Distal Modeling Framework for Pavlovian Conditioning Phenomena

Description Usage Arguments Value Author(s) See Also Examples

Calulates a realization of a proximal/distal model for a specified sequence of trials and paramter values. Use the verbose parameter to include underlying model components (distal and proximal estimates, weights, uncertainties and signal-reward association) in addition to the mean estimate.

1 2	computeModel(x, mFast, mSlow, n, g = 0, h, tau = 1/TV_DAY, threshold = 0, verbose = TRUE)

`x`	Object of class `TimedVector` specifying trials including whether signal was rewarded/unrewarded and times
`mFast`	Learning rate of proximal memory estimates
`mSlow`	Learning rate of distal memory estimates
`n`	Learning rate of uncertainty estimates
`h`	Decay rate of distal memory uncertainty estimator as time passes between trials
`g`	Association learning speed parameter
`tau`	Temporal scaling coefficient to translate time differences in `x` to fractional days. Defaults to `1/TV_DAY` assuming that the times in `x` are expressed in minutes.
`threshold`	Difference in real time that must pass before deflation kicks in (used for testing)
`verbose`	true to include supporting estimates, weights, etc.

Series of estimates

Chloe Bracis

calculateResponse, averageBySession

# Create 5 sessions of 20 rewarded trials,
# then 2 sessions of 20 unrewarded trials
trialTime = as.vector(sapply(0:6, function(x) 1:20 + x * TV_DAY))
trials =  TimedVector(c(rep(1, 5*20), rep(0, 2*20)), trialTime)

estimates = computeModel(trials, mFast = 0.7, mSlow = 0.1, n = 0.05,
						 g = 500, h = 0.2, verbose = TRUE)
plot(estimates, trials)