The impact of the weekly cycle on conscious experience in daily life Data and Scripts

Description

Data and scripts for Wallace et al., 2025 [pre-print] for reproducibility. In this repository, you will find analysis scripts and component solutions as word clouds to illustrate and interrogate the relationship between day of the week and times of the day on experiences in daily life. Our analysis aimed to establish the types of experiences reported in daily-life using samples from two different countries (Canada and the UK) in student and general population age groups. Participants responded to probes about their experiences by answering questions about the dimensions of their thoughts using multi-dimensional experience sampling (mDES) multiple times a day across the week. Our results reveal across our entire sample, as well as our country and age-group analyses, reveal 3 components of thought associated with experiences. Using these data, we empirically assess if the weekly cycle has an impact on conscious experiences and how these experiences relate to our daily activities. We establish two main findings from our results. First, participants thoughts become more task problem-focused, deliberate, and detailed (i.e. Detail Task-Focus) throughout the day, which is specific to weekdays rather than weekends. This thought pattern was most present when individuals reported working or studying and negatively associated with talking in person. On the other hand, experiences involving more intrusive and distracting features are most prevalent in passive states (resting or doing nothing), neither of which vary with the weekly cycle. The raw data used in the manuscript, are available on request.

Steps to reproduce

The first step of the analysis after collecting the experience sampling data is to decompose the mdes data into PCA components using the data file in the 'Data files' folder. Conveniently, also attached is a data file with the PCA components for the whole sample (referred to as the "Common" solution in the manuscript), as well as for each grouping variable of interest (e.g., Country [Canada; UK] and Age group [Students; General Population], and additional dempgrahic information. The item loading for each of these respective solutions are included as .csv files in the 'Wordclouds' folder. Included in this folder is a python script in which you can use to generate wordclouds to visualize the item loadings from the component solutions. This script requires reading the respective .csv files corresponding to each PCA output, which are attached. The majority of the analyses were computed using scripts in R and python, which can be found in the 'scripts' folder. The python scripts, 'Scatterplots_3d_2d(.)py' and 'ClockPlot_distribution_timeday_activity(.)py' were used to generate the plots embedded within the main script and supplementary figures. Data transformation, linear mixed models, chi-square, and contrast analyses were written in R, with annotations indicating which results section each analysis corresponds to. Additionally, the additional plots included in the figures such as barplots and boxplots are also included within the R script.

Institutions

• Queen's University

  ON, Kingston

Categories

Licence