1 Release notes

Created: 2/2/2021.

These files contains the data and analysis scripts for “Automatic Responses to Acoustically Rough Intervals” concerning the reaction time data. See also the repository related to computational_models.

All data files are available at OSF repository: https://osf.io/zmjpd/.

2 Main data files

RT_Data_Raw.csv: PsyToolkit Data pasted into a big csv file.
FULL_Data_munged.csv: RT_Data_Raw subject to 1_Munge_Script.R.
Wang_results.csv: Data for Figure 3.
cents_and_interval_labels.txt: labels for Figure 1.

3 R Scripts

1_Munge_Script: Deletes timeouts, each participants RT data is fitted with Gamma distribution. Outliers (i.e. RTs outside 250ms<RT<95th percentile of Gamma) and incorrect answers are deleted. Note that running this script takes considerable amount of time due to bootstrapping. It can be by-passed by reading the data provided by this script (“FULL_Data_munged.csv”).
2_GLMModel.R: Fits GLMM model to data, tests for significance. Two versions…inverse Gaussian or Gamma as underlying distribution: no difference to pattern of significance.
3_Contrasts_Roughness_Harmonicity.R: Make sure script 2 is run first; planned contrasts for High and Low Delta_Roughness and Delta_Harmonicity.
4_Correlation_Wang.R: Simple correlation test between Delta_Roughness for the expanded data to include artificial intervals. Uses raw (i.e. untransformed Wang roughness values).
5_Roughness_LHE.R: Compares Low vs (High and Extreme) with Low vs High vs Extreme. [NB: as we collapse across each participant to create a priming index, we switch to simple lm rather than glmer (i.e we don’t have random effect of participant).
6_Figure_1.R: creates Figure 1, needs cents_and_interval_labels.txt
7_Figure_2.R: creates Figure 2
8_Figure_3.R: creates Figure 3, needs Wang_results.csv
9_Supplementary_Wilcox_Test.R: Tests for congruency effects for each pair of intervals seperately
10_KSTESTS.R: Checks that data fits Gamma distributions

3.1 Auxiliary data (Computational models)

Separate repository produces the predictions by computational models (roughness and harmonicity). The model predictions are also compared to each other and to empirical data (the results past rating studies involving consonance and dissonance). The way the predictions are created (separation into High and Low deltas) are also created by the these analyses. Also, the stimulus visualisations are included in the scripts.

See computational_models.

3.2 Run GLMM and contrasts

# Only run if needed.
# source('1_Munge_Script.R')

source('2_GLMModel.R')
source('3_Contrasts_Roughness_Harmonicity.R')

3.3 Correlations between Roughness and priming

source('4_Correlation_Wang.R')


    Pearson's product-moment correlation

data:  df$deltarough and df$Index
t = 2.3004, df = 377, p-value = 0.02197
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.01712277 0.21583104
sample estimates:
      cor 
0.1176544

3.4 Additional contrasts (high and extreme)

source('5_Roughness_LHE.R')

Delta_R	I	SDI
E	11.295217	40.89082
H	10.856638	42.30070
L	-0.084045	46.88718

3.5 Additional tests

source('9_Supplementary_Wilcox_Test.R')

[1] “Test on each interval pair for automatic responses to acoustical rough musical intervals:”

Intervals	V.value	p.value	Cohen.s.d
m3M3	368	0.383	0.002
m2P5	252	0.027	0.214
m6M6	435	0.246	0.007
M7P5	325	0.349	0.056
ttP5	330	0.377	0.041
m2tt	173	0.002	0.357
m2M3	236	0.041	0.251
M2P5	191	0.056	0.176
d2P5	278	0.137	0.240
s2S5	249	0.040	0.306

source('10_KSTESTS.R')

[1] “Fit gamma function to each participant’s RT distribution” [1] “and use Kolmogorov-Smirnow test to assess the goodness of fit”

Exact binomial test

data: failures and 379 number of successes = 11, number of trials = 379, p-value = 0.9833 alternative hypothesis: true probability of success is greater than 0.05 95 percent confidence interval: 0.01636026 1.00000000 sample estimates: probability of success 0.02902375

4 Figures

6_Figure_2.R: creates Figure 1, needs cents_and_interval_labels.txt.
7_Figure_2.R: creates Figure 2
8_Figure_3.R: creates Figure 3, needs Wang_results.csv

4.1 Create Figure 1

source('7_Figure_1.R')

4.2 Create Figure 2

source('6_Figure_2.R')

4.3 Create Figure 3

source('8_Figure_3.R')

5 Appendix

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_GB.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggrepel_0.8.2      Rmisc_1.5          plyr_1.8.6         lattice_0.20-41   
 [5] ggplot2_3.3.3      fitdistrplus_1.1-1 survival_3.2-7     lsr_0.5           
 [9] emmeans_1.5.1      car_3.0-10         carData_3.0-4      lmerTest_3.1-2    
[13] MASS_7.3-53        dplyr_1.0.2        lme4_1.1-23        Matrix_1.2-18     

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5          mvtnorm_1.1-1       zoo_1.8-8          
 [4] digest_0.6.27       R6_2.5.0            cellranger_1.1.0   
 [7] evaluate_0.14       coda_0.19-4         highr_0.8          
[10] pillar_1.4.7        rlang_0.4.10        curl_4.3           
[13] multcomp_1.4-14     readxl_1.3.1        minqa_1.2.4        
[16] data.table_1.13.6   nloptr_1.2.2.2      rmarkdown_2.6      
[19] labeling_0.4.2      splines_4.0.3       statmod_1.4.34     
[22] stringr_1.4.0       foreign_0.8-80      munsell_0.5.0      
[25] compiler_4.0.3      numDeriv_2016.8-1.1 xfun_0.19          
[28] pkgconfig_2.0.3     htmltools_0.5.0     tidyselect_1.1.0   
[31] tibble_3.0.4        rio_0.5.16          codetools_0.2-16   
[34] withr_2.3.0         crayon_1.3.4        grid_4.0.3         
[37] nlme_3.1-149        xtable_1.8-4        gtable_0.3.0       
[40] lifecycle_0.2.0     magrittr_2.0.1      scales_1.1.1       
[43] zip_2.1.1           estimability_1.3    stringi_1.5.3      
[46] farver_2.0.3        ellipsis_0.3.1      generics_0.1.0     
[49] vctrs_0.3.6         boot_1.3-25         sandwich_3.0-0     
[52] openxlsx_4.2.2      TH.data_1.0-10      tools_4.0.3        
[55] forcats_0.5.0       glue_1.4.2          purrr_0.3.4        
[58] hms_0.5.3           abind_1.4-5         yaml_2.2.1         
[61] colorspace_2.0-0    knitr_1.30          haven_2.3.1