If you are reading this (slowly) it’s too late The limits of reading speed

image courtesy of Kevin McCloskey


Most readers read at a rate within the range of 200-400 words per minute. Multiple bottlenecks, both perceptual and pragmatic, limit reading speed.

In an era when soylent is being sold at Walmart, it’s no surprise that the specter of speed reading would return to entice the current generation of time savers even despite the tarnished reputation and unsupported claims. Proponents of speed reading claim that that reading can be sped up to rates beyond 600 words per minute without sacrificing understanding. Search “speed reading” online and you will find that a lot of people believe that anyone can be transformed into a speed reader. Wikipedia has a quasi-legitimizing entry for “Speed reading”. Stanislas Dehaene, one of the most famous living cognitive scientists, has written optimistically about it in a best-selling book .  Fortunately, for the speed-curious, there exists nearly half-a-centuries’ worth of research into the limits of reading speed and whether or not speed reading counts as “reading, only faster” or whether it amounts to little more than good ole’ skimmin’ and guessin’. Below I try to share some of these findings and demonstrate a few simple examples of reading capacity with interactive tasks. While I try to convince you that speed reading is not possible, I invite you to try out these tasks and then judge for yourself whether or not to believe in speed reading.

First, if you want to know where all of the time gets wasted during the reading process, here is my rendition of Mark Seidenberg’s equation for average reading speed:

  1. Only about seven to eight letters can be read clearly within the field of vision at any given point.
  2. While reading, your eyes stop to gather information for an average of 200-250 milliseconds (ms):
    200-250 ms = 4 to 5 stops-per-second = 240 times-per-minute.
  3. 240 stops-per-minute x seven letters-per-stop = 1680 letters per minute.
  4. The average word length is 5 letters.
  5. 1,680/6 (five letters per word + 1 space)= 280 words per minute.

To some readers, this may sound like a challenge. Can we bump these numbers up?

1. The windows of perception
The number of words you can identify in one glance is limited by the eye and the mind/brain.

Below are 3 rows of text. Each row has a word, then a seven-digit number, then another word. How wide is the area of your vision that can identify words? Try identifying the bolded middle number in each row (for example “8” in “2538217”). Then, while your eyes are on that number, try identifying the words on the right and/or left of the number.

      diluted 5743899 emerged
bootleg 5794360 voyages
whiskey 8456752 vectors

Was this difficult? You might have even tried moving away from the screen, and noticed that it does not get easier.

Not all of the area within your gaze is treated equally. The eye is set up so that the images in the center of your gaze are seen with more detail than images in the periphery (can you imagine it the other way?). The retina, the place in the back of your eye where all of the vision receptors are located, gets light from almost 180 degrees. You can hold your fingers to the sides of your head to see how far this span is, but although you might be able to sense your fingers are there, at the edges of your visual span you probably couldn’t tell if they were fingers or hotdogs if you didn’t know in advance. This is because we don’t use most of the information in our vision for identifying things, but simply for noticing objects exist and then dodging or grabbing them. To identify things, including people’s faces and words, you must use the privileged spot towards the middle of the retina called the fovea. The fovea gets light from only 1/36th of the area within your gaze at any given moment. If you want to know how much this, hold your two thumbs away from your chest at an arm’s length and stare at your thumbnails. That’s about it. The density, type (cone receptors), and connectedness of the receptor cells in the fovea allows the cells there to pass on information to the brain that is way more fine-grained than what the rest of the cells in the retina can. This is the kind of information necessary to tell an “n” from an “h” or an “O” from an “G”.

image courtesy of wikimedia commons

Do faster readers have bigger or better foveas than slow readers? No. Sometime before 1975 Mark Jackson and James McClelland found that slow and fast readers do not show different abilities when recognizing 2 letters spaced various distances apart. Since readers of different speeds can do just as well, they have no differences in the width of their perceptual windows.

While the width of the windows for fast readers and slow readers appears the same (is that a pun?), their brains use the information within that  window that hits the fovea differently. Identifying two letters is easy. When Jackson and McClelland flashed 8 unrelated letters (for example, VGSFDATU or FGIOVTKA) within this window, faster readers were better able to report which letters they had seen. The difference was not the width, but the ability for fast readers to do fast and reliable coding. The idea suggested by that experiment is that fast and slow readers could be working with the same visual area, but that faster readers are able to use more information from that area. By “use” it, I meant they are better able to quickly and accurately store it in memory. Isn’t that also the goal of reading?

Jackson and McClelland followed this up with a study published in 1979, demonstrating that faster readers tended to show better listening comprehension, faster responses when determining whether two letters  were the same (for example, A & a or B & d), and increased speed and accuracy at recognizing homophones viewed side-by-side (BOAR-BORE). This again stresses the idea that it’s all about coding, and further clarified the code of the fast reader: knowledge of words, their letters, and their sounds.

If you want to see what it means to code visual information with different levels of skill, try staring at these song lyrics for a few seconds, then look away and try to copy them from memory with a pen or pencil:

1. I thought you would be as big as a whale.
My nets were knit.

2. Eu quero é dar o fora
E quero que você venha comigo.

3. 那片永远不会融化洁白的雪

The task should show how increased knowledge of a language allows us to code the visual information more deeply (quickly and accurately put it into memory). The first (#1) can be coded by readers of English using grammar structure, word meanings, sounds and visually (you might have gotten a few words wrong, but probably got the gist). While most readers of English should be able to read the middle passage (# 2) out loud, they will be slower to move their eyes over it, and they will have difficulty spelling it from memory. Knowledge of the meaning and letter-sound correspondence allows you to code the text as sound. This, in turn, makes it easier to remember what the visual words looked like than if the words were in a different script. For the last section, unless you know Chinese script, you will not be able to remember how to write it (I can’t either), and unless you speak Mandarin, you will have even more trouble. Even in English, some readers will have different levels of vocabulary and/or letter-sound correspondence knowledge which will effect their ability to code what they are able to perceive. Dyslexia due to a difficulty in coding some aspect of letter-sound correspondence is another roadblock to efficient reading.

In the following section I’ve included some examples to help demonstrate how word-sound knowledge is coded.

2. Subvocalization vs phonology

It’s fun to compare reading to listening. According to a 2006 study of spoken communication, conversational speaking rates between familiar speakers are about 216 words per minute (277 milliseconds, per word). This is close to the average amount of time spent looking at a word while reading (220-280 milliseconds). The intuitive, theoretical notion is that written words are coded as sounds (phonological codes) or even motor movements of the mouth at some stage of reading. According to Alan Baddeley’s model of working memory, an articulatory mechanism, akin to repeating words in your head, kicks in to sustain words in memory after 1-2 seconds. It’s hard to distinguish which of these two processes the zealous leaders of speed reading cults wish to eliminate in order to increase reading speed. Is it the initial phonological coding or is it the secondary memory loop which these swindlers detest? Phonological coding is fast and automatic, possible even with massive distraction, so this is not going to be easy to consciously stop.

If you’re curious to see how easy it is to retrieve phonological codes during reading, try this task that Seidenberg gives at the end of his book. Put a pencil between your teeth and repeat the words “coca cola” to yourself out loud (if you don’t have a pencil, try mumbling, sanely, to yourself) while you determine whether the following words rhyme:


This should have been perfectly possible, despite the distraction — further evidence that you can retrieve sound information from words with relative ease. And what happens with ease is hard to turn off.

It is important to mention Dyslexia  again here. It could be that attempts to eliminate subvocalization come from reading coaches who are dyslexic themselves, and have claimed to find a better way around phonological coding. Even so, this method of ignoring spelling-sound correspondence has not been shown to help all readers with dyslexia, many of whom might stand to benefit from phonology training despite early diagnoses.

Perhaps attempts to eliminate subvocalization are a means to encourage readers to avoid remembering what they just read, to nudge them to drop what is in their memories and look forward. The extent to which readers rely on Baddeley’s style of phonological loop while reading might vary with the text, as certain texts make you use your short-term, or working, memory more.

Below I try to show situations where remembering what you just read might come in handy (when reading ambiguous phrases).

3. Word presentation speed

Your eyes are constantly moving and stopping, moving and stopping. When they move between two words it is done with a burst of speed in a jump called a saccade. Importantly, you are unable to see during the saccade. This limits how you look at words and so it is another place where reading can be sped up. The average amount of time you spend looking at one word is about 200-280 milliseconds (4-5 per second), although this can vary. This also accounts for roughly 30 milliseconds it takes the eyes to do the moving. But what if you could take away the need to move them?

Here is a paragraph from Harry Potter and the Sorcerer’s Stone played at 3000 wpm:

Here it is played at 3000 wpm:

Here it is played at 1000 wpm:

Here it is played at 800 wpm:

Here it is played at 400 wpm:

Here it is played at 200 wpm:

Which one did you prefer?

The technique for displaying one word at-a-time is called rapid serial visual presentation or RSVP. This method has allowed readers to strictly control the rate of word presentation. Back in 1986, Michael Masson tested to see if people could understand sentences presented fast (100 ms or 600 wpm) via RSVP. He found that at speeds of 400-600 words per minute via RSVP, people were able to use context to predict the final words in sentences such as “stamp” in “He mailed the letter without a stamp,” while they were unable to predict final words such as “stamp” in scrambled sentences like “Without he mailed letter a the stamp.” If context had an effect, the logic was that people are “doing comprehension” at these fast rates. After a series of experiments where subjects were asked to name or respond to the final words in the Ok vs Scrambled sentences, Masson concluded that context did have an effect at high presentation rates: readers could use the context to help them identify upcoming words, and they could do this faster than they could move their eyes during reading.

As mentioned above, it takes the eyes roughly 280 milliseconds (250 for fixation + 30 for saccade), on average, to begin reading one word and then get to the next one. Masson was saying that comprehension processes within that 280 millisecond window might take less time if they weren’t bogged down by the muscle movement in the eye. Does this mean that reading comprehension can happen at faster-than-normal (280 ms) presentation rates? Here, please consider that these “comprehension processes” being referred to in the above experiment are merely one part of what goes on during comprehension which includes coding information and the remembering it later. Eggs, for instance, are a part of the cake process, but you wouldn’t eat raw eggs and say you were having cake.

While Masson did not test comprehension in terms of recall for the material, the experiment shows that when words are presented rapidly, some early comprehension processes are able to cope with the speed. This example however, was for single, unrelated sentences with breaks in between. What happens when you try to use RSVP to string sentences together?

In another series of experiments, researchers tested RSVP reading at average (200 wpm), high (400 wpm), and really high (600 wpm) presentation rates. Readers who participated read paragraphs borrowed from an old experiment by John Brandsford and Marcia Johnson. One example is below.

You can try to guess what it is about. The paragraphs go like this:

The procedure is quite simple. First you arrange everything into different groups. One pile may be enough if you don’t have much to do. Then you have to go somewhere else if you do not have a machine. You put them into the machine and turn it on. It is better not to put too many in at once. Then you sit and wait. You have to stay there in case anything goes wrong. Then you put everything in another machine and watch it go around. When it stops, you take the things home and arrange them again. Then they can be put away in their usual places. Soon they will all be used again and you have to do it all over. The whole thing can be a pain.

In case you couldn’t tell, the paragraph describes the process of doing laundry! When RSVP and normal readers were given the same amount of time to read these passages, they performed equally well at getting the gist. When the word “Laundry” was inserted, readers in the RSVP condition were able to use this word to their advantage, more so than the regular readers. This shows how RSVP might provide a good gist based on single words, similarly to how speed readers might make educated guesses and inferences to make sense of a paragraph using only a few words, skipping the rest.

When else does RSVP not work?

Logical difficulties with RSVP arise when you read so-called garden path sentences:

“The raft floated down the river sank.”

or otherwise ambiguous sentences like this from a Time magazine headline:

“The FBI director responded to the President’s angry tweet storm calling for him to be jailed.”

In the ambiguous case, it is hard to tell who is calling whom to be jailed. When connecting sentences, ambiguity from the text or from the reader (because they blinked or spaced out) can lead to errors that require looking back at the text. As a result, RSVP hinders comprehension when larger texts like paragraphs. When paragraphs are cranked up to 600 wpm, you won’t have time to reanalyze garden-path sentences or resolve ambiguities.

An argument for skimming

Despite the love and dedication that you show an author by reading their work carefully, reading is often either rushed or laborious or both. I will admit that I’ve said that I’d “read” what in reality I had merely skimmed (of course I suspected what it was about before I read it). I skimmed for the reason most people become curious about speed reading: reading is often done under stress. Our bosses, our colleagues, advertisers, landlords, annoying family members, or teachers want us to read that email/report/book/lease/cell phone app privacy policy and do it fast. Even when we read for fun, we discover that writers of prose and poetry (and blogs) often choose to explain intricate details of something for pages on end! If you want to read fast, expect the expected. When you are confident you know what a correspondence is going to be about or if you are reading for fun it might be good to skim or to selectively skip over sections of a text. A skimmer’s strong pre-conceived notions of the content might even help when reading at normal pace. This way you can take a guess about what’s coming next: a strategy which might help you approach the material.

You’ve gotta get that reading done, so go ahead and skim around. Soon you will find something you want to actually read.

For more info check out:

For info on speed reading, check out Rayner, K., Schotter, E. R., Masson, M. E., Potter, M. C., & Treiman, R. (2016). So much to read, so little time: How do we read, and can speed reading help?. Psychological Science in the Public Interest, 17(1), 4-34.

For books on reading:
Check out The Psychology of Reading by Keith Rayner, Alexander Pollatseck, Jane Ashby and Charles Clifton Jr.

Language at the Speed of sight: Why How We Read, Why So Many Can’t, and What Can Be Done About It. by Mark Seidenberg

Here is a link to Seidenberg’s chapter on speed reading:


When good sentences sound bad Understanding the limits of how the mind processes language.

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Here’s a game for those of you who are native speakers of English. Which one of the following sentences has a grammatical error?

  1. The girls think the boys will eat ice cream.
  2. The girls think the boys who will eat ice cream.
ice cream
(Image: Pampered Chef)

That was easy, right? (In case you were wondering: sentence (1) is fine, sentence (2) is not. No ice cream for correct answers, sorry.) What about the next two sentences?

  1. Yesterday, the girls who love dogs said the boys who will eat ice cream.
  2. Yesterday, the girls who love dogs said the boys who will eat ice cream love cats.

That was pretty easy, too: Sentence (3) is bad, while sentence (4) is way better. As native speakers who grew up speaking a language, we can usually easily tell whether a sentence in that language contains grammatical errors or not, even though we might not be able to say what went wrong. We do so quickly and reliably even for fairly complicated sentences. If someone actually said sentences like (2) and (3) to you, you might conclude quickly that this person doesn’t speak English too well.

The ease with which we can identify whether a sentence contains grammatical errors says something about our mind, and one of the major goals in language science is to spell out exactly how the mind is set up to process language so efficiently.

Somewhat paradoxically, one way to learn about language and the mind is by investigating the exceptional circumstances when our language processing abilities falter and grammatical sentences consistently seem awful to us. “Consistently” is an important caveat here, since we all know from personal experience that no native speaker can be perfect all the time; we sometimes skip a word when we read, or mis-hear or fail to pay sufficient attention to an utterance. What researchers are interested in are grammatical sentences that pose a problem for everyone even in the most favorable environments. Our struggles with these sentences can then be blamed on some special cognitive “bug” that all of us have, rather than, say, the fact that we have different attention spans and cognitive ability (e.g. some people are good at doing mental arithmetic, others not so much).


A class of grammatical sentences that has long baffled native speakers and excited researchers involves the phenomenon known as “center-embedding.” To see how this works, let’s begin with acceptable simple sentences.

  1. The cat chased the mouse.
  2. The dog growled at the cat.
Left: The cat chased the mouse; Right: The dog growled at the cat.
(Left image: Jeroen Moes; Right image: http://www.dogclipart.com/)

We can add more words to the nouns “the cat” and “the mouse” to provide additional information about the cat and the mouse in question, as in (7) and (8). (Technically, what we added are “relative clauses” — think of them as almost-complete sentences that describe nouns.) Sentences (7) and (8) are longer than (5) and (6), but you can obviously nonetheless tell that they are fine.

  1. The cat that the dog growled at chased the mouse.
  2. The dog that the girl fed growled at the cat.

In fact, there doesn’t seem to be a cap on the number of relative clauses one could add or where they appear in a sentence. Sentence (9) is an even longer sentence with two relative clauses, where the relative clauses appear at the end, one inside the other. (I have underlined the outer relative clause, and bolded the inner one.) We can easily judge sentence (9) to be OK.

  1. The girl fed the dog that growled at the cat that chased the mouse.

Intuitively, we might predict that native speakers can judge a sentence to be fine or not regardless of the number of relative clauses it contains or where the relative clauses attach to. This prediction turns out to be false.

Here is an example. Let’s take a perfectly fine sentence like The cat that the dog growled at chased the mouse. In the middle of the relative clause that the dog growled at, let’s insert another perfectly fine relative clause, like that the girl fed. This nesting of relative clauses, which is technically known as “center-embedding,” creates a new sentence, which is shown in (10).

  1. The cat that the dog that the girl fed growled at chased the mouse.

What did you think about (10)? Does it sound like something you could say, or something that you could understand? If you are like most English native speakers, you probably found (10) incomprehensible when you first read it. You might have even taken a few seconds (or more) to try to figure out who did what in that sentence. In fact, sentences like (10) are not only difficult to understand, but experiments have shown that English speakers rate sentences like (10) as if they contained grammatical errors!

Note that the incomprehensibility of sentence (10) cannot be blamed on the complexity of the concept that is being conveyed, since it can be easily expressed in the form of (9) (or in the image below). 

A picture is worth a thousand words (or fewer)

This picture can be either described by sentence (9) "The girl fed the dog that growled at the cat that chased the mouse" or sentence (10) "The cat that the dog that the girl fed growled at chased the mouse." Both sentences seem to be grammatically fine, but only (9) is comprehensible to native speakers. (Images: Silhouette Design Store; iStockPhoto; LuCiD 2018)
This picture can be either described by sentence (9) “The girl fed the dog that growled at the cat that chased the mouse” or sentence (10) “The cat that the dog that the girl fed growled at chased the mouse.” Both sentences seem to be grammatically fine, but only (9) is easily comprehensible to native speakers. (Images: Silhouette Design Store; iStockPhoto; LuCiD 2018)

The difficulty of understanding (10) also cannot be simply blamed on the fact that it is a new sentence that we have never seen before, as there are many new utterances that we can easily understand or produce on the fly. For example, Superman accidentally swallowed a fly is a sentence that (presumably) no one has come across before, but we manage to figure out what it means.

Why are sentences with center-embedding difficult?

So why does a sentence like (10) create so much trouble for native speakers? What does it tell us about our minds? Elaborating on an intuition floated by George Miller and Noam Chomsky in the 1960s, Ted Gibson and James Thomas suggested the following hypothesis:

We know from existing research that we make predictions about upcoming words based on what we just read or heard. When we read “the cat,” we predict that we will see a verb like “chased” (or “meowed”, “played”, etc.). However, in a sentence like (10), we don’t see a verb right away; the next thing we see turns out to be the start of a relative clause: “that the dog.” Since English sentences must have verbs in them, we expect to see a verb eventually. For the time being, though, we park this prediction for a verb in our memory. Likewise, when we read “the dog,” we generate a second prediction for a verb, based on the fact that English relative clauses have verbs in them. Again, since the next thing read is “that the girl,” this prediction is not immediately borne out either, so it gets parked in our memory, too. The presence of multiple predictions of verbs then creates memory overload, interfering with our ability to interpret the sentence. In contrast, in a sentence like (9), our predictions for verbs (and nouns) are quickly borne out, and a memory overload problem does not arise.

Put differently, this memory constraint hypothesis says that our language processing resources are so limited that they can’t cope with center-embedding sentences. You might find this claim surprising. Our brains are powerful enough to deal with complex situations, such as planning a long trip, tracking and participating in a conversation at a noisy restaurant, memorizing the first 100 digits of pi, etc. Why aren’t there more resources allocated for language processing?

This hypothesis also predicts that speakers of all languages with center-embedding structures like the one in (10) should experience the same kind of difficulty with this kind of sentence. Cross-linguistic research on this topic has found only partial support for this prediction: while speakers of English, French, and Mandarin Chinese find sentences like (11) to be at least as bad as ungrammatical sentences of similar complexity, Shravan Vasishth and Stefan Frank and collaborators report that German and Dutch speakers do not. They observe that compared to English, German and Dutch sentences are more likely to have verbs appearing toward the end, and they suggest that this difference makes German and Dutch speakers more experienced with processing center-embedding sentences like (10).

(Here’s an example that Stefan Frank and Patty Ernst found was relatively OK:

Het spannende boek dat de populaire schrijver die de recensenten nauwlettend bekritiseerden met veel vertrouwen publiceerde miste een aantal pagina’s.

“The exciting book that the popular author who the reviewers meticulously criticized published with much confidence missed a number of pages.” Frank and Ernst’s paper can be found here — it’s open access, so free to read!)

Furthermore, even within English, there are center-embedding sentences that are easier to identify as grammatical than others. Janet Fodor, a linguist who spearheaded research on this topic, notes that the center-embedding sentence in (11), which is structurally similar to (10), is easier to understand. To see her point, compare both sentences.

  1. The rusty old ceiling pipes that the plumber that my dad trained fixed continue to leak occasionally.

(adapted from a Language Log post reporting on a presentation Fodor gave)

Fodor suggests that the difference between (10) and (11) might be due to how we say the sentences rather than memory constraints. The general idea behind her argument is this: when we read (10) aloud, we do so as if we were reading a list of nouns and verbs, and not a sentence, which affects our ability to make sense of it. (11), on the other hand, can be read like a regular sentence, which facilitates comprehension.

To sum up, center-embedding sentences provide a classic case study of how language science research works: researchers identify a class of sentences that native speakers exceptionally have problems processing and develop theories to explain why that might be the case. In the context of center-embedding, researchers have come up with competing hypotheses, attributing the difficulty of these sentences to memory limitations, linguistic experience, or how we pronounce sentences. While these hypotheses are very different from each other, their objective is the same: to use these bad sentences to shed light on how our minds process language.

Nick Huang is a PhD student in linguistics at the University of Maryland, interested in issues related to syntax and sentence processing.

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“Sticks and stones may break my bones…and words can hurt me, too.” Understanding the difference between a person with dyslexia and a dyslexic person

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Who am I? I am a sister. I am a speech pathologist. I am a researcher. I am kind. I bake cookies that will blow your mind. I also have a speech impairment, an attention deficit disorder, and anxiety. I embrace all of my unique characteristics, but only some of these traits define me. My attention deficit undoubtedly impacts the way I experience the world, but it has little influence on how I define myself as a person. Being a clinician, a researcher, a sister, a kind and thoughtful human being—these are attributes that constitute my identity, which I rely on to shape my values, establish priorities, and make choices.

A person’s quality of life fundamentally depends on his or her power to self-identify. I would be deeply unhappy if my medical conditions imposed on my sense of self. If I had been defined by my speech impairment, I never could have become a speech pathologist! But, not everybody is afforded the opportunity to choose which traits define them. People with disabilities in particular are often characterized by their disabilities, labeled by someone else in a position of power. Parents have an “autistic son.” Teachers have a “special needs student.” Doctors have a “dyslexic patient.” A child gets branded by a disorder before having a chance to develop his or her own identity.

Using person-first language is a decisive step that we as a society can take to empower people with disabilities to self-identify. Person-first language is a linguistic technique used to emphasize the individual. It describes what a person has, does, or needs, but not who a person is. Using person-first language means saying “a person who stutters,” rather than “a stutterer.” It’s referring to “the student who uses hearing aids” rather than “the hearing-impaired student,” or, “the family with a child who has Cerebral Palsy,” rather than “the Cerebral Palsy family.” It’s acknowledging the “Pulitzer Prize-winning investigative reporter from the New York Times who has arthrogryposis,” rather than portraying him like this. (This article by The Arc also discusses representation of people with disabilities.)

Nobody wants to be defined by what they can’t do, or by a circumstance they didn’t choose. Disabling conditions such as autism spectrum disorder, dyslexia, or Down syndrome, are heavily stigmatized. People with impairments are marginalized, mistreated, and perceived as “different,” or “not normal.” Negative attitudes towards individuals with disabilities can impact physical, emotional, and overall well-being even more than the condition itself. Using disability-first language emphasizes a person’s limitations. Using person-first language, on the other hand, features the individual, not the disability.

Stephen Hawking (1942-2018): a world-renowned cosmologist, space traveler, scientist, and author, who also used a wheelchair and an alternative communication system. Focus on the person’s strengths, skills, and accomplishments—not their limitations!

For those of us who value respect and equality, changing the stigma around disabilities is the ultimate goal. Laws and policies supporting the rights of people with disabilities have materialized and gained traction over the past 20 years (here’s a resource about person-first language from 1992!). But, a society where impairments are not disabling—where the environment is supportive and enriching for every person regardless of diagnoses or disorders—is still at least a few generations away. A critical step towards equality is changing our culture’s attitudes towards people with disabilities, and using respectful language is essential to progress. (Kathie Snow’s been working hard to change the paradigm through Disability is Natural!)

Using person-first language is especially important for anyone whose views are taken as authoritative. Descriptions used by experts such as scientists, doctors, policy-makers, principals, parents, teachers, and service providers permeate through society. Terms used by specialists are quoted by the media and recast into everyday language, which impacts not only how the world perceives individuals with disabilities, but also how individuals perceive themselves.

Updating the language we use to describe people or groups is a huge undertaking. Changing any habit takes time, practice, and perseverance. At first, it may feel unnatural to say “children from families with low socioeconomic status,” instead of the more common phrase, “poor children.” But, once the habit is formed, “the student who uses cochlear implants” will roll right off the tongue—I promise! Then, you’ll also start noticing the pervasiveness of disability-first language in everyday life, and you might wonder if mentioning the person’s disability was even necessary. It definitely requires more words to use person-first language. Scientists and reporters may balk because of strict word counts and the need for concise phrases in publication-quality writing. But, it’s worth the effort to promote a more inclusive society.

Some people feel their identity is inseparable from a diagnosis. For example, members of the Deaf community (take note of the capital ‘D’) who use American Sign Language do not consider hearing impairments disabling or restrictive in any way, and they are proud to identify as Deaf. Some people with autism spectrum disorder also feel strongly that their identity is intertwined with autism, and they prefer being identified as “an autistic person,” rather than “a person with autism.” Perhaps a better term for what I’m advocating for is “identity-first” language. Individuals who have a strong sense of self, integrate a medical condition into their identity, and use their powers to reframe the conversation about disabilities—all the more power to them!! I encourage everyone to make personal connections with people in their lives who have disabilities. Ask questions, and listen to the voices of people who are #actuallyautistic or #actuallydeaf. But, if you aren’t sure how a person chooses to identify, using person-first language is an explicit way to show respect and empower people to separate themselves from unwanted labels that provoke stereotypes, negative attitudes, or differential treatment. 

So…who am I? I am a sister. I am a speech pathologist. I am a researcher. I am kind. I bake cookies that will blow your mind. I also have a speech impairment, an attention deficit disorder, and anxiety. Only some of those traits define me, and I decide which ones. My speech impairment didn’t stop me from becoming a speech pathologist, nor will any other medical condition dictate my identity without my consent. Every person has the right to choose what characteristics define them, whether that includes a medical condition or not.

Below are 11 ways to use person-first language. Person-first language pertains not only to individuals with disabilities, but to anyone with a condition, characteristic, or behavior that might be stigmatized, used to cultivate a harmful perception, or stereotype.

Avoid: Try:
“A stutterer” “A person who stutters”
“Special needs students” “Students who require accommodations during exams”
or “students who need extra support”
“Cleft palate kid” “The kid with a cleft palate”
“The kid who was born with a cleft palate”
“Hearing-impaired client” “Client with a hearing impairment”
or “Client who uses hearing aids”
“HIV patient” “Patient with HIV”
or “Patient being treated for HIV”
“Dyslexic group” “Group of participants with dyslexia”
“A schizophrenic” “A person diagnosed with schizophrenia”
“The learning-disabled child” “The child with a learning disability”
“Normal speakers” “Speakers with typical development”
or “speakers with no history of hearing impairment”
or “speakers whose test scores fell within normal limits”
“Poor families” “Families living in poverty”
or “Families with low socio-economic status”
or “Families with income below the federal poverty line”

Allie Johnson is a PhD student in UMD’s Hearing and Speech Sciences department. She also has a Master’s degree in Speech Pathology from the University of Wisconsin-Madison. Her research focuses on speech development in children with cochlear implants. She really does bake good cookies!

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Seeds for Thought: Is there Structure in Birdsong? We may be underestimating the communication systems of birds.

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A group of alien linguists receives a modest grant to do fieldwork on the communication systems of animals on Earth. They notice two groups making a lot of noise: birds and humans, and since there are a lot fewer humans than birds, they decide it’s more feasible to focus on the featherless bipeds. They record all the different sounds we make, the order in which they are usually produced, and who we’re with and what’s around us when we converse. After analyzing terabytes worth of data, they gain some compelling insights into our communication system: they discover phonological rules, dialects, vowel formants, and categorical perception. They posit reasonable hypotheses about the function of language in building alliances, courting mates, and managing conflict. But, I would argue, following Chomsky, that they’d be missing out on fundamental properties about the nature of language.

For one thing, since the relationship between sounds and meanings is essentially arbitrary, they would have a difficult time uncovering what our words mean or even which segments of the sound signal are functioning as words. On top of that, without access to meaning, they would have very little chance at discovering rules and principles of how words fit together, i.e. syntax. As a result, these alien linguists would miss out on the structure of human language: how words combine to form phrases that can join in infinite ways with other phrases. Despite having only studied the surface of our language, they would conclude that these humans lack the infinite generativity and recursive rules of their own beautifully unique alien language.

After decades of humans studying the communication systems of birds, the predominant consensus is that birdsong is all surface: no compositional meaning, no hierarchical combinations of phrases, only strings of sounds. And, while I agree that there is no strong evidence of structure below the surface in birdsong, I would argue that those alien linguists, without our intuitions about how words fit together, without our judgements of what constructions are acceptable and not, would be hard pressed to find evidence of structure below the surface in human language.

So, when it comes to birdsong, are we the alien linguists? Are we missing out on structure below the surface?

Budgerigar (Melopsittacus undulatus) flock. Originally posted to Flickr by anna banana and licensed under the Creative Commons Attribution-Share Alike 2.0 Generic license.

Traditionally, researchers have analyzed the sequences of sounds produced by songbirds and parrots by using concepts from language and music: a note is a continuous trace on a spectrogram, a syllable is a collection of notes, and a motif or phrase is a grouping of syllables. They then characterize patterns in the order of syllables or phrases by using Markov chain models. These models capture how the probability of an element occurring in a sequence depends on the previous elements. If the two foregoing elements help to predict the next one, then it is a 2nd order Markov model. If three elements, then 3rd order, and so on. Most birdsong can be characterized by low (1st or 2nd) order Markov models. Vicky Hsu, a former PhD student at UMD, studied the complex and variable warble song of parakeets and found that it can arguably be best captured by a 5th order Markov model. This is very impressive but still categorically different than the hierarchical depths of human language.

Yet, think about this: if you just studied the sound or sign patterns of human language, then you could also characterize them with Markov models, as our own Bill Idsardi has argued. You would have no idea, of course, that there is structure below the surface. Like the alien linguists, you would confidently assert that human language patterns are Markovian in nature, easily computable by finite-state machines. Could that also be the case for us and the birds?

In my view, this question is still open and important. Uncovering the putative structure of birdsong would give us an unparalleled window into the minds of birds, possibly helping us to better understand the workings of the computational devices inside all of our skulls (whether bird or human). But needless to say, it is not satisfying just to claim that structure in birdsong is possible. We want to find evidence either way. Here are two possible steps forward I think we could take:

  • While there is no evidence for anything like words in birdsong in our studies of syllables and motifs, we might be wrong about the fundamental units in their communication systems. Recent studies have shown that songbirds and parakeets are exquisitely sensitive to temporal fine structure (TFS) — rapid changes in the amplitude and frequency of the acoustic waveform. Their perceptual abilities in this dimension actually exceed our own: zebra finches, for example, can hear changes in TFS for periods as short as 1-2ms while humans require periods 3-4ms long. This means that birdsong may sound very different to the birds than to us. Thus, while many birdsongs appear simple and repetitive to us, more complex patterns and variability could be embedded in the strings. Examining temporal fine structure patterns and understanding perception in this acoustic dimension could help us uncover any hierarchical structure, if it exists.
  • While we are stuck looking at strings of sound when analyzing birdsong, there are tools out there to help us decode structural rules governing the strings. Much like we can make up minimal pairs of sentences and ask human subjects to make acceptability judgments in order to test hypotheses about grammatical dependencies, we can ask the birds to “judge” strings of sounds as valid and invalid. This is not an easy road to take, since, as I’ve argued, we lack the intuitions of a native speaker for what elements might fit together. But we can draw on clues to structural rules: for example, in parakeet warble song, “contact call-like” elements are the most common, perhaps playing a role like function words in human language, in which case they may mark the beginning or ends of “phrases”. When it comes to asking the birds to “judge” strings, the path is a bit more straightforward. We can, for instance, ask birds to perform a preference task: you put two perches in a cage and when a bird stands on one it triggers a set of sounds manipulated in one way (with, for instance, hierarchical embedding), and when it stands on the other it triggers sounds manipulated in another way (for instance, a random arrangement of the same sounds). If a bird prefers one set of sounds over another, this could tell us what strings are acceptable and allow us to uncover any grammar in birdsong, again if it exists.

These are, of course, only two ideas of many possible paths forward. What we really need is linguists with theoretical insights, computer scientists with powerful algorithms and processors, biologists with expertise in animal behavior and cognition, birdsong researchers with innovative behavioral testing paradigms and other passionate folks to come together and approach this question with as open of minds as when it comes to presupposing the complexity of our own thoughts. My view could certainly turn out to be totally wrong. However, I’d rather run the risk of being wrong than to miss out on an entire dimension of animal cognition and communication, which could help us better understand our own. I, for one, would like those alien linguists to come back with more colleagues and new ideas (and perhaps a generous new grant) to take a chance at delving below the surface of our language and minds.

Adam Fishbein is a PhD student in the Neuroscience and Cognitive Science program at UMD, using comparative work with birds to study the evolution of human language and cognition. He also has a Master’s in Professional Writing from USC and has published several short stories and a novel.

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