Information in difference

increasingly unclear
10 min readMar 30, 2020

How we make meaning from patterns that perpetuate.

This is the second of a series of articles about information (here is the first one). I am collecting all my notes from years of teaching into these informal articles, and updating them as new information comes in and as my views change.

“out there [in the world], there is no light and no colour, there are only electromagnetic waves…there is no sound and no music, there are only periodic variations of the air pressure…there is no heat and no cold, there are only moving molecules with more or less mean kinetic energy” Heinz Von Foerster

What does it mean to talk about waves? Look to the sea, of course — ocean waves roll in and out of the shore, oscillating in cycles. The rate at which they rise and fall, come and go varies, depending on location, the topography of the land beneath them, and especially environmental conditions — big waves are generally driven by high winds and weather conditions that may be some distance away. The waves, then, tell us something about those distant conditions — they carry information. We glean such information from the size and rate of the waves. It’s not in one wave (though a single wave can carry important information, such as in a tsunami — waves indeed carry energy), or in the composition of water and substances within; no, it’s in the rise and fall of a sequence of waves collectively, over a given time period. The height of the waves (their amplitude) is a measure from the top of a cresting wave to the bottom of a trough, and the rate they come in (their frequency) is measured from when the top of one wave passes a certain point until the next one does. At some level, then, information is related to oscillations — to change over time, difference and sameness.

This is basically what Claude Shannon said, in formulating his mathematical model of communication, now known as information theory. His example was quite different, however — the alphabet. He looked at the frequency of each letter as it appears in the English language, so ‘E’ appearing most frequently, for example. Then, in a striking series of diagrammatic examples, he reconstructed the language based on this frequency of occurrence — the letter ‘U,’ for oexample, appears most often after ‘Q’ and so on. Groups of two, then three letters, to predict words and sentences. Then he asked, How much do we need to make sense, how much can we fill in the gaps? Cn yu rd ths? From the waves carrying meteorological information to the pages and screens and voices carrying words, can we read the signal in the midst of noise? Do we have the tools to decode the motions, the scribblings and mutterings into meaning?

A sea of sameness, of flatness and consistency, contains little information. It’s therefore easy to describe. More information comes with change, and difference. It’s a measure of how difficult something is to describe. As the anthropologist Gregory Bateson said, information is a difference that makes a difference.

Pickup & persistence

This is how we learn about our environment. The psychologist James J. Gibson described what he called the ambient optic array, a structured arrangement of light that provides visual information about the environment [1]. This departed from Shannon’s communication model. In human perception, according to Gibson, “The world does not speak to the observer. Words and pictures convey information, carry it, or transmit it, but the information in the sea of energy around each of us, luminous or mechanical or chemical energy, is not conveyed. It is simply there.”

Where physicists use light as a measure of information (for example when particles collide, as described in the previous article), Gibson is more concerned with the human scale: “Radiant light has no structure; ambient light has structure…Radiant light comes from atoms and returns to atoms; ambient light depends upon an environment of surfaces. Radiant light is energy; ambient light can be information.” Specifically, it is the differences in light bouncing off of different surfaces that constitutes information.

In Gibson’s theory, we simply pick up such information, primarily in terms of the features of the environment that persist versus those that change. The process of such information pickup depends not on the processing of input information through passive sensors that are separate from a processor, as in a computer, but on an active and unified perceptual system based on “the activities of looking, listening, touching, tasting, or sniffing.” He continues:

The five perceptual systems correspond to five modes of overt attention. have overlapping functions, and they are all more or less subordinated to an overall orienting system. A system has organs, whereas a sense has receptors. A system can orient, explore, investigate, adjust, optimize, resonate, extract, and come to an equilibrium, whereas a sense cannot.

Gibson’s ambient optic array is like Von Foerster’s sea of energy described above. Not just light that radiates off of a surface and is reflected to our eyes, it’s the collective pattern of reflections and refractions that together describe a particular environment. We perceive light directly, according to Gibson, and mostly unconsciously — there is a direct interaction between light, observer and environment. Information here is the perception of variations in the light that reaches us. “Light not only is transmitted but also reverberates,” he says, “that is, bounces back and forth between surfaces at enormous velocity and reaches a sort of steady state.” So too with sound and other things we sense—sound constitutes a “vibratory event,” chemical diffusion affords smelling and tasting.

A message sent by two bells of different notes carries more information than from a single one [2]. But when it comes to decoding the information — finding the signal in the noise, the meaning in bells’ message — we again come up against individual differences in perception and interpretation. Something as simple as changing your rate of breathing can change your perceptual experience. So an important corollary to Bateson’s dictum that information is a difference that makes a difference is in differences between individual perceivers. What I perceive as difference, you may not. Or you may see something different — the colour red that you see is probably different from the one I do.

What’s more, each of us not only perceives our environment, we’re a part of it. “The observer,” says Gibson, “being an organism, exchanges energy with the environment by respiration, food consumption, and behavior. A very small fraction of this ambient sea of energy constitutes stimulation and provides information.” But this fraction of the sea of energy, he says, is crucial for survival, because it contains information for things at a distance, as in those waves rolling in to the beach.

The converse of information as difference is in similarity — when we form categories to combine things. Classification facilitates communication. We perceive differently, but to communicate, we need to agree at some level on the meaning of words, or else all is noise and no signal. As P.W. Anderson said, we need to at least start with some kind of reductionism [see the previous article in this series].

Information in formations

Gibson describes the environment in which we live (indeed where any organism lives) as composed of a medium, substances and surfaces. Light, for example from the sun, travels through a medium (for us, air), and when it reaches a surface, is absorbed, reflected or scattered. This arrives at the human eye as a particular pattern.

Information is patterns of organisation, where meaning resides in the connections and diffractions between things. The physicist and philosopher Karen Barad eloquently appends Bateson’s dictum: information is in patterns of difference that make a difference. If we think of Gibson’s ambient optic array as light not simply bouncing off of a surface to reach our eye like a laser light, but like throwing a stone in a pond, causing waves to ripple outward in all directions, then we can look for information where and how these waves interact — some waves meet each other at the crest, some at the trough and everywhere in between, building on each other or cancelling each other out.

Humans, too, can be seen as patterns that perpetuate themselves [3], in turn creating and disseminating more patterns. A good example is the ancient technique of the memory palace, or theatre of memory [4]. When most human knowledge was held in the head and transmitted orally, an effective way to remember and recall the gods, say, or one’s prayers, was to imagine a space in which memorable images are arranged in a particular sequence, then assign bits of information to each one.

My wording is deliberate: in a computer, bits of information are assigned to specific physical locations in memory. Von Foerster, quoted at the top of this article, defines cognition as the computation of reality. The difference is that in a computer, a device based on numbers and built for calculation, these spaces of memory are numeric addresses, like apartments in a skyscraper. Humans, on the other hand, remember in images and emotions — just think of your most memorable moments.

There’s an even bigger difference. When a computer recalls something from memory, accuracy is important, grounded in Shannon’s approach to transmitting data from one place to another without loss. Communication within and between computers relies on sending and receiving an accurate signal amidst any noise. Constructing bytes from bits at either end means recognising patterns and checking for errors.

The memory palace technique does something similar, by associating information with particular (imagined) locations and sequences. This is a way of building a kind of pattern, which acts as a kind of carrier wave for knowledge, according to the computer scientist Philip Armour:

The ’strongest’ of these patterns are our most conscious and intentional thoughts — those that are strong enough to be accessible to and recognised by the ‘consciousness’ pattern. Our habits might also be strong patterns, thought we may be quite unaware of them. Some patterns resemble other patterns and these similarities are themselves signals. Some signals are so weak they are almost gone. When they weaken further or are completely buried in other patterns they will be gone and we will have ‘forgotten.’ Patterns can be made stronger by continually revisiting them as happens when we practice playing a musical instrument. Patterns that are very similar to others may become conflated over time and memories merge.

The memory palace, in this view, helps to reconstruct memories accurately by exploiting our tendency to remember images as a kind of pattern reconstruction. But images, or any other pattern of information, are not stored in specific locations in the brain, as in computer memory. Neuroscientists and psychologists know that the act of recall always changes a memory. If we think back to Barad’s analogy of diffraction patterns, we can think of a single memory as a pebble in a pond, but we always read its ripples through others. Have you ever tried to retrieve a stone thrown into a pond?

What is a single memory anyway? To recall a single number or a name, we can fairly easily emulate a computer and recall without error. What about an image? We can think of it as a particular pattern — a scene reaches our eyes in Gibson’s ambient optic array, but the mind isn’t a topological landscape.

We can, however, observe the brain at work. Neuroscientists can visualise waves of neutrons firing in various parts of the brain, and through this we know that perceiving or recalling a single image involves the whole brain, not single locations. Using computers, scientists can now observe and reconstruct actual images as they are recalled. The results, as of this writing, are fuzzy and imprecise, suggesting rough shapes and blocks of colours. But they almost recognisable [5].

When we observe the brain in this way, it is always at work and always in motion. Scientists use the latest imaging technologies and machine learning techniques, but these tools, in turn, affect how they interpret the phenomenon under study. As quantum physicists such as Barad know well, when we observe any system, whether of particles or people interacting, we simultaneously affect it and are part of it. The act of observation, therefore, is not only subjective but takes on an ethical dimension — how do we feel about what we observe? Again, I choose my words carefully: in saying our tools affect our interpretations, by ‘affect’ I refer not only to effects but emotions, and ethics.

At the neural level, Armour says, “It is likely that some of these patterns are functional rather than factual.” By enabling actions rather than storing data, we can think of these as verbs, not nouns.

At the social level, like throwing a stone into a pond, our actions have effects that ripple outwards, interacting with the actions of others. The information, then, that we send out and receive, is diffracted through our perspective and those of others — not just in difference but in patterns of difference that makes a difference. Voices may be silenced like stones sunk in a pond, but the ripples they send out may yet reach us in unexpected ways, places and times, diffracted through media, materials, minds and bodies.

Paul Miller calls these ripples “the algorithms of everyday life.” As he says, “patterns ain’t just about bein’ digital. They are global. They are universal. They are rhythms that hold everything we know and can understand together.” [6]

Go to the next article in this series.


  1. Gibson, J. J. (1979) The Ecological Approach to Visual Perception. (Routledge)
  2. Gleick, J. (2011) The Information: A History, a Theory, a Flood. (Pantheon)
  3. Weiner, N. (1954) The Human Use of Human Beings. (Da Capo)
  4. Yates, F. (1966) The Art of Memory. (Routledge)
  5. Shen Guohua, Dwivedi Kshitij, Majima Kei, Horikawa Tomoyasu and Kamitani Yukiyasu, “End-to-End Deep Image Reconstruction From Human Brain Activity,” Frontiers in Computational Neuroscience 13 (2019) DOI=10.3389/fncom.2019.00021
  6. Miller, P.D. (2004) Rhythm Science. (Mediawork/MIT Press)