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The Science of Word Recognition
or how I learned to stop worrying and love the bouma
Advanced Reading Technology, Microsoft Corporation
Evidence from the last 20 years of work in cognitive psychology indicate that we use the letters within a word to recognize a word. Many typographers and other text enthusiasts I’ve met insist that words are recognized by the outline made around the word shape. Some have used the term bouma as a synonym for word shape, though I was unfamiliar with the term. The term bouma appears in Paul Saenger’s 1997 book Space Between Words: The Origins of Silent Reading. There I learned to my chagrin that we recognize words from their word shape and that “Modern psychologists call this image the ‘Bouma shape.’”
This paper is written from the perspective of a reading psychologist. The data from dozens
of experiments all come from peer reviewed journals where the experiments are well specified so that anyone could reproduce the experiment
and expect to achieve the same result. This paper was originally presented as a talk at the ATypI conference in Vancouver in September, 2003.
The goal of this paper is to review the history of why psychologists moved from a word shape model of word recognition to a letter recognition model, and to help others to come to the same conclusion. This paper will cover many topics in relatively few pages. Along the way I will present experiments and models that I couldn’t hope to cover completely without boring the reader. If you want more details on an experiment, all of the references are at the end of the paper as well as suggested readings for those interested in more information on some topics. Most papers are widely available at academic libraries.
I will start by describing three major categories of word recognition models: the word shape model, and serial and parallel models of letter recognition. I will present representative data that was used as evidence to support each model. After all the evidence has been presented, I will evaluate the models in terms of their ability to support the data. And finally I will describe some recent developments in word recognition and a more detailed model that is currently popular among psychologists.
Model #1: Word Shape
The word recognition model that says words are recognized as complete units is the oldest model in the psychological literature, and is likely much older than the psychological literature. The general idea is that we see words as a complete patterns rather than the sum of letter parts. Some claim that the information used to recognize a word is the pattern of ascending, descending, and neutral characters. Another formulation is to use the envelope created by the outline of the word. The word patterns are recognizable to us as an image because we have seen each of the patterns many times before. James Cattell (1886) was the first psychologist to propose this as a model of word recognition. Cattell is recognized as an influential founder of the field of psycholinguistics, which includes the scientific study of reading.
Word shape recognition using the pattern of ascending, descending, and
Word shape recognition using the envelope around the word
Cattell supported the word shape model because it provided the best explanation of the available experimental evidence. Cattell had discovered a fascinating effect that today we call the Word Superiority Effect. He presented letter and word stimuli to subjects for a very brief period of time (5-10ms), and found that subjects were more accurate at recognizing the words than the letters. He concluded that subjects were more accurate at recognizing words in a short period of time because whole words are the units that we recognize.
Cattell’s study was
sloppy by modern standards, but the same effect was replicated in 1969 by
Reicher. He presented strings of letters
half the time real words, half the time not
for brief periods. The
subjects were asked if one of two letters were contained in the string, for
Reicher found that subjects were more accurate at recognizing
D when it was in the context of
than when in the context of
This supports the word shape model because the word allows the subject to
quickly recognize the familiar shape. Once the shape has been recognized,
then the subject can deduce the presence of the correct letter long after
the stimulus presentation.
The second key piece of
experimental data to support the word shape model is that lowercase text is
read faster than uppercase text. Woodworth (1938) was the first to report
this finding in his influential textbook Experimental Psychology.
This finding has been confirmed more recently by Smith (1969) and Fisher
(1975). Participants were asked to read comparable passages of text, half
completely in uppercase text and half presented in standard lowercase text.
In each study, participants read reliably faster with the lowercase text by a
5-10% speed difference. This supports the word shape model because lowercase
text enables unique patterns of ascending, descending, and neutral
characters. When text is presented in all uppercase, all letters have
the same text size and thus are more difficult and slower to read.
The patterns of errors that are missed while proofreading text provide
the third key piece of experimental evidence to support the word shape model. Subjects were asked to carefully read passages of text
for comprehension and at the same time mark any misspelling they found in the passage. The passage had been carefully designed to have
an equal number of two kinds of misspellings: misspellings that are consistent with word shape, and misspellings that are inconsistent
with word shape. A misspelling that is consistent with word shape is one that contains the same patterns of ascenders, descenders, and
neutral characters, while a misspelling that is inconsistent with word shape changes the pattern of ascenders, descenders, and neutral
characters. If test is the correctly spelled word,
would be an example of a misspelling consistent with word shape and
would be an example of a misspelling inconsistent with word shape. The word
shape model would predict that consistent word shapes would be caught less
often than an inconsistent word shape because words are more confusable if
they have the same shape. Haber & Schindler (1981) and Monk & Hulme (1983)
found that misspellings consistent with word shape were twice as likely to
be missed as misspellings inconsistent with word shape.
Consistent word shape
Misspellings that are consistent with word shape are missed more often
The fourth piece of evidence supporting the word shape model is that it is difficult
to read text in alternating case. AlTeRnAtInG case is where the letters of a word change from uppercase to lowercase multiple times
within a word. The word shape model predicts that this is difficult because it gives a pattern of ascending, descending, and neutral
characters that is different than exists in a word in its natural all lowercase form. Alternating case has been shown to be more
difficult than either lowercase or uppercase text in a variety of studies.
Smith (1969) showed that it slowed the reading speed of a passage
of text, Mason (1978) showed that the time to name a word was slowed,
Pollatsek, Well, & Schindler (1975) showed that same-difference matching was
hindered, and Meyer & Gutschera (1975) showed that category decision times
Model #2: Serial Letter Recognition
The shortest lived model of word recognition is that words are read letter-by-letter
serially from left to right. Gough (1972) proposed this model because it was easy to understand, and far more testable than the word shape
model of reading. In essence, recognizing a word in the mental lexicon was analogous to looking up a word in a dictionary. You start off
by finding the first letter, than the second, and so on until you recognize the word.
This model is consistent with Sperling’s (1963) finding that letters can be recognized
at a rate of 10-20ms per letter. Sperling showed participants strings of random letters for brief periods of time, asking if a particular
letter was contained in the string. He found that if participants were given 10ms per letter, they could successfully complete the task.
For example, if the target letter was in the fourth position and the string was presented for 30ms, the participant couldn’t complete the
task successfully, but if string was presented for 40ms, they could complete the task successfully. Gough noted that a rate of 10ms per
letter would be consistent with a typical reading rate of 300 wpm.
The serial letter recognition model is also able to successfully predict that shorter
words are recognized faster than longer words. It is a very robust finding that word recognition takes more time with longer words.
It takes more time to recognize a 5-letter word than a 4-letter word, and 6-letter words take more time to recognize than 5-letter words.
The serial letter recognition model predicts that this should happen, while a word shape model does not make this prediction. In fact,
the word shape model should expect longer words with more unique patterns to be easier to recognize than shorter words.
The serial letter recognition model fails because it cannot explain the Word Superiority
Effect. The Word Superiority Effect showed that readers are better able to identify letters in the context of a word than in isolation,
while the serial letter recognition model would expect that a letter in the third position in a word should take three times as long to
recognize as a letter in isolation.
Model #3: Parallel Letter Recognition
The model that most psychologists currently accept as most accurate is the parallel
letter recognition model. This model says that the letters within a word are recognized simultaneously, and the letter information is used
to recognize the words. This is a very active area of research and there are many specific models that fit into this general category.
I will only discuss one popular formulation of this model.
Figure 4 shows a generic activation based parallel letter recognition model.
In this example, the reader is seeing the word work. Each of the stimulus letters are processed simultaneously. The first step of processing is recognizing the features
of the individual letters, such as horizontal lines, diagonal lines, and curves. The details of this level are not critical for our purposes.
These features are then sent to the letter detector level, where each of the letters in the stimulus word are recognized simultaneously.
The letter level then sends activation to the word detector level. The
in the first letter detector position sends activation to all the words that
in the first position (WORD
in the second letter detector position sends activation to all the words
that have an O in the
second position (FORK,
have activation from three of the four letters,
WORK has the most activation because it has all four letters activated, and
is thus the recognized word.
Parallel Letter Recognition
Much of the evidence for the parallel letter recognition model comes from the eye
movement literature. A great deal has been learned about how we read with the advent of fast eye trackers and computers. We now have the
ability to make changes to text in real time while people read, which has provided insights into reading processes that weren’t previously possible.
It has been known for over 100 years that when we read, our eyes don’t move smoothly
across the page, but rather make discrete jumps from word to word. We fixate on a word for a period of time, roughly 200-250ms, then
make a ballistic movement to another word. These movements are called saccades and usually take 20-35ms. Most saccades are forward movements
from 7 to 9 letters,*
but 10-15% of all saccades are regressive or backwards movements. Most readers are completely unaware of the frequency of regressive saccades
while reading. The location of the fixation is not random. Fixations never occur between words, and usually occur just to the left of the
middle of a word. Not all words are fixated; short words and particularly function words are frequently skipped. Figure 5 shows a diagram of
the fixation points of a typical reader.
Saccadic eye movements
During a single fixation, there is a limit to the amount of information that can
be recognized. The fovea, which is the clear center point of our vision, can only see three to four letters to the left and right of
fixation at normal reading distances. Visual acuity decreases quickly in the parafovea, which extends out as far as 15 to 20 letters
to the left and right of the fixation point.
Eye movement studies that I will discuss shortly indicate that there are three zones
of visual identification. Readers collect information from all three zones during the span of a fixation. Closest to the fixation point
is where word recognition takes place. This zone is usually large enough to capture the word being fixated, and often includes smaller
function words directly to the right of the fixated word. The next zone extends a few letters past the word recognition zone, and
readers gather preliminary information about the next letters in this zone. The final zone extends out to 15 letters past the fixation
point. Information gathered out this far is used to identify the length of upcoming words and to identify the best location for the
next fixation point. For example, in Figure 5, the first fixation point is on the
Roadside. The reader is able to recognize the word
beginning letter information from the first few letters in
as well as complete word length information about the word
joggers. A more interesting fixation in Figure 5 is the word
In this fixation both the words
are short enough to be fully recognized, while beginning letter information
is gathered for
is a high frequency function word, this is enough information to skip this
word as well. Word length information is gathered all the way out to
which becomes the location of the next fixation.
There are two experimental methodologies that have been critical for understanding the
fixation span: the moving window paradigm and the boundary study paradigm. These methodologies make it possible to study readers while
they are engaged in ordinary reading. Both rely on fast eye trackers and computers to perform clever text manipulations while a reader is
making a saccade. While making a saccade, the reader is functionally blind. The reader will not perceive that text has changed if the
change is completed before the saccade has finished.
Moving Window Study
In the moving window
technique we restrict the amount of text that is visible to a certain number
of letters around the fixation point, and replace all of the other letters
on a page with the letter
x. The readers task is simply to read the page of text. Interestingly it
is also possible to do the reverse and just replace the letters at the
fixation point with the letter
but this is very frustrating to the reader. If just the three letters to the
left and right of the fixation point are replaced with
x, then reading rate drops to 11 words per minute. McConkie
& Rayner (1975) examined how many letters around the fixation point are
needed to provide a normal reading experience. Figure 6 shows a snapshot of
what a reader would see if they are reading a passage and fixated on the
If the reader is provided three letters past the fixation point, then they
won’t see the entire word for experiment, and their average reading rate
will be a slow 207 words per minute. If the reader is given 9 letters past
the fixation point, they will see the entire word
experiment, and part of the word
With 9 letters, reading rate is moderately slowed. If the reader is given 15
letters past the fixation point, reading speed is just as fast as if there
was no moving window present. Up to 15 letters there is a linear relation
between the number of letters that are available to the reader and the speed
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Linear relationship between letters available in moving window and reading
From this study we
learned that our perceptual span is roughly 15 letters. This is interesting
as the average saccade length is 7-9 letters, or roughly half our perceptual
span. This indicates that while readers are recognizing words closer to the
fovea, we are using additional information further out to guide our reading.
It should be noted that we’re only using information to the right of our
fixation point, and that we don’t use any letters to the left of the word
that is currently being fixated. In figure 6, where the user’s fixation
point is on the second
if the word An is removed,
it will not further slow reading rate.
The moving window study
demonstrates the importance of letters in reading, but is not airtight. The
word shape model of reading would also expect that reading speed would
decrease as word shape information disappears. The word shape model would
make the additional prediction that reading would be significantly improved
if information on the whole word shape were always retained. This turns out
to be false.
Figure 7 shows the
reading rate when three letters are available. It is roughly equivalent to
the reading rate when the fixated word is entirely there. That’s true even
though the entire word has 0.7 more letters available on average. When the
fixated word and the following word are entirely available, reading
rate is equivalent to when 9 letters are available. Reading rate is also
equivalent when three words or 15 letters are available. This means that
reading is not necessarily faster when entire subsequent words are
available; similar reading speeds can be found when only a few letters are
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1 word (3.7 letters)
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2 words (9.6 letters)
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experiment was condxxxxx xx
3 words (15.0
experiment was conducted xx
Full word information does not improve reading rate.
Pollatsek & Rayner (1982)
used the moving window paradigm to compare reading when the word spaces were
present to when they are replaced with an x. They found that saccade
length is shorter when word space information is not available.
The boundary study
(Rayner, 1975) is another innovative paradigm that eye
trackers and computers made possible. With the boundary study we can examine
what information the reader is using inside the perceptual span (15
letters), but outside of the word that is being fixated. Figure 8
illustrates what the reader sees in this kind of study. While reading the
words The old
reader will be performing ordinary reading. When the reader reaches the word
the key word of interest becomes available within the reader’s fixation
span. In this example the key word is
When the reader saccades from
the saccade will cross an invisible boundary which triggers a change in the
text. Before the saccade finishes, the text will change to the correct text
for the sentence, in this case
chart. The reader will always fixate on the correct word for the sentence.
The string of letters
ebovf after the boundary changes to
during the saccade.
The critical word in this
study is presented in different conditions including an identical control
similar word shape and some letters in common (chovt), dissimilar word shape with some letters in common (chyft),
and similar word shape with no letters in common (ebovf). The fixation times for the words both before and after
the boundary are measured. The fixation times before the boundary are the
same for the control condition and the three experimental conditions. After
the boundary, readers were fastest reading with the control condition (chart),
next fastest reading with similar word shape and some letters in common (chovt),
third fastest with the condition with only some letters in common (chyft),
and slowest with the condition with only similar word shape (ebovf).
This demonstrates that letter information is being collected within the
fixation span even when the entire word is not being recognized.
Similar word shape
Some letters in
Dissimilar word shape
Some letters in
Similar word shape
No letters in common
Relative speed of boundary study conditions
Having letters in common
played greater role in fixation times in this study. But it does not
eliminate the role of word shape because of the combination of word shape
and letters in common facilitates word recognition. Rayner
(1975) further investigated what happens with a capitalized form of the
critical word (CHART).
This eliminates the role of word shape, but retains perfect letter
information. They found that the fixation times are the same as the control
condition! This demonstrates that it is not visual information about either
word shape or even letter shape that is being retained from saccade to
saccade, but rather abstracted information about which letters are coming
The eye movement
literature demonstrates that we are using letter information to recognize
words, as we are better able to read when more letters are available to us.
We combine abstracted letter information across saccades to help facilitate
word recognition, so it is letter information that we are gathering in the
periphery. And finally we are using word space information to program the
location of our next saccade.
Evidence for Word Shape Revisited
So far I’ve presented
evidence that supports the word recognition model, evidence that contradicts
the serial word recognition model, and eye tracking data that contradicts
the word shape model while supporting the parallel letter recognition model.
In this section I will reexamine the data used to support the word shape
model to see if it is incongruent with the parallel letter recognition
The strongest evidence
for the word shape model is perhaps the word superiority effect which showed
that letters can be more accurately recognized in the context of a word than
in isolation, for example subjects are more accurate at recognizing
in the context of
than in the context of
(Reicher, 1969). This supports word shape because subjects are able to
quickly recognize the familiar word shape, and deduce the presence of letter
information after the stimulus presentation has finished while the nonword
can only be read letter by letter. McClelland & Johnson (1977) demonstrated
that the reason for the word superiority effect wasn’t the recognition of
word shapes, but rather the existence of regular letter combinations.
Pseudowords are not words in the English language, but have the phonetic
regularity that make them easily pronounceable.
Mave and rint are two examples of pseudowords. Because pseudowords do not have
semantic content and have not been seen previously by the subjects, they
should not have a familiar word shape. McClelland & Johnson found that
letters are recognized faster in the context of pseudowords (mave)
than in the context of nonwords (amve).
This demonstrates that the word superiority effect is caused by regular
letter combinations and not word shape.
The weakest evidence in
support of word shape is that lowercase text is read faster than uppercase
text. This is entirely a practice effect. Most readers spend the bulk of
their time reading lowercase text and are therefore more proficient at it.
When readers are forced to read large quantities of uppercase text, their
reading speed will eventually increase to the rate of lowercase text. Even
text oriented as if you were seeing it in a mirror will quickly increase in
reading speed with practice (Kolers & Perkins, 1975).
Haber & Schindler (1981)
found that readers were twice as likely to fail to notice a misspelling in a
proofreading task when the misspelling was consistent with word shape (tesf,
13% missed) than when it is inconsistent with word shape (tesc,
7% missed). This is seemingly a convincing result until you realize that
word shape and letter shape are confounded. The study compared errors that
were consistent both in word and letter shape to errors that are
inconsistent both in word and letter shape. Paap, Newsome, & Noel (1984)
determined the relative contribution of word shape and letter shape and
found that the entire effect is driven by letter shape.
Figure 10 shows the
example word than
of the four permutations of same and different word shape, and same and different letter shape. As with Haber & Schindler, subjects fail to notice
misspellings with the same word shape and same letter shape (tban,
15% missed) far more often than when there is a different word shape and
letter shape (tman,
10% missed). The two in between conditions of different word shape with same
letter shape (tnan,
19% missed) and same word shape with different letter shape (tdan, 8% missed) are illuminating. There is a statistically
reliable difference between the larger number of proofreading errors when
the letter shape is the same (tban
tnan) than when the letter shape is different (tdan
While there is no statistically reliable difference between conditions with
same word shape (tban
and different word shape (tnan
more errors are missed when the word shape is different. This trend sharply
contradicts the conclusions of the earlier studies.
Same word shape
Different word shape
Word shape and letter shape contributions to proofreading errors.
The final source of
evidence supporting the word shape model is that text written in alternating
case is read slower than either text in lowercase or uppercase. This
supports the word shape model because subjects are able to quickly recognize
the familiar pattern of a word written entirely in lowercase or uppercase,
while words written in alternating case will have an entirely novel word
shape. Adams (1979) showed that this is not the case by examining the effect
of alternating case on words, which should have a familiar pattern when
written in lowercase or uppercase words, and pseudowords, which should not
have a familiar pattern in any form because the subjects would never have
come across that sequence of letters before. Adams found that both words and
pseudowords are equally hurt by alternating case. Since pseudowords are also
impacted by alternating case, then the effect is not caused by word shape.
Further examination of
the evidence used to support the word shape model has demonstrated that the
case for the word shape model was not as strong as it seemed. The word
superiority effect is caused by familiar letter sequences and not word
shapes. Lowercase is faster than uppercase because of practice. Letter shape
similarities rather than word shape similarities drive mistakes in the
proofreading task. And pseudowords also suffer from decreased reading speed
with alternating case text. All of these findings make more sense with the
parallel letter recognition model of reading than the word shape model.
In the next section I
will describe an active area of research within the parallel letter
recognition model of reading. There are many models of reading within
parallel letter recognition, but it is beyond the scope of this paper to
discuss them all. Neural network modeling, sometimes called connectionist
modeling or parallel distributed processing, has been particularly
successful in advancing our understanding of reading processes.
Neural Network Modeling
In neural network
modeling we use simple, low-level mechanisms that we know to exist in the
brain in order to model complex, human behavior. Two of the core biological
principles have been known for a long time. McCulloch & Pitts (1943, 1947)
showed that neurons sum data from other neurons. Figure 11 shows a tiny two
dimensional field of neurons (the dark triangles) and more importantly the
many, many input and output connections for each neuron. Current estimates
say that every neuron in the cerebral cortex has 4,000 synapses. Every
synapse has a baseline rate of communication between neurons and can either
increase that rate of communication to indicate an excitatory event or
decrease the rate of communication to indicate an inhibitory event. When a
neuron gets more excitatory information than inhibitory information, it will
become active. The other core biological principle is that learning is based
on the modification of synaptic connections (Hebb, 1949). When the
information coming from a synapse is important the connection between the
two neurons will become physically stronger, and when information from a
synapse is less important the synapse will weaken or even die off.
A field of neurons and synapses in the cerebral cortex
The first well-known
neural network model of reading was McClelland & Rumelhart’s Interactive
Activation model (1981). Figure 12 diagrams how this model works. The reader
here is processing the letter
in the first position in a word. The flow of information here starts at the
bottom where there are visual feature detectors. The two nodes on the left
are active because they match the features of an uppercase
T, while the three nodes on the right are not active because
they don’t match. Every node in the visual feature detector level is
connected to every node in the letter detector level. The letters seen here
apply only to the first letter of a word. The connections between the visual
feature detector level and the letter level are all either excitatory
(represented with an arrow at the end of the connection) or inhibitory
(represented with a circle at the end of the connection). The letters
all received some excitatory activation from the two left feature detectors
because all three have a crossbar at the top of the letter (at least in this
font). The inhibitory connections between each of the letters will result in
being the most activated letter node because it has the most incoming
excitatory activation. The letter node for
T will then send excitatory activation to all the words that start with
and inhibitory activation to all the other words. As word nodes gain in
activation, they will send inhibitory activation to all other words,
excitatory activation back to letter nodes from letters in the word, and
inhibitory activation to all other letter nodes. Letters in positions other
than the first are needed in order to figure out which of the words that
start with T
is being read.
McClelland & Rumelhart’s Interactive Activation model: A few of the
neighbors of the node for the letter T in the first position in a word, and
One of the joys of neural
network modeling is that it’s specific enough to be programmed into a
computer and tested. The interactive activation model is able to explain
human behaviors that it was not specifically designed for. For example when
a human is shown the degraded stimulus in figure 13, it is very easy to
figure out that
is the degraded word, but the computer simulation of this model can also
solve this problem.
This degraded stimulus is easily read as
by human readers.
The computer simulation
does not attempt to solve the visual perception problem, but rather is told
which of the visual feature detectors are on for each letter position. For
the fourth letter position the computer simulation is told that there is a
vertical line on the left, a crossbar in the middle, and a diagonal pointing
towards the bottom right. Figures 14 and 15 show the activation levels of
certain letter and word nodes over time. Time in the computer is measured in
epochs of activation events. Figure 14 shows the early activation equally
rising for the
letter nodes. This is because the visual feature information supports both
of those letters, while the d letter node is unsupported. During the
early epochs the letter nodes are only receiving activation from the visual
feature nodes, but later activation is provided by the word nodes. Figure 15
shows the activation among four words:
Since the first three letters of the word are not degraded, the letter nodes
easily recognized them as
for the first three positions respectively. These letters provide early
activation for the words
but not for weak and
The word nodes then start to send activation back down to the letter node
level indicating that the fourth letter could be
k or d. Since k is already an active letter node while
is an inactive node, the
node is further strengthened. This allows the
k letter node and the word
to continuously increase in activation and send inhibitory activation to
their competitors, the letter
and the word word. Similar
activation patterns can also explain the word superiority effect.
The activation level over time for letter nodes in the fourth position of a
The activation level over time for four word nodes.
Seidenberg & McClelland
(1989) and Plaut, McClelland, Seidenberg, & Patterson (1996) have made great
progress in developing neural network models of reading that can account for
more human reading behaviors. Both of these models concentrate on the
reading processes that start after each of the letters in a word have been
recognized. The internal representations for these models convert the letter
information to phonemic information, which is seen as a mandatory step for
word recognition. It is well known that words that have a consistent
spelling to sound correspondence such as
hint are recognized faster than words that have an inconsistent
spelling to sound correspondence such as
(Glushko, 1979). These models are able to generate correct word
pronunciations (i.e. read) without the use of specific word nodes. The more
recent model is also able to read pseudowords at a near human rate and
account for consistency and frequency effects.
The Seidenberg &
McClelland and Plaut et. al. models are able to simulate not only adult
reading, but can also simulate a child learning to read. Initially the
neural network model starts out with no knowledge about the relationship
between letters and pronunciations, only that letters and sounds exist. The
neural net goes through a training phase where the network is given examples
of correct pronunciations for different words. After seeing a correct
sample, the network will calculate the error in its guess of the
pronunciation, and then modifies the strength of each of the nodes that are
connected to it so that the error will be slightly less next time. This is
analogous to what the brain does. After a few rounds of training, the model
may be able to read a few of the most high frequency, regular words. After
many rounds of training the model will be able to read not only words it has
seen before, but words it hasn’t seen before as well.
Given that all the
reading research psychologists I know support some version of the parallel
letter recognition model of reading, how is it that all the typographers I
know say that we read by matching whole word shapes? It appears to be a
grand misunderstanding. The paper by Bouma that is most frequently cited
does not support a word shape model of reading. Bouma (1973) presented words
and unpronounceable letter strings to subjects away from the fixation point
and measured their ability to name the first and last letters. He found
are more successful at naming letters to the right of fixation than to the
left of fixation.
distance to the right of the fixation point is controlled, subjects are
better able to recognize the last letter of a word than the first letter of
word. This data explains why it is that we tend to fixate just to the left
of the middle of a word.
Bouwhuis & Bouma (1979)
extended the Bouma (1973) paper by not only finding the probability of
recognizing the first and last letters of a word, but also the middle
letters. They used this data to develop a model of word recognition based on
the probability of recognizing each of the letters within a word. They
conclude that ‘word shape … might be satisfactorily described in terms of
the letters in their positions.’ This model of word recognition clearly
influenced the McClelland & Rumelhart neural network model discussed earlier
which also used letters in their positions to probabilistically recognize
Word shape is no longer a
viable model of word recognition. The bulk of scientific evidence says that
we recognize a word’s component letters, then use that visual information to
recognize a word. In addition to perceptual information, we also use
contextual information to help recognize words during ordinary reading, but
that has no bearing on the word shape versus parallel letter recognition
debate. It is hopefully clear that the readability and legibility of a
typeface should not be evaluated on its ability to generate a good bouma
Why I wrote this paper
I am a psychologist who has been working for Microsoft in different capacities since 1996. In 2000 I completed my PhD in cognitive psychology from the University of Texas at Austin studying word recognition and reading acquisition. I joined the ClearType team in 2002 to help get a better scientific understanding of the benefits of ClearType and other reading technologies with the goal of achieving a great on-screen reading experience.
During my first year with the team I gave a series of talks on relevant psychological topics, some of which instigated strong disagreement. At the crux of the disagreement was that the team believed that we recognized words by looking at the outline that goes around a whole word, while I believed that we recognize individual letters. In my young career as a reading psychologist I had never encountered a model of reading that used word shape as perceptual units, and knew of no psychologists who were working on such a model. But it turns out that the model had a very long history that I was unfamiliar with.
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If you’re just looking
for a couple of papers on reading psychology. I recommend these four:
1. Rayner, K. (1998). Eye movements in reading and information
processing: 20 years of research. Psychological Review, 124 (3), 372-422.
This paper is an account of the eye movement field from the premier eye
D.C., McClelland, J.L., Seidenberg, M.S., & Patterson, K. (1996).
Understanding normal and impaired word reading:
Computational principles in quasi-regular domains. Psychological Review,
This is the most recent of the major neural network papers, and is available
on David Plaut’s website.
3. Stanovich, K.E (1986). Matthew effects in reading: Some consequences
of individual differences in the acquisition of literacy. Reading
Research Quarterly, 21, 360-407.
This is one of the most cited reading papers of all time. If you are
interested in reading acquisition this is the place to start.
4. Hoover, W.A. & Gough, P.B. (1990). The simple view of reading.
Reading & Writing, 2(2), 127-160.
This paper demonstrates that word recognition and context are two separate
skills that are both necessary for reading.
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