Results and discussion

I analyzed data from three measures, as seen on Figure 4.3.4. The data were analyzed in lmer and glmer models (as appropriate) using thelme4 package (Bates et al., 2015) in R (R Core Development Team, 2019). All models reported below include Word Order and Remnant Type as fixed effects, with random intercepts for Participants and Items. Deviation coding was used throughout. Effects at p<.05 were considered statistically significant.

Firstly, let us look at acceptance rates for the four conditions (i.e. the proportion of trials where participants chose a Yes response). As seen on the left panel of Figure 4.3.4, overall acceptance rates for the experimental items were high (M = 85.5%, SE = 1.2). The glmer model showed an interaction between Word Order and Remnant Type (z = 3.953, p<.01), with OSV clauses with Subject CT remnants (M = 77.8%, SE = 2.8) accepted at lower rates than the other three conditions. There were no significant main effects of either Word Order or Remnant type. Pairwise analyses conducted using the emmeans package (Lenth,

13_{The mean response time across all items prior to exclusions was 1256 ms. The motivation for exclud-}

ing slower responders’ data from the analysis was two-fold – firstly, to reduce variance in the statistical analysis, and secondly, to reduce the amount of prescriptive judgments. I hypothesized that prescriptive judgments, being conscious, would generally take longer to compute than acceptability judgments based on the participant’s personal grammar.

Figure 4.6: Results from the speeded acceptability experiment. Differences significant at p<.05 are marked with an asterisk.

2019) with Tukey adjustments confirmed that there was a Remnant type effect for OSV clauses (z = 4.130, p <.01) but not for SOV clauses (z = -1.479, p = 0.4505). Following the assumption of information-structural parallelism between remnants and their correlates (that is, that contrastive remnant ellipsis disambiguates the information structure of its antecedent clause), this finding indicates that SOV clauses are as acceptable with CT status assigned to the Subject as they are with CT status assigned to the object, while in OSV clauses, CT status is more naturally applied to the clause-initial object.

I take the relatively high rates of acceptance for the experimental items as evidence that word order and contrast placement in V3+ clauses is flexible, in line with the syntactic anal- ysis proposed in Chapter 2 (contrary to the assumptions presented in previous theoretical literature on Estonian, e.g. Henk 2010). There is some inter-participant variability in the acceptability of the four conditions. 19.4% of participants consistently accepted all experi- mental items while only 5.6% of participants showed a clause-initial CT preference in both SOV and OSV clauses.

processing penalty that would indicate a mismatch between the constituent marked as con- trastive in the matrix clause and the CT remnant. I was interested in whether there is a processing penalty associated with any of the four conditions. I used response time (RT), or the time to make the acceptability judgment at the end of the sentence, as a proxy for pro- cessing difficulty. Considering the high acceptance rates for the experimental items overall, and possible differences between the cognitive processes behind “Yes” and “No” responses (e.g. erroneous “No” responses), only “Yes” responses were used for this analysis. Thus, RTs for the four conditions were compared on trials where participants accepted the sentence as grammatical.

Prior to statistical modeling, all RT data were winsorized (Dixon and Tukey, 1968), by replacing the top and bottom 5% of values in each experimental condition with the appropriate cut-off value. The center panel in Figure 4.3.4 depicts the RT findings. In the lmer model, there was a main effect of Word Order (t = -3.341, p<.01), with a penalty for OSV clauses (M = 1062 ms, SE = 36) compared to SOV clauses (M = 940 ms, SE = 30), and a main effect of Remnant Type (t = 6.507,p<.01), with a penalty for Subject remnants (M = 1108 ms,SE = 37) compared to Object remnants (M = 897 ms,SE = 29). However, these effects appear to be driven by OSV clauses with Subject remnants, as suggested by the significant interaction between Word order and Remnant Type (t = -4.622, p<.01). OSV clauses with Subject remnants (i.e. second-position CTs) were accepted as grammatical more slowly (M = 1267 ms, SE = 62)14 _{than the other three conditions. Pairwise analyses}

(conducted as previously) confirmed that only the OSV Subject CT condition significantly differed from other conditions (ps <.05). The slower RTs for acceptance in subject-contrast OSV clauses could indicate a processing penalty associated with revising the computed information structure of the clause, when the processor has previously committed to assigning CT status to the clause-initial object.

Finally, as shown in the rightmost panel on Figure 4.3.4, I analyzed comprehension

14_{Interestingly, this condition is also associated with the highest variance (as shown by the standard error),}

question accuracy as another measure of processing difficulty. Items in the disfavored OSV Subject CT condition showed lower comprehension question accuracy compared to the other three conditions, as shown by a trend towards an interaction between Word Order and Remnant Type in the glmer model (z = 1.915, p = .0555). There was also a main effect of Remnant Type (z = -2.489, p <.05) with responses to items with Subject CTs (M = 80% correct, SE = 3) being less accurate than responses to items with Object CTs (M = 89% correct, SE = 2). As seen in Figure 4.3.4, this effect is primarily driven by the OSV conditions, were Subject remnants impede comprehension. Interestingly, there is no main effect of Word Order (z = 0.622,p = 0.534). Readers are performing as well on OSV clauses with Object CTs as they do on SOV clauses. This suggests that the less canonical, object- initial OSV order (cf. Kristensen et al. 2014) is not comprehended more poorly than the more canonical SOV order. Rather, the processing profile of non-canonical word order is influenced by information structure.

In order to examine the source of lower comprehension question accuracy in the OSV Subject CT condition, let us compare comprehension question accuracy for questions about the matrix clause and for questions about the ellipsis clause. The examples in (80) show how the two types of question related to the target clause. As the matrix clause always contained negation, the correct answer to the Matrix Clause Questions was “No” and the correct answer to theEllipsis Clause Questions was “Yes”.

(80) Anna didn’t meet Mari, but Kadi did.

a. Matrix Clause Question: Did Anna meet Mari? b. Ellipsis Clause Question: Did Kadi meet Mari?

An overview of comprehension question accuracy for the two types of questions in the four conditions is shown in Table 4.7. Although the data are too sparse for a statistical analysis, we observe an interesting pattern in the descriptive data. The lowest accuracy is observed in response to questions inquiring about the ellipsis clause in the OSV conditions with a Subject remnant (67%). In the same experimental condition, participants were not

doing as poorly on questions about the matrix clause (82%). This suggests that readers were particularly struggling with disambiguating OSV clauses towards Subject contrast and parsing the ellipsis clause, while the computation of the basic argument structure of the matrix clause was not severely impeded by object-initial order.

Condition All Qs Matrix Clause Qs Ellipsis Clause Qs

SOV, Subject CT 85% 95% 75%

SOV, Object CT 87% 89% 84%

OSV, Subject CT 75% 82% 67%

OSV, Object CT 90% 88% 93%

Table 4.7: Comprehension question accuracy in Experiment 2, for questions inquiring about the Matrix clause and for questions inquiring about the Ellipsis clause

All in all, in contrast to OSV clauses where Subject CT penalties were observed across experimental measures, there are no robust asymmetries between Subject and Object rem- nants following SOV clauses. The findings from OSV clauses overall suggest that contrast is rapidly assigned to the clause-initial Object, resulting in processing difficulty for Subject CT remnant ellipsis. Interestingly, SOV clauses pattern similarly to OSV clauses with Object CTs in the different experimental measures. Let us consider two possibilities for why SOV clauses (regardless of contrast placement) would not be penalized compared to OSV clauses with Object CTs, where the processor appears to commit to a single information-structural representation early.

The first possibility is that in SOV clauses, the processor commits to a single interpre- tation for contrast assignment (perhaps stochastically, in the absence of biasing context). In the present experimental manipulation, this would result in that interpretation being re- vised in about a half of the trials when the remnant is encountered. All else being equal, this would be expected to yield an overall processing penalty in SOV clauses compared to OSV clauses with Object CTs, driven by trials where the initial interpretation had to be revised. The lack of this penalty could arise from an overall preference for SOV order over OSV order, perhaps due to a preference for independently more salient subjects preceding

less salient objects.15 _{However, an independent penalty for object-initial structures might}

be expected to show up in comprehension, with lower comprehension question accuracy for OSV conditions compared to SOV conditions. There was no main effect of Word Order in the statistical analysis of comprehension question accuracy.

The second possibility is that the placement of contrast remains underspecified until contextually disambiguated, and in this case, until the disambiguating remnant is encoun- tered. We observe no penalty for the SOV clauses upon disambiguation as the processor is anticipating upcoming contrast. If it was the case that an ambiguity pertaining to which constituent is contrastive prevents the processor from using V3+ order to anticipate up- coming contrast, we would expect to see a penalty when a CT alternative is encountered in contrastive remnant ellipsis (as was the case for Subject remnants following V2 clauses in Experiment 1). The data discussed here are compatible with the processor temporarily entertaining multiple possibilities for the assignment of contrast when the placement of the verb necessitates the presence of a CT but there isn’t sufficient contextual information to bias towards CT-marking on a particular constituent.