There never was a reading war. A war assumes there are two armies meeting on a field of battle. This didn’t happen. But there was a reading coup. There was a hostile takeover of the field of literacy instruction by profiteers who saw public education as their own private ATM machine. This group of profiteers is part of the educational industrial complex which includes Cambium-Lexia Learning, Pearson Education, Cengage Learning, Hough Mifflin Harcourt, McGraw-Hill Education, Voyager Sopris Learning, TAL Education Group, Bright Horizons, and KinderCare Learning. Their armies of well-paid toadies (consultants) promise schools simple solutions to complex problems.

“Just buy our shiny new products,” they say. “Pay for our services,” they say. “Get trained by our experts,” they say, “and all your literacy problems will go away. All your students will be reading above grade level.”

“Well, I don’t know,” the school says. “That’s a lot of money.”

“Look,” they say, “look at all the colorful charts and graphs. Look at all the pretty, pretty numbers.”

“Well,” the school says, “you do have numbers. That must mean it’s real.”

“Wouldn’t you like to have colorful charts and graphs like this? Wouldn’t you like to have pretty, pretty numbers?”

“Yes,” the school says. “Yes, I would.”

And that, my friends, is how education lost its soul.

Neuroscience is a study of the nervous system including the brain, spinal cord, and neurons (NIH, 2025). The neuroscience of reading looks at how the brain functions during reading using imaging techniques to detect blood flow and electrical energy (Gotlieb, et al., 2022). Cognitive science is based on the word ‘cognition’ which means thinking. Cognitive science looks at human thinking (Robinson-Riegler & Robinson-Riegler, 2012). One studies the physical brain as it thinks and the other studies the thinking the brain does. But we can’t observe thinking directly. We can only observe the effects of thinking. Thus, both fields look at the effects of thinking to make deductions about thinking itself.

The first part of this podcast is designed to help you understand how reading works from a purely cognitive perspective. This provides an important context for the second part where I examine the theory of orthographic mapping (Ehri, 2014). Orthographic mapping is a theory based on logical deductions made from research. The questions we must ask are how robust is the theory, how valid are the data upon which it is based, and how logical are the deductions? My conclusions are, not very, not very, and not very.

In this podcast, I try to make sense of orthographic mapping, a term invented by Linnea Ehri and introduced in Chapter 15 (Ehri, 2014). We’ll start with her definition:

“Orthographic mapping occurs when, in the course of reading specific words, readers form connections between written unit, either single graphemes or larger spelling patterns, and spoken units, either phonemes, syllables, or morphemes. These connections are retained in memory along with meanings and enable readers to recognize words by sight. An important consequence of orthographic mapping is that the spellings of words enter memory and influence vocabulary learning, the processing of phonological constituents in words, and phonological memory” (Ehri, 2014, pp. 5-6)

This is written with all the stunning clarity of a Rorschach inkblot. Let’s do a bit of unpack-O-rating.

The term “direct and explicit instruction” is often used to sell products or to persuade state legislators to make bad decisions. But everybody already uses direct instruction in some form. It's not the 'what' of direct instruction that is in question; it's the 'how' and 'how much' of direct instruction. T

Our big human brains have evolved to become very efficient predicting machines (Hawkins, 2004). They are constantly accessing multiple data sources in order to give us a sense of what will happen next. Most of this is done at levels below our conscious awareness. For example, baseball players are able to run to the right spot to catch balls in the outfield because they can predict where it’s going to come down. Their big human brains instantly process a variety of information related to the sound of the bat hitting the ball as well as the height, speed, and angle of trajectory.

The same prediction process is used in language comprehension and reading (Gavard & Ziegler, 2022; Lupyan & Clark). Here, our prediction machine uses semantic, syntactic, and phonological information to make micro-predictions about words and meaning during the process of reading (Goodman, 1967; Laroche & Decon, 2019). Very much like baseball players catching pop flies, this enables us to efficiently and effectively create meaning with the print before us.

The problem with Orton-Gillingham and similar for-profit products (Lindamood, Wilson Language Training, Barton System, etc.) is that they try to reduce teaching to an algorithm. An algorithm is a formula for solving problems in which you follow a step-by-step set of procedures (with fidelity) to achieve a specific outcome. In other words, by correctly following a prescribed set of steps in the specified order, you will be led to a predefined solution. Algorithms are useful in mathematics and computer science for calculation, data processing, and automatic reasoning. For teaching of any kind? Not so much.

However, Orton-Gillingham would have you believe that if the teaching algorithm is followed explicitly, the teacher can be assured that students will learn to read. And if the algorithm does not work, you run them through the algorithm again … and again … and again. What these algorithmic programs offer is a false sense of certainty. Despite all the certainty thrown about, research to support the long-term effectiveness of these “direct, explicit, multi-sensory, structured, sequential, diagnostic, and prescriptive” instruction, it is simply not evident (Compton, et. al., 2014).

When somebody askes you, “What’s the Science of Reading?” what do you say? Is it a process? Is it a set of strategies? An approach or method? A reading program? A group or organization? In this chapter, I will attempt to define the Science of Reading. And notice that I’m using capital letters. This enables us to differentiate between a science of reading as one of several sciences of reading and the Science of Reading as a proper noun or title,

The Science of Reading seems to refer to a general consensus related to the strategies and practices that lead to improved reading outcomes. These strategies and practices have been determined to be effective using experimental or quasi-experimental research and conducted in authentic learning environments. Also, this research has established a causal link between strategies or practices and student outcomes (reading achievement). Thus, the Science of Reading can be thought of as a process that uses the standards in Figure 16.2 when making decisions related to reading instruction and policy. However, the SoR might best be described today as a self-defined movement that advocates these standards be used for making decisions related to reading policy and instruction.

The really big point is this: It’s the semantic connections that are most important, not orthographic, graphemic, or phonemic connections. When you encounter the words ‘elephant’ you don’t connect with short /e/ words. You don’t activate words containing the /ant/ letter pattern or words with silent ‘ph’ blends. You connect with elephant things, regardless of the letter sounds or patterns.

Just hearing the word ‘elephant’ brought some of the elephant things in your elephant schemata to consciousness. Meaning that, if elephant were followed by the words sock, trunk, swallow, you’d be able to identify the word ‘trunk’ microseconds faster than the other non-related words (Higgins, Rholes, & Jones,1977). This is called priming. Priming looks at how something that comes before primes or impacts what follows. The very word ‘elephant’ primed the pump so that I would be able to identify elephant words quicker and more efficiently. We’ll be looking at some of these research studies below.