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When high-capacity readers slow down and low-capacity readers speed up: Working memory differences in unbounded dependencies for German and Spanish readers Bruno Nicenboim, Pavel Logaˇ cev, Carolina Gattei and Shravan Vasishth University of Potsdam 1 / 24
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Page 1: When high-capacity readers slow down and low-capacity ...phiz.c.u-tokyo.ac.jp/~hiroselab/TLMAPLL2014Slides/...I Storage costs: based on the number of heads required to complete the

When high-capacity readers slow down andlow-capacity readers speed up: Working memory

differences in unbounded dependencies forGerman and Spanish readers

Bruno Nicenboim, Pavel Logacev,Carolina Gattei and Shravan Vasishth

University of Potsdam

1 / 24

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Aim:

I examine whether the increase of distance in unboundeddependencies is associated with slower reading times (localityeffects) once several confounds are controlled

I examine whether locality effects are modulated by workingmemory capacity

I examine whether the results hold for SVO and SOV structures

2 / 24

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Unbounded dependencies

(1) Someone asked what Mary didx last summer.

(2) Someone asked what Mary [some words] didx last summer.

3 / 24

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Unbounded dependencies

(1) Someone asked what Mary didx last summer.

(3) Someone asked what Mary, the girl that we met at school, didx last

summer.

3 / 24

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Unbounded dependencies

(1) Someone asked what Mary didx last summer.

(4) Someone asked what Mary’s sister didx last summer.

3 / 24

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Unbounded dependencies

(1) Someone asked what Mary didx last summer.

(2) Someone asked what Mary [some words] didx last summer.

3 / 24

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Memory-based explanations

I Dependency Locality Theory (DLT) (Gibson, 2000)

I Activation-based account (Lewis & Vasishth, 2005)

4 / 24

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Memory-based explanations: DLT

Processing costs in DLT (Gibson, 2000)

I Integration costs: based on the distance between dependentand head

I Storage costs: based on the number of heads required tocomplete the current input as grammatical

5 / 24

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Memory-based explanations - Activation-based account

Retrieval costs in the activation-based account (Lewis & Vasishth,

2005)

I Decay: more time has passed from the encoding of anargument

I Similarity-based interference: other items with similarfeatures serve as distractors

6 / 24

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Memory-based explanations

(1) Someone asked what Mary didx last summer.

(2) Someone asked what Mary [some words] didx last summer.

Prediction of processing difficulty at “did”:

Higher processing difficulty in (2) than in (1)

Can we translate costs to RTs?

RTs at “did” should also be:Longer reading times in (2) than in (1)

7 / 24

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Memory-based explanations

(1) Someone asked what Mary didx last summer.

(2) Someone asked what Mary [some words] didx last summer.

Prediction of processing difficulty at “did”:

Higher processing difficulty in (2) than in (1)

Can we translate costs to RTs?

RTs at “did” should also be:Longer reading times in (2) than in (1)

7 / 24

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Memory-based explanations

(1) Someone asked what Mary didx last summer.

(2) Someone asked what Mary [some words] didx last summer.

Prediction of processing difficulty at “did”:

Higher processing difficulty in (2) than in (1)

Can we translate costs to RTs?

RTs at “did” should also be:Longer reading times in (2) than in (1)

7 / 24

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Memory-based explanations

(1) Someone asked what Mary didx last summer.

(2) Someone asked what Mary [some words] didx last summer.

Prediction of processing difficulty at “did”:

Higher processing difficulty in (2) than in (1)

Can we translate costs to RTs?If so, we have locality effects.

RTs at “did” should also be:Longer reading times in (2) than in (1)

7 / 24

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Memory-based explanations

(1) Someone asked what Mary didx last summer.

(2) Someone asked what Mary [some words] didx last summer.

Prediction of processing difficulty at “did”:

Higher processing difficulty in (2) than in (1)

Can we translate costs to RTs?If so, we have locality effects.

RTs at “did” should also be:Longer reading times in (2) than in (1)

7 / 24

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Differential effects for different WMC

I low-WMC readers have more difficulty especially with complexsentences in comparison to high-WMC ones

I for garden-path vs. non-garden path sentences: Christianson,

Williams, Zacks, and Ferreira (2006)

I for comprehension reaction times in subject- vs. object-relativeclauses: King and Just (1991), Vos, Gunter, Schriefers, and Friederici

(2001)

I WMC influences the probabilities of success in integratinginformation over a distance in a text (Daneman & Carpenter, 1980)

I WMC is associated with the ability to maintain on-taskthoughts (McVay & Kane, 2011)

8 / 24

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Differential effects for different WMC

I low-WMC readers have more difficulty especially with complexsentences in comparison to high-WMC ones

I for garden-path vs. non-garden path sentences: Christianson

et al. (2006)

I for comprehension reaction times in subject- vs. object-relativeclauses: King and Just (1991), Vos et al. (2001)

I WMC influences the probabilities of success in integratinginformation over a distance in a text (Daneman & Carpenter, 1980)

I WMC is associated with the ability to maintain on-taskthoughts (McVay & Kane, 2011)

8 / 24

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Differential effects for different WMC

I low-WMC readers have more difficulty especially with complexsentences in comparison to high-WMC ones

I for garden-path vs. non-garden path sentences: Christianson

et al. (2006)

I for comprehension reaction times in subject- vs. object-relativeclauses: King and Just (1991), Vos et al. (2001)

I WMC influences the probabilities of success in integratinginformation over a distance in a text (Daneman & Carpenter, 1980)

I WMC is associated with the ability to maintain on-taskthoughts (McVay & Kane, 2011)

8 / 24

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Problems

(5) a. Someone asked what Mary didx last summer.

b. Someone asked what Mary [some words] didx last summer.

Potential Confounds:

I Word position effects (Ferreira & Henderson, 1993)

I

9 / 24

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Problems

(6) a. Someone [some words] asked what Mary didx last summer.

b. Someone asked what Mary [some words] didx last summer.

Potential Confounds:

I Word position effects (Ferreira & Henderson, 1993)

I

9 / 24

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Problems

(6) a. Someone [some words] asked what Mary didx last summer.

b. Someone asked what Mary [some words] didx last summer.

Potential Confounds:

I Word position effects (Ferreira & Henderson, 1993)

I Systematic change of the structure of the sentence:I

I

I

I

9 / 24

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Problems

(6) a. Someone [some words] asked what Mary didx last summer.

b. Someone asked what Mary [some words] didx last summer.

Potential Confounds:

I Word position effects (Ferreira & Henderson, 1993)

I Systematic change of the structure of the sentence:I Increasing the distance also increases the expectations for the

head (Antilocality effects!) (Levy, 2008; Hale, 2001)I

I

I

9 / 24

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Problems

(7) a. A Someone [some words] asked what Mary didx last summer.

b. Someone asked what Mary [some words] didx last summer.

Potential Confounds:

I Word position effects (Ferreira & Henderson, 1993)

I Systematic change of the structure of the sentence:I Increasing the distance also increases the expectations for the

head (Antilocality effects!) (Levy, 2008; Hale, 2001)I Facilitation at the verb due to preactivation (Vasishth, 2003)I

I

9 / 24

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Problems

(8) a. Someone [some words] asked what Mary didx last summer.

b. Someone asked what Mary [some words] didx last summer.

Potential Confounds:

I Word position effects (Ferreira & Henderson, 1993)

I Systematic change of the structure of the sentence:I Increasing the distance also increases the expectations for the

head (Antilocality effects!) (Levy, 2008; Hale, 2001)I Facilitation at the verb due to preactivation (Vasishth, 2003)I Encoding interferenceI

9 / 24

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Problems

(8) a. Someone [some words] asked what Mary didx last summer.

b. Someone asked what Mary [some words] didx last summer.

Potential Confounds:

I Word position effects (Ferreira & Henderson, 1993)

I Systematic change of the structure of the sentence:I Increasing the distance also increases the expectations for the

head (Antilocality effects!) (Levy, 2008; Hale, 2001)I Facilitation at the verb due to preactivation (Vasishth, 2003)I Encoding interferenceI Encoding ”facilitation” (Hofmeister, 2007)

9 / 24

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Problems

(9) a. Someone [some words] asked what Mary didx last summer.

b. Someone asked what Mary [some words] didx last summer.

c. Someone [some words] asked if Mary did something last summer.

d. Someone asked if Mary [some words] did something last summer.

Potential Confounds:

I Word position effects (Ferreira & Henderson, 1993)

I Systematic change of the structure of the sentence:I Increasing the distance also increases the expectations for the

head (Antilocality effects!) (Levy, 2008; Hale, 2001)

I Facilitation at the verb due to preactivation (Vasishth, 2003)

I Encoding interferenceI Encoding ”facilitation” (Hofmeister, 2007)

9 / 24

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Predictions

10 / 24

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Predictions

10 / 24

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Predictions

10 / 24

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Predictions

10 / 24

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Aim:

I examine whether the increase of distance in unboundeddependencies is associated with slower reading times (localityeffects) once several confounds are controlled

I examine whether locality effects are modulated by workingmemory capacity

I examine whether the results hold for SVO and SOV structures

11 / 24

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Self-paced reading in Spanish (Argentina) and German(Germany)

Experiment 1 - Spanish Self-paced reading

I operation span task for working memory capacity (WMC)

I rapid automatized naming for reading skill (RS)

I self-paced reading task on 48 items with 4 conditions

I subjects: 79

Experiment 2 - German Self-paced reading

I operation span task

I rapid automatized naming

I self-paced reading task on 48 items with 4 conditions

I subjects: 72

12 / 24

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(10) a. short unbounded dependency

LaThe

hermanasister

menoryounger

deof

SofıaSofia

preguntoasked

a quienwho.ACC

fuewas

quethat

MarıaMarıa

habıa saludadohad greeted

enat

lathe

puertadoor

delof the

colegioschool

ayeryesterday

aat

lathe

tarde.afternoon

b. long unbounded dependency

SofıaSofia

preguntoasked

a quienwho.ACC

fuewas

quethat

la hermana menor de Marıathe sister younger of Marıa

habıa saludadohad greeted

enat

lathe

puertadoor

delof the

colegioschool

ayeryesterday

aat

lathe

tarde.afternoon

c. short baseline

LaThe

hermanasister

menoryoungerr

deof

SofıaSofia

preguntoasked

siif

MarıaMaria

habıa saludadohad greeted

ato

lathe

primacousin

deof

PaulaPaula

enat

lathe

puertadoor

delof the

colegioschool

ayeryesterday

aat

lathe

tarde.afternoon

d. long baseline

SofıaSofia

preguntoasked

siif

la hermana menor de Marıathe sister younger of Maria

habıa saludadohad greeted

ato

lathe

primacousin

deof

PaulaPaula

enat

lathe

puertadoor

delof the

colegioschool

ayeryesterday

aat

lathe

tarde.afternoon

13 / 24

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(10) a. short unbounded dependency

LaThe

hermanasister

menoryounger

deof

SofıaSofia

preguntoasked

a quienwho.ACC

fuewas

quethat

MarıaMarıa

habıa saludadohad greeted

enat

lathe

puertadoor

delof the

colegioschool

ayeryesterday

aat

lathe

tarde.afternoon

b. long unbounded dependency

SofıaSofia

preguntoasked

a quienwho.ACC

fuewas

quethat

la hermana menor de Marıathe sister younger of Marıa

habıa saludadohad greeted

enat

lathe

puertadoor

delof the

colegioschool

ayeryesterday

aat

lathe

tarde.afternoon

c. short baseline

LaThe

hermanasister

menoryoungerr

deof

SofıaSofia

preguntoasked

siif

MarıaMaria

habıa saludadohad greeted

ato

lathe

primacousin

deof

PaulaPaula

enat

lathe

puertadoor

delof the

colegioschool

ayeryesterday

aat

lathe

tarde.afternoon

d. long baseline

SofıaSofia

preguntoasked

siif

la hermana menor de Marıathe sister younger of Maria

habıa saludadohad greeted

ato

lathe

primacousin

deof

PaulaPaula

enat

lathe

puertadoor

delof the

colegioschool

ayeryesterday

aat

lathe

tarde.afternoon

13 / 24

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(11) ......

preguntoasked

{a quien{who.ACC

fuewas

que;that;

si}if}

(la(the

hermanayounger

menorsister

de)of)

|||

MarıaMarıaprecritical

|||

habıahadcritical 1

|||

saludadogreetedcritical 2

|||

{a; en}{to; in}spillover 1

|||

lathespillover 2

|||

...

...

14 / 24

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Results for Spanish SPR

15 / 24

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Results for Spanish SPRLocality effects + Locality effects x WMC

15 / 24

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Results for Spanish SPRLocality effects + Locality effects x WMC

15 / 24

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(12) a. short unbounded dependency

MariasMary’s

außerstextremely

kaltschnauzigeuncaring

Lehrerinteacher

fragte,asked

wenwho.ACC

die Mutterthe mother

gesternyesterday

beimat.the

Treffenmeeting

angeschrien hatyelled had

mitwith

schrillershrill

Stimme.voice

b. long unbounded dependency

DieThe

Lehrerinteacher

fragte,asked

wenwho.ACC

Marias außerst kaltschnauzige Mutter

Mary’s extremely uncaring mother

gesternyesterday

beimat.the

Treffenmeeting

angeschrien hatyelled had

mitwith

schrillershrill

Stimme.voice

c. short baseline

MariasMary’s

außerstextremely

kaltschnauzigeuncaring

Lehrerinteacher

fragte,asked

obif

die Mutterthe mother

jemandensomeone.ACC

beimat.the

Treffenmeeting

angeschrien hatyelled had

mitwith

schrillershrill

Stimme.voice

d. long baseline

DieThe

Lehrerinteacher

fragte,asked

obif

Marias außerst kaltschnauzige Mutter

Mary’s extremely uncaring mother

jemandensomeone

beimat.the

Treffenmeeting

angeschrien hatyelled had

mitwith

schrillershrill

Stimme.voice

16 / 24

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(12) a. short unbounded dependency

MariasMary’s

außerstextremely

kaltschnauzigeuncaring

Lehrerinteacher

fragte,asked

wenwho.ACC

die Mutterthe mother

gesternyesterday

beimat.the

Treffenmeeting

angeschrien hatyelled had

mitwith

schrillershrill

Stimme.voice

b. long unbounded dependency

DieThe

Lehrerinteacher

fragte,asked

wenwho.ACC

Marias außerst kaltschnauzige Mutter

Mary’s extremely uncaring mother

gesternyesterday

beimat.the

Treffenmeeting

angeschrien hatyelled had

mitwith

schrillershrill

Stimme.voice

c. short baseline

MariasMary’s

außerstextremely

kaltschnauzigeuncaring

Lehrerinteacher

fragte,asked

obif

die Mutterthe mother

jemandensomeone.ACC

beimat.the

Treffenmeeting

angeschrien hatyelled had

mitwith

schrillershrill

Stimme.voice

d. long baseline

DieThe

Lehrerinteacher

fragte,asked

obif

Marias außerst kaltschnauzige Mutter

Mary’s extremely uncaring mother

jemandensomeone

beimat.the

Treffenmeeting

angeschrien hatyelled had

mitwith

schrillershrill

Stimme.voice

16 / 24

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Results for German SPR

17 / 24

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Results for German SPRLocality effects + Locality effects x WMC

17 / 24

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Results for German SPRLocality effects + Locality effects x WMC

17 / 24

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General results

I WMC affects retrieval times at unbounded dependencyresolution

I but high-WMC readers showed the strongest slow-down

I and low-WMC readers showed a speed-up

18 / 24

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General results

I WMC affects retrieval times at unbounded dependencyresolution

I but high-WMC readers showed the strongest slow-down

I and low-WMC readers showed a speed-up

18 / 24

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General results

I WMC affects retrieval times at unbounded dependencyresolution

I but high-WMC readers showed the strongest slow-down

I and low-WMC readers showed a speed-up

18 / 24

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Increased processing difficulty does not necessarily lead to longerRTs

I low-WMC subjects may take less time when ambiguities arepresent (but they had worst accuracy) than high-WMCs(MacDonald, Just, & Carpenter, 1992; Pearlmutter & MacDonald, 1995;

Malsburg & Vasishth, 2012)

I low-WMC subjects can read superficially enough to drawcontradicting conclusions from a text (Oberauer, Weidenfeld, &

Hornig, 2006)

I similar results in previous study (only for SPR) (Nicenboim,

Vasishth, Gattei, Sigman, & Kliegl, 2014)

19 / 24

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Increased processing difficulty does not necessarily lead to longerRTs

I low-WMC subjects may take less time when ambiguities arepresent (but they had worst accuracy) than high-WMCs(MacDonald et al., 1992; Pearlmutter & MacDonald, 1995; Malsburg &

Vasishth, 2012)

I low-WMC subjects can read superficially enough to drawcontradicting conclusions from a text (Oberauer et al., 2006)

I similar results in previous study (only for SPR) (Nicenboim

et al., 2014)

19 / 24

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Increased processing difficulty does not necessarily lead to longerRTs

I low-WMC subjects may take less time when ambiguities arepresent (but they had worst accuracy) than high-WMCs(MacDonald et al., 1992; Pearlmutter & MacDonald, 1995; Malsburg &

Vasishth, 2012)

I low-WMC subjects can read superficially enough to drawcontradicting conclusions from a text (Oberauer et al., 2006)

I similar results in previous study (only for SPR) (Nicenboim

et al., 2014)

19 / 24

Page 49: When high-capacity readers slow down and low-capacity ...phiz.c.u-tokyo.ac.jp/~hiroselab/TLMAPLL2014Slides/...I Storage costs: based on the number of heads required to complete the

Increased processing difficulty does not necessarily lead to longerRTs

I low-WMC subjects may take less time when ambiguities arepresent (but they had worst accuracy) than high-WMCs(MacDonald et al., 1992; Pearlmutter & MacDonald, 1995; Malsburg &

Vasishth, 2012)

I low-WMC subjects can read superficially enough to drawcontradicting conclusions from a text (Oberauer et al., 2006)

I similar results in previous study (only for SPR) (Nicenboim

et al., 2014)

19 / 24

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Explanation

1. longer RTs of high-WMC readers may be due tomemory-driven locality effects

2. shorter RTs of low-WMC readers may be due to failure atretrieval and incomplete parsing

3. Can (1) and (2) explain the data?

20 / 24

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Explanation

1. longer RTs of high-WMC readers may be due tomemory-driven locality effects

2. shorter RTs of low-WMC readers may be due to failure atretrieval and incomplete parsing

3. Can (1) and (2) explain the data?

20 / 24

Page 52: When high-capacity readers slow down and low-capacity ...phiz.c.u-tokyo.ac.jp/~hiroselab/TLMAPLL2014Slides/...I Storage costs: based on the number of heads required to complete the

Explanation

1. longer RTs of high-WMC readers may be due tomemory-driven locality effects

2. shorter RTs of low-WMC readers may be due to failure atretrieval and incomplete parsing

3. Can (1) and (2) explain the data?

20 / 24

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Model

Average RT at the verb assuming only one dependent:av RT = Baseline+ pr (dep) (Tr (dep) + Tintegration) + (1-pr (dep)) Tfailure

Probability of successfull retrieval of a dependent:pr (dep) = f(distance, wmc, encoding)

Time required for a successfull retrieval of a dependent:Tr (dep) = g(distance, encoding)

Other assumptions:

I Tfailure < Tr (dep)

I Tintegration > 0

Average RT at the verb:

av RT = Baseline+∏

pr (dep) (∑

Tr (dep) + Tintegration) + ...

21 / 24

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Model

Average RT at the verb assuming only one dependent:av RT = Baseline+ pr (dep) (Tr (dep) + Tintegration) + (1-pr (dep)) Tfailure

Probability of successfull retrieval of a dependent:pr (dep) = f(distance, wmc, encoding)

Time required for a successfull retrieval of a dependent:Tr (dep) = g(distance, encoding)

Other assumptions:

I Tfailure < Tr (dep)

I Tintegration > 0

Average RT at the verb:

av RT = Baseline+∏

pr (dep) (∑

Tr (dep) + Tintegration) + ...

21 / 24

Page 55: When high-capacity readers slow down and low-capacity ...phiz.c.u-tokyo.ac.jp/~hiroselab/TLMAPLL2014Slides/...I Storage costs: based on the number of heads required to complete the

Model

Average RT at the verb assuming only one dependent:av RT = Baseline+ pr (dep) (Tr (dep) + Tintegration) + (1-pr (dep)) Tfailure

Probability of successfull retrieval of a dependent:pr (dep) = f(distance, wmc, encoding)

Time required for a successfull retrieval of a dependent:Tr (dep) = g(distance, encoding)

Other assumptions:

I Tfailure < Tr (dep)

I Tintegration > 0

Average RT at the verb:

av RT = Baseline+∏

pr (dep) (∑

Tr (dep) + Tintegration) + ...

21 / 24

Page 56: When high-capacity readers slow down and low-capacity ...phiz.c.u-tokyo.ac.jp/~hiroselab/TLMAPLL2014Slides/...I Storage costs: based on the number of heads required to complete the

Model

Average RT at the verb assuming only one dependent:av RT = Baseline+ pr (dep) (Tr (dep) + Tintegration) + (1-pr (dep)) Tfailure

Probability of successfull retrieval of a dependent:pr (dep) = f(distance, wmc, encoding)

Time required for a successfull retrieval of a dependent:Tr (dep) = g(distance, encoding)

Other assumptions:

I Tfailure < Tr (dep)

I Tintegration > 0

Average RT at the verb:

av RT = Baseline+∏

pr (dep) (∑

Tr (dep) + Tintegration) + ...

21 / 24

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Model

Average RT at the verb assuming only one dependent:av RT = Baseline+ pr (dep) (Tr (dep) + Tintegration) + (1-pr (dep)) Tfailure

Probability of successfull retrieval of a dependent:pr (dep) = f(distance, wmc, encoding)

Time required for a successfull retrieval of a dependent:Tr (dep) = g(distance, encoding)

Other assumptions:

I Tfailure < Tr (dep)

I Tintegration > 0

Average RT at the verb:

av RT = Baseline+∏

pr (dep) (∑

Tr (dep) + Tintegration) + ...

21 / 24

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Average RT at the verb assuming only one dependent:av RT = Baseline+ pr (dep) (Tr (dep) + Tintegration) + (1-pr (dep)) Tfailure

22 / 24

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Average RT at the verb assuming only one dependent:av RT = Baseline+ pr (dep) (Tr (dep) + Tintegration) + (1-pr (dep)) Tfailure

22 / 24

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Summary

I Increased dependency length leads to more processingdifficulty

I But we see locality effects (slow downs) only when thedependency can be resolved

I And speed-ups associated with failed retrieval if thedependency can not resolved

I A computational model can capture the pattern found inthe data (under very specific assumptions)

23 / 24

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Summary

I Increased dependency length leads to more processingdifficulty

I But we see locality effects (slow downs) only when thedependency can be resolved

I And speed-ups associated with failed retrieval if thedependency can not resolved

I A computational model can capture the pattern found inthe data (under very specific assumptions)

23 / 24

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Summary

I Increased dependency length leads to more processingdifficulty

I But we see locality effects (slow downs) only when thedependency can be resolved

I And speed-ups associated with failed retrieval if thedependency can not resolved

I A computational model can capture the pattern found inthe data (under very specific assumptions)

23 / 24

Page 63: When high-capacity readers slow down and low-capacity ...phiz.c.u-tokyo.ac.jp/~hiroselab/TLMAPLL2014Slides/...I Storage costs: based on the number of heads required to complete the

Summary

I Increased dependency length leads to more processingdifficulty

I But we see locality effects (slow downs) only when thedependency can be resolved

I And speed-ups associated with failed retrieval if thedependency can not resolved

I A computational model can capture the pattern found inthe data (under very specific assumptions)

23 / 24

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24 / 24


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