+
AAAL 2014
What makes second language lexical competition so competitive? Nick B. Pandža, University of Maryland Phillip Hamrick, Kent State University
+
Motivation + L2 lexical competition in spoken word recognition + Gating paradigm + Hypotheses
2
+
3
Motivation L2 lexical competition in spoken word recognition n L2
learners activate more candidates that compete for longer than native speakers (Broersma, 2012; Cutler, 2011)
n L2
learners have:
n More
lexical entries n Extra candidates from inaccurate phonemic processing (e.g., Broersma & Cutler, 2008) n A reduced ability to inhibit incorrect competitors (e.g., Ruschemeyer, Nojack, & Limback, 2008)
+
4
Motivation L2 lexical competition in spoken word recognition n L2
word recognition is:
n slower
and less accurate than L1 n the “victim” of prolonged activation and competition n Even
with sentential context cues
(e.g., gating task in Field, 2008) n Learners
still suffer from prolonged activation and persistence from incorrect lexical competitors/parses
+
5
Motivation L2 lexical competition in spoken word recognition n Previous
research has focused on:
n Factors
(e.g., frequency, ND) that influence accurate L2 spoken word recognition (e.g., Imai, Walley, & Flege, 2005)
n Ways
in which participants activate L2 lexical competitors in listening (e.g., Broersma & Cutler, 2008)
n However, we
still don’t precisely know what causes prolonged activation/persistence
+
6
Motivation L2 lexical competition in spoken word recognition n What
are the characteristics of L2 lexical candidates prior to accurate word recognition?
n What
makes lexical competition persist late in hearing a word?
+
7
Motivation L2 lexical competition in spoken word recognition n In
psycholinguistic models of the lexicon, frequency is assumed to influence activation levels n Either
less stimulation is required to activate HF
words n Or HF words are simply activated to a higher degree than LF words n In
models that incorporate neighborhoods (e.g., NAM), phonological similarity resonates among neighbors, essentially ‘amplifying’ the overall signal
+
8
Motivation Gating paradigm (Grosjean, 1980) n Words
broken down into successive increments
n Participants
must guess the word after each gate and provide a confidence rating
n Requires
learners to generate lexical competitors
n Allows
for analysis of L2 learner-produced lexical competitors (e.g., frequency, ND)
+
9
Motivation Gating paradigm (Grosjean, 1980) n
1: _____
1234
n
2: _____
1234
n
3: _____
1234
n
4: _____
1234
n
5: _____
1234
n
6: _____
1234
+
10
Hypotheses I.
We predict that learners will recognize words less often and at later gates than is often reported for short words for NSs. (i.e., 200-350ms,
Grosjean, 1980; Marslen-Wilson & Warren, 1994; Metsala, 1997; Wayland et al., 1989) II.
a) Prior to accurate word recognition learners will produce words that are higher in frequency than the target words they are hearing. n b)
Because competition persists for longer in the L2 (e.g., Broersma, 2012; Broersma & Cutler, 2011), we predict that learners will continue to produce words that are high in frequency across all gates (cf. Wayland et al., 1995).
III.
We predict that learners will produce words that are high in neighborhood density (>20) across all gates. (a la NAM, Luce & Pisoni, 1998)
+
Methods + Participants + Materials + Procedure
11
+
12
Methods Participants n 22
ESL learners (7 F; Age 19-36)
n NSs
of Arabic n Collected across 3 intact ESL listening courses at a large midwestern university n High-intermediate and advanced courses n Mean TOEFL 450-500 n 2-24 months in an English-speaking country (M: 15.43) n 1 early bilingual participant excluded
+
13
Methods Participants: Arabic n Arabic
has a single phoneme, /b/, of which [b] and [p] are allophones in complementary distribution n [p]
occurs before voiceless stops n [b] elsewhere n For present stimuli, all English /b/ and /p/ occupy Arabic’s ‘elsewhere’ distribution
+
14
Methods Materials n 12
CVC(C) targets
n 6
[b]-initial n 6 [p]-initial n Controlled for frequency & length, split by phon. ND n 12
fillers (+2 practice)
n Washington
University Hearing and Speech Lab Neighborhood Database n Hoosier
Mental Lexicon Database
(Nusbaum et al., 1984)
+
15
Methods Procedure n Words
broken down into 70ms increments
n Gates
start at 140ms n 140, 210, 280, 350, 420, 490, 560, 630, 700, 770
n Participants
must guess the word after each gate and provide a confidence rating
+
16
Methods Procedure n Split n 6
into 4 timed PowerPoint sessions
stimuli per session
n Pseudo-randomized n Each
order
set of six are randomized by gate: n Participant hears first 140ms of all six words n Participant hears first 210ms of all six words n Etc. n Proficiency
(LHQ)
17
+
Targets and fillers by session
+
Results
α-level = .05
+ Accuracy analysis + Competitor analyses – Frequency characteristics – Neighborhood density effects 18
+
19
Results Accuracy analyses n
Accuracy: n
n
Mean isolation point: n
n
Correct: 192, 14.7%; Incorrect: 1115, 85.3%
440.38ms (SD: 107.60; 420-490ms/gates 5-6)
One-sample t-test: n
Isolation point against 350(ms) n
n
Conservative estimate of English NS spoken word recognition
t(78) = 7.47, p < .001
0.8
*
0.7 Mean Accuracy
20
0.6
*
0.5
*
0.4
/b/-initial
*
0.3
/p/-initial
*
0.2 0.1 0 1
2
3
4
5
6
7
8
9 10
Gate
Mean accuracy by gate + and initial phoneme 2 X 10 rm ANOVA Within-subjects variables: initial phoneme, gate (1-10) /b/-initial: M = .11, SD = .01, /p/-initial: M = .21, SD = .02 Initial Phoneme, (F(1, 1288) = 33.46, p < .001, ηp2 = .03) Gate, (F(9, 1288) = 35.93, p < .001, ηp2 = .20), Initial Phoneme*Gate interaction, (F(8, 1288) = 5.77, p < .001, ηp2 = .04).
+
21
Results Competitor analyses n Gating
errors ≈ Competitors
n Conservative n Removed
data trim
all non-English words, including: n Misspelled near-words (“bause”) n Non-words(“bau”, “paazze”) n Single letters (“p”) n No response n Removed 23.66% (405) observations
Log-Transformed Frequency
2.5
22
2 1.5 1 0.5 0 P2R Words
Target Words
Mean log-transformed frequencies + of P2R and Target words overall t(20) = 13.71, p < .001
Log-Transformed Frequency
2.2
23
2.0 1.8 M (P2R)
1.6
M(Target)
1.4 1.2 1.0 1
2
3
4
5
6
7
8
9
Mean log-transformed frequencies + of P2R words and target words across gates 1-9 2 X 9 rm ANOVA Sphericity not violated No effect of gate: F(8, 112) = 1.11, p = .36
Neighborhood Density
35
24
30 25
P2R (High)
20
P2R (Low)
15
Target ND (High)
10
Target ND (Low)
5 0 1
2
3
4
5
6
7
Mean neighborhood densities for + P2R words and Target words across gates 1-7 2 X 7 rm ANOVA, Greenhouse-Geisser corrected Target word neighborhood density, F(1, 7) = 1.96, p = .20, Effect of Gate, F(6, 42) = 2.73, p = .02, ηp2 = .28, Target*Gate interaction, F(2, 18.57) = 11.15, p < .001, ηp2 = .61.
+
Discussion 25
+
26
Discussion Initial phoneme accuracy analysis n Arabic /b/ n [p] before voiceless consonants; [b] elsewhere n Arabic
learners of English were more accurate for the recognition of /p/-initial words compared to /b/-initial words in Arabic /b/’s ‘elsewhere’ context
n Response
bias for producing /p/-initial words
(signal detection theory, Wickens, 2001)
n Smaller
cross-language neighborhoods for /p/-initial words
+
27
Discussion Hypothesis I I.
We predict that learners will recognize words less often and at later gates than is often reported for native speakers.
n Across
gates, ESL learners on average took longer to recognize a word than NSs (based on our conservative estimate).
+
28
Discussion Hypothesis II II.
a) Prior to accurate word recognition learners will produce words that are higher in frequency than the target words they are hearing.
n Overall, ESL
learners preferred to produce words with higher frequencies than the target words
+
29
Discussion Hypothesis II II.
b) Because competition persists for longer in the L2 (e.g., Broersma, 2012; Broersma & Cutler, 2011), we predict that learners will continue to produce words that are high in frequency across all gates (cf. Wayland et al., 1995).
n
Across gates, ESL learners were producing P2R words with consistently high frequencies
+
30
Discussion Hypothesis III III.
n
We predict that learners will produce words that are high in neighborhood density (>20) across all gates. (a la NAM, Luce & Pisoni, 1998)
ESL learners produced competitors from higher density neighbors (>20) than the target words (with one exception)
+
31
Discussion n
n
Participants persist in producing competitors n
Even in the absence of context
n
Even in a block design that minimizes chances of perseveration
n
This helps generalize the results of Field (2008), who found protracted competition with gated sentential context cues
Frequency and ND appear to contribute to prolonged activation in L2 spoken word recognition
+
Limitations & Future directions 32
+
33
Limitations n No
familiarity/knowledge assessment of targets
n Low
number of targets (12) due to controlling variables
n Gating
elicits data consistent with online measures, but it’s still an offline task n Explicit
strategies?
+
34
Future directions n Take
learner IDs into account (LHQ)
n Look
at learner neighborhoods (LINGUA)
n Analyze n Collect
additional L2 data (L1 Chinese)
data from NSs
+
Thank you!
Questions?
[email protected] [email protected] Nick B. Pandža, University of Maryland Phillip Hamrick, Kent State University
+
36
Methods Materials
! !
High%Neighborhood%Density% Low%Neighborhood%Density% (Freq.,!ND,!#ofGates)% (Freq.,!ND,!#ofGates)!
/b/4initial%
bell!(19,!27,!6)! bid!(22,!25,!5)! boot!(13,!32,!4)!
boss!(20,!11,!9)! bull!(14,!13,!6)! bath!(26,!17,!8)!
/p/4initial%
pat!(35,!39,!6)! patch!(13,!21,!8)! pitch!(22,!24,!7)!
push!(37,!5,!8)! pause!(21,!9,!10)! pig!(8,!19,!5)!
!
