Tracking the able in stable: Toward an understanding of morphological decomposition in processing and representation Alec Marantz, Olla Somolyak, Ehren.
Post on 13-Dec-2015
213 Views
Preview:
Transcript
Tracking the able in stable:Toward an understanding of morphological
decomposition in processing and representation
Alec Marantz, Olla Somolyak, Ehren Reilly
Bill Badecker, Asaf Bachrach, Susan Gabrieli, John Gabrieli
KIT/NYU MEG Joint Research Lab (et al.)Departments of Psychology and
Linguistics NYU
Outline
• Strawmen Burnt cartoon dual route model cartoon obligatory decomposition model
• Real Issues Identified modality specific access “lexicon”? access lexicon contains morphologically complex forms?
knowledge contributions to affix-stripping/decomposition?
effects of decomposition on stem access? effects of stages of processing on RT in, e.g., lexical decision?
• Experiment Done evidence for, at least, full decomposition or interactive dual route model
no support for whole word access route support for early effects of surface frequency of affixed forms relative to stem frequency – at decomposition stage
• Experiment Planned tracking the -able in amiable and stable
Competing Models of Lexical Access – take your pick
(or, the field’s take on Words and Rules)
Saturday Morning Models of Lexical Access
(as most people read Pinker)
• Full storage model: all complex words (walked, taught) stored and accessed as wholes: only surface frequency effects on access predicted
• Full decomposition model: no complex words stored and accessed as wholes: only stem frequency effects on access predicted
• Dual Route Model (hooray!): irregular complex forms are stored and accessed as wholes; regular complex forms (except high frequency regulars) are not: surface frequency effects on access for irregulars and high frequency regulars
stem frequency but no surface frequency effects on access for regulars
Facts Support Dual Route Model, if Alternatives are these Cartoon
Versions
• Fact: Stem Frequency effects in access for complex words
• Fact: These effects are arguably not attributable to post-access decomposition particularly when considered in connection with masked priming studies showing morphological priming when neither form nor semantic priming are found
• But, fact: Surface frequency effects in lexical access are found in wide variety of cases, including completely regular morphology (e.g., for most inflected words in Finnish)
Problems for Cartoon Dual Route Model
• The representation of even irregular derived or inflected forms must be complex from the grammatical point of view, from morphology, syntax and semantics, felt is as complex as walked e.g., behavior with respect to do support, further inflection, impossibility of derivation, rigidity of meaning…
from the psycho and neurolinguistic point of view, irregulars contain the stem in the same way that regulars do taught-teach identity priming in long-lag priming and for M350 brain response
• So, the issue of “whole word” access to complex irregulars must be focused on possible “whole word” representations in something like a modality-specific “access lexicon,” not on the lexical representations of such forms, which must be complex and as complex as the representations of regulars.
Comparing surface and base frequency effects: Level 1 vs Level 2 Morphology
Surface Frequency Effect:Biggest for “Level 1”
Base Frequency Effect:Only for “Level 2”
Groups of Affixes
Base Frequency Effect:Nothing significant for groups of affixes
Surface Frequency Effect
Semantic Transparency Doesn’t Explain Lack of Base Frequency
Effects
Whole Word “Representations” for Regulars
• Surface frequency effects on access are seen for a variety of completely regular derivations and inflections.
• This should not surprise any obligatory decompositionalist, since surface frequency effects could be tied to the decomposition (the more you’ve decomposed a particular letter/sound sequence into stem and affix, the faster you are at it) or recombination (the more often you’ve put together a particular stems and affix, the faster you are at it) stages of processing. But any such effects imply representation of whole word as complex structure, regardless of regularity.
“Representations”
• Saying that every combination of morphemes in perception or production, no matter how regular, leaves a trace in the language system of the speaker is saying that frequency information is part of the grammar and that all combinations of morphemes are stored in some sense.
• Theories of morphology in particular have made explicit the difference between stored information about combinations of morphemes that may have grammatical effects on syntax, morphology, phonology and certain types of compositional semantics and stored information about combinations that may only have an effect on “idiomatic” or phrasal meanings.
• It’s fairly straightforward to claim that walked is “stored” as a complex form with a certain frequency in the same way that And now for something completely different is. Both must be composed with the grammar when heard or produced, but both may have frequency and special meaning information associated with them that, as far as the kinds of things most linguists study, have no implications for the grammar whatsoever.
Beyond the Cartoons
Realistic Full Decomposition Models Must…
• Recognize that complex words, both regular and irregular, are stored in some sense, leading to possible surface frequency effects
• Investigate the role of surface frequency in decomposition stem access recombination
Realistic Dual Route Models Must…
• Recognize that all complex forms must be representationally complex, containing structures of morphemes and contrasting with monomorphemic constituents
• Focus on the possible existence of stored “whole word” representations at modality-dependent “access lexicons” (whole word word form representations) to distinguish themselves from obligatory decomposition models
u n r e a l
un unrealreal
[un[real]]
“not” REAL
form code
modality specific access lexicon(?)
lemma
(lexical entry)
EncyclopediaStored info about encountered items)
And now for something completely different
UN+REAL (??)
Realistic Interactive Dual Route Models
• Discuss possible sources for facilitation or difficulty for identifying the affixes for decomposition
• These include phonotactic clues to morpheme boundaries (cf. sixths) as well as statistically properties of the the combinations of morphemes, particularly conditional probabilities.
Effect of “Dominance” on Lexical Access
• Jen Hay has made some about the importance of the relative frequency of a morphologically complex form with respect to the frequency of its stem.
• Complex words with high frequencies relative to their stems are “affix-” or “surface dominant”; those with low frequencies are “stem” or “base dominant”
• Hay: affix dominance leads to difficulty in parsing/decomposition, thus reliance on whole-word recognition and suppression of complexity of representation.
Simplistic Prediction of Hay Model
• Affix dominant words should show surface frequency effects since they are accessed via the whole word route.
• Stem dominant words should show stem (cumulative) frequency effects since they are accessed via the decomposition route.
matched for stem frequency (9), difference in surface dominant (mere(ly)) or stem dominant
(sane(ly))
• meremerely• meremerely• meremerely• meremerely• merely
• sanesanely• sane• sane• sane• sane• sane• sane• sane
Taft (2004): “Morphological Decomposition and the Reverse Base Frequency Effect”
Makes same predictions as Hay for RT with full parsing theory
• Base frequency effects… RT to complex word correlates with freq of stem
• …reflect accessing the stem of morphological complex forms whereas
• Surface frequency effects… RT to complex word correlates with freq of complex word
• …reflect the stage of checking the recombination of stem and stripped affix for existence and/or well-formedness.
How can we distinguish these accounts of RT differences?
PL-Dominant PL (High Surface Freq)
SG-Dominant PL (Low Surface Freq)
Post-Access processing (until response)Latency of Lexical Access
Post-Access processing (until response)Latency of Lexical Access
Full Parsing*
Latency of Lexical AccessPost-Access processing (until
response)
Latency of Lexical AccessPost-Access processing (until
response)
Full Listing / Parallel Dual Route
PL-Dominant PL (High Surface Freq)
SG-Dominant PL (Low Surface Freq)
Reilly, Badecker & Marantz 2006 (Mental Lexicon)
Sequential processing of words
Sequential processing of words
Pylkkänen and Marantz, 2003, Trends in Cognitive Sciences
(Pylkkänen, Stringfellow, Flagg, Marantz, Biomag2000 Proceedings, 2000)
Repetition Frequency
1 2 3 4 5 6
Frequency Category (Frequent -- Infrequent)
Behavioral Data: Reaction Time
Categories (n/Million):
1: 7002: 1403: 30 4: 6 5: 1 6: .2
1 2 3 4 5 6
Frequency Category (Frequent -- Infrequent)
Latency of m350 Component
Categories (n/Million):
1: 7002: 1403: 30 4: 6 5: 1 6: .2
(Embick, Hackl, Shaeffer, Kelepir, Marantz, Cognitive Brain Research, 2001)
Latency of M350 sensitive to lexical factors such as lexical frequency and
repetition
Experiment: parallel behavioral and MEG processing
measures • Lexical Manipulation (Baayen, Dijkstra
& Schreuder, 1997, JML) Lemma frequency (CELEX database) Morphological dominance (surface frequency)
Stem Frequenc
y:Stem Dominant
Affix Dominant
High desk – desks crop – crops
Mid deck – deckscliff – cliffs
Low chef – chefschord – chords
Stimuli: 3 Lexical Categories
• Nouns: singular/plural bone bones
• Verbs: stem/progressive chop chopping
• Adjectives:adjective/-ly adverb clear clearly
Experiment: behavioral measures
• Reliable effect of stem frequency in RT
High Medium Low
620
640
660
680
700
720
740
760
Stem Frequency
Experiment: behavioral measures
• Interacting effects on RT of affixation (base vs. affixed) and dominance (base-dominant vs. affix-dominant
B
B
J
J
Unaffixed Affixed
640
660
680
700
720
740
760
780
Affixation
B Base-Dominant
J Affix Dominant
Analysis of M350 peak latency
• Reliable effect of Stem frequency for unaffixed words and for affixed words
High Medium Low
250
300
350
400
Stem Frequency
High Medium Low
250
300
350
400
Stem Frequency
Unaffixed Words Affixed Words
Analysis of M350 peak latency
• Reliable effect of Affixation (base vs. affixed)
Unaffixed Affixed
250
300
350
400
Affixation
Analysis of M350 peak latency
• No effect of Dominance (base-dominant vs. affix-dominant) on M350 peak latency
Affix Dominant Base Dominant
250
300
350
400
Affixed Words
Analysis of M350 peak latency
• No interaction between Dominance (base-dominant vs. affix-dominant) and Affixation (base vs. affixed)
B
B
J
J
Unaffixed Affixed
335
345
355
365
375
385
Affixation
B Base-Dominant
J Affix Dominant
B
B
J
J
Unaffixed Affixed
640
660
680
700
720
740
760
780
Affixation
M350 peak latencyCumulative Response Time
Analysis of M350 peak latency
• Evidence that early stages of access for affixed words is based on full parsing: Whole word frequency affects post-access stages.
Affix Dominant
Base Dominant
0 100 200 300 400 500 600 700 800
M350 Peak Latency and Residual RT for Base-Dominant and Affix-Dominant Affixed Words
Problem with this Conclusion
• No acknowledgement of the effects of dominance and/or surface frequency on parsing stage of decomposition
• No acknowledgement of possible effects of finding affix on stem access
Possible effects of dominance at different stages in word
recognition
parsing affix
stem access recombin-ation and checking
RT
affix dominantmerely
harder, since tighter connection (possibly only at high surface freq values)
based on stem frequency, possibly speeded if high conditional probability of stem given affix
faster, should correlate with surface frequency at high freq values
might correlate (-) with surface frequency, given speed up in recom-bination
stemdominantsanely
easier than for affix dominant (lower transition probability)
based on stem frequency
at lower surface freq values, no effect of surface freq
at lower surface freq value, should correlate (-) with stem freq
• Let’s examine these effects with correlational analysis
• [For affixed words, dominance and surface frequency are correlated in these materials, perhaps unfortunately for our purposes]
Correlations with RT
For all words:• Stem Frequency
r = -0.11; p << 0.01• Surface Frequency
r = -0.15; p << 0.01• Word Frequency
r = -0.18; p << 0.01• Dominance
r = -0.07; p < 0.01• Affixation
r = 0.08; p < 0.01 For affixed words only:
• Stem Frequency r = -0.07; p < 0.05
• Surface (word) Frequency r = -0.17; p << 0.01
• Dominance r = -0.13; p << 0.01
For affixed surface dominant words:• Stem Frequency
r = -0.14; p < 0.01
• Surface Frequency r = -0.14; p < 0.01
Correlational Analyses
• Within a subject (or two): for each sensor, for each point in time, correlate measured magnetic field strength for each stimulus with the value of some stimulus variable (length, frequency, etc.)
correlation with left vs. right hemifield presentation
- 1 0 0 - 5 0 0 5 0 1 0 0 1 5 0 2 0 0 2 5 0
- 0 . 4
- 0 . 3
- 0 . 2
- 0 . 1
0
0 . 1
0 . 2
0 . 3
0 . 4
0 . 5
L e f t V s R i g h t
S i g n i f i c a n c e L e v e l = 0 . 0 9 0
t i m e f r o m s t i m u l u s o n s e t ( m s )
s
t
r
e
n
g
t
h
o
f
c
o
r
r
e
l
a
t
i
o
n
150ms
RMS Correlations Across Subjects
• For some set of sensors, calculate at each time point in each experimental “epoch” the root mean square (RMS) = the square root of the mean of the squares of the values at each sensor (after normalization of values)
• So, for each subject, for each item, an RMS “wave” can be provided for the correlational analysis
• At each time point, the RMS value for each stimulus is correlated with a stimulus variable
Grand Average All Stimuli All Subjects (11)
M170 Sensors Chosen on the basis of field pattern, subject by subject
M350 sensors chosen subject by subject
M170 RMS Across Subjects:Affixed Words Split Half Divided by
Affix Dominance
M170 Correlation with Dominance:Significant “parsing” effect
No M170 Stem Frequency Effect for unaffixed words
No M170 Word/Non-Word “lexicality” effect
Although surface frequency correlates with affix dominance in these words, no
surface frequency M170 effect
RMS Averages Support Previous Conclusion that M350 peak lags for Affixed as
Compared to Un-Affixed Words
Affix Dominance RMS Averages Suggest Dominance Effects at M350
Base Frequency Correlation for Affixed Surface Dominant Words at M350 Supports
Full Decomposition
Effects of Dominance on M350: In Contrast to M170, Now Bigger Effect for Stem Dominant Words(so, dominance effects affix-dominant words early = parsing, stem-dominant words later =
recombination?)
Recombination Effect?:Correlation with Conditional Probability of
Stem, Given Affix, for Affixed Words
Evidence For Modality-Specific Access Lexicon?
• At M170, where there are large effects of dominance, it’s difficult to find word-form frequency effects or lexicality effects
• However, does “parsing” at M170 require access to “lexicalized” word forms (recall Zweig & Pylkkänen’s winter/farmer contrast) or to high-n n-grams or what?
• Dominance effects at M170 suggest frequency information associated with word-forms, as does winter/farmer contrast dominance reflects the conditional probability of the affix given the stem
Sophisticated Interactive Dual-Route Models predict Whole Word WordForm
Effects
• Note that there is no evidence from the current study (or any study I know of) that whole word wordforms are available for complex words
• To the contrary, complex words with high affix dominance – the very words that should have whole word wordforms according to Hay – show the biggest effect of complexity at the M170, where wordform effects are expected (i.e., these don’t act like simplex wordforms)
word length; 2 subjects combined
- 1 0 0 - 5 0 0 5 0 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 3 5 0 4 0 0
- 0 . 2
- 0 . 1 5
- 0 . 1
- 0 . 0 5
0
0 . 0 5
0 . 1
0 . 1 5
0 . 2
0 . 2 5
T i m e f r o m S t i m u l u s O n s e t ( m s )
S
t
r
e
n
g
t
h
o
f
C
o
r
r
e
l
a
t
i
o
n
V a r i a b l e 1
S i g n i f i c a n c e L e v e l = 0 . 0 5 7
Localization of M100 from averaged data
one subject, with orthographic neighborhood size
- 1 0 0 - 5 0 0 5 0 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 3 5 0 4 0 0
- 0 . 2
- 0 . 1 5
- 0 . 1
- 0 . 0 5
0
0 . 0 5
0 . 1
0 . 1 5
0 . 2
T i m e f r o m S t i m u l u s O n s e t ( m s )
S
t
r
e
n
g
t
h
o
f
C
o
r
r
e
l
a
t
i
o
n
3 1 0 1 : O r t h o g r a p h i c N e i g h b o u r s
S i g n i f i c a n c e L e v e l = 0 . 0 8 0
fMRI data, same experiment, same variable
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.
Correlation in One Subject with Word Length
Correlation with Word Frequency
Correlation with Lexicality
if there were a modality specific word form lexicon…
• Since word-form access lexicon effects would be expected at the M170, given the nature of the morphological complexity results for the M170, the whole-word word form hypothesis would predict: if word-form frequency effects are at all visible for unaffixed words at the M170, there should be word-form frequency effects at the M170 for complex forms once effects of parsing are regressed away
Experiment Planned, Continuous Variables:Investigate Factors Influencing Stages of
Processing for Morphologically Complex Words AND Provide Neural Indices of Morphological
Decomposition for Controversial Cases• Initial parsing and decomposition
affix frequency? transition probabilities, computed over morphemes or over strings?
• stem activation frequency “family” structure (family size, frequency, semantic transparency, etc.)
conditional probability from affix recognition?
• re-merger of pieces surface frequency and conditional probabilities?
• evaluation of combined structure semantic transparency?
status of bound stems
• durable same root in duration predicts durability
• amiable (stable?) no other uses of root but, predicts amiability (stability)
• cable false parse, compared to stable
tracking the -able in amiable
• If words like tolerable with a recurring root and amiable with a unique root nevertheless are parsed and computed as is workable with a word root, then M170 “parsing” effects should be visible for these “opaque” words, since effects are strong for stem-dominant words
M350 effects should be modulated by morphological family and conditional probability of word given the affix
correlational contrasts with “matched” unaffixed words should be seen at all stages of access
Categories of Affixed Words for New Experiment
• 1. Word-Affix taxable
• 2. Root-Affix tolerable
• 3. Pseudo-Affix (mono-morphemes) capable
• Morphological parsing as from English Lexicon Project
Nine Affixes
•able•ary•ant•ity•ate
•ic•er•al•ion
More Examples
Word-Affix Root-AffixPseudo-Affix
rationality
quantity vicinity
destroyer sorcerer character
classic specific empiric
•6 words per category per affix
•6 x 3 x 9 = 162 affixed words
The 27 subgroups…
• All groups are equivalent and well-distributed over:
length mean bigram count frequency
• All word-affix groups are well-distributed over:
surface vs stem frequency dominance
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.
Length
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.
Log Frequency
Mean Bigram Count
QuickTime™ and aTIFF (Uncompressed) decompressorare needed to see this picture.
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.
Matched Words
• Matched on frequency of ending: Calculated affix frequencies from CELEX database
For each affix, found a list of mono-morphemes with roughly the same ending frequency. (log frequency of +/- 0.3 from affix log frequency)
Chose 18 matched words for each affix that had same parameter distributions as affixed words.
Examples
• er evening, lightning, mediocre
• ity caricature, terrain, pertain
• able sentence, thought, profound
Parsability
• Affixed and Matched words are equally distributed over “parsability” - transitional probability between the last 2 letters of the stem and the first two letters of the affix (or affix-match).
• Example - for “ability” this would be: given that you see “il” 5 letters from the end of a word, what is the probability that “it” will follow it?
Non Words
• 324 non-words chosen randomly to have the same distributions over length and mean bigram count as the words.
• 648 total words and non-words
Homographs
• Added 20 homographs to compare the effects of word frequency with word-form frequency (to test for nature of modality specific access lexicon, if any)
• Examples: content, contract, wind
• Matched with a group of words and a group of non-words.
Total = 728 stimuli
• 162 affixed words 3 categories, 54 per category
• 162 words matched to affixed words• 20 homographs• 20 words matched to homographs• 324 non-words matched to affixed words
• 40 non-words matched to homographs
That’s all, fo..(oh, you know the rest…)
top related