森林警備隊が空にいるワシを見た。

The ranger saw the eagle in the sky.

• What is the shape of ワシ/eagle?

- Zwaan et al. (2002)

「森林警備隊が空にいるワシを見た」

Fig. 1: Eagle in the Sky

「森林警備隊が巣にいるワシを見た」

Fig. 2: Eagle in the Nest

• We can mentally visualize the objects from words
• It depends on the context

- Language comprehension involves reproducing representations acquired through the surrounding environment, body, and mind

（e.g., Barsalou et al., 2008）

• Strong connection between words and their referents

Fig. 3: Concept of Embodied Cognition

- Visual Aspects (e.g., shape, color, size)

• Faster reaction times for sentence and picture matched conditions
  (e.g., de Koning et al. 2017)

- Motor

• Involvement of action/motion area during reading action related nouns (e.g., Desai et al. 2016)

- Does language comprehension in L2 involve such a process?

• The difference in acquisition process between L1 and L2
  （e.g., Jiang, 2000; Kühne & Gianelli, 2019; Li & Jeong, 2020）

  • L1 acquisition:
    Involving rich contextual input
    (e.g., interacting object while hearing the sound of the word)

  • L2 learning:
    word-association
    (e.g., dog = 犬)

- Is it possible for L2 learners to immediately mentally visualize the image of an object?

- Does L2 proficiency determine the degree of visualization?

- 36 native Japanese speakers who primarily learned English in EFL context

• Vocabulary Size: (the V_YesNo (Meara & Miralpeix, 2016)

- 15 nouns

• e.g., apple, bear, steak, tomato

- Receiving inconsistent information causes inhibition.

• font color - meaning of the word

- Indicate a font color

Fig. 5: Two Types of the Sentences

• brown - bear (Typical）

• white - bear (Atypical)

• green - bear（Unrelated）

Fig. 6: Typical and Atypical Sentence

• A Semantic Stroop Task

- Error bars represent Standard Error

What I Found

• With increase of L2 proficiency, readers immediately represent the color of the images.

• But only typical color (e.g., 〇 brown bear × white bear)

What the Results Indicate

• Not just orthographic processing, also quality of the understanding

sessionInfo()

## R version 4.1.1 (2021-08-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 22621)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Japanese_Japan.932  LC_CTYPE=Japanese_Japan.932   
## [3] LC_MONETARY=Japanese_Japan.932 LC_NUMERIC=C                  
## [5] LC_TIME=Japanese_Japan.932    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] patchwork_1.1.1    ggsignif_0.6.3     grateful_0.1.11    moments_0.14      
##  [5] performance_0.9.0  fitdistrplus_1.1-6 survival_3.2-11    MASS_7.3-54       
##  [9] kableExtra_1.3.4   ggmosaic_0.3.3     ggpubr_0.4.0       qqplotr_0.0.5     
## [13] plotly_4.9.4.1     sjPlot_2.8.9       lmerTest_3.1-3     lme4_1.1-27.1     
## [17] Matrix_1.3-4       forcats_0.5.1      stringr_1.4.0      dplyr_1.0.7       
## [21] purrr_0.3.4        readr_2.0.1        tidyr_1.1.4        tibble_3.1.4      
## [25] ggplot2_3.3.5      tidyverse_1.3.1   
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1        backports_1.2.1     systemfonts_1.0.3  
##   [4] lazyeval_0.2.2      splines_4.1.1       crosstalk_1.1.1    
##   [7] TH.data_1.1-0       digest_0.6.27       htmltools_0.5.2    
##  [10] fansi_0.5.0         magrittr_2.0.1      tzdb_0.1.2         
##  [13] openxlsx_4.2.4      modelr_0.1.8        sandwich_3.0-1     
##  [16] svglite_2.0.0       colorspace_2.0-2    rvest_1.0.1        
##  [19] ggrepel_0.9.1       haven_2.4.3         xfun_0.33          
##  [22] crayon_1.4.2        jsonlite_1.8.0      zoo_1.8-9          
##  [25] glue_1.4.2          gtable_0.3.0        emmeans_1.6.3      
##  [28] webshot_0.5.3       sjstats_0.18.1      sjmisc_2.8.7       
##  [31] car_3.0-11          DEoptimR_1.0-9      abind_1.4-5        
##  [34] scales_1.1.1        mvtnorm_1.1-2       DBI_1.1.1          
##  [37] rstatix_0.7.0       ggeffects_1.1.1     Rcpp_1.0.7         
##  [40] viridisLite_0.4.0   xtable_1.8-4        foreign_0.8-82     
##  [43] datawizard_0.4.0    htmlwidgets_1.5.4   httr_1.4.2         
##  [46] RColorBrewer_1.1-2  ellipsis_0.3.2      pkgconfig_2.0.3    
##  [49] farver_2.1.0        sass_0.4.0          dbplyr_2.1.1       
##  [52] utf8_1.2.2          tidyselect_1.1.1    labeling_0.4.2     
##  [55] rlang_1.0.6         effectsize_0.6.0.1  munsell_0.5.0      
##  [58] cellranger_1.1.0    tools_4.1.1         cli_3.1.0          
##  [61] generics_0.1.1      pacman_0.5.1        sjlabelled_1.1.8   
##  [64] broom_0.7.9         evaluate_0.17       fastmap_1.1.0      
##  [67] yaml_2.2.1          knitr_1.40          fs_1.5.0           
##  [70] zip_2.2.0           robustbase_0.93-8   nlme_3.1-152       
##  [73] xml2_1.3.2          compiler_4.1.1      rstudioapi_0.13    
##  [76] curl_4.3.2          reprex_2.0.1        bslib_0.3.0        
##  [79] stringi_1.7.5       highr_0.9           parameters_0.17.0  
##  [82] lattice_0.20-44     nloptr_1.2.2.2      vctrs_0.3.8        
##  [85] pillar_1.6.4        lifecycle_1.0.3     jquerylib_0.1.4    
##  [88] estimability_1.3    data.table_1.14.2   insight_0.17.0     
##  [91] R6_2.5.1            rio_0.5.27          codetools_0.2-18   
##  [94] boot_1.3-28         assertthat_0.2.1    withr_2.4.2        
##  [97] multcomp_1.4-18     bayestestR_0.11.5   hms_1.1.0          
## [100] grid_4.1.1          coda_0.19-4         minqa_1.2.4        
## [103] rmarkdown_2.17      carData_3.0-4       numDeriv_2016.8-1.1
## [106] lubridate_1.7.10

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• Assistant Professor @ Aichi University of Technology

  • twitter: @uniquefreshman

  • Qiita: @masato-terai