CHAPTER IV. GENERALIZATION TO OTHER TYPES OF SCHWA WORDS
IV.4 Experiment 6: Medial schwa words in Swiss French
Experiments 4 and 5 showed that speakers who produce schwa words with two different pronunciation variants (i.e., alternating schwa words) store these words in their lexicon with two lexical representations, one per variant. In Experiment 6 we examine the lexical representations of non-alternating schwa words. Our aim is to determine whether speakers only store variants that they use (i.e., usage-based storage hypothesis) or whether they can store non-used variants provided that these variants correspond to the spelling of the word (i.e., usage and orthographic-based hypothesis). We examine the production of medial schwa words (e.g., as in casserole ‘pot’ or allemand ‘German’) by Swiss speakers. Medial schwa words are always realized without their schwa by Swiss speakers25 (Lyche & Durand, 1996; Jetchev, 1999). For these words, the spelling of the word contains a graphic
25 As mentioned in Chapter I, Swiss French is very similar to standard French. With regard to schwa words, we observe the same tendency to use only the non-schwa variants for words with a medial schwa. According to our observations, Swiss French speakers are even more systematic on this particular use of medial schwa words than standard French speakers.
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representation of schwa. Hence the non-used variant (i.e., the schwa variant) corresponds to the word’s spelling.
As in Experiments 4 and 5, participants performed three tasks: an immediate pseudohomophone and pseudoword naming task, a delayed pseudohomophone and pseudoword naming task, and a variant relative frequency estimation task. Our predictions for this experiment are as follows. If speakers only store variants that they use, only non-schwa variants will show a Pseudohomophone effect. Alternatively, if speakers also store variants which correspond to the spelling of the word, both variant types will show a Pseudohomophone effect.
IV.4.1 Method
IV.4.1.1 Participants
Twenty-four participants took part in the experiment. They were all monolingual French speakers, aged between 18 and 35 years, with no reported hearing, reading or language impairment. They were all born in the French part of Switzerland (in the districts of Neuchatel, Fribourg, Jura or Vaud), and have always lived there. They were paid for their participation.
IV.4.1.2 Material
The material used in this study was identical to the material used in Experiment 5.
IV.4.1.3 Design and procedure
Design and procedure were the same as in Experiment 4 except that the experiment was run in a soundproof cabin at the Neuropsycholinguistic Laboratory in Neuchâtel.
IV.4.2 Results
IV.4.2.1 Variant relative frequency estimation task
The mean relative frequency for non-schwa variants is 8.3 (95% confidence interval: ± 0.14) and the median is 9. The distribution of responses for these variants is shown in Figure 24.
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As can be seen, most words are estimated as always produced without the schwa by most participants (69% of the ratings)26.
Figure 24. Distribution of participants’ responses in the variant relative frequency estimation task in Experiment 6 (medial schwa words produced by Swiss speakers). 1 means that the word is never realized without its schwa for a
given participant and 9 means that it is always realized without its schwa.
Like in our previous experiments, there is a correlation between our participants’ estimations (values averaged over speakers) and Racine’s values (Swiss speakers: Spearman rho = 0.52, S = 31706.2, p < 0.0001; French speakers: Spearman rho = 0.51, S = 31477.8, p < 0.001).
Like in the ratings of southern French speakers for these same stimuli (Experiment 5), there is no correlation between the ratings for the non-schwa variants and the words’ frequencies in films (Spearman rho = 0.016, S = 21162.0, p > 0.1).
Overall, these estimations confirm that our participants show a strong preference for non-schwa variants. According to these estimations, however, we cannot conclude that all non-schwa words are non-alternating for all participants. In the analyses presented below for the naming tasks, we will thus systematically conduct our statistical models on the whole data set as well
26 Responses that are different from 9 (i.e., words estimated as alternating or always produced with the schwa) are not restricted to some items or participants. One participant had a slightly different behavior, with more “1”
responses than all other participants. Removing this participant from the analyses did not change the outcome.
1 2 3 4 5 6 7 8 9
Non-schwa variants' relative frequencies Number of responses 0100200300400500600700
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as on the data set restricted to non-alternating tokens according to the participants’
estimations.
IV.4.2.2 Pseudohomophone and pseudoword naming tasks
Each vocal response was checked for accuracy. Four participants were excluded as their error rate was over 35 % in a given condition (either pseudohomophone or pseudoword naming, in the immediate or delayed naming task). For the remaining 8000 responses, hesitations, disfluencies, reading errors, productions of the wrong variant, onset uncertainty measures, variant uncertainty, and anticipations in the delayed naming task were considered as errors and removed from the analysis.
Delayed naming task
In the delayed naming task, there were 273 errors (7%), most of them reading errors (n = 145, 53% of errors). Latencies below 100 ms (7 data points) were automatically removed. A visual inspection of the distribution further led us to disregard the 25 data points above 1200 ms. Latencies for the 3695 remaining correct responses ranged from 103 to 1198 ms, with an overall mean of 435 ms. No further analyses were conducted on these latencies. They were used to control for differences in ease of articulation between pseudohomophones and pseudowords in the statistical model for the immediate naming latencies.
Immediate naming task
Analysis of responses
In the immediate naming task, the number of errors totaled 478 (12%). Most errors were reading errors (n = 302, 63% of errors). An analysis of error type for pseudohomophones showed that six non-schwa variants were produced with the schwa and 24 schwa variants were produced without the schwa.
Excluding errors due to uncertainty in the onset of the response or in the variant produced, we ran a generalized mixed-effects model on errors. Participant and item were entered as random terms, variant type and stimulus type (pseudohomophone versus pseudoword) were entered as fixed effects. Results show that the probability of making an error is higher for pseudowords (16%) than for pseudohomophones (7%, β = 1.04, F(1,3988) = 86.7, p <
0.0001) and for schwa variants (16%) than for non-schwa variants (8%, β = 0.91, F(1,3988)
= 75.1, p < 0.0001).
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Analysis of latencies: pseudohomophones and pseudowords
We further withdrew the 226 data points for which we did not have a value for the delayed latencies (including the 32 outliers defined above). The latencies for the remaining responses were adjusted whenever necessary using the software CheckVocal. Visual inspection of the resulting latencies showed that the distribution was right-skewed. Most of this skewness was removed by performing a reciprocal transformation (following the Box-Cox test) and taking out the 12 data points above 1800 ms. Further analyses were restricted to the 3284 remaining correct responses. Figure 25 shows the mean latencies and 95% confidence intervals as a function of stimulus type and variant type.
Figure 25. Mean production latencies as a function of stimulus type for each variant type in Experiment 6 (medial schwa words produced by Swiss participants). The bars represent the 95% confidence intervals (n = 3284).
We analyzed the data again by means of a mixed-effects model with the reciprocal latencies as the dependent variable and with word and participant as crossed random effects. As in Experiment 4, four different models were run (Models 1 to 4). In Model 1, only the variables related to our research questions were entered as predictors, together with Order of presentation. In Model 2, we added other variables known to influence latencies in written and oral naming tasks. In Model 3, the data set was restricted to non-alternating words according to our participants’ estimations. Model 4 is identical to Model 2 with an additional predictor accounting for the phonological similarity of the pseudohomophones with words.
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Model 1
In this first model, we entered the following predictors: Stimulus type, Variant type, the interaction between these two predictors, Order of presentation (whether the item was the first pseudohomophone or pseudoword of a given schwa word to be produced by a given speaker in the experiment) and production latencies for the items in the delayed naming task.
Residuals larger than 2.5 times the standard deviation (51 data points forming 1.6% of the data) were considered outliers and removed. The random terms for participant and for word significantly improved the final model according to likelihood-ratio tests (participant: χ2(1) = 2380.9, p < 0.0001; word: χ2(1) = 472.2, p < 0.0001). The final model is summarized in Table 25.
Table 25. Summary of Model 1 for Experiment 6. The intercept represents a schwa variant pseudohomophone, being the first pseudohomophone of a given schwa word to be produced by the speaker in the experiment. For categorical variables, the statistical values correspond to the contrast between the intercept and the level of the variable indicated in round brackets.
Variable β F p
Order of presentation -2.97 10-5 12.11 <0.001
Stimulus type (Pseudoword) 1.98 10-4 450.15 <0.0001
Variant type (Without schwa) -1.45 10-4 415.92 <0.0001
Variant type by Stimulus type -4.10 10-5 6.44 <0.05
The model showed main effects for all predictors except for the delayed latencies. Removing this predictor did not change the effects of the other predictors in the model. Latencies decreased when a given item was the second pseudohomophone or pseudoword of a given schwa word to be produced. Importantly, latencies were shorter for pseudohomophones than for pseudowords (e.g., Pseudohomophone effect) and shorter for non-schwa variants compared to schwa variants. In addition, there was an interaction between variant type and stimulus type. This interaction is shown in Figure 26. It shows that the advantage for pseudohomophones over pseudowords was greater for schwa variants than for non-schwa variants. Separate analyses for schwa and non-schwa variants confirm that the Pseudohomophone effect is present for both variant types.
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Figure 26. The effects of stimulus type and variant type on production latencies in Model 1, Experiment 6 (medial schwa words produced by Swiss speakers).
Model 2
As in Experiments 4 and 5, we conducted an additional analysis to examine to which extent the advantages for pseudohomophones and for non-schwa variants could be explained by phonological and/or orthographical differences between pseudohomophones and pseudowords and between schwa and non-schwa variants. The selection of the variables to enter in the mixed-effects model was again performed through random forests and hierarchical clustering analyses (see APPENDIX 22 and APPENDIX 23). The following variables were selected: number of letters, phonemes and syllables, orthographic neighborhood density, phonological neighborhood density, positional and non-positional bigram frequency, positional diphone frequency and initial syllable frequency.
The selected variables were then entered sequentially in the mixed-effects model, together with Order of presentation, Delayed latencies, Stimulus type, Variant type and the interaction between Variant type and Stimulus type. Again, predictors with a correlation coefficient above 0.3 were orthogonalized.
Residuals larger than 2.5 times the standard deviation (54 data points, forming 1.6% of the data) were considered outliers and removed. The random terms for participant and for word significantly improved the final model according to likelihood-ratio tests (participant: χ2(1) =
Variant type -0.00150-0.00140-0.00130-0.00120
Naming latency (rec)
With schwa Without schwa
Pseudohomophone Pseudoword
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2661.9, p < 0.0001; word: χ2(1) = 315.5, p < 0.0001). The final model is summarized in Table 26. In this model, the variables Number of letters and Initial syllable frequency are orthogonalized with Variant type.
Table 26. Summary of Model 2 for Experiment 6. The intercept represents a schwa variant pseudohomophone, being the first pseudohomophone of a given schwa word to be produced by the speaker in the experiment. For categorical variables, the statistical values correspond to the contrast between the intercept and the level of the variable indicated in round brackets.
Variable β F p
Order of presentation -3.03 10-5 12.73 <0.001
Number of letters 5.68 10-5 35.79 <0.0001
Initial syllable frequency -2.38 10-9 28.06 <0.0001
Positional diphone frequency -1.05 10-3 51.07 <0.0001
Stimulus type (Pseudoword) 1.97 10-4 419.86 <0.0001
Variant type (Without schwa) -1.43 10-4 417.85 <0.0001
Stimulus type by Variant type -4.34 10-5 7.30 <0.01
Results for this second analysis replicate the results for the previous analysis; latencies were shorter for pseudohomophones compared to pseudowords and for non-schwa variants compared to schwa variants. As in the previous analysis, variant type and stimulus type interacted; the difference between pseudohomophones and pseudowords was greater for schwa variants than for non-schwa variants. Separate analyses for schwa and non-schwa variants confirmed the advantage for pseudohomophones over pseudowords for both variant types. Hence, we can be confident that the Pseudohomophone effect found in Model 1 was not due to the phonological or orthographical properties of the items.
In addition, the model shows that latencies were shorter when the variant to be produced was the second pseudohomophone or pseudoword of a given schwa word to be produced. They increased with the number of letters and decreased with initial syllable frequency and positional diphone frequency.
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Model 3
Importantly, the same model fitted to the 2287 tokens estimated as non-alternating by the participants who produced them yields similar results. All predictors remain significant but the interaction between variant type and stimulus type is no longer significant. Statistical values for this model are presented in APPENDIX 24.
Model 4
In Model 4, we again ran Model 2 with an additional predictor; a two-level variables coding whether the item has or does not have at least one phonological neighbor. This variable is not significant and does not affect the other predictors in the model.
Analysis of latencies: Pseudohomophones
We then restricted the data set to the pseudohomophones (n = 1758) and examined whether their latencies were predicted by variant relative frequency and variant type. We again performed two analyses (Model 5 and Model 6). The first analysis (Model 5) concerned all correct responses to pseudohomophones. The second analysis (Model 6) only concerned correct responses for non-alternating words according to our participants’ estimations.
Model 5
In this model, we entered the predictors related to our research questions (i.e., Variant type, Variant relative frequency) together with Delayed latencies, Order of presentation and other variables known to influence oral and written naming latencies. In order to select the most relevant variables, we again performed random forests and hierarchical clustering analyses.
These procedures led to the selection of the following variables: number of letters, phonemes and syllables, orthographic neighborhood frequency, phonological neighborhood density, lexical frequency, positional and non-positional bigram frequency, positional segment frequency, and initial syllable frequency. Details for the random forest and hierarchical clustering analyses are presented in APPENDIX 25 and APPENDIX 26 respectively.
We ran a mixed-effects model with the reciprocal of the immediate naming latencies as the response and participant and item as random terms. We added the selected variables described above sequentially to the model together with Delayed naming latencies, Order of presentation, Number of changes from the base word’s spelling, Stimulus type, Variant type and Variant relative frequency as fixed effects. We systematically tested for correlation between predictors. The predictors that were correlated above 0.3 were orthogonalized.
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Residuals larger than 2.5 times the standard deviation (32 data points, forming 1.8% of the data) were considered outliers and removed. The random terms for participant and for word significantly improved the final model according to likelihood-ratio tests (participant: χ2(1) = 1507.8, p < 0.0001; word: χ2(1) = 233.3, p < 0.0001). The final model is summarized in Table 27. In this model, the variable Number of letters is orthogonalized with Variant type.
Table 27. Summary of Model 5 for the pseudohomophones of Experiment 6. The intercept represents a schwa variant pseudohomophone, being the first pseudohomophone of a given schwa word to be produced by the speaker in the experiment. For categorical variables, the statistical values correspond to the contrast between the intercept and the level of the variable indicated in round brackets.
Variable β F p
Order of presentation -5.20 10-5 24.37 <0.0001
Number of letters 5.57 10-5 15.40 <0.0001
Positional segment frequency -4.75 10-4 7.69 <0.05
Variant type (Without schwa) -1.55 10-4 239.33 <0.0001
Results show that latencies were longer when the variant to be produced was the first pseudohomophone of a given schwa word to be produced by the speaker in the experiment.
They increased with the number of letters and decreased with positional segment frequency.
In addition, non-schwa variants were produced with shorter latencies. There was no effect of variant relative frequency and no interaction between variant relative frequency and variant type. Figure 27 shows the partial effects of this model.
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Figure 27. Partial effects of Model 5 (production latencies for
pseudohomophones) in Experiment 6 (medial schwa words produced by Swiss speakers).
Model 6
In this last model, we ran Model 5 again but on a data set restricted to the 1213 tokens estimated as non-alternating by the participants who produced them. This model yields very similar results. Statistical values for Model 6 are presented in APPENDIX 27.
IV.4.3 Discussion
The main result of Experiment 6 is the advantage of pseudohomophones over pseudowords (i.e., Pseudohomophone effect) for both schwa and non-schwa variants. Importantly, this result is found for the entire data set as well as for the data set restricted to non-alternating schwa words according to our participants’ estimations. Additional analyses showed that this effect was not a by-product of structural (phonological or orthographical) differences between pseudohomophones and pseudowords. The finding that both schwa and non-schwa variants show a Pseudohomophone effect strongly suggests that even though Swiss speakers
Naming latencies (rec)
1.0 1.2 1.4 1.6 1.8 2.0
-0.00155-0.00140-0.00125
Order of presentation
Naming latencies (rec)
-2 -1 0 1 2 3
-0.00155-0.00140-0.00125
Number of letters
0.1 0.2 0.3 0.4 0.5
-0.00155-0.00140-0.00125
Positional segment frequency
Naming latencies (rec) -0.00155-0.00140-0.00125
Variant type
Naming latencies (rec)
With schwa Without schwa
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only use the non-schwa variant for most medial schwa words, their mental lexicon nevertheless contains two representations for these words, one per variant. This finding is in line with our second hypothesis, i.e., that the storage of phonological variants is not based on usage only, but that speakers can store variants they never use in their everyday speech.
They do so at least for schwa words whose non-used variant corresponds to the spelling of the word.
The question now arises whether only non-used variants which correspond to the spelling of the word are stored or whether speakers also store non-used variants which do not correspond to the spelling of the word (i.e., all variants stored hypothesis). This issue is addressed in Experiment 7.
Note that contrary to the results for the same experiment conducted with southern French speakers (Experiment 5), latencies for pseudohomophones are not influenced by the variants’
relative frequencies in the present experiment. This result was expected as there is almost no variation in the responses for the relative frequency estimation task. As for the effect of variant type, it mirrors that of Experiments 4 and 5; non-schwa variants are produced with shorter latencies than schwa variants. The two explanations proposed in the Discussion of Experiment 4 to account for this effect, i.e., an advantage for more frequent variants or an effect of structural differences between the two variant types, still hold. Our data do not allow us to disentangle them so far. Experiment 7 will allow us to further address this issue.