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Grammatical aspect influences motion event perception: findings from a cross- linguistic non-verbal recognition task*

Published online by Cambridge University Press:  29 January 2014

MONIQUE FLECKEN*
Affiliation:
Radboud University Nijmegen, Donders Institute for Brain, Cognition, and Behaviour, the Netherlands
CHRISTIANE VON STUTTERHEIM
Affiliation:
Heidelberg University, Germany
MARY CARROLL
Affiliation:
Heidelberg University, Germany
*
†Address for correspondence: Monique Flecken, Donders Centre for Cognition, Radboud University, Montessorilaan 3, 6525 HR Nijmegen, The Netherlands. e-mail: m.flecken@donders.ru.nl
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Abstract

Using eye-tracking as a window on cognitive processing, this study investigates language effects on attention to motion events in a non-verbal task. We compare gaze allocation patterns by native speakers of German and Modern Standard Arabic (MSA), two languages that differ with regard to the grammaticalization of temporal concepts. Findings of the non-verbal task, in which speakers watch dynamic event scenes while performing an auditory distracter task, are compared to gaze allocation patterns which were obtained in an event description task, using the same stimuli. We investigate whether differences in the grammatical aspectual systems of German and MSA affect the extent to which endpoints of motion events are linguistically encoded and visually processed in the two tasks. In the linguistic task, we find clear language differences in endpoint encoding and in the eye-tracking data (attention to event endpoints) as well: German speakers attend to and linguistically encode endpoints more frequently than speakers of MSA. The fixation data in the non-verbal task show similar language effects, providing relevant insights with regard to the language-and-thought debate. The present study is one of the few studies that focus explicitly on language effects related to grammatical concepts, as opposed to lexical concepts.

Information

Type
Research Article
Copyright
Copyright © UK Cognitive Linguistics Association 2014 
Figure 0

Fig. 1. Screenshot of a critical item: two people are walking in a park (towards a climbing rack).

Figure 1

table 1. Relative frequency of endpoint encoding

Figure 2

Fig. 2. Bar charts showing the mean relative frequency of endpoint encoding in the verbal task, in the critical and control conditions (EP condition ‘EP not reached’ and ‘EP reached’) (error bars indicate 95% confidence intervals).

Figure 3

table 2. Fixed effects in mixed model on data of endpoint encoding (language data)

Figure 4

table 3. Mean total number and duration (in ms) of fixations on EPs in the verbal task, in the critical and control conditions

Figure 5

Fig. 3. Mean number (top) and duration (bottom) of fixations on EPs, in the critical and control conditions.

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table 4. Fixed effects in mixed model on data of endpoint fixation frequency

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table 5. Fixed effects in mixed model on data of endpoint fixation duration

Figure 8

table 6. Mean total number and duration (in ms) of fixations on EPs in the non-verbal task, in the critical and control conditions

Figure 9

Fig. 4. Mean number (top) and duration (bottom) of fixations on EPs, in the critical and control conditions.

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table 7. Fixed effects in mixed model on data of endpoint fixation frequency (non-verbal task)

Figure 11

table 8. Fixed effects in mixed model on data of endpoint fixation duration (non-verbal task)