In dalejbarr/truthiness: Illusory Truth Longitudinal Study

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truthiness

The truthiness package contains data, code, and utilities related to the Longitudinal Illusory Truth study by Henderson, Simons, and Barr (2021). The package includes anonymized data from the study, the key preprocessing and analysis scripts that underlie the published results, tables describing the design of the study in full detail, and functions for data simulation that the authors used in study planning. The package was created to enable verification of the analytical methods, to facilitate reproduction of the main findings, and to encourage replication, extension, and further exploration of a rich longitudinal dataset on the Illusory Truth phenomenon.

The Illusory Truth phenomenon is the tendency of people to consider statements they have heard before as more likely to be true relative to novel statements (Hasher, Goldstein, & Toppino, 1977). Our study examined the persistence of this effect across four time intervals: immediately, one day, one week, and one month following exposure. Five hundred and sixty seven participants saw 64 out of 128 statements of ambiguous veracity during an exposure phase, and later rated the full set of statements on a scale from 1 (definitely false) to 7 (definitely true). (Each repeated statement was repeated only once, at one of the four intervals.)

More details about the study can be found in the project repository at https://osf.io/nvugt/ and in the article at the Journal of Cognition.

Installation

Package version 1.2.5 is available on the Comprehensive R Archive Network (CRAN). Install it using:

install.packages("truthiness")

We put a lot of effort into making our data and analysis scripts readily available. If you find this package useful in your work, please cite the paper:

Henderson, Emma L., Simons, Daniel J., and Barr, Dale J. (2021). The trajectory of truth: A longitudinal study of the Illusory Truth Effect. Journal of Cognition, 4, 29. http://doi.org/10.5334/joc.161

  @Article{,
    title = {The trajectory of truth: A longitudinal study of the Illusory Truth Effect},
    author = {Emma L. Henderson and Daniel J. Simons and Dale J. Barr},
    journal = {Journal of Cognition},
    year = {2021},
    volume = {4},
    pages = {29},
    url = {http://doi.org/10.5334/joc.161},
  }

If you encounter any problems using the package, or have any suggestions, please file an issue at the package development site on github.

Basic Usage

All of the datasets collected during Stage 2 of the study are included in the package, in four data objects:

• sessions: information about each participant
• phases: information about each participant's performance in each phase
• cjudgments : category judgments (exposure task)
• ratings : truth ratings

Information about the design of the study is available in these objects:

• stimulus_materials : list of statements used in the study
• presentation_lists : presentation lists used for counterbalancing
• stimulus_conditions : which condition each statement appeared in within each presentation list
• stimulus_categories : the correct categorizations for each statement

Finally, the fitted model objects from the analysis have also been made available:

• truth_trajectory_models : cumulative link mixed-effects models (CLMMs) fitted to the data for analysis

See the help documentation ?truth_trajectory_data, ?truth_trajectory_design, and ?truth_trajectory_models for further information.

Reproducing the findings

Re-run the analysis script on the data and produce an HTML report of the results using reproduce_analysis().

library(truthiness)

report <- reproduce_analysis()
browseURL(report) # view the report in your browser

Alternatively, you can access the R Markdown file through the RStudio interface using File -> New File -> R Markdown... -> From Template -> Illusory Truth Analysis.

Exploring and visualizing the data

The code below calculates the illusory truth trajectory for each participant, and plots the distribution of participant means along with the cell means.

library(truthiness)
library(tidyverse)

## combine info from tidy tables
all_ratings <- ratings %>%
  inner_join(phases, c("ID", "phase_id")) %>%
  filter(keep) %>%                         # apply exclusions
  inner_join(sessions, "ID") %>%           # link to participant-level info
  inner_join(stimulus_conditions,          # map to experimental condition
             c("list_id", "stim_id")) %>% 
  select(ID, repetition, interval, trating)

## calculate subject means
subj_means <- all_ratings %>%
  group_by(ID, repetition, interval) %>%
  summarize(mean_rating = mean(trating), .groups = "drop")

## calculate cell means
cell_means <- subj_means %>%
  group_by(repetition, interval) %>%
  summarize(mean_rating = mean(mean_rating), .groups = "drop")

ggplot(subj_means,
       aes(interval, mean_rating)) +
  geom_violin(aes(fill = repetition), color = NA,
              alpha = .1, position = "identity") +
  geom_point(aes(color = repetition), data = cell_means, size = 3) +
  geom_line(aes(color = repetition, group = repetition), data = cell_means) +
  labs(y = "truth rating (1 = definitely false, 7 = definitely true)") +
  scale_y_continuous(breaks = 1:7) +
  theme_light() +
  theme(panel.grid.minor.y = element_blank())

References

Hasher, Lynn, Goldstein, David, and Toppino, Thomas (1977). Frequency and the conference of referential validity. Journal of Verbal Learning and Verbal Behavior, 16, 107--112.

Henderson, Emma L., Simons, Daniel J., and Barr, Dale J. (2021). The trajectory of truth: A longitudinal study of the Illusory Truth Effect.. Journal of Cognition, 4, 29.

dalejbarr/truthiness documentation built on June 10, 2021, 2:10 p.m.