The Role of Structure 7
7.2. EXPERIMENT 4: STRUCTURAL DIFFERENCES I 119 Table 7.1: Schema of a Stimulus Set of Experiment 4
Short: [WH-nom] 3W [NP2-acc] MV
RC: [WH-nom] [relative clause] 3W [NP2-acc] MV
ADV: [WH-nom] 3W [adverbial clause] [NP2-acc] MV
Long: [WH-nom] [relative clause] 3W [adverbial clause] [NP2-acc] MV
Short: [WH-acc] 3W [NP2-nom] MV
RC: [WH-acc] [relative clause] 3W [NP2-nom] MV
ADV: [WH-acc] 3W [adverbial clause] [NP2-nom] MV
Long: [WH-acc] [relative clause] 3W [adverbial clause] [NP2-nom] MV
length. The condition ‘ADV’ contains an adverbial clause also consisting of five words in length. The number of NDRs in both clauses are matched (always two NDRs per clause). The condition ‘Long’ contains both a relative clause and an adverbial. Example (1) shows a relative clauses used in the current experiment.
(1) . . . der
‘who lives right next to the principal’
The relative clause always modifies the sentence-initial wh-phrase and is always adjacent to it. Following the assumptions of the DLT, the material was monitored for new discourse referents. All relative clauses contain two NDRs. One referent is always introduced by an NP like dem Rektor. A second NDR is always intro-duced by the RC-verb (wohnt). In order to be able to compare the two different intervening syntactic structures the number of NDRs is identical in both condi-tions ‘RC’ and ‘ADV’. As shown in (2), the intervening adverbial also always consists of five words.
Just as in the ‘RC’ condition, the adverbial clause also introduces two new dis-course referents. As in the relative clause in (1), the NDRs are introduced by a definite NP and a verb; in (2) the NP die Klasse and the verb betrat. The adverbial is always located between the three-word region ‘auxiliary + temporal adverbial’
(3W) and the region of NP2. Table 7.2 shows a stimulus set of Experiment 4.
The number of words (and of NDRs) varies across conditions due to the different specifications of the factor Length. ‘Short’ sentences consist of eight words. Both the relative clause and the adverbial clause add five words each to the sentence.
Table 7.2: Sample Stimuli for Experiment 4 Nom-initial sentences:
‘Short’ sentences:
Welcher Lehrer hat am Montag den Schüler gegrüßt?
Which teacher has at monday the student greeted
‘Which teacher has greeted the student on monday?’
‘RC’ sentences:
Welcher Lehrer, der neben dem Rektor wohnt, hat am Montag which teacher who next-to the dean lives has at monday
den Schüler gegrüßt?
the student greeted
‘Which teacher who lives next to the dean has greeted the student on monday?’
‘ADV’ sentences:
Welcher Lehrer, hat am Montag bevor er die Klasse betrat which teacher has at monday before he the classroom entered
den Schüler gegrüßt?
the student greeted
‘Which teacher has greeted the student on monday before he entered the classroom?’
‘Long’ sentences:
Welcher Lehrer, der neben dem Rektor wohnt, hat am which teacher who next-to the dean lives has at Montag bevor er die Klasse betrat den Schüler gegrüßt?
monday before he the classroom entered the student greeted
‘Which teacher who lives next to the dean has greeted the student on monday before he entered the classroom?’
Acc-initial sentences:
‘Short’ sentences:
Welchen Lehrer hat am Montag der Schüler gegrüßt?
Which teacher has at monday the student greeted
‘Which teacher was greeted by the student on monday?’
‘RC’ sentences:
Welchen Lehrer, der neben dem Rektor wohnt, hat am Montag which teacher who next-to the dean lives has at monday
der Schüler gegrüßt?
the student greeted
‘Which teacher who lives next to the dean was greeted by the student on monday?’
‘ADV’ sentences:
Welchen Lehrer, hat am Montag bevor er die Klasse betrat which teacher has at monday before he the classroom entered
der Schüler gegrüßt?
the student greeted
‘Which teacher was greeted by the student on monday before he entered the classroom?’
‘Long’ sentences:
Welchen Lehrer, der neben dem Rektor wohnt, hat am which teacher who next-to the dean lives has at Montag bevor er die Klasse betrat der Schüler gegrüßt?
monday before he the classroom entered the student greeted
‘Which teacher who lives next to the dean was greeted by the student on monday before he entered the classroom?’
7.2. EXPERIMENT 4:STRUCTURAL DIFFERENCES I 121 Therefore, conditions ‘RC’ and ‘ADV’ each consist of 13 words. ‘Long’ sen-tences add up to 18 words: 8 words as in ‘Short’ sentence; plus 5 words for the relative clause; plus 5 words for the adverbial clause.
Procedure. Experiment 4 was run using a moving-window self-paced reading procedure (cf. Just, Carpenter, and Wolley, 1982). Participants read sentences in a word-by-word non-cumulative fashion on a computer screen. In the beginning of each trial, all words were replaced by underlines. Participants were asked to press a predetermined key on a standard keyboard in order to uncover each new word of the sentence. Every single key press uncovered the next word while the previous word was replaced by underlines again. The time between key presses was recorded and taken as the estimated reading time of the respective word. After a sentence has been read, participants were either presented the next sentence in the same word-by-word procedure, or a simple yes-no-question that related to the previous sentence appeared on the screen. All questions had to be answered via two keys J for Ja (‘yes’) or N for Nein (‘no’). Half of the experimental sentences of Experiment 4 involved a related question. Participants did not receive feedback as to the correctness of their answers.1
Predictions. Locality-based and expectation-based models make different pre-dictions in terms of measures (here: reading times) as to the outcome of Experi-ment 4. The DLT suggests that processing complexity increases with an increased number of NDRs in the interim between related items. Thus, the shorter a dis-tance, the less complex the parsing process. Sentences in the ‘Short’ condition are predicted to be the least complex sentences as they introduce the lowest number of NDRs. In return, the condition ‘Long’ is suggested to exhibit the most complex sentences. ‘Long’ sentences contain both a relative clause and an intervening ad-verbial. They are supposed to reveal high processing complexities in the regions of both the relative clause and the adverbial. Additionally, those costly regions increase integration and storage costs of the clause-final matrix verb.
Table 7.3 illustrates the processing costs for nom-initial sentences. As can be seen, the processing costs are split into individual cost components. This partly follows the examples given in chapter 4. Discourse processing costs (DPC) are the costs for integrating new discourse referents. Syntactic integration costs mark costs for structural integration of the current item with a previously processed entity. Those syntactic integration costs increase with an increase in the number of intervening new discourse referents (cf. (9) in chapter 4). Table 7.3 is completed with storage costs (StC). This are costs associated with the syntactic heads that are still required to complete the CPPM as a grammatical sentence (cf. (12) in
1For details about the self-paced reading task see also Appendix A
Table 7.3: Processing Costs of ‘RC’- and ‘ADV’-sentences (nom-initial)
‘RC’-sentences:
Welcher Lehrer der neben dem Rektor wohnt hat am Montag tden Schüler gegrüßt?
DPC 0 1 0 0 0 1 1 0 0 1 0 1 1
SIC 0 0 0 0 0 0 0+1 2 0 0 0 0 0+4
StC 3 2 3 3 4 3 2 2 3 2 2 1 0
TC 3 3 3 3 4 4 4 4 3 3 2 2 5
‘ADV’-sentences:
Welcher Lehrer hat am Montag bevor er die Klasse betrat tden Schüler gegrüßt?
DPC 0 1 0 0 1 0 0 0 1 1 0 1 1
SIC 0 0 0 0 0 0 1 0 0 0+1 0 0 0+4
StC 3 2 2 3 2 3 3 4 3 2 2 1 0
TC 3 3 2 3 3 3 4 4 4 4 2 2 5
DPC = discourse processing cost; SIC = syntactic integration cost StC = storage cost; TC = total costs
chapter 4). Finally, Total costs (TC) add up all accumulating costs (integration costs (DPC+SIC) and storage costs (StC)).
The DLT framework predicts an increase in the usage of computational re-sources depending on the number of NDRs. Thus, the conditions ‘RC’ and ‘ADV’, containing the same amount of additional NDRs, imply the same level of process-ing complexity in all but two regions. Table 7.3 shows that patterns of predicted costs for ‘RC’ and ‘ADV’-sentences are alike. In ‘RC’-sentences, it is assumed that integrating hat with Lehrer is costly, as it crosses referents. This is assumed to be cost-free in ‘ADV’-sentences. In ‘ADV’-sentences, it is assumed to be costly to integrate er with Lehrer, as it crosses one NDR. Table 7.3 also shows, that all conditions are predicted to ask for the highest amount of resources at the matrix verb. The costs are supposed to be equally high in ‘RC’- and ‘ADV’-sentences. In both conditions, introducing the verb costs 1EU (DPC). Integrating the verb with its related arguments costs 4EUs (SIC). The process of integrating gegrüßt with the NP den Schüler is cost-free, as the items are adjacent to each other. However, integrating the verb with the noun Lehrer in sentence-initial position is costly, as various NDRs (Rektor; wohnt; Montag and Schüler) intervene in the interim.
Costs for the matrix verb are supposed to be higher in ‘RC’ and ‘ADV’, than in
‘Short’-sentences, but smaller than in ‘Long’-sentences. Furthermore, both condi-tions, ‘RC’ and ‘ADV’, also reveal increased resource usage in the regions RelCl and RCverb, respectively Adv and AdvV 2.
In ‘RC’-sentences, the relative clause is supposed to ask for a high amount of computational resources in the clause-final region dem Rektor wohnt. Both
2Partition of sentences into regions and their respective labels are given in Table 7.4
7.2. EXPERIMENT 4:STRUCTURAL DIFFERENCES I 123