fitModel

Object of class <code><a rd-options="" href="/link/TimedVector?package=pdmod&version=1.0.1" data-mini-rdoc="pdmod::TimedVector">TimedVector</a></code>
 specifying trials including whether signal was
 rewarded/unrewarded and times

dataX

Corresponding observations of
 subject's response to signal

dataResponse

The function to use to transform
 the mean estimate into a response

responseFunction

(optional) Vector defining how
 to group the trials into sessions where the items are the
 starting indicies for each session (so the last value can
 be the index after the last trial) and <code>NA</code>s are
 used for gaps between sessions

sessionBoundaries

<code>TRUE</code> (default) to estimate g, or
 <code>FALSE</code> to fix g at 0

fitG

Estimates parameters for proximal/distal model using
 multi-criteria estimation (<code><a rd-options="=nsga2" href="/link/mco?package=pdmod&version=1.0.1&to=%3Dnsga2" data-mini-rdoc="=nsga2::mco">mco</a></code>)

Fits a model of Pavlovian conditioning phenomena, such as response extinction and spontaneous recovery, and partial reinforcement extinction effects. Competing proximal and distal reward predictions, computed using fast and slow learning rates, combine according to their uncertainties and the recency of information. The resulting mean prediction drives the response rate.

Chloe Bracis

pdmod

Proximal/Distal Modeling Framework for Pavlovian Conditioning
Phenomena

fitModel function

Object of class <code><a rd-options='' href='TimedVector'>TimedVector</a></code>
 specifying trials including whether signal was
 rewarded/unrewarded and times

Estimates parameters for proximal/distal model using
 multi-criteria estimation (<code><a rd-options='=nsga2' href='mco'>mco</a></code>)

fitModel: Fit model parameters

Description

Usage

Arguments

Value

See Also