Figure 1
Plot of synthetic data generated from an observer detecting the flash of a light onscreen. The abscissa represents stimulus intensity, while the ordinate axis represents proportion correct.
Figure 2
Example of how data from a single psychophysical experiment should be formatted when passing these data to BayesFit for model fitting.
Figure 3
Plot of psychometric function using a Cumulative Normal sigmoid fit to data using BayesFit.
Figure 4
Plot of prior distributions used during Bayesian inference of parameters of psychometric function.
Figure 5
Plot of marginal distributions of parameters extracted from posterior.
Figure 6
Plot of posterior surface for scale and slope parameters, collapsed across gamma and lambda.
Figure 7
Example of how multiple datasets should be combined into a single dictionary object before being passed as an argument to BayesFit for batch fitting.
Figure 8
Three plots comparing the accuracy of predicted parameter values versus the true values used to generate data for each simulated observer. The ordinate axis provides the value of the parameter, either estimated or true, and the abscissa is an index for each simulated observer.
Figure 9
Boxplots of parameter estimates generated using BayesFit compared to target distributions.
Figure 10
Boxplots of distributions for values of threshold estimated at 75% correct performance using BayesFit and Psignifit 4.0 compared to target distribution.
Figure 11
Schematic of the architecture for BayesFit, also displaying module dependencies for each function, whether directly or using a function that also depended upon use of a specific module.