1. 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

2. Outline Strawmen Burnt Real Issues Identified 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?

3. Experiment Done Experiment Planned
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

4. Competing Models of Lexical Access – take your pick (or, the field’s take on Words and Rules)

5. 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

6. Dual Route Model (hooray
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

7. Fact: Stem Frequency effects in access for complex words
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

8. 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)

9. 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

10. 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.

11. Comparing surface and base frequency effects: Level 1 vs Level 2 Morphology

12. Surface Frequency Effect: Biggest for “Level 1”

13. Base Frequency Effect: Only for “Level 2”

14. Groups of Affixes

15. Base Frequency Effect: Nothing significant for groups of affixes

16. Surface Frequency Effect

17. Semantic Transparency Doesn’t Explain Lack of Base Frequency Effects

18. 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.

19. “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.

20. 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.

21. Beyond the Cartoons

22. 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

23. 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

24. And now for something completely different
Encyclopedia
Stored info about encountered items)
[un[real]]
UN+REAL (??)
lemma
(lexical entry)
“not”
REAL
modality specific access lexicon(?)
unreal un
real
form code
u n r e a l

25. 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.

26. 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.

27. 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.

28. mere merely merely sane sanely sane
matched for stem frequency (9), difference in surface dominant (mere(ly)) or stem dominant (sane(ly))
mere merely
merely
sane sanely
sane

29. Base frequency effects…
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.

30. How can we distinguish these accounts of RT differences?
Full Listing / Parallel Dual Route
PL-Dominant PL (High Surface Freq)
Latency of Lexical Access
Post-Access processing (until response)
SG-Dominant PL (Low Surface Freq)
Latency of Lexical Access
Post-Access processing (until response)
PL-Dominant PL (High Surface Freq)
SG-Dominant PL (Low Surface Freq)
Post-Access processing (until response)
Latency of Lexical Access
Full Parsing*
Reilly, Badecker & Marantz 2006 (Mental Lexicon)

31. Sequential processing of words

32. Sequential processing of words
Pylkkänen and Marantz, 2003, Trends in Cognitive Sciences

33. Latency of M350 sensitive to lexical factors such as lexical frequency and repetition
(Pylkkänen, Stringfellow, Flagg, Marantz, Biomag2000 Proceedings, 2000)
Repetition
Frequency
(Embick, Hackl, Shaeffer, Kelepir, Marantz, Cognitive Brain Research, 2001)

34. Experiment: parallel behavioral and MEG processing measures
Lexical Manipulation (Baayen, Dijkstra & Schreuder, 1997, JML)
Lemma frequency (CELEX database)
Morphological dominance (surface frequency)
Stem Frequency:
Stem Dominant
Affix Dominant
High
desk – desks
crop – crops
Mid
deck – decks
cliff – cliffs
Low
chef – chefs
chord – chords
162 Words in lexical decision:
half inflectionally affixed (nouns [90], verbs [42] & adjectives [30])
half the corresponding base forms
corresponding items (e.g., high stem dominant and high affix dominant base / affixed forms were matched on length, category, and surface and lemma frequency,
162 Non-words
81 affixed nonce stems
81 illicit combinations of stem and affix (e.g., thick-s)

35. Stimuli: 3 Lexical Categories
Nouns: singular/plural
bone
bones
Verbs: stem/progressive
chop
chopping
Adjectives: adjective/-ly adverb
clear
clearly

36. Experiment: behavioral measures
Reliable effect of stem frequency in RT

37. Experiment: behavioral measures
Interacting effects on RT of affixation (base vs. affixed) and dominance (base-dominant vs. affix-dominant

38. Analysis of M350 peak latency
Reliable effect of Stem frequency for unaffixed words and for affixed words
Unaffixed Words
Affixed Words

39. Analysis of M350 peak latency
Reliable effect of Affixation (base vs. affixed)

40. Analysis of M350 peak latency
No effect of Dominance (base-dominant vs. affix-dominant) on M350 peak latency

41. Analysis of M350 peak latency
No interaction between Dominance (base-dominant vs. affix-dominant) and Affixation (base vs. affixed)
Cumulative Response Time
M350 peak latency

42. 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.

43. 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

44. Possible effects of dominance at different stages in word recognition
parsing affix
stem access
recombin-ation and checking RT
affix dominant
merely
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
stem
dominant
sanely
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

45. 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]

46. Correlations with RT For affixed surface dominant words:
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:
r = -0.07; p < 0.05
Surface (word) Frequency
r = -0.17; p << 0.01
r = -0.13; p << 0.01
For affixed surface dominant words:
Stem Frequency
r = -0.14; p < 0.01
Surface Frequency

47. 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.)

48. correlation with left vs. right hemifield presentation

49. 150ms

50. 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

51. Grand Average All Stimuli All Subjects (11)

52. M170 Sensors Chosen on the basis of field pattern, subject by subject

53. M350 sensors chosen subject by subject

54. M170 RMS Across Subjects: Affixed Words Split Half Divided by Affix Dominance

55. M170 Correlation with Dominance: Significant “parsing” effect

56. No M170 Stem Frequency Effect for unaffixed words

57. No M170 Word/Non-Word “lexicality” effect

58. Although surface frequency correlates with affix dominance in these words, no surface frequency M170 effect

59. RMS Averages Support Previous Conclusion that M350 peak lags for Affixed as Compared to Un-Affixed Words

60. Affix Dominance RMS Averages Suggest Dominance Effects at M350

61. Base Frequency Correlation for Affixed Surface Dominant Words at M350 Supports Full Decomposition

62. 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?)

63. Recombination Effect?: Correlation with Conditional Probability of Stem, Given Affix, for Affixed Words

64. 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

65. 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)

69. word length; 2 subjects combined

71. Localization of M100 from averaged data

72. one subject, with orthographic neighborhood size

74. fMRI data, same experiment, same variable

75. Correlation in One Subject with Word Length

76. Correlation with Word Frequency

77. Correlation with Lexicality

78. 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

79. Initial parsing and decomposition
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?

80. status of bound stems durable amiable (stable?) cable
same root in duration
predicts durability
amiable (stable?)
no other uses of root
but, predicts amiability (stability)
cable
false parse, compared to stable

81. 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

82. 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

83. Nine Affixes
able
ary
ant
ity
ate ic er al
ion

84. More Examples Word-Affix Root-Affix Pseudo-Affix rationality quantity
vicinity
destroyer
sorcerer
character
classic
specific
empiric

85. 6 words per category per affix
6 x 3 x 9 = 162 affixed words

86. The 27 subgroups… length mean bigram count frequency
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

87. Length

88. Log Frequency

89. Mean Bigram Count

90. 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.

91. Examples er ity able evening, lightning, mediocre
caricature, terrain, pertain
able
sentence, thought, profound

92. 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?

93. 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

94. 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.

95. 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

96. That’s all, fo..(oh, you know the rest…)