Chapter 7 Topic
7.2 Speech production
7.2.4 Results
This section presents the results of the speech production study. For the statistical analysis of the data I calculated a generalized linear mixed effect (GLME) model with the fixed factor GIVENNESSand the random factors
SPEAKER and ITEM (only intercepts) using the glmer function from R’s lme4 library (Bates et al. 2015). Afterwards I fitted a null model without the factor GIVENNESSto the same data set and compared the full model and the reduced model by using the likelihood ratio test of the function anova.
This test compares the relative fits (=log-likelihoods) of the two models and examines whether the full or the reduced model fits better to the results. For each language I report theχ2-score, the degrees of freedom and thep-value of the model comparison which indicates if the factor GIVENNESS has a statistically significant effect on the linearization preferences.
7.2.4.1 Turkish
7.2.4.1.1 Subjects and Non-subjects
Agents vs. Patients
The aim of the first experiment is to investigate the effect of givenness on the linearization preferences of agents and patients. The absolute numbers and the means of the valid descriptions produced by the Turkish native speakers are summarized in Table 7.2.
Table 7.2: Turkish:agents vs. patients AG=GIV PAT=GIV
n % n %
AG<PAT<V 64 100 57 93.4
PAT<AG<V - - 4 6.6
total 64 100 61 100
Table 7.2 illustrates that the Turkish speakers only produced V-final constructions and that the participants show a strong preference for AG<PAT orders in both conditions. Consider for instance the target picture descriptions in (245).
(245) Turkish: Exp1, Item 01 a. AG=GIV
[Kız]AG girl
[bir one
elma]PAT apple
yi-yor.
eat-PROG[3]
‘The girl is eating an apple.’ (Tu03)
b. PAT=GIV
[Kız]AG girl
[elma-yi]PAT apple-ACC
yi-yor.
eat-PROG[3]
‘The girl is eating the apple.’ (Tu12)
Nevertheless, Table 7.2 reveals four instances of PAT<AG orders in the patient given condition, which implies that givenness has a small effect on the linearization of agents and patients in Turkish. The overall means of the valid descriptions with PAT<AG orders are illustrated in Figure 7.5.
AG=giv PAT=giv
0 20 40 60 80 100
∅ofPAT<AGorders
Figure 7.5: Turkish: PAT<AG linearizations
The GLME analysis reveals no significant effect of the factor GIVEN
-NESS. However, the model comparison shows that the full model (see Table 7.3) is significantly different from the null model (χ2(1) = 4.59, p <.05) which indicates that a model including the factorGIVENNESSfits slightly better to the results than a model without the factor. The positive estimate of the factorGIVENNESSimplies that PAT<AG linearizations in Turkish occur significantly more often with given patients than with given agents.
Table 7.3: Turkish: Fixed effect estimates for PAT<AG linearizations
Fixed effects Estimate SE zvalue Pr(>|z|) (Intercept) -22.92 664.46 -.03 .97 GIVENNESS 19.17 664.461 .03 .98
*p< .05;**p< .01;***p< .001
1 SE inflation occurred due to the null-values for PAT<AG orders
Themes vs. Locatives
The second experiment tests the effect of givenness on the linearization of themes and locatives. Due to the fact that non-subjects with inherent cases (e.g., locatives) can scramble easier over subjects than non-subjects with structural cases (e.g., accusatives), I expect that the speakers show a preference for THE<LOC orders in cases where the theme is contextually given and a preference for LOC<THE orders in cases where the locative is contextually given. The total numbers and the means of the valid descriptions are summarized in Table 7.4.
Table 7.4: Turkish:themes vs. locatives THE=GIV LOC=GIV
n % n %
THE<LOC<(V) 54 84.1 10 16.1
LOC<THE(<V) 10 15.9 52 83.9
total 63 100 62 100
Table 7.4 reveals that the order of themes and locatives in Turkish is strongly influenced by givenness, i.e., the speakers predominantly realized the contextual given referent before the new one, see for instance the examples in (246).
(246) Turkish: Exp2, Item 17 a. THE=GIV
[Çanta]THE bag
[masa-nın table-GEN
üst-ün-de]LOC. top-POSS.3-DAT
‘The bag [is] on the table.’ (Tu05)
b. LOC=GIV
[Masa-nın table-GEN
üst-ün-de]LOC top-POSS.3-DAT
[bir one
çanta]THE
bag
var.
exist
‘On the table is a bag.’ (Tu16)
The amount of LOC<THE orders triggered by the two different condi-tions (THE=giv, LOC=giv) is illustrated in Figure 7.6.
THE=giv LOC=giv
0 20 40 60 80 100
∅ofLOC<THEorders
Figure 7.6: Turkish: LOC<THE linearizations
The GLME analysis of the data reveals a significant effect of the factor GIVENNESS. The model comparison confirms that the model including the factor can explain the Turkish results highly significantly better (χ2(1)= 63.6, p= <.001) than the null model, which indicates that the factor GIVENNESS
cannot be reduced from the model without a significant loss of information.
Consider the winning model in Table 7.5.
Table 7.5: Turkish: Fixed effect estimates for LOC<THE lineariza-tions
Fixed effects Estimate SE zvalue Pr(>|z|) (Intercept) 1.71 .39 4.35 1.34e-05***
GIVENNESS -3.45 .58 -5.94 2.77e-09***
*p< .05;**p< .01;***p< .001
7.2.4.1.2 Configurations with non-subject arguments
Recipients vs. Patients
The third experiment tests the effect of givenness on the linearization of recipients and patients. Since Scrambling among verbal arguments is gen-erally less restricted than scrambling non-subjects over subjects, I assume that the order of recipients and patients highly interacts with givenness. The total number and the means of the valid descriptions of the Turkish native speakers are presented in Table 7.6.
Table 7.6: Turkish:recipients vs. patients REC=GIV PAT=GIV
n % n %
REC<PAT<V 42 85.7 15 28.9
PAT<REC<V 7 14.3 37 71.1
total 49 100 52 100
The data in Table 7.6 reveal a strong effect of givenness on the order of recipients and patients, i.e., the participants show a preference for REC<PAT orders if the recipient is contextually given and a preference for PAT<REC orders if the patient is contextually given. Consider for instance the examples in (247).
(247) Turkish: Exp3, Item 11 a. REC=GIV
Bir one
adam man
[çocu˘g-a]REC child-DAT
[hediye]PAT present
ver-iyor.
give-PROG[3]
‘A man is giving the child a present.’ (Tu03) b. PAT=GIV
Bir one
adam man
[hediye]PAT present
[çocu˘g-a]REC child-DAT
ver-iyor.
give-PROG[3]
‘A man is giving the present to a child.’ (Tu06) The means of the descriptions with PAT<REC orders triggered by the two conditions (REC=giv, PAT=giv) are also illustrated in Figure 7.7.
REC=giv PAT=giv 0
20 40 60 80 100
∅ofPAT<RECorders
Figure 7.7: Turkish: PAT<REC linearizations
The GLME analysis reveals a significant effect of the factorGIVENNESS. The likelihood ratio test shows that the full model (see Table 7.7) fits highly significantly better (χ2(1) = 37.48, p= <.001) to the results than the null model. This implies that the factorGIVENNESS cannot be excluded from the model without a significant loss of information.
Table 7.7: Turkish: Fixed effect estimates for REC<PAT lin-earizations
Fixed effects Estimate SE zvalue Pr(>|z|) (Intercept) 1.09 .47 2.31 .0206* GIVENNESS -3.35 .72 -4.67 3e-06***
*p< .05;**p< .01;***p< .001
Instruments vs. Patients
Similar to the linearization of recipients and patients, I assume that the order of instruments and patients is depending on discourse context. The total number and the means of the valid Turkish descriptions are summarized in Table 7.8.
Table 7.8: Turkish:instruments vs. patients INS=GIV PAT=GIV
n % n %
INS<PAT<V 37 74 15 40.5
PAT<INS<V 13 26.3 22 59.5
total 50 100 37 100
Table 7.8 demonstrates that the Turkish speakers have a strong preference forGiven-before-Neworders. They mainly produced INS<PAT orders if the instrument was contextually given, whereas they predominantly produced PAT<INS orders if the patient was contextually given, consider the examples in (248).
(248) Turkish: Exp4, Item 25 a. INS=GIV
Adam man
[¸semsiye-yle]INS umbrella-with
[ine˘g-i]PAT cow-ACC
dövü-yor.
beat-PROG[3]
‘A man is beating the cow with the umbrella.’ (Tu09) b. PAT=GIV
Ya¸slı old
bir one
adam man
[ine˘g-i]PAT cow-ACC
[¸semsiye-yle]INS umbrella-with
dövü-yor.
beat-PROG[3]
‘An old man is beating the cow with an umbrella.’ (Tu12) The total amount of PAT<INS orders triggered by the two different contexts (PAT=giv, INS=giv) is also summarized in Figure 7.8.
INS=giv PAT=giv
0 20 40 60 80 100
∅ofPAT<INSorders
Figure 7.8: Turkish: PAT<INS linearizations
The GLME analysis shows a significant effect of the factorGIVENNESS. The likelihood ratio test reveals that the full model can explain the deviance of the results significantly better (χ2(1) = 10.28, p < .01) than a model without the factorGIVENNESS. Consider the winning model in Table 7.9.
Table 7.9: Turkish: Fixed effect estimates for PAT<INS lineariza-tions
Fixed effects Estimate SE zvalue Pr(>|z|) (Intercept) -1.1 .39 -2.87 .00470**
GIVENNESS 1.54 .55 2.79 .00522**
*p< .05;**p< .01;***p< .001
7.2.4.2 Russian
7.2.4.2.1 Subjects and Non-subjects
Agents vs. Patients
Table 7.10 summarizes the absolute numbers and the means of the valid descriptions of the Russian speakers produced in the agents vs. patients experiment.
Table 7.10: Russian:agents vs. patients AG=GIV PAT=GIV
n % n %
AG<V<PAT 53 96.4 62 100
PAT<V<AG 2 3.6 -
-total 55 100 62 100
Table 7.10 shows that the Russian speakers only produced V-medial constructions. Similar to the Turkish speakers, they moreover show a very strong preference for AG<PAT orders in both conditions (AG=giv, PAT=giv).
Consider for instance the examples in (249).
(249) Russian: Exp1, Item 01 a. AG=GIV
[Devochka]AG girl
kushayet eat:IPFV[3]
[yabloko]PAT. apple
‘The girl is eating an apple.’ (Ru12)
b. PAT=GIV
[Devochka]AG girl
kushayet eat:IPFV[3]
[yabloko]PAT. apple
‘The girl is eating the apple.’ (Ru13)