Experiment 7: Initial schwa words in southern French

In Experiment 6 we found that schwa words that are only used in their non-schwa variants by a given group of speakers (i.e., medial schwa words in Swiss French) and whose non-used variants correspond to the spelling of the word showed a Pseudohomophone effect for their schwa and their non-schwa variants. We concluded that both variants are lexically represented. The aim of the present experiment is to examine whether non-alternating schwa words whose non-used variant does not correspond to the spelling of the word are also stored with two lexical representations.

In order to do so, we study the production of initial schwa words in a group of southern French speakers, again by means of pseudohomophone and pseudoword naming tasks. As seen above, initial schwa words are always realized in their schwa variant for these speakers

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(e.g., Eychenne, 2006). Importantly, the schwa variant also corresponds to the spelling of the word. For instance, the schwa variant of the initial schwa word cerise ‘cherry’ corresponds to the variant used by southern French speakers (e.g., [siz]) and to the spelling of the word (e.g., “cerise”). As a consequence, the non-schwa variant is neither used nor in correspondence with the spelling of the word.

Two alternative hypotheses are examined. According to the first hypothesis, variants are only stored if used or represented in the spelling of the word (i.e., usage and orthographic-based hypothesis). If this hypothesis is correct, results for Experiment 7 will show a Pseudohomophone effect for schwa variants only, as non-schwa variants are neither used nor represented in the spelling of the word. According to the second hypothesis, speakers store both variants for non-alternating schwa words, even when the variant which they do not use does not correspond to the spelling of the word (i.e., all variants stored hypothesis). If this second hypothesis is correct, results for Experiment 7 will show a Pseudohomophone effect for schwa and for non-schwa variants.

The use of non-alternating initial schwa words will also allow us to examine whether the advantage for non-schwa variants over schwa variants we found in Experiment 4 is due to the higher relative frequency of these variants or to structural differences between schwa and non-schwa variants (e.g., number of syllables or letters, bigram frequency, etc.). If the effect of variant type in Experiment 4 is due to structural differences between schwa and non-schwa variants, the same advantage for non-non-schwa variants should be found in the present experiment, since the stimuli are identical to that of Experiment 4. If, by contrast, the advantage for non-schwa variants in Experiment 4 is due to the higher relative frequency of these variants, we should observe the reverse in the present experiment (i.e., an advantage for schwa variants over non-schwa variants) since schwa variants of initial schwa words are more frequent than non-schwa variants for southern French speakers.

IV.5.1 Method

IV.5.1.1 Participants

Twenty-four students from the University of St-Charles in Marseille 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 area, and have always lived there. They were paid for their participation.

181 IV.5.1.2 Material

The material used in this study is identical to the material used in Experiment 4.

IV.5.1.3 Design and procedure

Design and procedure are the same as in Experiment 4 except that the experiment was run in a quiet room at the University of St-Charles.

IV.5.2 Results

IV.5.2.1 Variant relative frequency estimation task

The mean relative frequency for non-schwa variants is 2.1 (95% confidence interval: ± 0.19) and the median is 1. The distribution of responses is shown in Figure 28. As can be seen, the majority of words were estimated as never produced in their non-schwa variant by the majority of participants (61% of responses).

Figure 28. Distribution of participants’ responses in the variant relative frequency estimation task in Experiment 7 (initial schwa words produced by southern French 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.

Variant relative frequency estimations differ between Experiment 4 (Swiss participants) and Experiment 7 (southern French participants) as shown by an unpaired t test (t(86) = -15.93, p

< 0.0001). Furthermore, unlike in Experiment 4, estimations in the present experiment differ

1 2 3 4 5 6 7 8 9

Non-schwa variants' relative frequencies Number of responses 0100200300400500600700

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from the estimations of Experiment 1 for the 19 words present in both studies (t(22) = 9.51, p < 0.0001). These comparisons suggest that, as expected, the rating behavior of southern French speakers on initial schwa words differs drastically from that of Swiss speakers toward the same set of words.

As in previous experiments, we find a correlation between our participants’ estimations (values averaged over speakers) and Racine’s values (Swiss speakers: Spearman rho = 0.34, S = 27990.5, p < 0.05; French speakers: Spearman rho = 0.51, S = 31368.8, p < 0.001). In contrast with Experiment 4, we find no correlation between the ratings for the non-schwa variants and the words’ frequencies in films (Spearman rho = 0.0007, S = 20840.0, p =1).

Overall, these estimations confirm that our participants show a strong preference for schwa variants. According to these estimations, however, we cannot conclude that all 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 as on the data set restricted to non-alternating tokens.

IV.5.2.2 Pseudohomophone and pseudoword naming tasks

Each vocal response was checked for accuracy. Three participants were excluded as their error rate was over 35% in a given condition. For the remaining 8400 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 398 errors (10%), most of them reading errors (n = 210, 53% of errors). Latencies below 100 ms (11 data points) were automatically removed.

A visual inspection of the distribution further led us to disregard the 21 data points above 1200 ms. Latencies for the 3770 remaining correct responses ranged from 105 to 1187 ms with an overall mean of 412 ms. No further analyses were conducted on these latencies.

They were used to control for differences in ease of articulation in the statistical model for the immediate naming latencies.

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Immediate naming task

Analysis of responses

In the immediate naming task, the number of errors totaled 657 (16%). Most errors were reading errors (n = 438, 67% of errors). An analysis of error type for pseudohomophones showed that 36 non-schwa variants were produced with the schwa whereas six schwa variants only were produced without the schwa.

Excluding errors due to measurements, we ran a generalized mixed-effects model on errors.

Participant and item were entered as random terms, variant type and stimulus type were entered as fixed effects. Results show an effect of stimulus type (β = 1.54, F(1,4159) = 150.01, p < 0.0001), the probability of making an error being higher for pseudowords (22%) than for pseudohomophones (8%). There was no effect of variant type (15% of errors for both variants, β = 0.360, F(1,4159) = 0.02, p > 0.1) but an interaction between variant type and stimulus type (β = 0.54, F(1,4159) = 7.17, p < 0.01). The interaction showed that the difference between pseudowords and pseudohomophones was greater for schwa variants than for non-schwa variants, as shown in Figure 29.

Figure 29. The effects of stimulus type and variant type in the statistical model for errors in Experiment 7 (initial schwa words produced by southern French

speakers).

0.820.840.860.880.900.920.94

Variant type

Probability of correct response

With schwa Without schwa

Pseudohomophone Pseudoword

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Analysis of latencies: pseudohomophones and pseudowords

We further withdrew the 266 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 7 data points above 1800 ms. Further analyses were restricted to the 3270 remaining correct responses. Figure 30 provides the mean latencies and 95% confidence intervals as a function of stimulus type and variant type.

Figure 30. Mean production latencies as a function of stimulus type for each variant type in Experiment 7 (initial schwa words produced by southern French

participants). The bars represent the 95% confidence intervals (n = 3270).

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 the previous experiments of this chapter, 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 the order of presentation of the stimuli. In Model 2, we added other variables known to influence latencies in written and oral naming tasks. Model 3 is identical to Model 2 but the data set is 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.

600 620 640 660 680 700 720 740

With schwa Without schwa

Latencies (ms)

Variant type

Pseudohomophone Pseudoword

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Model 1

In this first model, the following variables were entered as fixed effects: stimulus type, variant type, the order of presentation of the stimuli and the latencies for the items in the delayed naming task. We also included in the model the interaction between variant type and stimulus type.

Residuals larger than 2.5 times the standard deviation (57 data points forming 1.7% 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) = 3108.4, p < 0.0001; word: χ² (1)= 808.4, p < 0.0001). The final model is summarized in Table 28.

Table 28. Summary of Model 1 for Experiment 7. 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

Delayed latencies 7.31 10^-8 10.42 <0.01

Order of presentation -6.32 10^-5 50.80 <0.0001

Stimulus type (Pseudoword) 9.58 10^-5 122.87 <0.0001

Variant type (Without schwa) 5.39 10^-5 37.96 <0.0001

The model showed main effects for all predictors and no interaction between variant type and stimulus type. Latencies decreased when a given item was the second pseudohomophone or pseudoword of a given schwa word to be produced. They increased when the latencies for the delayed naming task increased. In addition, as in Experiments 4, 5 and 6, we observed a Pseudohomophone effect; pseudohomophones were produced with shorter latencies than pseudowords. Unlike in Experiments 4, 5, and 6, non-schwa variants were produced with longer latencies than schwa variants.

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Model 2

As in Experiments 4, 5, and 6 we conducted an additional analysis to examine to which extent the effects of stimulus type and variant type could be explained by phonological and/or orthographical variables. The selection of the variables to enter in the mixed-effects model was again performed through random forests and hierarchical analyses of clustering (see APPENDIX 28 and APPENDIX 29). The following variables were selected: number of letters, phonemes and syllables, orthographic neighborhood density, phonological neighborhood density, positional and non-positional bigram frequency, positional segment 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 and Variant type. Again, predictors with a correlation coefficient above 0.3 were orthogonalized.

Residuals larger than 2.5 times the standard deviation (61 data points, forming 1.9% 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) = 3483.5, p < 0.0001; word: χ²(1) = 209.2, p < 0.0001). The final model is summarized in Table 29. In this model, the variable Number of letters is orthogonalized with Variant type.

Table 29. Summary of Model 2 for Experiment 7. 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

Delayed latencies 6.56 10^-8 11.30 <0.001

Order of presentation -6.21 10^-5 50.54 <0.0001

Number of letters 8.07 10^-5 138.89 <0.0001

Orthographic neighborhood density -4.37 10^-5 55.87 <0.0001

Stimulus type (Pseudoword) 9.72 10^-5 128.70 <0.0001

Variant type (Without schwa) 4.39 10^-5 24.92 <0.0001

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Results for this second analysis replicate the results of the previous analysis; latencies are shorter for pseudohomophones compared to pseudowords and for schwa variants compared to non-schwa variants. Hence, we can be confident that the advantages for pseudohomophones and for schwa variants in our previous model were not due to the phonological or orthographical properties of the items.

In addition, the model showed that latencies increased when delayed latencies increased.

They were shorter when the variant to be produced was the second pseudohomophone or pseudoword of a given schwa word to be produced. They also increased with the number of letters of the stimuli and decreased as initial syllable frequency increased.

Model 3

The same model fitted to the 1923 tokens estimated as non-alternating by the speakers who produced them yields similar results. All predictors remain significant except for Delayed latencies. Importantly, there is an equal advantage for pseudohomophones over pseudowords for both variants (i.e., no interaction between variant type and stimulus type). Statistical values for this model are presented in APPENDIX 30.

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 a significant predictor; pseudohomophones and pseudowords which have at least one phonological neighbor are produced faster than pseudohomophones and pseudowords which do not have at least one phonological neighbor. All other predictors remain significant except for Delayed latencies and, in addition, the interaction between variant type and stimulus type is significant. Separate analyses for schwa and non-schwa variants, however, show that the advantage for pseudohomophones over pseudowords is present for both variant types. Statistical values for Model 4 are presented in APPENDIX 30.

Analysis of latencies: Pseudohomophones

We restricted the data set to pseudohomophones (n = 1788) and examined whether variant relative frequency and variant type predicted their immediate naming latencies. 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 as estimated by the given participants. For both