Psy1302 Psychology of Language Lecture 10 Ambiguity Resolution Sentence Processing I

agenda Connecting word recognition with sentence processing via ambiguity resolution. Connecting word recognition with sentence processing via ambiguity resolution. Lexical Ambiguity Lexical Ambiguity Syntactic Ambiguity Syntactic Ambiguity –& MORE MODELS!!! Garden-Path Model Garden-Path Model Constraint-Satisfaction Model Constraint-Satisfaction Model –& CLEVER but difficult to explain experiments! (so ask questions if you are lost!!!) (so ask questions if you are lost!!!)

Ambiguity Time flies like an arrow –Time proceeds as quickly as an arrow proceeds. –Measure the speed of flies in the same way that you measure the speed of an arrow. –Measure the speed of flies in the same way that an arrow measures the speed of flies. –Measure the speed of flies that resemble an arrow. –Flies of a particular kind, time flies, are fond of an arrow.

Qs about Online Ambiguity Resolution What alternatives are available at different time points? What alternatives are available at different time points? What degree of commitment is made to one or more alternatives? What degree of commitment is made to one or more alternatives? What information is used to guide these commitments? What information is used to guide these commitments?

Lexical Ambiguity (semantic, lexical)

Cross-Modal Priming

Cross-Modal Priming Exp. 1 (Swinney et al. 1978; Onifer & Swinney, 1981) Rumour had it that for many years, the government building had been plagued with problems. The man was not surprised when he found several (spiders, roaches, and other) bugs in the corner of his room. ANT SPY SEW ANT SPY SEW {

immediate3 syll delay Amount of Priming (unrelated word RT minus related word RT) “ant” “spy” Cross-Modal Priming Exp. 1 (Swinney et al. 1978; Onifer & Swinney, 1981)

Rumour had it that for many years, the government building had been plagued with problems. The man was not surprised when he found several (spiders, roaches, and other) bugs (insects) in the corner of his room. ANT SPY SEW ANT SPY SEW {

Riddle What has wheels and flies, but is not an airplane? What has wheels and flies, but is not an airplane? What [has wheels] and [flies], but is not an airplane? What [has wheels] and [flies], but is not an airplane? What has [wheels and flies], but is not an airplane? What has [wheels and flies], but is not an airplane? V N

Cross-Modal Priming Exp. 2 (Tanenhaus, Leiman, & Seidenberg, 1979; Seidenberg, Tanenhaus, Leiman, & Bienkowski, 1982) Noun reading: I bought a watch. Noun reading: I bought a watch. Verb reading: I will watch. Verb reading: I will watch CLOCK

Cross-Modal Priming Exp. 2 (Tanenhaus, Leiman, & Seidenberg, 1979; Seidenberg, Tanenhaus, Leiman, & Bienkowski, 1982) Noun reading: I bought a watch. Noun reading: I bought a watch. Verb reading: I will watch. Verb reading: I will watch clock

Seidenberg, Tanenhaus, Leiman, & Bienkowski, 1982

Effects of Frequency in Ambiguity Resolutions pitcher port Equibias Ambiguous Word Non-Equibias Ambiguous Word

Duffy, Morris, & Rayner (1988) Varied frequency of homonyms Varied frequency of homonyms Varied whether supportive context came before word or after word. Varied whether supportive context came before word or after word.

low-level infrared light  eye low-level infrared light  eye reflections from cornea and lens indicate position of eye fixation. reflections from cornea and lens indicate position of eye fixation. Older Eye-tracker Head movements messes up calibration  Bite bar or head rest is needed

*Control words in Parentheses *For Non-Equibiased, Context supports non-dominant reading. Duffy, Morris, & Rayner (1988) Supportive Context No Supportive Context Supportive Context No Supportive Context

pitcher port Equibias Ambiguous Word Non-Equibias Ambiguous Word whiskey Non-Ambiguous Control soup Non-Ambiguous Control No Supportive Context -- Thickness of the line indicates amount of activation.

pitcher port Equibias Ambiguous Word Non-Equibias Ambiguous Word whiskey Non-Ambiguous Control soup Non-Ambiguous Control Adding Supportive Context -- Thickness of line indicates amount of activation. + supportive context

pitcher + supportive context port Equibias Non-Equibias + supportive context Supportive ContextNo Supportive Context

= High reaction time Equibiased: Equibiased: –Processing time lower when provided with prior disambiguating contextual support. (Reason: because accessing both meanings) Non-equibiased: Non-equibiased: –Processing time high when provided with prior disambiguating contextual support supporting the less frequent meaning. (Reason: made the less frequent more “equal” to the other meaning) –Processing time low when not provided disambiguating contextual support for the less frequent meaning. (Reason: not considering the less frequent meaning. In fact, time spent later in disambiguating region is higher due to a need to reanalyze). Supportive ContextNo Supportive Context

Lexical Ambiguity Current conclusions Parallel, rather than serial activation Parallel, rather than serial activation Relative strength of activation depends on: Relative strength of activation depends on: –Degree of contextual constraint available –Frequency of use of each meaning

Syntax Another level up! Another level up! Parsing: figuring how the words in a phrase or sentence combine, using the rules in a grammar. Parsing: figuring how the words in a phrase or sentence combine, using the rules in a grammar. Parser Parser

Syntactic Ambiguity

Is the woman insured? Woman drives off with what she thought was her date’s car (but wasn’t) and then totaled it. Can she get money from her insurance company: Woman drives off with what she thought was her date’s car (but wasn’t) and then totaled it. Can she get money from her insurance company: Contract says: Contract says: –Such insurance as is provided by this policy applies to the use of a non-owned vehicle by the named insured and any person responsible for use by the named insured provided such use is with the permission of the owner.

Does he deserve jail time? Drug dealer tried to swindle an (unbeknownst to him) undercover cop by selling a bag of powder that had only a minuscule trace of methamhetamine. The quantity was not harmful. Drug dealer tried to swindle an (unbeknownst to him) undercover cop by selling a bag of powder that had only a minuscule trace of methamhetamine. The quantity was not harmful. Law says Law says –Every person who sells any controlled substance which is specified in subdivision (d) shall be punished. –(d) Any material, compound, mixture, or preparation which contains any quantity of the following substance having a potential abuse associated with a stimulant effect on the central nervous system: Amphetamine, Methamphetamine…

The bully hit the girl with the... The bully hit the girl with the......stick....stick....wart. (*garden-pathed)...wart. (*garden-pathed) The woman felt the fur... The woman felt the fur......and then left....and then left....was very expensive. (*garden-pathed)...was very expensive. (*garden-pathed) Local Ambiguities (Being led down the “garden-path”)

Local Ambiguities The bully hit the girl with the wart and then… The bully hit the girl with the wart and then… The bully hit the girl with the stick and then… The bully hit the girl with the stick and then…

Ambiguous Sentences time yesterday today Last night, the car crashed. time yesterday today The car crashed. The reporter said the car crashed last night. Homework sentence

Ambiguous Sentences time …car... time …car.... The reporter said the car crashed last night. S NP VP S AdvP NP V VP The reporter said last night the car crashed V The reporter NP VP S NP V VP said the car crashed S V AdvP last night

Ambiguous Sentences Jamie saw the man with the telescope. Homework sentence

Ambiguous Sentences Jamie saw the man with the telescope. S NP VP PN NP PP V Det NPNP Jamie saw theman with the telescope S NP VP PN NP PP V Det N P NP Jamie saw theman with the telescope NP

Traditional Views (contrasting lexical and syntactic ambiguity) Table from MacDonald, Pearlmutter, & Seidenberg Paper

Garden-Path Model (Frazier & Fodor, 1978) Serial: the processor initially identifies only one analysis Serial: the processor initially identifies only one analysis –selected based on structural simplicity Modular: Initial structure built on the basis of syntactic category labels. Modular: Initial structure built on the basis of syntactic category labels. –revision process incorporates other information.

Garden Path Model Selecting the initial analysis When word is identified, its syntactic category is retrieved When word is identified, its syntactic category is retrieved Parser identifies which rules of the grammar contain that category Parser identifies which rules of the grammar contain that category Analysis selected on the basis of structural simplicity Analysis selected on the basis of structural simplicity –Late Closure –Minimal Attachment

Garden Path Model Heuristics for Simplicity Late Closure Late Closure –When possible, attach incoming lexical items into the clause or phrase currently being processed (i.e., the lowest possible nonterminal node dominating the last item analyzed). Minimal Attachment Minimal Attachment –Attach incoming lexical items into the phrase- marker being constructed with the fewest nodes consistent with well-formedness rules of language.

Late Closure time yesterday today..car… time yesterday today..car… S NP VP S AdvP NP V VP The reporter said last night the car crashed V The reporter NP VP S NP V VP said the car crashed S V AdvP last night

Late Closure The reportersaidthe car crashed last night S VP V said The reporter NP the car S VP crashed V AdvP last night 1 or 2? 1 2

Late Closure Choice #1 Choice #1 Choice #2 Choice #2 …car... time Last night… S NP VP S AdvP NP V VP The reporter said last night the car crashed V The reporter NP VP S NP V VP said the car crashed S V AdvP last night

Minimal Attachment S NP VP PN NP PP V Det NPNP Jamie saw theman with the telescope S NP VP PN NP PP V Det N P NP Jamie saw theman with the telescope NP

Minimal Attachment NP PN Jamie S VP V saw Jamie saw the manwith PP P with 1 or 2? NP the Det man N 1 2

Minimal Attachment Choice #1 Choice #1 PP P with NP PN Jamie S VP V saw NP the Det man N PP P with NP PN Jamie S VP V saw NP the Det NP Choice #2 Choice #2 NP  Det + N NP  NP + PP man N 1 extra node

Garden Path Model Heuristics for Simplicity Late Closure Late Closure –When possible, attach incoming lexical items into the clause or phrase currently being processed (i.e., the lowest possible nonterminal node dominating the last item analyzed). Minimal Attachment Minimal Attachment –Attach incoming lexical items into the phrase- marker being constructed with the fewest nodes consistent with well-formedness rules of language.

Ambiguities: Late Closure and Minimal Attachment NP/VP Attachment Ambiguity: NP/VP Attachment Ambiguity: –The cop [saw [the burglar] [with the binoculars]] –The cop saw [the burglar [with the gun]] In-Class Exercise (see also homework)

Ambiguities: Late Closure and Minimal Attachment NP/S Complement Attachment Ambiguity: NP/S Complement Attachment Ambiguity: –The athlete [realized [his goal]] last week –The athlete realized [[his shoes] were across the room] In-Class Exercise (see also homework)

Ambiguities: Late Closure and Minimal Attachment Clause-boundary Ambiguity: Clause-boundary Ambiguity: –Since Jay always [jogs [a mile]] the race doesn’t seem very long –Since Jay always jogs [[a mile] doesn’t seem very long] In-Class Exercise (see also homework)

Ambiguities: Late Closure and Minimal Attachment Reduced Relative-Main Clause Ambiguity: Reduced Relative-Main Clause Ambiguity: –[The woman [delivered the junkmail on Thursdays]] –[[The woman [delivered the junkmail]] threw it away] In-Class Exercise (see also homework)

Ambiguities: Late Closure and Minimal Attachment Relative/Complement Clause Ambiguity: Relative/Complement Clause Ambiguity: –The doctor [told [the woman [that he was in love with]] [to leave]] –The doctor [told [the woman] [that he was in love with her]] In-Class Exercise (see also homework)

Garden-Path Model Strengths: Considers our working memory capacity Considers our working memory capacity Speed achieved by considering one interpretation Speed achieved by considering one interpretation Explains broad range of phenomena Explains broad range of phenomena

Models of Sentence Processing Garden-Path Model Garden-Path Model –Autonomous Late closure Late closure Minimal attachment Minimal attachment Constraint-Based Model Constraint-Based Model –Interactive Lexical Biases Lexical Biases Referential Contexts Referential Contexts Structural Biases Structural Biases } Cues from multiple sources constrain interpretation

Traditional Views (contrasting lexical and syntactic ambiguity) Table from MacDonald, Pearlmutter, & Seidenberg Paper Constraint-Satisfaction Model SAYS it’s not the right characterization!