A package to compute a cumulative history for time-series of perceptual dominance in bistable displays.
Estimates cumulative history, an estimate of accumulating adaptation/prediction error for the dominant percept, for time-series for continuously viewed bistable perceptual rivalry displays. Computes cumulative history via a homogeneous first order differential process. I.e., it assumes exponential growth/decay of the history as a function of time and perceptually dominant state. Supports Gamma, log normal, and normal distribution families.
If you use the toolbox in your work, please cite Pastukhov, A., (2022). bistablehistory: an R package for history-dependent analysis of perceptual time series. Journal of Open Source Software, 7(70), 3901, https://doi.org/10.21105/joss.03901
For current stable version use
The master branch is the development version. To install it please use
The main function is
fit_cumhist that takes a data frame with time-series as the first argument. Minimally, you need to specify
state — string with the column name that encodes perceptually dominant state — and either
duration (column name with duration of individual dominance phases) or
onset (column name with onset times of individual dominance phases). Thus, for a simplest case of a single subject and single run/block measurement with all defaults (gamma distribution, fitted cumulative history time constant but fixed mixed state value and history mixing proportion) the call would be
library(bistablehistory) data(br_singleblock) gamma_fit <- fit_cumhist(br_singleblock, state = "State", duration = "Duration")
library(bistablehistory) data(br_singleblock) gamma_fit <- fit_cumhist(br_singleblock, state = "State", onset = "Time")
Now you can look at the fitted value for history time constant via
and main effect of history for both parameters of gamma distribution