Archive for the ‘Illusions’ Category

I spoke at TED in NYC in December of 2012 on my grand unified theory of illusions. For more information, see my earlier book, The Vision Revolution. (For those with a strong stomach, see this journal article.)


Mark Changizi is Director of Human Cognition at 2AI, a managing director of O2Amp, and the author of HARNESSED: How Language and Music Mimicked Nature and Transformed Ape to Man and THE VISION REVOLUTION. He is finishing up his new book, HUMAN 3.0, a novel about our human future, and working on his next non-fiction book, FORCE OF EMOTION.

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You’ve heard that space is curved – that’s gravity. You’ve also been told that you cannot really understand curved space. Sure, you can come to know curvy mathematics by studying general relativity or differential geometry, but you cannot grasp curved space in your bones…for the obvious reason that, in our everyday human-level world, space is flat, and so we have a brain for thinking flat.

Or, at least, that’s what they say.

But there is at least one variety of curvy mathematics that your brain comprehends so completely that you don’t even know you know it. It concerns your visual field, and your innate understanding of the directions from you to all the objects in your environment.

In thinking about your visual field, it is best to imagine a sphere around your head, recording the directions to all objects in one’s environment. Call it the “projection sphere,” since it records in which directions objects project light toward us.

So, if you are standing in front of a row of six vertical poles, then they will project onto your sphere as shown below. In this figure, one imagines that you, the observer, are at the center of the sphere, looking in the direction of the cross.

Consider now the way these poles project…

First, notice that each pole appears straight in your visual field. They are not straight in the figure above, but remember that the observer in the figure is at the center of the sphere looking out. Each pole is straight on this projection sphere — and thus in your visual field — because each is what is called a “great circle,” extending in this case from the bottom to the top of the sphere like lines of longitude.

Second, observe that the poles are parallel to one another at the equator.

Yet, despite being straight lines that are parallel to one another, they intersect! Namely, the lines intersect at the top and bottom of the sphere.

Can this really be?

It can really be, and it is possible because of the non-Euclidean nature of the geometry of the visual field.   The geometry that is appropriate for the visual field is the surface of a projection sphere, and the surface of a sphere is not flat / Euclidean, but, well, spherical.

There are three main kinds of geometry for space: elliptical (including spherical), Euclidean (or flat), and hyperbolic.  How does one tell them apart? One way is to simply measure the sum of the angles in a square drawn in that space.

In Euclidean geometry, the sum of the angles in a square is 360 degrees. But for elliptical geometry the sum adds up to more than 360 degrees. In hyperbolic geometries, on the other hand, the sum comes to less than 360 degrees.  Back to the visual field, then, let’s “draw” a square on it and sum up its angles.

The figure above shows a square in your visual field. Why does it count as a square? Because (i) it has four sides, (ii) each side is a straight line (being part of a great circle), (iii) the lines are the same length, and (iv) the four angles are the same.

Although it is a square, notice that each of its angles is larger than 90 degrees, and thus the square has a sum of angles greater than 360 degrees.  The visual field is therefore elliptical, and spherical in particular.

One does not need to examine figures like those above to grasp this. If you are inside a rectangular room at this moment, look up at the ceiling. The ceiling projects toward you as a four-sided figure. Namely, you perceive its four edges to project as straight lines. Now, ask yourself what each of its projected angles is. Each of its angles projects toward you at greater than 90 degrees (a corner would only project as exactly 90 degrees if you stood directly under it).

Thus, you are perceiving a figure with four straight sides, and where the sum of the angles is greater than 360 degrees.

Your visual field conforms to an elliptical geometry!

(The perception I am referring to is your perception of the projection, not your perception of the objective properties. That is, you will also perceive the ceiling to objectively, or distally, be a rectangle, each angle having 90 degrees. Your perception of the objective properties of the ceiling is Euclidean.)

It is often said that non-Euclidean geometry, the kind needed to understand general relativity, is beyond our everyday experience, since we think of the world in a Euclidean manner. While we may think in a Euclidean manner for our perception of the objective lines and angles, our perception of projective properties — i.e., the directions from us to the world around us — is manifestly non-Euclidean, namely spherical.

We do have tremendous experience with non-Euclidean geometry, it is just that we have not consciously noticed it. But once one consciously notices it, it is possible to pay more attention to it, and one then sees examples of non-Euclidean geometry at every glance.


This piece was adapted from my book, The Brain from 25000 Feet (Kluwer), and first appeared so adapted at Sept 30, 2010, in Science 2.0.


Mark Changizi is Director of Human Cognition at 2AI, and the author of The Vision Revolution (Benbella Books) and the upcoming book Harnessed: How Language and Music Mimicked Nature and Transformed Ape to Man (Benbella Books).

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In the world of Harry Potter the one thing you don’t want to be is a “muggle”. Muggles are the regular folk lacking magical powers, and discrimination and prejudice against them is rampant. Muggles are not merely unable to attend Hogwarts School of Witchcraft and Wizardry, but are kept ignorant of the school’s very existence. In fact, muggles are kept in the dark about the entire cryptic world of magic altogether. The sorcerers struggle for the heart and soul of humankind’s freedom as the muggle folk blindly graze. Muggles are mutton.

In our world we are all muggles, but at least we can be content knowing we’re not missing out on all the cool stuff.

…because in the real world there’s no magic.

Or is there?

There may not be “true” magic in our world, but we have illusions. Gobs of them. And who’s to say illusions are not magic? Perhaps in Harry Potter’s world the spells only seem like magic because the natural principles underlying them are not well understood. And maybe sorcerers like Harry Potter have an inborn knack for employing these natural principles.

Illusions, I submit, are examples of real world magic. And the purveyors of illusions – artists and cognitive scientists – are our world’s sorcerers.

But not all real-world sorcery is made alike. Just as the spells taught at Hogwarts vary in potency, the illusions of our world vary in potency.

The three tiers of magic. 

The three tiers of illusion potency.

And Chris Chabris and Dan Simons, the authors of the new book The Invisible Gorilla, are among the most powerful of the real-world sorcerers. Their book is an engaging romp through a variety of cognitive illusions, with the theme that our intuitions often fail us. The book is written so well it would make Gladwell envious (and maybe a wee bit angry), and yet we must remember that these are the scientists themselves discussing their own discoveries and experiments. They have somehow mastered both the science and its communication.But it’s their master sorcery I wish to focus on here. Their illusions are at the top tier of real-world magic.

To appreciate just how powerful is their sorcery, let’s start at the lowest level of real world sorcery, and build up to “the invisible gorilla.”

The first level of visual sorcery includes illusions such as the Ames Room, the rotating mask illusion, and the uphill marbles illusion. Such illusions are exciting, but they are conceptually simple parlor tricks. These entry-level visual illusions rely upon ambiguity, or the fact that the light coming toward the eye does not uniquely determine what the world is like out there. The strategy is to devise an unusual scene that happens to look like something more common, and then let the hilarity ensue. For the Ames Room the true scene is a radically geometrically distorted room that happens to look like a normal square room from the right viewpoint; and the hilarity ensues when people stand in the two corners, in which case one person appears to be a giant relative to the other. Other examples of this first level are illusions like the Necker cube, rabbit/duck, vase/face and old-hag/young-maiden, where the strategy is to devise an image that could equally be due to two different scenes.

Why are these ambiguity illusions mere muggle magic? For starters, it is entirely unmysterious why the illusions occur. Magic needs to be enigmatic! And there’s a deeper reason why these ambiguity illusions don’t count as high sorcery: in a sense they are not illusions at all. One’s brain elicits a perception of its best guess about what is out there given the light the eye received. It just happens that the light sent to the eye was rigged so that the best guess would fail.

The more potent tiers of magic are not of this “ambiguity” kind. In “real” illusions the brain creates a perception that is not even consistent with the light that was sent to the eye! That is, for the higher tiers of sorcery, of all the scenes that could potentially have sent that light to your eye, your brain guesses a scene not among those! Now that’s an illusion!

The second level of visual sorcery includes the geometrical ones like the Hering illusion, where the two vertical lines project nearly parallel to one another in your eye, but you perceive them as bowing away from one another at the center. That’s a perception that is not consistent with the stimulus, and that makes it a real illusion, and a real mystery. These stimuli have the power of making people perceive something that couldn’t possibly exist out there. In my research I have argued that illusions of this kind are explained by your brain attempting to anticipate what the scene will look like when the perception is finally constructed a tenth of a second later, in order to overcome neural delays. In short, I have argued that the brain has mechanisms for anticipating the near future so as to thereby perceive the present. Here’s a short, four-slide introduction to the idea.

And now we get to level-3 sorcery, the dark and gorilla-ey stuff. Whereas the level-2 illusions involve perceiving angles a few degrees off, level-3 illusions can be radically stronger. …like invisible gorillas. In the real stimulus, as everyone knows, Chabris and Simons have a movie of basketball players, and during the video a person dressed up as a gorilla walks in, bangs on her chest, and walks off. For observers asked to count the number of ball passes, about half fail to see the gorilla. Their visual system has “gorilla all over it,” but they don’t see it because they don’t attend to it.

Now that’s magic. And not only is the invisible-gorilla illusion a radically stronger illusion than level-2 illusions, but it is also as or more enigmatic: although it is somewhat plausible that a lack of attention will lead to missed stuff, why shouldn’t the brain have bottom-up mechanisms that shift attention to large dangerous beasts that approach you menacingly?!

The sorcery of Chabris and Simons is so potent one suspects they’ve been dabbling with the darker forces of he who shall not be named. It can make those of us involved with level-2 optical illusions cower with respect at these superior sorcerers. It certainly made a muggle out of me.


This originally appeared June 29, 2010, at Psychology Today.

Mark Changizi is Director of Human Cognition at 2AI, and the author of The Vision Revolution (Benbella Books, 2009) and the upcoming book Harnessed: How Language and Music Mimicked Nature and Transformed Ape to Man (Benbella Books, 2011).

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Why do we see illusions? I talk about it with Jorge Salazar at EarthSky. See the podcast, at about 4 minutes in.



Mark Changizi is Professor of Human Cognition at 2AI, and the author of The Vision Revolution (Benbella Books) and the upcoming book Harnessed: How Language and Music Mimicked Nature and Transformed Ape to Man (Benbella Books).

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Our everyday visual perceptions rely upon unfathomably complex computations carried out by tens of billions of neurons across over half our cortex. In spite of this, it does not “feel” like work to see. Our cognitive powers are, in stark contrast, “slow and painful,” and we have great trouble with embarrassingly simple logic tasks.

Might it be possible to harness our visual computational powers for other tasks, perhaps for tasks cognition finds difficult? I have recently begun such a research program with the goal of devising ways of converting digital logic circuits into visual stimuli – “visual circuits” – which, when presented to the eye, “tricks” the visual system into carrying out the digital logic computation and generating a perception that amounts to the “output” of the computation. That is, the technique amounts to turning our visual system into a programmable computer.

This is not the first time scientists have attempted to make use of biological computation; this was first tried with DNA, tapping into the computational prowess inside cells. My research is the second kind of biological harnessing that has been attempted for computation, aiming to commandeer our very brains. Because this new kind of computation – “visual computation,” or “eye computation” – is carried out in people’s brains, its outputs can be directly and immediately fed into humans, making for true human-computer interaction, all in one head!

Why with the Eye?

People are notoriously poor reasoners, whether in the probabilistic domain or logical domain, something I have also personally witnessed when teaching logic and computer science. That’s one of the reasons we all appreciate computers. Although our reasoning and logic powers are poor, we are all walking around with computers in our heads that are far more powerful in many respects to any computing device ever built, or likely to be built in the foreseeable future.

There are several reasons why the visual modality is a promising one for biological computation.  First, the computations underlying our elicited perceptions are extraordinarily powerful, our visual system taking up about half our cortex. Second, our eyes and visual system are capable of inputting and processing large amounts of information in a short period of time. Third, in spite of the billions of  calculations carried out at each glance, it feels effortless to perceive. Fourth, visual neuroscience is by far the most well understood subfield of neuroscience, both at the level of neurobiological mechanisms and perceptual phenomenology. Finally, visual stimuli are a much easier modality for presenting stimuli – e.g., on paper – whereas audition, say, requires a computer or play-back device.

The idea of tapping into vision for computation is not new. Mathematical notation itself is a visualization technique and aid to cognition. The invention of writing, more generally, moved language from the auditory modality to a visual one, and enhanced our reasoning capabilities.

Visualization within science in the form of graphs, charts, etc. has been crucial for understanding complex phenomena. Visualization has also been employed for over two hundred years in logic.

For example, Leonhard Euler invented diagrams to visually represent the contingent relationships among concepts, John Venn utilized visual diagrams to show the logical structure relating concepts, and Charles Sanders Peirce invented existential graphs (Figure 1a) for depicting logical formulae. For digital logic there has long been a standard visual notation scheme that depicts each logical formula as if it were a physical electrical circuit, as shown in Figure 1b. Figure 1c shows a preview of the kind of visual circuit I have been designing; we’ll see more of these later.

Pierce  contingent relationships among concepts

Vision has, then, long been harnessed for computation, in particular with the aim of facilitating human reasoning.

The “visual computation” technology I am designing is something altogether different. The aim is not simply to use visual stimuli to aid one’s cognitive computations. Rather, the aim is to get our visual system itself to obediently carry out the computations.

The broad strategy is to visually represent a computer program in such a way that, when one looks at the visual representation, one’s visual system naturally responds by carrying out the computation and generating a perception that encodes the appropriate output to the computation. That is, there would be a special kind of image that amounts to “visual software,” software our “visual hardware” (or brain) computes, and computes in such a way that the output can be “read off” the elicited perception.

Ideally, we would be able to glance at a complex visual stimulus—the program with inputs—and our visual system would automatically and effortlessly generate a perception that would inform us of the ouput of the computation. Visual stimuli like this would not only amount to a novel and useful visual notation, but would actually trick our visual systems into doing our work for us. Other visual notation systems for logic and computation – such as Charles Peirce’s “existential logics” or standard digital circuit notation (see Figure 1a and 1b above) – cannot do this.

Escher Circuits
In my attempt to hack into our visual system and program it as I wish, I have experimented with hundreds of varieties of visual circuit instantiations, for much of that time with little success.

Figure 2 illustrates some of the broad classes of visual circuit types that failed for any of a number of reasons. Stimuli that were not successful include: (i) cases where the bistability was figure-ground (and for these circuits a large problem was that the “wire” was not “insulated,” and would quickly leak and spread everywhere in the circuit), (ii) some stimuli looked like pipes and tubes (but although NOT was easy to achieve, AND and OR were not even conceivably computed), (iii) some kinds of circuits tried to affect the probable illumination direction, thereby modulating the perceived convexity of a bump/crater, (iv) some utilized dynamic stimuli with dots alternating positions, with ambiguous motion signals, (v) others had a similar idea, but for grouping ambiguously grouped pairs of objects, and (vi) color spreading was utilized in some attempts.

failed visual  circuits for computer logic

Finally, though, I found a variety of visual circuit that (kind of) works. The design relies on depth ambiguity, and was the first design that enabled perceptual AND and OR operations, as well as satisfying the other early constraints for the simplest digital visual circuits. Because this variety of visual circuit can often look vaguely Escherian, I call them Escher circuits.
To understand Escher circuits, we must start with the most fundamental part in them: wire.

Wire: Circuits need wire in order to transmit signals to different parts of the circuit, and an example case of “visual wire” is shown on the left in Figure 3. It is bistable, and can be perceived either as tilted away (0) or tilted toward you (1). Stimuli of this sort serve as wire because your perception of its tilt at the top propagates all the way down it to the bottom. This kind of stimulus also serves as insulated wire, because state changes tend to be confined to the wire itself. Many circuit varieties I experimented with before the current variety suffered from leaky wires, where the state would spread across the page: for example, this was a key problem when trying to use figure-ground perceptual ambiguity for digital state. In this style of Escher circuit, wire has a canonical form, directed down and to the left as in the orientation shown at the input and output of the wire on the left in Figure 3.

Wire can also be bent as in the case shown, which – with the increased junction information – can make the perception of depth more pronounced and stable. But these circuits are designed so that any such bends must eventually “unbend” when being input into another component of the circuit. This feature of visual circuits is important in understanding the design difficulties in building a NOT gate, something I’ll discuss in a moment.

wire escher circuits

Inputs: An input to an Escher visual circuit is an unambiguous cue to the tilt at that part of the circuit. Here I utilize simple unambiguous boxes as inputs, as shown in Figure 3 on the middle and right. One advantage to inputs of this kind is that differential depth cues lead to pop out: in larger circuits there will be many inputs, and it will be crucial for the pattern of tilt-towards and tilt-aways – i.e., the binary input – to stand out as a perceptible pattern so that it can induce the computations in the circuit.

escher inputs not gate

Negation: NOT gates are crucial for digital circuit computations, inverting the signal from a 0 to a 1 or vice versa. Figure 4a shows one kind of visual NOT gate for Escher circuits. It begins as a special kind of wire—roughly a wire-frame box—which undergoes a “break” below it. The portion of wire below the break tends to be perceived as having the opposite tilt to that above the “break.” The curvy portion below it is required here in order to bring the wire back into the down-and-leftward canonical orientation for wire in these circuits. Another variety of NOT gate is shown in Figure 4b, this one relying on an ambiguous prism-like shape to correct the circuit orientiation, or handedness. A third type is inside the circuit shown in Figure 6.

escher circuit  negation and or gate

Disjunction and conjunction: Escher circuits allow ORs and ANDs as shown in Figure 5. The visual OR gate in Figure 5a is designed with transparency cues so that the tilted-toward-you, or 1, interpretation is favored, and tends to be overridden only when both inputs are 0s. A similar idea works for an AND gate, but with a distinct kind of transparency cue. That is, the OR and AND gates are designed so that, without inputs, 1 and 0 output interpretations are favored, respectively.

These gates – NOT, AND, and OR – are sufficiently powerful that any digital circuit can, in principle, be built from them. (In fact, {NOT, AND} and {NOT, OR} are each universal.) In the circuit shown in Figure 7b are two NAND gates (relying on similar transparency cue tricks as for OR and AND), and a NAND gate is, by itself, universal. Figure 6 shows an example larger circuit, an exclusive-OR (XOR).

escher circuit XOR

Most of the interesting computations possible with digital circuits require feedback, and Figure 7 gives two examples, including a simple variety of flip-flop for memory storage.

escher circuit  visual circuit feedback

What’s the Point?

Why do any of this? I can imagine a variety of possible long-term benefits (some of them quite fantastic).

Enhanced computation: One general potential payoff concerns the possibility that some programs could be run more quickly on an “eye computer” than on an electronic computer. These would be programs that critically rely on the visual system’s specialized “GPU” (graphical processing units), something unparalleled by computational vision algorithms. This is analogous to the original hopes for DNA computation.

Computation that interacts with the brain: DNA computation did not end up useful for carrying out computations faster than electronic circuits. Instead, it was realized that the advantage of molecular computation was that it allowed the direct communication and interaction with the cell biology. Analogously, whether or not eye computation can ever be employed to carry out computations more efficiently than on an electronic computer, the benefit may be that visual circuits can directly interact with the neural machinery – because the neural machinery is the computer here.

State-dependent perceptions in static stimuli:
One of the directions of interaction can be from brain to computation, where different observers – having different brain states – may react differently to a certain visual circuit. For example, one can imagine a circuit component whose perceptual resolution is modulated by the observer’s, say, thirst (actually, there are such stimuli, something from 2001 research of mine). The visual circuit would be designed to communicate (via its perceptual resolution) something relevant for thirsty observers when thirsty, and something else for non-thirsty observers.

Diagnostic Rorschach-like tests: One of the hopes of molecular computation is to have molecular computers that can interact with cells, and whose output will depend upon the state of the cell. In this way, molecular computation hopes to be a diagnostic tool. Similarly, eye circuits may potentially have value as a diagnostic tool for neurology and psychiatry: the patient reports the perceptual output to the doctor, and this output is diagnostic about which condition the patient likely has. Like a Rorschach inkblot test, eye computation relies upon ambiguity; but unlike Rorschach tests, visual circuits carry out specific algorithms, and can be explicitly designed.

Treatment: The other direction in the brain-computation interaction is from computation to brain. For molecular computing, the idea would be that the molecular computer can selectively affect the cellular environment. For eye computation, the goal would be to develop circuits that can leave a particular lasting impact on the visual system and brain. Just as with flip-flop circuits it is possible to create and control a long-lasting state change (used for memory storage), visual circuits can potentially induce perceptual states, and in such a way that even once the input stimulus inducer is removed, the perceptual state remains “frozen in”. There may, then, someday be routes by which visual computation could not only diagnose psychiatric and neurological disorders, but also be involved in treatment.

Programmable perceptions: Visual computation could provide powerful tools for manipulating an observer’s perception, despite much or all of the visual stimulus remaining identical. For example, a three input visual circuit can have up to eight different perceptual states, and which perceptual state the observer is in can be controlled by modulating the three unambiguous input visual stimuli. It is also possible to program for arbitrary kinds of perceptual ambiguity: for any visual circuit without inputs, one’s perceptions will tend to settle only on the logically satisfiable solutions to the circuit, and so one can purposely engineer which of multiple perceptions are possible for a viewer.

Enhancement of human logical capabilities: Despite the presence of computers, people rely more and more on visual displays aimed at aiding our thinking. For example, digital circuit notation is used more than ever among engineers, and visual notation for mathematicians and scientists is likely to always be with us. Visual computation makes new inroads into visual displays, and radically extends its horizons so that the visual modality is not just a medium for the iteraction of vision and cognition, but lets loose the computational dynamics of the visual system. For example, rather than an engineer programming digital circuits via thinking his or her way through traditional digital circuit notation, with visual circuits the engineer’s visual system will be harnessed and allow him or her to much more quickly see – literally see – the computational steps.

Manipulation of perceptual memory: Manipulation of computer memory (in RAM) relies upon digital circuits like flip-flops, where a brief signal to one of the inputs leads to a state change (a bit flip) at one of the outputs, and this new state remains even after the brief input signal is removed. Such digital memory circuits can be implemented via visual circuits as well, allowing a short presentation of an input stimulus to cause a long-term shift in the perceptual output. Circuits like this rely upon feedback, which in the case of visual computation amounts to one’s own perceptual state being fed back to earlier parts of the circuit, affecting the perceptual state there. I foresee memory circuits such as these eventually being crucial building blocks for visual circuits, helping to maintain greater circuit perceptual stability. In the long run one hope would be that visual circuits for bit storage could be utilized as an aid to working memory, allowing us to artificially enhance our working memory limits by tapping into visual working memory.

Mnemonic device: One common technique for enhancing recall is to create imagery connected to the list of terms to be recalled. The imagery is more easily recalled than the list all by itself, and the imagery then helps one recall each of the terms. Visual computation allows something like this. The list of terms to be recalled is now the input to a visual circuit, and the visual circuit is designed so that there is a one-to-one correspondence between the possible inputs and the resultant visual circuit perceptual state. The list of terms now computationally induces a particular imagery, and the person just needs to remember the look of the induced imagery. To recall the list, the visual circuit is presented without inputs, the observer recalls the imagery, the imagery helps induce the visual circuit into the earlier perceptuo-computational state, and this state leads to perceptual states at the inputs (now empty), which can be read off one’s perception to attain the original list.

Just the Beginning

Although the Escher visual circuits I just described are a great improvement over my many earlier attempts (see Figure 2), there are serious technical difficulties to overcome.

First, the larger circuits currently appear to require – at least without training — “perceptually walking through the circuit” from the inputs downward toward the output. One does not yet immediately and holistically perceive the output. Second, the visual logic gates do not always faithfully transmit the appropriate signal at the output. For example, although AND gates tend to elicit perceptions that are AND-like, it is a tendency only, not a sure-fire physical result as in real digital circuits. Third, even if a logic gate works well, in the sense that it unambiguously and robustly cues the perception at the output, our perception can be somewhat volatile, capable of sudden Escher-like flips to the alternate state. The result is that it can be difficult to perceive one’s way through these visual circuits. And, fourth, building larger and more functionally complex circuits will require smaller and/or more specialized visual circuit components in order to fit the circuit on an image (analogous to the evolution of electronic circuits).

A major problem to overcome is how to do this while still ensuring that the visual system reacts to the circuit as intended.

The current visual circuit design is only the first step, demonstrating the basic concept. It should be thought of as analogous to the early research stages in DNA computation, an idea that was “miles away” from the ideal promise at the inception…and still is.

This first appeared on March 25, 2010, as a feature at ScientificBlogging.com.


Mark Changizi is a professor of cognitive science at Rensselaer Polytechnic Institute, and the author of The Vision Revolution (Benbella Books).

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David DiSalvo is a science writer for places like Scientific American, with his own Brainspin column at the True/Slant Network, and another column he calls Neuronarrative.

He recently interviewed me about my book, The Vision Revolution

Neuronarrative interview with me


Mark Changizi is a professor of cognitive science at Rensselaer Polytechnic Institute, and the author of The Vision Revolution (Benbella Books).

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Migraine sufferers have long complained about how their headaches worsen with bright light, and in case you ever doubted their complaints, Rami Burstein and other researchers from Harvard Medical School and the Moran Eye Center at the University of Utah recently made a giant step in understanding the light-to-headache mechanism in Nature Neuroscience. They found neurons in the rat thalamus sensitive to both light and to the dura (the membrane surrounding the brain).

More intriguing than the “how” of light and headaches is the “why”. Why should light be linked with pain mechanisms at all? Why should the retina be in the business of eliciting pain in your brain?

Upon reflection, however, we all know of occasions where looking hurts. The most obvious case is when we look at the sun. And another obvious case is when someone shines a flashlight in our eyes in the dark. In each case we are likely to respond, “Ouch!” From these real-world links between light and pain can we discern what the link may be for?

light aggravates headaches

The example of the sun may coax us into suggesting that it is the retina-scorching amount of light that hurts. However, the fact that the same kind of discomfort occurs when someone shines a flashlight in our eyes shows it is not the intrinsic amount of light that is the source of the pain. A flashlight can be so dim that we can hardly see it in daytime, and yet hurt when shone in our eyes at night. The flashlight’s beam is not scorching anything, although the pain it elicits is every bit as real.

Instead, I suggest that these light/pain phenomena are similar to pain in other domains of our life. The general role of pain is not merely to tell us that something has been damaged, but to motivate us to modify our behavior toward safer or smarter action (and to so without our having to consciously think about it). For example, subtle pain signals are constantly causing me to shift my weight as I sit here and type this, leading to healthier blood flow in my lower extremities. Our eye fixations are like fingertips, reaching out and touching things in the world; just as fingertips need a pain sense to help optimally guide their behavior, so do our eye fixations.

In our normal viewing experiences there are very often wild fluctuations in brightness in our visual field, often due to the sun or to reflections of the sun. We are typically not interested in looking at objects having this full breadth of brightnesses, but, instead, at a range of “interesting objects” at a narrower range of brightnesses. To help us best see the objects of current interest, our visual system adapts to the brightness levels among them. If we were to fixate on a part of the scene that is much brighter than these interesting objects (perhaps a spot of glare), then our eyes would begin to adapt to the new brightness level, and when we look back at the objects of interest, we will be unable to see them well.

“Eye pain” of this kind may be the principal unconscious mechanism that keeps us fixating in a smart fashion within our visual field; it is what keeps our eyes performing at their best given our interests at the time. Although this kind of mechanism is unconscious, it by no means needs to be stupid. Instead, it may be able to infer where the brightest parts of the scene are on the basis of global cues in the scene.

For example, look at the earlier photograph of the glaring sun. It feels somewhat discomforting to look at this photograph, and our eyes want to steer clear from the sun. Yet the brightest spot at the center of the sun in the photograph is no brighter than the white elsewhere on this web page which causes us no discomfort to look at. Our brain seems to be able to recognize the sun-glare-like cues in the photograph, and elicits the glare-avoidance pain mechanisms for it but not for the white elsewhere on screen.

In light of these ideas for the role of light in pain, could it be that migraine-like headaches are the normal kind of pain elicited for these light/pain mechanisms, and that the trouble for migraine sufferers is the overactivation of these usually-functional mechanisms?

This first appeared on February 26, 2010, as a feature at ScientificBlogging.com.

Mark Changizi is a professor of cognitive science at Rensselaer Polytechnic Institute, and the author of The Vision Revolution (Benbella Books).

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Noah Hutton’s online magazine Beautiful Brain burst onto the scene over the last year, filled with reporting and pieces about the intersection of the neuroscience and the arts. He recently interviewed me about The Vision Revolution, and the podcast of the interview is here.

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It’s nearing the end of American football season, with the Super Bowl fast approaching. These games involve displays of tremendous strength, agility and heart. What you may not have known is that some of the most talented players out on the field are doing it all with their eyes closed.   Literally.    The American football player Larry Fitzgerald of the Arizona Cardinals made news last year when photographers captured him catching the ball with his eyes closed. He apparently does this all the time. And it is not just Fitzgerald who does this: after just five minutes searching online I found evidence that acclaimed college wide receiver Austin Pettis of Boise State, this year’s Fiesta Bowl Champion’s, closes his eyes when catching, as seen in the photo here.

Austin Pettis Boise State
How can these athletes be the best in the world, and yet close their eyes at what would appear to be the most important moment? It is less surprising than it first seems.

Our brains are slow: it takes about a tenth of a second between the time that light lands on your eye to the time that the resultant perception occurs. That is a long time. A receiver running at 10 meters per second (or about 20 mph) moves one meter in a tenth of a second. If the receiver’s brain were to take the information at the eye and turn it directly into a perception of what the world was like, then by the time the perception occurs a tenth of a second later, that perception would be tenth-of-a-second-old news.

The receiver would be perceiving the world as it was a tenth of a second before. And because he may move a meter in that amount of time, anything that he perceives to be within one meter of passing him will have already passed him – or collided into him – by the time he perceives it. The ball may be moving faster still, maybe 30 meters per second (about 70 mph) or more, in which case it can move 3 meters in a tenth of a second.

Seeing the world a tenth of a second late is a big deal. That’s why our brains evolved strategies for overcoming this delay. Rather than attempting to build a perception of what the world was like when light hit the eye, the brain tries to figure out what the world will probably look like a tenth of a second after that time, and build a perception of that. By the time that perception (of the guessed-near-future) is generated in the brain, it is a perception of the present, because the near-future has then arrived. A lot of evidence exists suggesting that our brains have such “perceiving-the-present” mechanisms. And I have argued in my research that a great many of the famous illusions are due to these mechanisms – the brain anticipates a certain kind of dynamic change that never ends up happening (because it is just a drawing in a book, say), so one gets a misperception.

Back to catching with your eyes closed. Consider now that the perception you have at time t is actually a construction of your brain: the brain constructs that perception on the basis of evidence the eye got a tenth of a second earlier. So, to accurately perceive the world at time t, one need not actually have any light coming into the eye at time t. …so long as one had light coming in a tenth of a second earlier. Perhaps Pettis can get away with his eyes closed at the catch because his brain has already rendered the appropriate perception by that time.

Of course, when his eyes are closed at time t (the time of the catch), it means he won’t have a perception of the world a tenth of a second after the catch; but by then he’s being tackled and would only see stars anyway.

Mark Changizi is a professor of cognitive science at Rensselaer Polytechnic Institute, and the author of The Vision Revolution (Benbella Books).

This first appeared on February 1, 2010, as a feature at ScientificBlogging.com.

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Click on each slide to see it in higher resolution.

Mark Changizi is a professor of cognitive science at Rensselaer Polytechnic Institute, and the author of The Vision Revolution (Benbella Books).

For more information about my theory, see LiveScience, New York Times, BoingBoing, SciAm. The best introduction is chapter 3 of The Vision Revolution. And the journal article is here.

This first appeared on December 24, 2009, as a feature at ScientificBlogging.com.

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