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Beyond rule versus rote?

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christian bentz

on 22 August 2013

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Transcript of Beyond rule versus rote?

Beyond Rule versus Rote?
Processing of distinctive dative and genitive case markers in German
Christian Bentz
Department of Theoretical and Applied Linguistics
University of Cambridge

Häus-er-n

houses.PL.DAT
Rule
N
N
stem+Umlaut
Suffix
PL
DAT
Häus
-er
-n
English past tense -ed suffix:

Marslen-Wilson & Tyler (2007)
Tyler, Stamatakis, Post, Randall & Marslen-Wilson (2005)
Pinker & Ullman (2002)

German s-plural and participles:
Marcus, Brinkmann, Clahsen, Wiese & Pinker (1995)
Clahsen (1999)
Rote
- Häusern
- Auto
- Schiffahrtskapitänkabinenschlüssel
etc.

Mental lexicon
Entries stored whole,
no decomposition
German plural suffixes except -s:


Irregular past tense in English:
Marslen-Wilson & Tyler (2007)
Tyler, Stamatakis, Post, Randall & Marslen-Wilson (2005)
Pinker & Ullman (2002)

Marcus, Brinkmann, Clahsen, Wiese & Pinker (1995)
Clahsen (1999)
Methods
-
lexical decision
- priming
- EEG
- fMRI
- neural networks
etc.
Lexical Decision Tasks

dependent variable:
reaction time
Full parsing model
predictor variables: frequencies
Rote
Load
German suffix -n
no grammatical meaning
plural
plural + dative
Rule


[Stem] + [Suffix.PL] + [Suffix.DAT]


Full listing model
[Häusern]
Dual route models
see Baayen, Dijkstra & Schreuder (1997) for a discussion
Pinker & Ullman (2002)
Pinker (1991)
Pinker & Prince (1991)
Butterworth (1983)
See Baayen Dijkstra & Schreuder (1997) for a discussion
Some words stored whole, some are decomposed
[Häusern] [Auto] [-s.PL]
Clahsen (1999)
Clahsen, Eisenbeiss & Sonnenstuhl-Henning (1997)
Baayen, Dijkstra & Schreuder (1997)
Baayen & Schreuder (1995)
...

(low)
(medium)
(high)
LD Experiment on Grammatical Load
Methods
Results
Participants


26 native speakers of German
(mean age 27, 14 females, 12 males)
Stimuli
Stimuli
}
}
-n suffix
-s suffix
WEBCELEX (Baayen et al. 1995)
Target words
20
20
20
20
20
20
matched for
surface frequency*
(1-20 per mio)
and
length in letters
120
Filler words
+
120
Non-words
120
120
240
changing stem
changing suffix
+
selected from webcelex
480
Analysis
Dependent: log(RT)
Predictors: grammatical load (factor, 3 levels)
(surface frequency), stem frequency,
family frequency
Method: Mixed effects models with lme4 in R
Baayen, Davidson & Bates (2008)
Barr, Levy, Scheepers, Tily (2013)
Bates & Maechler (2010)
R Development Core Team (2012)


Null model
Load model
Load+Freq
Load model
Load+Freq
Null model
χ2(2) = 8.6, p = 0.01
χ2(2) = 13.12, p = 0.001
χ2(3) = 2.72, p = 0.43
χ2(3) = 4.19, p = 0.24
*Additional frequency measures:
stem frequency, family frequency, family size
VIF < 2
if family size is excluded
VIF < 2
if family size is excluded
Introduction
Experiment
Discussion
What is it about grammatical load?
Post-test questionnaire:
Did you have problems with dative and genitive forms? Did you use a strategy to decide?
52 % nope
24 % imagined the correct article
20 % imagined whole phrases
6% used Umlaut patterns
}
44% used
word-external
strategy
wegen d
es

Koch
s
because of the.
GEN.SG
cook.
GEN.SG
N
N
Suffix.GEN
word
internal
case dependency
prepositional trigger
word
external
Example given by one of the subjects:
wegen des Kochs
'because of the cook'
Conclusion


Removing
single word stimuli
from
sentential context
does not entail that processing
will be independent of that context.
Remaining issues

- type frequencies are tied with
grammatical load. Which is the predictor?





- individual variation


Thanks.

‘brave-ly’

‘walk-ed’

Bozic et al. (2013)

Bozic et al. (2010)

Does the distinction present for syntactically complex phrases
extend to morphologically complex words that code syntactic information?

Complex morphology in Russian verbs
Ongoing research by Anastasia Klimovich-Smith
Neurolex Group, Cambridge
Beyond lexical decision
Frequency
Processing
Baayen, Milin, Đurđević, Hendrix, Marelli (2011)
Milin, Đurđević & Moscoso del Prado Martín (2009)
Lukatela, Gligorijević & Turvey (1980)
For Serbian cases:
Full transcript