Exploring the conceptualisation of locative events in French, English, and Dutch: Insights from eye-tracking on two memorisation tasks
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Mégane Lesuisse
Abstract
The present study addresses the influence of language on the conceptualisation of locative events (e.g., the bottle on the table) in French, English, and Dutch which differ greatly in their habitual encoding of locative events. Dutch obligatorily expresses the disposition of the Figure (viz. the bottle) via a Cardinal Posture Verb (CPV) like liggen ‘lie’, staan ‘stand’, or zitten ‘sit’. In French, the preferred locative marker is the neutral copula être ‘be’ which leaves dispositional nuances habitually unexpressed. English straddles the middle: while the neutral copula be is usually preferred, the CPVs are sometimes found because of diachronic reasons. Our study assesses the potential repercussions of these cross-linguistic differences on the perception of locative events via a recognition task involving eye-tracking and is run in a non-verbal and a verbal condition. Our findings show that, irrespective of the condition, recognition performance is affected by the linguistic preferences, which confirms the permanent effect of language on thought even beyond verbal contexts. The analysis of eye-movements corroborates this finding: depending on their language, the participants attend to the stimuli differently. In the verbal condition, language is used as a strategic tool to enhance memorisation and the participants’ eye-movements still reflect cross-linguistic differences.
Acknowledgements
I would like to thank Maarten Lemmens as well as the editors Beate Hampe and Anja Binanzer for their constructive feedback and valuable comments on an earlier version of this paper. Responsibility for any errors or inaccuracies in this final version is mine.
Appendix
Modeling Semantic Density for Dutch
| Call: mlogit(formula = SemanticDensity ∼ 1 | Series + Position, data = data Dutch, method = “nr”) |
|||||
| Frequencies of alternatives: | N | NVE | V | VE | |
| 0.05 | 0.0008 | 0.89 | 0.04 | ||
| Coefficients | Estimate | Std. Error | z-value | Pr(>|z|) | Significance |
| (Intercept):NVE (Intercept):V (Intercept):VE Series2:NVE Series2:V Series2:VE Series3:NVE Series3:V Series3:VE PositionCP:NVE PositionCP:V PositionCP:VE PositionNCP:NVE PositionNCP:V PositionNCP:VE GroundNCG:NVE GroundNCG:V GroundNCG:VE |
−2.950942 2.530437 −0.538598 0.11765 0.386097 0.034507 −16.380548 0.604699 −0.037211 −17.296914 −0.189811 −1.307101 1.052074 −0.053333 1.933351 −17.61952 −0.088687 −0.517935 |
1.279654 0.202299 0.338325 1.438461 0.21644 0.348896 4609.154388 0.22464 0.34288 4554.992258 0.20374 0.456682 1.446823 0.273288 0.358978 4111.664829 0.191135 0.292641 |
−2.306 12.5084 −1.592 0.0818 1.7839 0.0989 −0.0036 2.6919 −0.1085 −0.0038 −0.9316 −2.8622 0.7272 −0.1952 5.3857 −0.0043 −0.464 −1.7699 |
0.021108 <2.2e-16 0.111395 0.934814 0.074448 0.921214 0.997164 0.007105 0.913579 0.99697 0.351526 0.004208 0.467127 0.845272 7.22E-08 0.996581 0.642648 0.07675 |
* *** . ** ** *** . |
| Log-Likelihood: −831.81 McFadden R2: 0.08 Likelihood ratio test: X2 = 149.88 p < 2.22e−16 | |||||
Modeling Semantic Density for English
| Call: mlogit(formula = SemanticDensity ∼ 1 | Series + Position, data = data English, method = “nr”) |
|||||
| Frequencies of alternatives: | N 0.80 |
NVE 0.11 |
V 0.07 |
VE 0.009 |
|
| Coefficients | Estimate | Std. Error | z-value | Pr(>|z|) | Significance |
| (Intercept):NVE (Intercept):V (Intercept):VE |
−1.990391 −2.270818 −5.216412 |
0.166143 0.191165 0.655402 |
−11.98 −11.8788 −7.9591 |
<2.2e–16 <2.2e–16 1.78E–15 |
*** *** *** |
| Series2:NVE Series2:V Series2:VE Series3:NVE Series3:V Series3:VE |
−0.155533 0.044147 −0.312267 −0.667779 −0.26463 0.06386 |
0.167239 0.199334 0.62999 0.173751 0.203795 0.483768 |
−0.93 0.2215 −0.4957 −3.8433 −1.2985 0.132 |
0.3523705 0.8247239 0.6201277 0.0001214 0.1941117 0.8949801 |
*** |
| PositionCP:NVE PositionCP:V PositionCP:VE PositionNCP:NVE PositionNCP:V PositionNCP:VE |
−0.356056 −0.702786 0.014215 1.766024 0.868021 2.607661 |
0.182764 0.207132 0.723366 0.165949 0.20441 0.581868 |
−1.9482 −3.3929 0.0197 10.6419 4.2465 4.4815 |
0.0513936 0.0006915 0.9843216 < 2.2e–16 2.17E–05 7.41E–06 |
. *** *** *** *** |
| GroundNCG:NVE GroundNCG:V GroundNCG:VE |
−0.176373 −0.051507 −0.150917 |
0.143599 0.167869 0.453068 |
−1.2282 −0.3068 −0.3331 |
0.2193618 0.7589735 0.7390584 |
|
| Log-Likelihood: −1400.4 McFadden R2: 0.07 Likelihood ratio test: X2 = 241.64 p < 2.22e–16 | |||||
Modeling Semantic Density for French
| Call: mlogit(formula = SemanticDensity ∼ 1 | Series + Position, data = data French, method = “nr”) |
|||||
| Frequencies of alternatives: | N 0.91 |
NVE 0.037 |
V 0.05 |
||
| Coefficients | Estimate | Std. Error | z-value | Pr(>|z|) | Significance |
| (Intercept):NVE (Intercept):V |
−3.07348 −3.27688 |
−0.27951 −0.2822 |
10.9958 11.6118 |
<2.2e–16 <2.2e–16 |
*** *** |
| Series2:NVE Series2:V Series3:NVE Series3:V |
0.32205 0.12588 −0.31356 −0.21532 |
0.27786 0.27184 0.2981 0.2498 |
1.159 0.4631 −1.0519 −0.862 |
0.2464485 0.6433088 0.2928659 0.3886851 |
|
| PositionCP:NVE PositionCP:V PositionNCP:NVE PositionNCP:V |
−1.27928 −1.39448 1.29257 2.24976 |
0.36395 0.42775 0.25654 0.23908 |
−3.515 −3.26 5.0386 9.4103 |
0.0004397 0.0011141 4.69E–07 < 2.2e–16 |
*** ** *** *** |
| GroundNCG:NVE GroundNCG:V |
−0.31732 −0.26259 |
0.23748 0.21761 |
−1.3362 −1.2067 |
0.1814789 0.2275488 |
|
| Log-Likelihood: −665.88 McFadden R2: 0.14 Likelihood ratio test: X2 = 231.89 p < 2.22e–16 | |||||
Modeling Figure versus Ground orientedness in the non-verbal task
| Linear mixed model fit by maximum likelihood [‘lmerMod’] |
||||
| Formula: Dwell.Time.ms. ∼ Language * AOI.Name + Position + Ground + Series. + (1 | Participant) + (1 | Stimulus) | ||||
| AIC 207966.9 |
BIC 208071.1 |
logLik −103969.4 |
deviance 207938.9 |
df.resid 12586 |
| Estimate | Std. Dev. | T-value | ||
| (Intercept) LanguageEnglish LanguageFrench AOI.NameGround PositionCP PositionNCP GroundNCG Series.Series2 Series.Series3 LanguageEnglish:AOI.Ground LanguageFrench:AOI.Ground |
2540.286 −260.52 3.618 −1592.118 −4.826 22.858 6.041 −45.138 −57.238 200.357 −79.294 |
41.503 53.658 53.194 28.107 18.542 23.794 17.066 20.4 20.093 40.097 39.749 |
61.207 −4.855 0.068 −56.645 −0.26 0.961 0.354 −2.213 −2.849 4.997 −1.995 |
|
Modeling orientational scanpaths on vertical Figures in the non-verbal task
| Linear mixed model fit by maximum likelihood [‘lmerMod’] |
|||||
| Formula: Extremities ∼ Language + Position + Ground + Series + (1 | Participant) + (1 | Stimulus) | |||||
| AIC 4099.1 |
BIC 4160 |
logLik −2039.6 |
deviance 4079.1 |
df.resid 3247 |
|
| Estimate | Std. Dev. | T-value | p-values | Significance | |
| (Intercept) LanguageEnglish LanguageFrench PositionCanonical PositionNonCanonical GroundNonCanonical Series2 Series3 |
−0.3447 0.181 0.1234 −0.6159 −0.751 0.5326 0.7459 0.6944 |
0.4419 0.1321 0.1326 0.414 0.6291 0.349 0.4334 0.4346 |
−0.78 1.37 0.931 −1.487 −1.194 1.526 1.721 1.598 |
0.4353 0.1707 0.3519 0.1369 0.2326 0.127 0.0852 0.1101 |
|
Modeling orientational scanpaths on horizontal Figures in the non-verbal task
| Linear mixed model fit by maximum likelihood [‘lmerMod’] |
|||||
| Formula: Extremities ∼ Language + Position + Ground + Series + (1 | Participant) + (1 | Stimulus) | |||||
| AIC 2789.6 |
BIC 2847.2 |
logLik −1384.8 |
deviance 2769.6 |
df.resid 2336 |
|
| Estimate | Std. Dev. | T-value | p-values | Significance | |
| (Intercept) LanguageEnglish LanguageFrench PositionCanonical PositionNonCanonical GroundNonCanonical Series2 Series3 |
0.65745 −0.42702 −0.46622 −0.86413 −0.18237 −1.20217 0.28855 −0.07791 |
0.61001 0.16267 0.16376 0.50962 0.52299 0.44328 0.51522 0.57518 |
1.078 −2.625 −2.847 −1.696 −0.349 −2.712 0.56 −0.135 |
0.28114 0.00866 0.00441 0.08995 0.72732 0.00669 0.57544 0.89226 |
** ** . ** |
Modeling Figure versus Ground orientedness in the verbal task
| Linear mixed model fit by maximum likelihood [‘lmerMod’] |
||||
| Formula: Dwell.Time.ms. ∼ Language * AOI.Name + Position + Ground + Series. + (1 | Participant) + (1 | Stimulus) | ||||
| AIC 217585.2 |
BIC 217689.4 |
logLik 108778.6 |
deviance 217557.2 |
df.resid 12544 |
| Estimate | Std. Dev. | T-value | ||
| (Intercept) LanguageEnglish LanguageFrench AOI.NameGround PositionCanonical PositionNonCanonical GroundNonCanonical SeriesSeries2 SeriesSeries3 LanguageEnglish:AOI.NameGround LanguageFrench:AOI.NameGround |
2867.3 −216.1 −494.46 −650.69 −10.73 −121.06 88.72 14.4 115.75 212.95 347.82 |
74.86 86.36 85.43 41.91 47.21 59.69 43.61 52.29 49.85 60.48 59.52 |
38.304 −2.502 −5.788 −15.526 −0.227 −2.028 2.035 0.275 2.322 3.521 5.844 |
|
Modeling orientational scanpaths on vertical Figures in the verbal task
| Linear mixed model fit by maximum likelihood [‘lmerMod’] |
|||||
| Formula: Extremities ∼ Language + Position + Ground + Series + (1 | Participant) + (1 | Stimulus) | |||||
| AIC 3448.2 |
BIC 3509.2 |
logLik −1714.1 |
deviance 3428.2 |
df.resid 3275 |
|
| Estimate | Std. Dev. | T-value | p-values | ||
| (Intercept) LanguageEnglish LanguageFrench PositionCanonical PositionNonCanonical GroundNonCanonical Series2 Series3 |
1.0764 0.1016 0.1367 −0.5902 −0.3315 0.5753 0.623 0.1368 |
0.5518 0.1496 0.1485 0.5201 0.784 0.4368 0.5419 0.5409 |
1.951 0.679 0.921 −1.135 −0.423 1.317 1.15 0.253 |
0.0511 0.497 0.3573 0.2565 0.6724 0.1878 0.2503 0.8004 |
|
Modeling orientational scanpaths on horizontal Figures in the verbal task
| Linear mixed model fit by maximum likelihood [‘lmerMod’] |
|||||
| Formula: Extremities ∼ Language + Position + Ground + Series + (1 | Participant) + (1 | Stimulus) | |||||
| AIC 2827.1 |
BIC 2884.9 |
logLik −1403.5 |
deviance 2807.1 |
df.resid 2390 |
|
| Estimate | Std. Dev. | T-value | p-values | Significance | |
| (Intercept) LanguageEnglish LanguageFrench PositionCanonical PositionNonCanonical GroundNonCanonical Series2 Series3 |
0.62 −0.1217 −0.58368 −1.43134 −0.05375 −0.5142 0.79417 0.2253 |
0.89 0.17335 0.17216 0.74509 0.7372 0.67301 0.74857 0.82078 |
0.69 −0.702 −3.39 −1.921 −0.073 −0.764 1.061 0.274 |
0.48 0.482637 0.000698 0.05473 0.941874 0.444856 0.288726 0.783702 |
*** |
Modeling orientational scanpaths on vertical Figures across conditions
| Linear mixed model fit by maximum likelihood [‘lmerMod’] |
|||||
| Formula: Extremities ∼ Language + Position + Ground + Series + (1 | Participant) + (1 | Stimulus) | |||||
| AIC 7575.4 |
BIC 7670.4 |
logLik −3773.7 |
deviance 7547.4 |
df.resid 6528 |
|
| Estimate | Std. Dev. | T-value | p-values | Significance | |
| (Intercept) LanguageEnglish LanguageFrench ConditionNV PositionCanonical PositionNonCanonical PositionNonCanonical GroundNonCanonical Series2 Series3 LanguageEnglish:ConditionNV LanguageFrench:ConditionNV |
0.93719 0.07131 0.13037 −1.16907 −0.58655 −0.84147 −0.21029 0.5384 0.66812 0.40781 0.10573 −0.0162 |
0.37558 0.12661 0.12595 0.27631 0.32733 0.63916 0.64184 0.27527 0.34168 0.34166 0.1408 0.1395 |
2.495 0.563 1.035 −4.231 −1.792 −1.317 −0.328 1.956 1.955 1.194 0.751 −0.116 |
0.0126 0.5733 0.3006 2.33E–05 0.0731 0.188 0.7432 0.0505 0.0505 0.2326 0.4527 0.9075 |
* *** . . . |
Modeling orientational scanpaths on horizontal Figures across conditions
| Linear mixed model fit by maximum likelihood [‘lmerMod’] |
|||||
| Formula: Extremities ∼ Language + Position + Ground + Series + (1 | Participant) + (1 | Stimulus) | |||||
| AIC 5614.8 |
BIC 5705.3 |
logLik −2793.4 |
deviance 5586.8 |
df.resid 4732 |
|
| Estimate | Std. Dev. | T-value | p-values | Significance | |
| (Intercept) LanguageEnglish LanguageFrench ConditionNV PositionCanonical PositionNonCanonical PositionNonCanonical GroundNonCanonical Series2 Series3 LanguageEnglish:ConditionNV LanguageFrench:ConditionNV |
0.77875 −0.11634 −0.56898 −0.24462 −1.15366 −0.34651 0.10558 −0.85659 0.50167 0.06327 −0.32252 0.09294 |
0.57025 0.15357 0.15337 0.44454 0.44509 0.57609 0.57929 0.39312 0.44929 0.49773 0.16362 0.16232 |
1.366 −0.758 −3.71 −0.55 −2.592 −0.601 0.182 −2.179 1.117 0.127 −1.971 0.573 |
0.172053 0.448677 0.000207 0.582129 0.009542 0.547522 0.855378 0.029334 0.264176 0.898855 0.048709 0.56693 |
*** ** * * |
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Articles in the same Issue
- Frontmatter
- Editorial: Cognitive Linguistics as an interdisciplinary endeavour
- How vector space models disambiguate adjectives: A perilous but valid enterprise
- Death, enemies, and illness: How English and Russian metaphorically conceptualise boredom
- The status of nominal sub-categories: Exploring frequency densities of plural -s
- No big deal: Situation-backgrounding uses of the Polish dative reflexive pronoun sobie/se
- Hand gestures with verbs of throwing: Collostructions, style and metaphor
- Exploring the conceptualisation of locative events in French, English, and Dutch: Insights from eye-tracking on two memorisation tasks
- Extending structural priming to test constructional relations: Some comments and suggestions
- Lexical Integrity: A mere construct or more a construction?
- Cognitive Linguistics meets Interactional Linguistics: Language development in the arena of language use
- Cognitive Linguistics meets multilingual language acquisition: What pattern identification can tell us
- Constructionist approaches to creativity
Articles in the same Issue
- Frontmatter
- Editorial: Cognitive Linguistics as an interdisciplinary endeavour
- How vector space models disambiguate adjectives: A perilous but valid enterprise
- Death, enemies, and illness: How English and Russian metaphorically conceptualise boredom
- The status of nominal sub-categories: Exploring frequency densities of plural -s
- No big deal: Situation-backgrounding uses of the Polish dative reflexive pronoun sobie/se
- Hand gestures with verbs of throwing: Collostructions, style and metaphor
- Exploring the conceptualisation of locative events in French, English, and Dutch: Insights from eye-tracking on two memorisation tasks
- Extending structural priming to test constructional relations: Some comments and suggestions
- Lexical Integrity: A mere construct or more a construction?
- Cognitive Linguistics meets Interactional Linguistics: Language development in the arena of language use
- Cognitive Linguistics meets multilingual language acquisition: What pattern identification can tell us
- Constructionist approaches to creativity
