Attention and Reward Task (ART)

This is the main task administered to subjects. Each trial started with a fixation cross lasting 750ms. Then, one of the two lateral placeholders, either the left or right one, changed colour for 100 ms (cue). After additional 600 ms (on average) a target - either square or circle - was presented either on the left or right side of the screen. The position of the cue was not predictive of the side of target presentation (50%). However, the color of the cue predicted (100%) the amount of points that the subject could earn for a correct and fast response. A feeback was presented at the end of each trial reporting whether the response was correct, incorrect, too fast, or too slow; the number of points rewarded for the current trial; the overall gain since the beginning of the task.

Time course of a typical trial of the ART.

Time course of a typical trial of the ART.

There’s a whealth on information we can extract from this task. We expected, to begin with, a main effect of Reward, suggesting that our manipulation effectively modulated motivation. Then, we sought to look for the interaction of Reward and Validity. In theory, motivational and spatial cues might interact, and the validity gain/inhibition of return commonly observed in Posner-like tasks might be modulated consequently. With this respect, studies in literature have been inconsistent, though the specific paradigm employed changed sensibly as well. In a study very similar to ours, high rewards were found to enhance inhibition of return specifically (Bucker and Theeuwes, 2014). However, the key interests of our study were the possible interactions with a vestibular stimulation (GVS, see the companion paper). The first question was whether GVS modulates sensitivity to rewards. With this respect, we wondered whether reward-based performance boosts could be further modulated by GVS, either in the sense of an abolishment, or in the sense of a further enhancement (bi-directional hypotheses). Finally, a three-way interaction GVS by Reward by Validity was also predicted, showing that GVS might affect the interplay between motivation and spatial attention (again, please refer to the main text for the rationale).

This document presents results for reaction times, a companion file, S4, reports the results for accuracy.

Preliminary setup

It’s sometimes good to clean the current environment to avoid conflicts. You can do it with rm(list=ls()) (but be sure everything is properly saved for future use).

In order to run this script we need a few packages available on CRAN. You might need to install them first, e.g. by typing install.packages("BayesFactor") in the console.

#list packages
packages= c("plyr", "magrittr", "tidyverse", "BayesFactor", "lme4", "multcomp",
            "gridExtra", "ez" , "lsmeans", "retimes")

#load them
lapply(packages, require, character.only= T)

For package afex I’m using the developer version on github (you will need to install devtools).

#devtools::install_github("singmann/afex@master")
library(afex)

For reproducibility, here’s details of the system on which the script was tested and a seed for random numbers:

set.seed(1)

sessionInfo()
## R version 3.4.2 (2017-09-28)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 15063)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United Kingdom.1252 
## [2] LC_CTYPE=English_United Kingdom.1252   
## [3] LC_MONETARY=English_United Kingdom.1252
## [4] LC_NUMERIC=C                           
## [5] LC_TIME=English_United Kingdom.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] afex_0.18-0          retimes_0.1-2        lsmeans_2.27-2      
##  [4] estimability_1.2     ez_4.4-0             gridExtra_2.3       
##  [7] multcomp_1.4-7       TH.data_1.0-8        MASS_7.3-47         
## [10] survival_2.41-3      mvtnorm_1.0-6        lme4_1.1-14         
## [13] BayesFactor_0.9.12-2 Matrix_1.2-11        coda_0.19-1         
## [16] dplyr_0.7.4          purrr_0.2.3          readr_1.1.1         
## [19] tidyr_0.7.1          tibble_1.3.4         ggplot2_2.2.1       
## [22] tidyverse_1.1.1      magrittr_1.5         plyr_1.8.4          
## [25] printr_0.1          
## 
## loaded via a namespace (and not attached):
##  [1] nlme_3.1-131        pbkrtest_0.4-7      lubridate_1.6.0    
##  [4] RColorBrewer_1.1-2  httr_1.3.1          rprojroot_1.2      
##  [7] tools_3.4.2         backports_1.1.1     R6_2.2.2           
## [10] rpart_4.1-11        Hmisc_4.0-3         lazyeval_0.2.0     
## [13] mgcv_1.8-20         colorspace_1.3-2    nnet_7.3-12        
## [16] mnormt_1.5-5        compiler_3.4.2      rvest_0.3.2        
## [19] quantreg_5.33       htmlTable_1.9       SparseM_1.77       
## [22] xml2_1.1.1          sandwich_2.4-0      checkmate_1.8.4    
## [25] scales_0.5.0        psych_1.7.8         pbapply_1.3-3      
## [28] stringr_1.2.0       digest_0.6.12       foreign_0.8-69     
## [31] minqa_1.2.4         rmarkdown_1.7       base64enc_0.1-3    
## [34] pkgconfig_2.0.1     htmltools_0.3.6     htmlwidgets_0.9    
## [37] rlang_0.1.2         readxl_1.0.0        bindr_0.1          
## [40] zoo_1.8-0           jsonlite_1.5        gtools_3.5.0       
## [43] acepack_1.4.1       car_2.1-5           modeltools_0.2-21  
## [46] Formula_1.2-2       Rcpp_0.12.13        munsell_0.4.3      
## [49] stringi_1.1.5       yaml_2.1.14         grid_3.4.2         
## [52] parallel_3.4.2      forcats_0.2.0       lattice_0.20-35    
## [55] haven_1.1.0         splines_3.4.2       hms_0.3            
## [58] knitr_1.17          reshape2_1.4.2      codetools_0.2-15   
## [61] stats4_3.4.2        glue_1.1.1          evaluate_0.10.1    
## [64] latticeExtra_0.6-28 data.table_1.10.4   modelr_0.1.1       
## [67] nloptr_1.0.4        MatrixModels_0.4-1  cellranger_1.1.0   
## [70] gtable_0.2.0        assertthat_0.2.0    coin_1.2-1         
## [73] xtable_1.8-2        broom_0.4.2         lmerTest_2.0-33    
## [76] bindrcpp_0.2        cluster_2.0.6

Thanks to the function retrieved here, not displayed, the following hyperlinks download the Rdata files: