Differences in dogs’ event related potentials in response to human and dog vocal stimuli: A non-invasive study
Data files
Apr 11, 2022 version files 109.06 MB
-
all_avgs_17dogs_long2.mat
39.74 MB
-
all_avgs_17dogs_spec_val.mat
69.19 MB
-
data_17dogs_long.csv
70.27 KB
-
data_17dogs.csv
49.29 KB
-
README_ERP.txt
1.94 KB
Abstract
Recent advances in the field of canine neuro-cognition allow for the non-invasive research of brain mechanisms in family dogs. Considering the striking similarities between dog’s and human (infant)’s socio-cognition at the behavioural level, both similarities and differences in neural background can be of particular relevance. The current study investigates brain responses of N=17 family dogs to human and conspecific emotional vocalisations using a fully non-invasive ERP paradigm. We found that similarly to humans, dogs show a differential ERP response depending on the species of the caller demonstrated by a more positive ERP response to human vocalisations compared to dog vocalisations in a time-window between 250-650 ms after stimulus onset. A later time-window between 800-900 ms also revealed a valence sensitive ERP response in interaction with the species of the caller. Our results are the first ERP evidence to show the species sensitivity of vocal neural processing in dogs along with indications of valence sensitive processes in later post-stimulus time-periods.
All of the data was collected at the Department of Ethology, ELTE, Budapest, Hungary. The Matlab datasets contain averaged ERP values of 17 family dogs, recorded on 4 different channels, with three additional channels created for the visual artefact rejection process, both for the Main (1 second long segments) and the Extended analysis (2 seconds long segments), this later is refered to with the 'long' tag. The subject averages of the 17 subjects are also provided.
The two excel files contain the averaged values of the 100 ms long, overlapping time-windows, which were used for the statistical analysis. The RStudio script for the statistics and the RStudio and Matlab scripts for the visualization are also included.
The README file further elaborates about the usage of the dataset.
There are no missing values in the dataset. The needed packages for each script are also indicated in the README file.