Computer and communication engineers specialize in systems that transmit information encoded as electromagnetic signals. For example, a microphone generates an electric signal as someone speaks, a magnetic disk records a copy of the signal, and a speaker generates a sound wave from that signal. A radio transmitter superimposes an audio signal on a radio frequency (RF) signal so that the RF amplitude tracks the audio signal, and a receiver subtracts out the RF signal to extract the audio. Engineers must be very precise and unambiguous about how they encode representations and their intended meanings. Otherwise, the physical systems they build will not work. Computer and communication engineers settled on the bit (short for binary digit) as their primary information unit. Claude Shannon introduced the term “ bit ”(Martell & Denning, 2015).
All we care about is what all that engineering adds up to when it succeeds in transmitting signals so that they become the physical basis for constituting the perceptible, meaningful patterns of our sign and symbol systems.
So we have an essential, foundational principle: the engineering principles for E-information (signals transmission and reception) form an intentionally designed subsystem for our primary meaning systems, that is, our sign and symbol systems (language and writing, mathematics, graphics, images, sound, film/video), which can be represented as data in the computing and E-information context (more on data later). In our contemporary electronics environment, we need the knowledge provided by both semiotics (the study of human symbolic cognition and sign systems) and the engineering theory of E-information (mathematics + physics) for all information systems engineered to use sections of the electromagnetic energy spectrum (electricity, radio waves, light waves (Irvine, 2021).
In the world of information science, the meaning is often tied to the notion of representation. A principle that underlies the whole concept of computation is that one state can be represented by another state. The states need not be in the same system. To give two examples: the words you type on a keyboard can be represented by voltages and current flows inside an electronic computer; music performed by a human artist can be characterized by a pattern of silver and black dote on a DVD. When a particular representation affects you, it has meaning to you. (Mayfield, 2013).
We need to understand the core concepts and design principles for E-information as a subsystem, and then go on to explain how the E-information subsystem is designed to serve our larger symbolic systems. We complete the whole picture with the knowledge provided by other fields (linguistics, semantics, pragmatics, semiotics, and other communication approaches), but not modelling those fields on the E-information transmission model.
As we’ve just reviewed, the signal-code-transmission model of information theory was initially developed as a set of models for transmitting error-free electronic signals in telecommunication systems where networks and radio frequencies’ physical limits and capacity could be precisely defined and engineered. This model provides an essential abstraction layer in the designs of all electronic and digital systems. It does not provide an extensible model for the larger sense of communication and meaning systems that these symbolic cognitive technologies allow us to implement. The meanings and social uses of communication are left out of the signal transmission model because they are assumed or presupposed as what motivates using signals and E-information at all. This is why we need to understand that the designs and engineering techniques for E-information are used for creating a data or semiotic subsystem using binary electronics.
“Information” in this context is thus primarily unobservable (we cannot observe energy fields, electronic pulses, or signals used as binary representations). (Irvine, 2021).
Why is the information theory model essential for everything electronic and digital, but insufficient for extending to models for meanings, uses, and purposes of our sign and symbol systems?
The transmission model of E-information is essential to understand. Still, it cannot be used for extrapolating to a model for communication and meaning more generally (though some schools of thought have tried unsuccessfully to use the model this way). The signal transmission theory is constrained by a signal-unit, point-to-point model, with the “conduit” and “container.”
The larger context surrounding E-information also includes what cognitive science research calls “meta-symbolic” knowledge, the understanding of meaning frameworks for meanings, and the essential meta-information (information about the information in the generic information sense) known to all communicators using a symbolic medium. This includes cultural knowledge of various kinds/genres of messages, social conventions, categories of meanings or cultural codes, and assumed background knowledge, which, of course, as meta-information is not – and cannot be — represented in the signal information, the E-information, itself. (Barwise, 1986 in Gleick, 2011).
- Gleick, J. (2011). The Information: A History, a Theory, a Flood. Bantheon Books: NY
- Irvine, M. (2021). Introducing Information Theory: The Context of Electrical Signals Engineering and Digital Encoding.
- Martell, C & Denning, P (2015). Great Principles of Computing. The MIT Press: London
- Mayfield, J. E., (2013). The Engine of Complexity: Evolution as Computation. NY: Columbia University Press: NY