Although most language processing models assume (at least implicitly) that all healthy people are competent speakers of their mother tongue, it has been found that not all of them are able to understand complex syntactic structures equally. Object relative clauses in English, for example, have often been found to be very hard to understand, even for English native speakers, because they make high demands on the language processing system (see, for example, Rayner, Carlson, & Frazier, 1983; King & Just, 1991). Im- portantly, these sentences are plausible, grammatical structures in English. Nonetheless, some readers are obviously not aware of the ambiguities and fail to find a grammatical interpretation of the sentence structure, often even after rereading the sentence several times. Accordingly, it has been proposed that linguistic knowledge might be an important factor for language processing in general (see, e.g., Wells, Christiansen, Race, Acheson, & Macdonald, 2009; Rayner et al., 2016) and for the interpretation of these sentences in particular, which has been also indicated by empirical evidence (MacDonald & Christiansen, 2002; Wells et al., 2009).
59 The differences in the ability to understand complex syntactic structures may also explain the results of Schotter et al. (2014; see chapter 2.3) who found that trigger- ing a regressive eye movement did not necessarily lead to better comprehension: It is possible here that some readers were simply not able to diagnose the error correctly and perform a useful regressive eye movement. In particular, this points to the important role of linguistic knowledge for the selection of regression targets.
If we assume that regressions are an attempt to try and solve a problem, we must first acknowledge that in order to solve a problem, it is necessary to diagnose the source of the problem and direct the eyes to the position in the sentence causing the problem. And this, of course, requires a high amount of linguistic knowledge.
As we have already discussed in chapter 2.2.1, the sentence location where diffi- culties become apparent is not necessarily consistent with the location causing the diffi- culties. A very prominent example for this phenomenon are garden path sentences. Con- sider the following example (an adapted version of the sentences used by Rayner, Carlson, Frazier, 1983):
(7) The teacher sent the flowers that looked beautiful was very pleased.
In this object relative sentence, the assumed preferred interpretation according to the minimal attachment principle of the garden path model is the main clause reading that analyzes the verbal phrase ‘sent the flowers’ as a predicate to the initial noun phrase ‘The teacher’. At the second verbal phrase ‘was very pleased’, however, this main clause in- terpretation turns out to be wrong and a reanalysis is performed (towards an object rel- ative clause reading), which leads to increased processing costs at the point of disambig- uation19.
But critically, the disambiguation region is the location in the sentence where the problem can be detected but not where it can be solved (at least in the case additional information is needed and the problem cannot be solved covertly, i.e., without triggering a regression; see Fodor & Inoue, 1994, for a discussion of detection and reanalysis
19 As becomes apparent from this example, the focus on single word identification fre-
quently falls short because not every word carries an autonomous meaning. Rather, words appear in phrases and hierarchical dependencies which is why word identification better has to be viewed as an interactive process that involves the identification of several words instead of a sin- gle word. Another opportunity is to view the interaction between the identification of single words in terms of predictions, thus facilitating the identification of words in highly predictive contexts of other words.
60 processes during sentence reading as well as Friederici, Hahne, & Saddy, 2002, for a discussion on this issue within the ERP literature). In order to solve the problem, how- ever, the sentence structure has to be reanalyzed (according to the garden path model) by interpreting the ambiguous verb phrase ‘sent the flowers’ as the sub-clause verb phrase, thus leading to a reduced object relative clause analysis.
The question whether and how a reanalysis is performed during sentence inter- pretation has received considerable attention in the last 35 years of psycholinguistic re- search (see Fodor & Ferreira, 1989, for an overview and von der Malsburg & Vasishth, 2013, for a recent discussion of sentence processing models in the context of regression landing sites), and it is well beyond the scope of this thesis to discuss the different ap- proaches in more detail. The precise description of this reanalysis process would also depend on our own underlying lexicon and the production rules which are currently not specified with regard to these aspects. But despite the diversity in the models of sentence processing, all of the approaches conclude (at least implicitly) that a high amount of lin- guistic knowledge is necessary in order to reanalyze a sentence efficiently.
Frazier and Rayner (1982; see also chapter 2.4.1) discussed this issue of reanal- ysis by investigating the landing sites of regressions. They used regressive eye move- ments as a tool to provide insights into reanalysis processes during sentence reading and implicitly assumed (according to the garden path model) that there is one common strat- egy shared by all readers. Interestingly, their results showed clearly that the majority of regressions targeted the ambiguous region, but that the results also revealed a high var- iability in landing sites. Further evidence for a variability in landing sites is provided by the findings of von der Malsburg and Vasishth (2011; 2013), who also reported different regression patterns that additionally varied among individuals (see chapter 2.4.3).
This points to the opportunity (although it has not been discussed or examined by the authors themselves) that regression patterns may vary as a function of language knowledge and the ability of diagnosing the potential error. Specifically, this means that the ability to diagnose the potential source of the problem requires a high amount of linguistic knowledge which is not available to all readers.
In terms of the Information Gathering Framework, the ability to solve a problem is closely linked to the production rules (and to a lower extent to the specification of the underlying lexicon) because such production rules allow for the integration of constitu- ents into the sentence structure. As we have discussed above, the production rules define condition-action pairs. Thus, in the case where predictions are not met by the current
61 input, the production rules may define the exact condition within the perceptual span that is not met by the action. This may be an open syntactic node, for example, that is not filled by an argument. The production rules therefore reduce the confidence in the con- dition-argument, because the action-argument is not provided and subsequently a re- gression to the condition-argument is performed. This regression aims to gather addi- tional information about the condition-argument in order to increase the confidence level.
In the case where the appropriate production rules are not available or the wrong production rules are assessed (i.e., linking the missing action to the wrong condition), this may lead to two scenarios: First, a low confidence level for the wrong word is com- puted, or second, that the confidence levels of more than one word or even all words are reduced as some kind of chaos response, because the error source cannot be determined. According to the Information Gathering Framework, therefore, the backward mecha- nism selects the target position on the basis of a strategy that has been developed draw- ing upon the language experience.