10/6/10Psyc / Ling / Comm 525 Fall 10 More on predicting word properties in context Dikker,...

10/6/10 Psyc / Ling / Comm 525 Fall 10

More on predicting word properties in context

Dikker, Rabagliati, Farmer, & Pylkkanen (2010) Psych Science

• MagnetoEncephaloGraphy (MEG) study– MEG better than EEG for source localization– BESA (Brain Electrical Source Analyzer) software

• Words have form properties correlated with syntactic category (Farmer et al., 2006; Monaghan et al., 2007)

– Function morphemes, but also phono/ortho properties– Words with properties more typical of their category are read

faster

• Dikker et al. (2009) found M100 sensitive to function morphology on content words– M100 (100-130 msec) larger when content words include

salient unexpected/ungrammatical function morpheme– e.g., The discovery was in the report / reported.

– Brain source = occipital (visual) cortex!!!


Dikker, et al (2010) cont’d

Is salient category-typical morphology required?

Example Stimuli- Nouns w/ derivational noun morph: princess, farmer - Nouns w/ non-morph-marked but typical form: soda- Nouns w/o form-correlated properties: infant

- (Form typicality derived from large corpus analysis by Farmer et al., 2006 and Monaghan et al., 2007)

Presented in context that predicts: - Noun: The tasteless soda …- Participle: The tastelessly soda …


In participle-predicting contexts:

M100 bigger for morphologically marked nouns - Replicates Dikker et al. (2009)

No M100 difference for neutral nouns - Also replicates Dikker et al. (2009)

M100 also bigger for nouns with more noun-likephonology/orthography - So, salient function morphology not necessary


Probabilistic Word Prediction

The more the form properties of a word are typical for its syntactic category,the bigger the M100 in a context that predicts a word from a different category - By 100-130 msec (!) in visual regions sensitive to letter/non-letter distinctions (!) - Dikker argues it’s visual word form properties that don’t match prediction, NOT syntactic category per se


Issues in Sentence Comprehension

• Parallel vs Serial– Parallel = Construct multiple interpretations & wait for

evidence about which is right– Serial = Construct only 1 interpretation at first, & then

try another only if that turns out to be wrong (two stages)

• Which makes more sense depends on whether easier to– Construct multiple interpretations & hold onto them long

enough to avoid making a mistake – Or construct just one interpretation & hope it’s right

– If first choice usually right, serial approach could be more efficient


• Modular vs Interactive– Modular = Take only syntactic properties of words

(N, V, Prep, etc.) & phrase structure options into account at first

– Interactive = Take word meaning, context, & general knowledge into account all along

• Again, which makes more sense depends on whether it’s easier to– Make quick first guesses based on just syntactic

categories of words & some basic phrase structure patterns

– Or combine syntax, meaning, context, & knowledge fast enough in first place


Garden Path Model (Frazier, Rayner, Clifton, F. Ferreira)

• Influential serial (two-stage) modular parsing model– Name comes from claim that we frequently

garden-path during comprehension• But only become aware of it occasionally

• Minimal Attachment Strategy– Whenever multiple structural options, start

with simplest one• i.e. one requiring adding fewest nodes to phrase

structure tree at that point in sentence


Structural Ambiguity

S S

VP VP

NP

NP V NP PP NP V NP PP

A thief shot the cop in the park. A thief shot the cop in the park.

Attachment ambiguity

MinimalNonminimal


Prediction of GP Model

• All sentences with Nonminimal Attachment (NMA) structures should be harder than all sentences with Minimal Attachment (MA) structures– Because always have to revise a wrong first guess

in NMA sentences– That should take some time

• Though often not enough to become aware of problem


Rayner, Carlson, & Frazier (1983)

• Measured how long people spent reading different regions of sentences using an eyetracker

• Stimuli:– Minimally Attached:

The doctor examined the patient with the stethoscope, but he couldn’t figure out what was wrong.

– Non-minimally Attached:The doctor examined the patient with the headache,but he couldn’t figure out what was wrong.

• Prediction of GP Model:– People should read headache & the words after it more

slowly than stethoscope & the words after it


Results – First Pass Times


Interpretation

• Results support GP Model– Slower reading times after headache suggest

people garden-pathed & had to reinterpret

• But, do Rayner et al.’s stimuli provide fair test of MA Strategy?– Temporary ambiguity starts at with– Is there anything earlier in sentence that might

bias interpretation one way or other?

– The verb examined, maybe?• Does it lead to expectations for certain kinds

of phrases following it? (Bresnan)


Verb Argument Structure• Verbs are the most important words in sentences (in English)

– All other words are interpreted relative to the verb

– The librarian put the book on the shelf.• put requires all 3 of these arguments to be in the sentence,• i.e. all 3 arguments are obligatory• Subject / Agent = librarian• Object / Patient (Theme) = book• Location = shelf

* The librarian put.* The librarian put the book.

• The librarian put on the shelf.

• Most verbs have some optional arguments– The doctor examined the patient.– The doctor examined the patient with a stethoscope.– * The doctor examined.


Verb Bias

• Verbs differ in how often they are used with their optional arguments– examine is probably often used with an optional

Instrument

• Maybe Rayner et al. happened to use lots of verbs that often take the kinds of arguments that were present in MA versions?– Would bias results in favor of MA sentences


Taraban & McClelland (1988)

• Measured reading times using self-paced moving window technique

• Stimuli:– Used Rayner et al.’s materials– Plus just as many more like: MA: John read the article in the bathtub

while he was waiting for a call.

NMA: John read the article in the magazine while he was waiting for a call.

• Where John read the article in the ... seems to lead to expectation of– something modifying article (= NMA) rather than a

location where the reading event took place (= MA)


Results


Taraban & McClelland’s Conclusions

• No general Minimal Attachment Strategy• People use knowledge about particular words &

how they’re most likely to be used to guide their interpretation– Especially verbs?

• Serial vs Parallel – Supports serial model – should be no GP if fully parallel

• Modular/Interactive– If verb bias is syntactic, results have no consequence– If word meaning or extra-sentential context had an

immediate effect, that would clearly support Interactive


Another Kind of Temporary Structural Ambiguity

“Marge Schott, managing partner of the Cincinnati Reds, at first did not want to apologize for her remark that Hitler‘was good at the beginning but he just went too far’. Under pressure, she finally said that she regretted her remarks ‘offended many people’.”

- NY Times, 7/21/96, D. Tannen, I’m sorry, I won’t apologize


The referees warned the spectators would probably get too rowdy.against heckling the other team.

- Ambiguity arises because that is optional (in English – obligatory in German, Dutch)

that

^

Temporary ambiguity about relationship between

the Verb

- Is the noun a- Direct Object (DO) (= Minimal Attachment), or- Subject of an Embedded Clause (= NMA)?

& the Noun after it


Verb Bias

• Verbs differ in how often they’re used in particular sentence structures– Compare The referees warned the spectators ... with The bus driver worried the passengers ...

warned = Direct-Object Biased Verb worried = Clause-Biased Verb

Does a verb's most likely use guide initial choicesabout the interpretation of words following it?


Plausibility of Nounas Direct Object

• Plausibility of particular Verb + Noun combos in particular relationships vary

– Compare The referees warned the spectators ... with The referees warned the game ...

– Meaning of game completely rules out DO possibility

• Does that lead people to expect a verb following game because it must be the beginning of an embedded clause?

• If yes, how long does it take to develop that expectation?


Example Stimuli(Garnsey et al., 1997)

DO-Bias Verbs (warned, discovered, heard, …)

The referees warned (that) the spectators would probably get too rowdy.The referees warned (that) the game would probably go into overtime.

Clause-Bias Verbs (worried, realized, suspected, …)

The bus driver worried (that) the passengers were starting to get annoyed.The bus driver worried (that) the tires were starting to go flat.

Equi-Bias Verbs (regretted, predicted, knew, …)

The senior senator regretted (that) the decision had ever been made public.The senior senator regretted (that) the reporter had ever seen the report.


Stimulus Norming & ControlVerb Properties

Verb Type Freq Length DO-Bias Cl-Bias that-Pref

DO-Bias 146 8.3 14% 75% 89%

Equi-Bias 178 7.6 36% 38% 71%

Clause-Bias

128 7.7 79% 12% 67%

Ambiguous Noun Properties

Plausible as DO Implausible as DO

Verb Type Freq LenDO

RateCl

RateFreq Len

DO Rate

Cl Rate

DO-Bias 132 6.3 6.5 5.7 166 5.5 2.9 5.4

Equi-Bias 106 6.5 6.7 5.9 62 6.3 2.9 5.7

Clause-Bias 67 6.7 6.9 6.3 47 6.3 2.6 6.1


DO-Bias VerbsThe referees warned the spectators would probably get too rowdy.The referees warned the game would probably go into

overtime.

Clause-Bias VerbsThe bus driver worried the passengers were starting to get

annoyed.The bus driver worried the tires were starting to go flat.

Equi-Bias Verbs The senior senator regretted the decision had ever been made

public.The senior senator regretted the reporter had ever seen the report.

Reading Time Results(Same pattern in button-pushing moving window & eyetracking first

pass)

read slowly

read slowly


Verbs Rule

• No immediate effect of Plausibility of V + N combination when V had a strong bias

• But Plausibility did have an immediate effect when no V-bias

• So, the two factors interact, with Verb Bias dominating the interaction• Consistent with Constraint-Satisfaction

Models, with some kinds of constraints being more influential


Event-Related Brain Potentials (ERPs) & Language

• N400 (Plausibility, Contextual fit)– Negative, 400 msec– Centro-parietal maximum, sometimes larger on right

• P600 (Garden-paths, grammar errors, complexity,…)– Positive, >500 msec– Centro-parietal maximum


Stimulus Presentationin ERP Study

READY 1Therefereeswarnedthegamewouldprobablygointoovertime.Were the referees expecting a long game?QUESTION


Waveforms Starting at Temporarily Ambiguous Noun

Plausible Noun:The referees warned the spectators ...

DO-Bias Verbs (warned)

6

3

0

-3

-6

6

3

0

-3

-6

Msec

Pz

0 650 1300 1950 2600

spectators

Finalword

Question





I mplausible NounThe referees warned the game ...

Pz N400

6

3

0

-3

-6

6

3

0

-3

-6

Msec0 650 1300 1950 2600

spectators/game



Pz N400

spectators/game

passengers/tires

Plausible Noun:The bus driver worried the passengers ...

Clause-Bias Verbs (worried)

I mplausible Noun:The bus driver worried the tires ...




6

3

0

-3

-6

6

3

0

-3

-6

Msec0 650 1300 1950 2600

6

3

0

-3

-6

6

3

0

-3

-6

Msec0 650 1300 1950 2600



Pz N400

spectators/game

passengers/tires

Plausible Noun:The bus driver worried the passengers ...

Clause-Bias Verbs (worried)

I mplausible Noun:The bus driver worried the tires ...




P600

6

3

0

-3

-6

6

3

0

-3

-6

Msec0 650 1300 1950 2600

6

3

0

-3

-6

6

3

0

-3

-6

Msec0 650 1300 1950 2600


Verbs still rule. Why?

• Principled reasons:

– They provide the most useful information

– Information about them may be simply retrieved, while plausibility requires combining information from multiple words

• More accidental reasons:– They precede the critical nouns in these

sentences [ but, see Trueswell (1996) ]

– They generally appear early in English sentences, leading English speakers to rely on them


Back to Usual Issues• Effect of plausibility supports Interactive

Constraint-Satisfaction Models• With some kinds of info outweighing &/or available

earlier than others

• Any evidence about Serial vs Parallel?

• Reliable correlations between RT at disambiguation & strength of bias toward less preferred structure when plausibility supported the less-preferred structure

• Clause-bias V + Plausible-as-DO N:- Difficulty increased as DO-bias strength of V increased (r =

+.53)

• DO-bias V + Implausible-as-DO N:- Difficulty decreased as Cl-bias strength of V increased (r =

-.58)

• Strongly suggests weighing both options = parallelism


GP Modelers’ Response

• Studies show various factors make easier the structures predicted by GP Model to be hard– So, all of these effects COULD be during 2nd

stage reanalysis, since should only try harder option when forced to reanalyze

• To be definitive, need to show that same factors can make harder the structures predicted by GP Model to be easier– Then effects can’t be due to reanalysis, since

no reanalysis should be necessary according to GP Model


Wilson & Garnsey (2009)

• Are sentences with DO structures made harder by having a Clause-bias verb?

• Sentences with either Clause or DO structures

DO-bias VThe ticket agent admitted the mistake might not have been

caught.The ticket agent admitted the mistake because she had been

caught.

Clause-bias VThe CIA director confirmed the rumor could mean a security leak.The CIA director confirmed the rumor when he testified to

Congress.


Moving Window RT Resultsat disambiguating region

C C

B

B

Clause-bias DO-bias0

20

40

60

80

Verb Bias

C Clause with "that"

B Clause without "that"

J

J

B

B

Clause-bias DO-biasVerb Bias

J Sentence with DO

B Sentence with Clause

a b


Eyetracking First Pass Timesat disambiguating region

C C

B

B

Clause-bias DO-bias-50

-25

0

25

50

Verb Bias

C Clause with "that"

B Clause without "that"

J

J

B

B

Clause-bias DO-biasVerb Bias

J Sentence with DO


a b


Regression-Path Timesat disambiguating region

(also called Right-Bounded or Go-Past Times)

J

J

B

B

Clause-bias DO-bias-50

-25

0

25

50

75

100

Verb Bias

J Sentence with DO


Time from when:- First enter a region- Until leave that region with forward-going saccade-Includes time regressing back to previous regions


Back to Usual Issues

• So, evidence supports both Parallelism & Interaction of multiple within-sentence constraints

• And shows Verb Bias effects in sentences that should not require any reanalysis according to GP– But Verb Bias effects could be part of 1st stage, since

may be a kind of syntactic knowledge– And N+V plausibility effects are only testable in

sentences with Clause structure, so could be due to reanalysis

• What about constraints from outside sentence?


The Role of Prosody

• Embedded Clause / Direct Object sentences can be disambiguated with prosodic phrasing

• Acoustic correlates:– Pause– Pre-boundary lengthening– Pitch contour– Pitch reset– …


How does this kind of prosodic boundary marking influence

sentence interpretation?

• Example of DO Prosody– The basketball star accepted the contract…

…because it paid so well.

Example of Clause Prosody- The basketball star accepted …… the contract required him to play every

game.


Do speakers produce different prosody

in DO and Clause structures?

Gahl & Garnsey (2004)


Boundaries marked more strongly when Structure not consistent with Verb Bias

10/6/10Psyc / Ling / Comm 525 Fall 10 More on predicting word properties in context Dikker,...

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