.scientificblogging.com/mark_changizi/music_sounds_moving_people ]
Many other aspects of the arts are potentially treatable in a similar fashion. For example, color vision, I have argued is optimized for detecting subtle spectral shifts in other people’s skin, indicating modulations in their emotion, mood or state. That is, color vision is a sense designed for the emotions of other people, and it is possible to understand the meanings of colors on this basis, e.g., red is strong because oxygenated hemoglobin is required for skin to display it. The visual arts are expected to have harnessed our brain’s color mechanisms via using colors as found in nature, namely principally as found on skin. Again, the strategy is to understand art without having to unravel the brain’s mechanisms.
One of the morals I want to convey is that you don’t have to be a neuroscientist to take a brain-based approach to art. The brain’s competencies can be ferreted out without going inside, by carving nature at its joints, just the joints the brain evolved to carve at. One can then search for signs of nature in the structure of the arts. My hope is that via the progress I have made for writing, speech and music, others will be motivated to take up the strategy for grappling with all facets of the arts, and cultural artifacts more generally.
This first appeared on March 4, 2010, as a feature at ScientificBlogging.com.