Zona y población afectada

Contrast, which has been argued to be an information-structural category independent of Topic and Focus (see e.g. Moln´ar 2002), offers unique insights when it comes to processing

20_{This is assuming that when encountering the canonical SVO matrix clause, the processor does not mark}

information structure. This is because unlike other information-structural categories like Aboutness Topics and informational Focus, the use of CTs and CFs requires that a salient set of alternatives be accessible within the discourse context. The processing of a contrastive element (say, a CT) could thus be conceptualized as involving two steps – firstly, encoding information structure by marking the constituent of interest as a CT, and secondly, resolving contrast by pairing the CT element with a salient contrastive alternative, which may be overtly present in the discourse or otherwise accessible. Previous psycholinguistic work on syntactic dependency resolution offers an analogy for how the parser may be treating contrast during online comprehension. Before discussing CT-marking and contrast resolution, let us first consider the analogy from the processing of wh-dependencies.

3.5.1 An analogy from processing syntactic dependencies

Research shows that filler-gap dependencies introduced bywh-movement are actively resolved through predictive syntactic processing (e.g. Frazier et al. 1983; McElree and Bever 1989; Omaki et al. 2015). Namely, in sentences like (63), readers typically experience processing difficulty when encountering at the object “Anna” (underlined), which has been taken to indicate that a gapsite for the wh-word “who” is initially postulated in the object position of the verb. A processing penalty occurs when the initially preferred analysis (with “who” being the direct object of “see”) is ruled out by bottom-up, lexical input.

(63) Whoi did Mary see Anna with ti yesterday?

This finding suggests that the processor immediately marks left-peripheral wh-words as moved, and attempts to reconstruct the original position of the moved element. This requires representing the wh-word in memory. Holding the moved constituent in memory is costly, leading to the processor forming the required dependency as soon as grammatically permissible. Thus, wh-words remain active in memory during sentence comprehension and the resolution of the wh-dependency takes precedence over other parsing considerations.

3.5.2 CT-marking

As discussed in Chapter 2, CT structure can be marked through several means – prosody, discourse context, topic particles, and – in Estonian – word order. How does the language processor handle these cues during comprehension? Different models of sentence processing make different assumptions about the extent to which the language processor rapidly and automatically specifies structural relations during comprehension (as opposed to leaving certain dependencies underspecified). For instance, the traditional serial Garden Path Model (e.g. Frazier 1987a) and various constraint-based models of parsing (e.g. Altmann and Steedman 1988) propose that syntactic structure is fully specified on a moment-by-moment basis, while under the Construal Model (Frazier and Clifton, 1996) the resolution of some dependencies (such as modifier attachment) is delayed compared to the computation of basic argument structure. Others, e.g. the Good Enough Model (Ferreira et al., 2002) propose that certain syntactic and semantic relations may remain underspecified in online processing, depending on task demands. More recent constraint-based models of semantic and pragmatic processing (e.g. Degen and Tanenhaus 2015) also allow for contextual variability in the extent to which certain inferences are drawn during comprehension. There is thus variability in the conceptual options when it comes to how rapidly and automatically the processor specifies information-structural relations, such as marking a particular constituent as a CT during incremental comprehension.

In Chapter 4, I test the hypothesis that constituents are marked as CTs during online processing (rather than at a delay), provided that there is sufficient (contextual or word order) information for doing so. Following Hoeks et al. (2002), I assume that in the absence of contextual (or grammatical) cues biasing towards a CT structure, the processor would maintain the simplest, topic-comment representation for the information structure of the clause.

What does it mean for a constituent to be marked as a CT? I assume that marking a constituent as a CT is minimally associated with representing the clause it occurs in as a CTopP, rather than a FinP (see discussion of the Estonian left periphery in Chapter 2).

Additionally, information-structural processing also involves non-syntactic components. In silent reading,21 _{a CT-marked constituent would be assigned contrastive implicit prosody}

(see e.g. Breen 2014, for a discussion of implicit prosody effects in reading). When no overt QUD is present but there is CT-marking in the clause itself (e.g. prosody, word order), the comprehender is also expected to accommodate a QUD that the clause containing the CT offers a partial answer to. I will not experimentally address prosodic and broader discourse/QUD processing in the present dissertation, but we will return to these topics in the discussion in Chapter 6.

Most importantly for the present work, CT-marking on a constituent presupposes the existence of a contrastive alternative to this CT in the context. If no such alternative is explicitly mentioned (seebackward-looking contrast in section 3.4), the processor may either infer the identity of this contrastive alternative based on contextual information, or anticipate a contrastive alternative to be mentioned later in the discourse (seeforward-looking contrast in section 3.4). I use the term contrast resolution for matching a CT-marked constituent with a contrastive alternative.

3.5.3 Contrast resolution

In Section 3.3, I alluded to the possibility of the processor actively anticipating a CT alterna- tive in the upcoming discourse after having encountered a CT-marked element. Intuitively, whether a_ctis anticipated by the processor depends on the earlier encoding (CT-marking) of ct.

Depending on how accessiblectis from the preceding context, more or less of its features

may be specified at the point of encoding ct. Let us consider the following example. In 21_{This dissertation only involves experiments using silent reading, but future work using overt prosody}

may well reveal that prosody mediates between syntactic CT-marking and the computation of information- structural representations. But clearly, information-structural processing cannot just be reduced to prosodic processing – work by Stolterfoht et al. (2007) shows that prosodic reanalysis and focus-structural revision are associated with different ERP effects. In a similar vein, work on ambiguity resolution in relative clause attachment shows that overt prosodic disambiguation does not fully eliminate processing difficulty associated with a less preferred syntactic structure (Harris et al., 2019).

the most straightforward cases, a conjunctive QUD is partially answered using a clause containingct, as shown in (64). Here, ctcan be straightforwardly inferred by comparing

the Topic in the question (“Anna and Mary”) and _ct (“Anna”). (64) Q: Did Anna and Mary stay at the hotel during the conference?

A: Annact did. ct = Mary

In other instances, the comprehender would need to consult world knowledge in order to infer the intendedct, as exemplified in (65).22

(65) Q: Where did the linguists stay during the conference? A: [The psycholinguists]ct stayed at the hotel.

ct∈ {syntacticians, phonologists, sociolinguists, morphologists ...}

Previous work on Focus alternatives in ellipsis suggests that parallelism (or similarities between syntactic and semantic features between contrastive alternatives) is also relevant for establishing contrastive relations (see Carlson 2013). So, for instance, if _ct is an animate Nominative subject, ct might be expected to also be animate and occur in the subject

position. Since the set is open and the syntactic and semantic properties of the first member are relevant for the evaluation potential contrastive alternatives, it is actively maintained in memory. In Chapter 5, I test the hypothesis that following the encoding of CT structure, the processor actively anticipates an upcoming contrastive alternative.

Finally, after identifying ct and ct, the processor would need to either draw the

appropriate inference (e.g. “Mary did not stay at the hotel during the conference” in 64) or, when the proposition applying to ct is encountered in the discourse, compare the two

propositions in order to establish that semantic conditions on the use of a CT structure are met.

As CT structure will primarily be conveyed through non-canonical V3+ word order in the experimental work reported in this dissertation, it is worth considering some concep-

tual options regarding how non-canonical word order contributes to the processing of CT structure.