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Language at the Speed of Sight Mark Seidenberg

MARCH 13-15, 2017HILTON NEW ORLEANS RIVERSIDE

PLAIN TALK ABOUT LITERACY AND LEARNING

(504) 840-9786 | [email protected] | www.cdl.org

About the Presenter

www.cdl.org | [email protected] | (504) 840-9786

About CDLWhat does CDL do when we aren’t doing Plain Talk? Plenty.

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Mark Seidenberg Mark Seidenberg, Ph.D., is a cognitive scientist/neuroscientist/psycholinguist who has studied language, reading and dyslexia since the disco era. He is the Vilas Research Professor and Donald O. Hebb Professor in the department of psychology at the University of Wisconsin. His reading research addresses the nature of skilled reading, how children learn to read, dyslexia, and the brain bases of reading, using the tools of modern cognitive neuroscience: behavioral experiments, computational models, and neuroimaging. Mark’s language research addresses what people know when they know a language, how this knowledge is represented in the brain, and how it is acquired and used. His book, Language at the Speed of Sight: How We Read, Why So Many Can’t, and What Can Be Done About It was published to acclaim and derision in January 2017. Mark attended Columbia University

as an undergraduate, where, like many students, he worked part-time. He received a Ph.D. from Columbia, where he was a student of Tom Bever during the notorious Nim Chimpsky era. He did postdoctoral research at the Center for the Study of Reading splitting time between Bolt Beranek & Newman and the University of Illinois. His first academic appointment was at McGill University in Montreal, home of fantastic food and a very long winter. After 10 years in the cold, he moved to the University of Southern California, where he had appointments in psychology, linguistics, and in the neuroscience program. In 2001 he moved to the University of Wisconsin Madison, where he lives in a house on a hill and bikes to work when he can. He has published many scientific articles in fine journals such as Science, Psychological Review, Nature Neuroscience, Language, Psychological Science, and Semiotica, and was honored as one of the 250 most-cited researchers in the areas of psychology and psychiatry by those Web of Science citation-counting people.

Center for Development and Learning(504) 840-9786 | www.cdl.org | [email protected]

Language at the Speed of Sight

Mark Seidenberg

Vilas Research Professor

University of Wisconsin-‐Madison

What can reading science contribute to be@er reading?

2/26/17 Mark Seidenberg 1 2/26/17 Mark Seidenberg 2

"Every teacher of young children as well as those who train them should read this book.”

Wall Street Journal, 1/10/2017.

On sale here!

My basic assumpRon

2/26/17 Mark Seidenberg 3

Too much to learn, too li0le 1me


Beginning reader’s task


Learn how print relates to spoken language they already know (Gough)

But now we know just how deeply they become intertwined.


Rueckl et al. PNAS 2014

Shankweiler et al.: integra:on greater for good readers compared to poor readers

PLAIN TALK ABOUT LITERACY AND LEARNINGNew Orleans, LA | March 13-15, 2017

1



Reading and speech are “Like two co-‐dependents with serious boundary issues.” What the child has to learn

• spoken language • print: orthographic structure • relaRons between print and spoken language


You can’t teach all of this Too much to learn, too li0le 1me

• Vocabulary • Phonics • Spelling

(Concepts, grammar, things about the world, much more)


The Learning Puzzle

• Readers do learn all this • There isn’t Rme to teach all of it • Teaching a relaRvely small amount of it is beneficial

anyway


What is going on?

To explain this, let look at something everyone just loves to discuss…

Phonics!

Bear with me! It’s a useful example Same story for vocabulary, morphology, syntax, concepts, etc.


One view

Why bother?

Not good



2


Another view

• There are rules Mat pat sat hat generalize: nat

• There are excepRons Have said done was aisle

Therefore • Teach the rules, memorize the excepRons (“sight

words”)


Problem

1. No one knows what the rules are 2. You can’t say which words are excepRons without

knowing what the rules are 3. See (1)



“Words in the COOK/LOOK/BOOK neighborhood seem to obey a rule governing the pronunciation of-OOK, which makes SPOOK an exception.

But SPOOK is fine if grouped with SPOON and SPOOL.

BOOK is rule governed if grouped with LOOK and COOK but irregular compared to BOON, BOOM, and BOOT.

Which -OWN words are rule governed and which are “exceptions”: CLOWN, TOWN, and FROWN or OWN, FLOWN, and BLOWN?

Worse, however they are stated, the number of rules is so large that only a small subset can be taught, surely the bane of every teacher charged with providing phonics instruction. How the child catches on to the rest is a

From the book:

What’s the answer?

• The system isn’t rule-‐governed. • It’s probabilisRc

“Rule” means: given X, do Y • Like rules for moving pieces in chess

“ProbabilisRc” means: given X, certain probabiliRes of doing X or Y or Z.

• Like wri@en English

StaRsRcal tendencies, not rules and excepRons


Degrees of consistency

• ”rule-‐governed” words have excepRons • But the excepRons are not arbitrary

HAVE is not pronounced “glorp”

It overlaps with “rule-‐governed words” Had, has, hive, etc.

• There is no line between rule-‐governed and sight words. Just degrees of consistency. ConRnuum.


How is this learned?

• “Deep learning” network • Given a spelling pa@ern,

produce the correct pronunciaRon

• Do that for every spelling pa@ern

• Use the same network for all words



3


What happens

StaRsRcal learning procedure • The network discovers regulariRes • Lots of pa@erns, subpa@erns, weird pa@erns not

visible to naked eye • Eventually converges on configuraRon that allows it

to produce correct pronunciaRons for many words • Regulars, excepRons and everything in-‐between

• And generalize to novel items


Involves two types of learning

• Implicit, unsupervised • Goes on all the Rme in the background • Network/brain learns from experience ”on its own”

• Explicit, supervised • Feedback about whether behavior is correct or not • Like, instrucRon


Important finding

• Learning in such systems is most effecRve when it combines

• Large amount of implicit learning • Smaller amount of explicit instrucRon

• We think this is true of children


ExplanaRon

• What is learned about one word carries over toother words

• Learning about SAVE makes it easier to learn PAVE and RAVE

• Facilitated by well-‐aimed explicit instrucRon Doesn’t just help the child learn that specific example.It influences all of the overlapping ones.


Same thing for vocabulary

• StaRsRcal learning over the contexts in which words occur

• Words with similar meanings appear in similar linguisRc contexts

• Learning about lion, Rger prepares the way for lynx

• Before the child experiences it!


We think this is the soluRon to the “too much to learn, too li@le Rme” problem

• Don’t have to teach it all Lots of learning goes on as the child reads, writes, talks, listens

• But instrucRon is also importantTunes the system in ways that help many words at a Rme



4


What’s going on in the child’s head is not apparent from observaRon We do the research

Behavioral, neuroimaging studies of children, adults ComputaRonal models of learning

Explicit learning: visible Rp of the iceberg Implicit learning: mass hidden below the surface


Side effect

What you think you are teaching the child may be very different from what they are learning


What’s missing from this account?

• How to opRmize limited opportuniRes for explicit instrucRon

• What we need Given the current state of the child’s knowledge, what kind

of explicit learning trial would have the biggest overall impact on learning?

Affect the most words at a Rme.

AdapRve learning procedures. Coming very soon!


Would it help teachers to know this stuff? I think so, do you?

Good theory of how children learn —> good pracRces

Bad theory: harder for child, teacher


In my Concurrent Session @1.30 Ask me (almost) anything

Topics to be determined by the audience. Such as:

How Much Do Readers/Learners Vary?

Reading vs. Literacy: Does it ma@er?

MulRple Literacies: Good for whom?

Dyslexia: Does it exist? Yes. Is it a “desirable difficulty” (Gladwell 2013)? No.

Achievement gaps: More than poverty, opportunity?

Other topics at the intersecRon of the science of reading/language/development and educaRon. Bring yours.


See you there!



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