EXPERIMENT 2: NEW DISCOURSE REFERENTS I 97 Table 6.1 shows that sentences consisted of a matrix clause with an embedded

CP. Case checking had to be accomplished between the sentence-final verb cluster and the initial NP1. The four different sized types of relative clauses were always embedded between the related items die/der Professorin (‘the professor_{NOM, DAT}’) and the verb-cluster hat/ wurde (‘has/was’). The relative clauses always consisted of: A = 4 words; B and C = 6 words; D = 8 words.

Procedure. The stimuli were presented visually using a speeded grammaticality judgment task. Participants were seated in front of a computer screen. Before each trial began, the words ’Bitte Leertaste drücken’ (please press space bar) appeared.

After pressing the space bar a fixation cross appeared and the sentence was pre-sented in a word-by-word fashion in the middle of the screen. After the last word, three red question marks were shown. This was the visual stimulus to judge the sentence’s grammaticality by pressing one of two designated keys on a standard keyboard. Participants were asked to judge as fast as possible. The task ended automatically after a period of 2000ms. Reaction times were mostly in a range between 700ms and 1400ms. Reaction types and reaction times were recorded.

Subjects handedness was taken before the experiment began. All subjects had at least 10 practice items before the experiment started.

Predictions. There is a wide range of predictions as to the outcome of Exper-iment 2. First, there is the distinction between locality-based and anti-locality based hypotheses. Furthermore, the question is if the results meet assumptions of a simplified binary cost formula or if the findings reveal support for a finer-grained increase in sentence complexity.

(i) Dependency Locality Theory: Predictions of the DLT for correct judgments are straight forward. Conditions A and B do not introduce NDRs and are therefore not supposed to impose additional costs. Although sentences in condition B con-tain more words than sentences in condition A, both conditions are treated alike.

Both A and B are predicted to be judged more accurately than C and D. In the lat-ter pair of conditions, additional NDRs are introduced and inlat-tervene between the related items. Thus, Gibson (2000) predicts an increase in resource requirements which manifests in an increase in processing complexity. Just as in the compari-son between A and B, conditions C and D reveal a difference in length with regard to the number of words. However, C and D are also treated alike as the amount of intervening NDRs is the same in both conditions. Predictions of the dependency locality theory for judgment accuracy are shown in (4).

(4) Predictions according to the ‘Dependency Locality Theory’

(A = B)>(C = D)

(ii) Pure Length: Next to predictions of the DLT, one also has to consider alter-native locality-based hypotheses. One alteralter-native to the assumptions made by the DLT is what I will call ‘Pure Length’. This hypothesis states that the distance between two related items depends on the number of intervening words. Contrary to a simple binary distinction as suggested in the DLT, ‘Pure Length’ suggests that (i) all intervening words consume resources and increase processing complexity and that (ii) all words ask for the same amount of computational resources. Thus, sentence complexity is predicted to increase as a function to the sheer number of intervening words between related items. The more intervening entities that have to be processed, the more complex is the retrieval of memory traces at some later point in the sentence. Contrary to a separation between costly and cost-free items, ‘Pure Length’ ignores any inherent linguistic information. For the current Experiment 2, ‘Pure Length’ assumes that both factors (NDR and Adv) affect sen-tence processing in the same degree. Thus, condition A is supposed to receive the highest percentage of correct judgments. Condition D, consisting of the highest number of words, is supposed to reveal the highest error rates. Conditions B and C are matched with regard to the number of words. Thus, ‘Pure Length’ predicts that B and C reveal identical findings. Predictions for judgment accuracy according to

‘Pure Length’ are as shown in (5).

(5) Predictions according to ‘Pure Length’

A>(B = C)>D

(iii) ‘Fine-Grained Hierarchy’: Predictions in (i) and (ii) illustrate pure models of processing costs. The DLT introduces a binary distribution of costs. Thus, the predictions in (ii) suggest that an item is either costly, or it is cost-free. In (ii) ‘Pure Length’: all intervening words are treated alike with regard to resource usage and are said to increase sentence complexity to the same degree. Between those assumptions lies a wide range of possible alternatives. The ‘Fine-Grained Hierarchy’ introduces one of those possible alternatives. The predictions made by the ‘Fine-Grained Hierarchy’ take into account that Gibson (2000) oversimplifies previous findings of a fine-grained ranking of processing costs (cf. Warren and Gibson, 1999). The ‘Fine-Grained Hierarchy’ suggests that every linguistic en-tity consumes a certain amount of computational resources. This does not only hold for discourse referents (focused, unfocused or new), but is also true for ma-terial that does not introduce any referent at all. The underlying presumption is that adverbials consume computational resources (to a lesser degree), too. There-fore, usage of computational resources is supposed to succumb a fine-grained gradation. The prediction for the current experiment are as follows: condition A introduces no additional material and therefore asks for the least amount of re-sources. Condition B is more costly as integrating adverbials also consumes small

6.2. EXPERIMENT 2:NEW DISCOURSE REFERENTS I 99 amounts of resources. Nevertheless, this amount is smaller than integrating two NDRs. Therefore, condition B is supposed to consume less activation than con-dition C. Adding up all costly integrations makes concon-dition D the most complex one. Hence, the highest amount of resources have to be spent sentence process-ing in condition D. Predictions for judgment accuracy accordprocess-ing to ‘Fine-Grained Hierarchy’ are shown in (6).

(6) Predictions according to ‘Fine-Grained Hierarchy’

A>B>C>D

(iv) ‘Anti-locality’: Models of anti-locality contrast with all locality-based as-sumptions. Previous findings (e.g. Konieczny, 2000) suggest that an increased distance sometimes eases sentence processing. It is suggested that intervening material can facilitate parsing of upcoming items when they are an indispensable part of the sentence. Contrary to the DLT, Konieczny (2000) does not specify the roles of old or new discourse referents. As the role of NDRs is not specified, pre-dictions for the two conditions containing the same numbers of words (B and C) remain unclear. Either beneficial effects are the same for both conditions, or one of the two sentence conditions reveals an advantage over the other.

(7) Predictions according to ‘Anti-locality’

D>C ? B>A

Anti-locality assumptions suggest that longer distances may result in better mem-ory performance than short sentences, as the items following the relative clause are an indispensable part of the clause. Thus, predictability of upcoming items is supposed to result in facilitated processing of sentence final verbs. An increase in the distance between the two related items is therefore predicted to result in an increase of facilitation as illustrated in (7).

6.2.2 Results

The results of Experiment 2 are shown in Table 6.2 (judgments) and Table 6.3 (reaction times). Just as in Experiment 1, analyses of findings have been ac-complished with LMEs, as recent work criticizes the appliance of ANOVAs on categorical data (Jaeger, 2008). Jaeger (2008) states that ANOVAs have been dis-putable for categorical data for a long time. This even holds for ‘[. . . ] ANOVAs over arcsine-square-root transformed proportions of categorical outcomes [that also] can lead to spurious null results and spurious significances’ (Jaeger, 2008:2).

LMEs fit well for binary data (as the ones acquired here with the SGJ paradigm).

Furthermore, LMEs only require a single analysis for subjects and sentences.

A B C D

Grammatical

Judgments correct (%) 020406080100 92 89 91 87 84 86

79 78

A B C D

Ungrammatical

Judgments correct (%) 020406080100

61 60 68

58 55 51 55 51

nom−initial dat−initial

.

A B C D

Grammatical

Reaction times (ms) 04008001200

902 880 971 925 10061007

898 978

A B C D

Ungrammatical

Reaction times (ms) 04008001200 ¹¹⁰⁷

1234 11771270

1168 1307

1126 1285

nom−initial dat−initial

. Figure 6.2: Upper Row: Correct Judgments (%), Below: Reaction Times (msec)

(A = -NDR,-Adv; B = -NDR,+Adv; C = +NDR,-Adv; D = +NDR,+Adv)

Judgments. Experiment 2 reveals some clear-cut results. Significant results are found for the factors NDR and Status. Sentences introducing new discourse ref-erents (+NDR) are processed significantly less reliable (correct judgments: 67%) than sentences not introducing new referents (-NDR; correct judgments: 76%; z

= 4.81; p < .001; Estimate =.57). Ungrammatical sentences receive a smaller percentage of correct judgments (correct judgments: 57%) than grammatical sen-tences (correct judgments: 86%; z = 14.17; p< .001; Estimate = 1.7). The factor Order only reveals a marginal effect. Nom-initial sentences are judged somewhat better (correct judgments: 73%) than dat-initial sentences (correct judgments:

70%; z = 1.81; p =.07; Estimate =.21). The factor Adv and all interactions fail to turn significant.

6.2. EXPERIMENT 2:NEW DISCOURSE REFERENTS I 101