In press: In: Dimitrova-Vulchanova, M - NTNU
In press: In: Dimitrova-Vulchanova, M - NTNU
In press: In: Dimitrova-Vulchanova, M - NTNU
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former has higher diversity in the motion lexicon. This result is highly coherent with the data<br />
and observations at the outset, namely the two-verb split for each motion scene. An inspection<br />
of the two plots revealed another interesting general result worth mentioning. While the<br />
multiset–based cluster analysis provides a grouping based on core similarities across stimuli<br />
and scene types, the Jaccard-based analysis, provides grouping based on similarity regarding<br />
marginal features, a fact which may be useful in planning future research and obtaining<br />
subtler results. We address the more detailed results in the discussion in section 5. below (cf.<br />
also <strong>Dimitrova</strong>-<strong>Vulchanova</strong>, Matínez, Edsberg & Eshuis, in preparation). The dendrograms<br />
for each language are enclosed in the Appendix.<br />
5. Discussion<br />
5.1 General comments<br />
Our basic results confirm that English and Norwegian have one basic term per motion scene<br />
(modulo the crawl verbs in Norwegian), and confirm the two-way split for each motion scene<br />
in Bulgarian. The target verbs in all three languages were used for the naming of basic motion<br />
scenes involving humans, with canonical figure orientation (front forwards), vector<br />
orientation (left-to-right or towards), and underspecified (straight path shape) performed at<br />
characteristic rate. Most scenes featuring a variation from the default on one or more of these<br />
conditions produced an increase in the range of lexical items preferred across subjects. Below<br />
we discuss the most salient findings. We first consider the selection of factors that have<br />
influenced responses for a particular scene type and for particular lexical items.<br />
5.2 Factors<br />
5.2.1 Biological factors – cycle, body and limb structure, species<br />
The most distinctive feature of at least three of the four types of targeted biological motion<br />
(running, walking and crawling), was their cycle – the number of limb movements, and the<br />
pattern of limb movement - the way the Agent moves its limbs and body, in order to achieve<br />
translational motion. <strong>In</strong> this respect, two factors were of utmost importance: suspended vs.<br />
supported motion (i.e. running vs. walking/ crawling), and erect posture vs. ‘supine’/ ‘lower<br />
than usual’ posture (i.e. walking vs. crawling).<br />
Our prediction that the naming of the target scenes would consistently reflect the value<br />
of the parameter supported vs. suspended was borne out. All scenes with suspended motion<br />
were overwhelmingly identified with verbs of running in all three languages. <strong>In</strong> addition, the<br />
cluster analysis revealed a major split between the clustering of suspended (running) scenes<br />
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