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ABSTRACT
We study to what extent linguistic differences in grammatical aspect systems and verb lexicalisation patterns of Dutch and mandarin Chinese affect how speakers conceptualise the path of motion in motion events, using description and memory tasks. We hypothesised that speakers of the two languages would show different preferences towards the selection of endpoint-, trajectory- or location-information in Endpoint-oriented (not reached) events, whilst showing a similar bias towards encoding endpoints in Endpoint-reached events. Our findings show that (1) groups did not differ in endpoint encoding and memory for both event types; (2) Dutch speakers conceptualised Endpoint-oriented motion focusing on the trajectory, whereas Chinese speakers focused on the location of the moving entity. In addition, we report detailed linguistic patterns of how grammatical aspect, verb semantics and adjuncts containing path-information are combined in the two languages. Results are discussed in relation to typologies of motion expression and event cognition theory.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 The verb “to park” was excluded from analysis for both languages because it conveys features of manner (not all moving entities can park) and features of path (a change of location) at the same time (1.038% of the data were excluded: 12 cases out of 1156 sentences in total).
2 Utterances that did not mention a specific endpoint location but implied one through an action (e.g. supermarket was implied in “going shopping”; gas station was implied in “go filling in gas”) were considered as endpoint mentioning. Utterances that implied a specific endpoint location (e.g. hij loopt naar binnen “he goes inside”) or an unspecific endpoint (e.g. ta xiang qian zou “he towards front walk”) were considered as endpoint mentioning as well.
3 The deictic verb “go” (gaan in Dutch and qu in Chinese) was coded as a path verb in both languages. It should be noted that in both languages, the deictic verb can be followed by a spatial location (e.g. go to a supermarket) or an action (e.g. go shopping). The deictic verb “go” was also coded as a path verb for the latter case in which the directed motion meaning is bleached/less evident.
4 Subordinate ZHE clauses (for example, qi zhe zixingche jin mendong “ride ZHE bike enter gate”) were not coded as part of serial verb constructions in this study. We coded the verb/verbs following these subordinate clauses. In this example, the verb jin “enter” was coded as path verb. The utterance qi zhe zixingche zou xiang mendong “ride ZHE bike walk towards gate” the verb was coded as containing a manner verb; if the verbal construction following the ZHE clause was a serial verb, the utterance was coded as containing a serial verb construction (e.g. qi zhe zixingche zou-jin mendong “ride ZHE bike walk enter gate”).
5 We are happy to share our data (transcriptions, coded data, memory data, experiment and analysis scripts) upon request.
6 glmer (Endpoint ∼ Language*EventType + (1|PP) + (1|Stimulus), data = data, family = binomial)
glmer (Location_Only ∼ Language*EventType + (1|PP) + (1|Stimulus), data = data, family = binomial)
glmer (Trajectory ∼ Language*EventType + (1|PP) + (1|Stimulus), data = data, family = binomial).
7 mlogit(Path∼1|EventType, data = PathC2, reflevel = 1) #reflevel 1 = endpoint
mlogit(Path∼1|EventType, data = PathD2, reflevel = 1) #reflevel 1 = endpoint.
8 All the dependent variables in the multinomial logistic regression models were dummy coded. When one category of a dependent variable was coded as 0, the other categories of the dependent variable were all coded as 0. So if there were N categories, there were N-1 dummy variables.
9 We got these within-event-type statistics via the intercepts information after running the binomial/multinomial logistic regression models.
10 glmer (MV ∼ Language*EventType + (1|PP) + (1|Stimulus), data = data, family = binomial)
glmer (PV ∼ Language*EventType + (1|PP) + (1|Stimulus), data = data, family = binomial, control = glmerControl(optimizer="bobyqa”,optCtrl = list(maxfun = 100000))).
11 mlogit(VerbType∼1|EventType, data = VerbC2, reflevel = 1) #reflevel 1 = MV
glmer(VerbType∼EventType + (1|PP) + (1|Stimulus), data = VerbD3, family = binomial).
12 glmer (MaEnd ∼ Language*EventType + (1|PP) + (1|Stimulus), data = data, family = binomial)
glmer (PaEnd ∼ Language*EventType + (1|PP) + (1|Stimulus), data = data, family = binomial)
glmer (MaLoc∼ Language*EventType + (1|PP) + (1|Stimulus), data = data, family = binomial)
glmer (MaTra ∼ Language + EventType + (1|PP) + (1|Stimulus), data = data, family = binomial) # The model that included the interaction did not converge.
glmer (PaTra ∼ Language*EventType + (1|PP) + (1|Stimulus), data = data, family = binomial).
13 mlogit(VerbAdjunct∼1|EventType, data = ChAll3, reflevel = 1) #reflevel 1 = MV_endpoint
mlogit(VerbAdjunct∼1|EventType, data = DuAll3, reflevel = 1) #reflevel 1 = MV_endpoint.
14 mlogit(VerbAspect∼1|EventType, data = Csyntax4, reflevel = 1) #reflevel 1 = MV.
