R/sRSA_iterative_indep_pr.R

In CognitiveModeling/priorinference_iterative: Prior Inference Iterative

#' Simple pragmatic speaker with all prior preferences iterative function
#' Iterative function independent of trial order (prior rate).
#' \code{posterior = (1 - prior rate) x evidence + (prior rate)  x  prior}.
#'
#' @description Simple-RSA (iterative, independent on trial order)
#' The simple pragmatic speaker considers all "imaginable" (i.e. implemented)
#' preference distributions over objects of the listener.
#'
#' Starting with a prior assumption over the possible listener's preferences. It
#' then infers the posterior over these preferences given the listener makes a
#' particular object choice. P(listener's feature value preferences | utterance,
#' object choice by the listener, prior over listener's feature value
#' preferences).
#'
#' This function takes the evidence from the current trial in consideration and also
#' the prior from the trials before and computes the posterior over the feature preferences of the listener.
#' \code{posterior = (1 - prior rate) x evidence + (prior rate) x prior}.
#'
#' @param utterance The uttered word by the speaker that the listener hears.
#'
#' An index referring to one of the values in the vector validUtterances.
#'
#' @param obj The object chosen by the listener. A value referring to the index
#'   1,2 or 3.
#'
#' @param preferencesPriorAll A vector of length 9.
#'
#' Probability mass over all feature values.
#'
#' Gives a prior preferences distribution over all (nine) feature values.
#'
#' \code{preferencesPriorAll <- rep(1/9, 9)}
#'
#' @param validUtterances A vector of utterances that correspond to all feature
#' values present in the current objects in the scene.
#'
#' For example, it only makes sense to utter \emph{"red"} in a scene if there
#' are \emph{red} objects present.
#'
#' @param currentObjects Vector of three values in \code{{1,...,27}} specifying
#' the target and the other two objects.
#'
#' The target is the first object in the vector \code{(index = 1)}.
#'
#' @param uttToObjProbs A matrix. The rows map each possible utterance that
#' corresponds to each present feature value of the current objects. The
#' columns represent the three objects in the scene.
#'
#' This reflects the obedience-parameter and which objects match the
#' respective utterance. The matrix shows the probability that a certain
#' object is chosen following a certain utterance, that is valid in the scene.
#' The number of rows of the matrix match the length of the validUtterances
#' vector.
#'
#' @param objectPreferenceSoftPriors A list of preference priors for all valid
#' utterances based on the object in the scene.
#'
#' The list has as many rows as the length of the validUtterances vector + 1.
#'
#' Each row in the list contains a vector of length 3, as there are three
#' objects in the scene.
#'
#' The extra row is for the case of no feature preferences whatsoever, i.e.
#' uniform prior over all three objects in the scene.
#'
#' @param priorRate A parameter specifying how much prior information is weighed into the decision.
#'
#' @return A vector of length 9. It contains the normalized probability over
#'   preferences (priors).
#'
#' @details
#' This is function is the first of two functions that are used in the iterative setting using the prior rate parameter.
#' The second one is: \code{\link{simplePragmaticSpeakerWithPrefPriorAll_indepOfOrder_pr}}.
#'
#' @examples
#' \donttest{simplePragmaticSpeakerWithPrefPriorAll_indepOfOrder_pr(utterance, obj,
#' preferencesPriorAll, validUtterances, currentObjects, uttToObjProbs, objectPreferenceSoftPriors, priorRate)}
#'
#' output:
#' [1] 0.5333333 0.1333333 0.3333333 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#' [9] 0.0000000
#' @export
simplePragmaticSpeakerWithPrefPriorAll_indepOfOrder_pr <-
  function(utterance,
           obj,
           preferencesPriorAll,
           validUtterances,
           currentObjects,
           uttToObjProbs,
           objectPreferenceSoftPriors,
           priorRate) {
    #cat("preferencesPriorAll", preferencesPriorAll, "\n")
    preferencesPrior <- preferencesPriorAll[validUtterances]
    prefPost <- rep(0, length(validUtterances))

    for (pref in c(1:length(validUtterances))) {
      print("pref")
      print(pref)
      if (preferencesPrior[pref] > 0) {
        # evidence pp from the current trial
        pp <- simpleListener(utterance,
                             uttToObjProbs,
                             objectPreferenceSoftPriors[[pref]])
        # take half of the evidence and half of the prior
        prefPost[pref] <- (1 - priorRate) * pp[obj] + priorRate * preferencesPrior[pref]

      } # else{} # prior preference for this preference is zero
    } # done with considering all possible preferences.
    if (sum(prefPost) == 0) { # no evidence for any preferences... -> no inference
      return(preferencesPriorAll)
    }
    # normalizing relevant posterior preferences such that the sum is equal to their prior probability mass
    #   sum(preferencesPrior) is the probability mass of the full prior that we are "entitled" to redistribute
    #           because it concerns the features present in the trial
    #   prefPost / sum(prefPost) is the normalized posterior, so that the updated vector sums up to 1
    prefPost <- sum(preferencesPrior) * (prefPost) / sum(prefPost)

    # replacing the relevant old prior preferences values in preferencesPriorAll with their posteriors (which become the new priors)
    preferencesPriorAll[validUtterances] <- prefPost
    #
    return(preferencesPriorAll / sum(preferencesPriorAll)) # Sidenote: the renormalization here should not be really necessary because it is taken
    # care of by a scaled insertion of new values in the two commands above.
  }

#' Determine speaker's inference of the posterior listener preferences (iterative setting, independent of trial order)
#' \code{posterior = (1 - prior rate)  x  evidence + (prior rate)  x  prior}
#'
#' @description
#' Simple RSA (iterative, independent of trial order (prior rate))
#'
#' This function calculates the speaker's posterior guess of the
#' feature value preferences of the listener in the iterative setting.
#' That means how the speaker infers the preferences of the listener based on the object choice.
#'
#' @param currentObjects A vector of three values in \code{{1,...,27}} specifying the target and the other two objects in the scene.
#'
#' The target is the first object in the vector \code{(index = 1)}.
#' @param featureUtt One of the values \code{{1,2,3}} specifying which feature is uttered (i.e. shape = 1 / texture = 2 / or color = 3).
#'
#' @param softPrefValue A parameter value between \code{[0,infinity)} (The larger the higher the tendency towards uniform liking).
#'
#' Value reflects how categorical the listener's preferences are:
#'
#' \strong{0:} The listener always picks her preferred object.
#'
#' If the listener prefers \emph{red} objects, she will always pick the \emph{red} object in the scene.
#'
#' \strong{infinity:} It is as likely for the listener to pick \emph{green}, \emph{blue} or \emph{red} objects.
#' @param notObeyInst Determines the extent to which the instruction of the speaker is obeyed by the listener.
#'
#' (0 = full obedience, infinity = full instruction ignorance).
#'
#' \strong{Example:}
#'
#' \strong{0:} Listener always picks \emph{red} objects following the utterance \emph{"red"}.
#'
#' \strong{infinity:} Listener as likely to pick \emph{green, blue} or \emph{red} objects even if the utterance is \emph{"red"}.
#'
#' @param priorPrefAll A vector of length 9.
#'
#' Probability mass over all feature values.
#'
#' Gives a prior preferences distribution over all (nine) feature values.
#'
#' @param priorRate This parameter specifies how much the prior information is weighed into the speaker's decision is weighed into the decision of the speaker regarding the feature preferences of the listener.
#'
#' @export
#' @return A vector of length 9. It contains the speaker's inference of the feature value preferences of
#' the listener independent of the trial order.
#'
#' @examples
#' \donttest{determineSpeakerPostListPrefsSimpleRSAWithPriorPref_indep_pr(currentObjects, featureUtt,
#' softPrefValue, notObeyInst, priorPrefAll, priorRate)}
#'
#' output:
#' [1]  6.666667e-01 6.666667e-101  3.333333e-01  0.000000e+00  0.000000e+00  0.000000e+00
#' [7]  0.000000e+00  0.000000e+00  0.000000e+00
determineSpeakerPostListPrefsSimpleRSAWithPriorPref_indep_pr <- function(currentObjects, featureUtt,
                                                                                      softPrefValue, notObeyInst, priorPrefAll, priorRate) {
  validUtterances <- determineValidUtterances(currentObjects)
  mapObjToUtt <- mapObjectToUtterances(currentObjects)
  uttToObjProbs <- determineUttToObjectProbs(validUtterances,
                                             currentObjects,
                                             mapObjToUtt, notObeyInst)
  mapUttToObjDeterministic <- determineUttToObjectProbs(validUtterances,
                                                        currentObjects,
                                                        mapObjToUtt, 0)
  #  print(uttToObjProbs)
  objectPreferenceSoftPriors <- getObjectPreferencePriors(validUtterances, currentObjects,
                                                          softPrefValue, mapUttToObjDeterministic)
  prefPostAll <- simplePragmaticSpeakerWithPrefPriorAll_indepOfOrder_pr(which(validUtterances==allObjectsToUtterancesMappings[currentObjects[1],featureUtt]),
                                                                        1, priorPrefAll, validUtterances, currentObjects,
                                                                        uttToObjProbs, objectPreferenceSoftPriors, priorRate)
  return(prefPostAll)
}

# ------------------------
# functions for optimization (independent of trial order) prior rate

#' Simple RSA model Kullback-Leibler divergence determination
#' (iterative setting, independent of trial order)
#'
#' @description
#' Simple RSA (iterative, independent of trial order (prior rate))
#'
#' The function calculates the optimal parameter values of the free parameters by
#' estimating the log-likelihood of the RSA model given model parameters and data.
#' It also determines the actual RSA model Kullback-Leibler divergence.
#'
#' 2 parameter optimization considering only the available feature values present in the scene, i.e. feature values of shape, texture and color.
#' This function is used in the iterative dependent on the trial scenario.
#'
#' @param data A matrix with data rows.
#'
#' column structure: \code{[1:OC1,OC2,OC3,4:numUttOptions,7-X:TurkerSliderValues]}
#'
#' \strong{1:OC1} Object 1. A value between 1 and 27.
#'
#' \strong{2:OC2} Object 2. A value between 1 and 27.
#'
#' \strong{3:OC3} Object 3. A value between 1 and 27.
#'
#' \strong{4:numUttOptions} The number of valid utterances in the scene.
#'
#' \strong{7-X:TurkerSliderValues} These columns contain the participants' slider values.
#'
#' @param par1 \describe{
#' \item{softness parameter}{The strength of "preferring one entity over others". (The larger the value the higher the tendency towards uniform liking)}
#' }
#'
#' @param par2 \describe{
#' \item{non-obedience parameter}{The extent to which the instruction of the speaker is obeyed by the listener.
#' (0 = full obedience, infinity = full instruction ignorance).}
#' }
#'
#' @param par3 \describe{
#' \item{prior rate parameter}{This parameter specifies how much the prior information is weighed into the speaker's decision is weighed into the decision of the speaker regarding the feature preferences of the listener.
#' }}
#' @return Minimized Kullback-Leibler divergence and the optimal parameter values.
#'
#' @details
#' This function is used in \code{\link{LL1_1_Iterative_pr_notObey0_pr0.5}},
#'
#' \code{\link{LL1_2_Iterative_pr_pref0_pr0.5}},
#'
#' \code{\link{LL1_3_Iterative_pr_pref0_notObey0}},
#'
#' \code{\link{LL2_13_Iterative_pr_notObey0}},
#'
#' \code{\link{LL2_13_Iterative_pr_notObey0.1}},
#'
#' \code{\link{LL2_23_Iterative_pr_pref0}}.
#'
#' @export
#' @return Minimized Kullback-Leibler divergence and the optimal parameters.
RSAModelKLDiv3params_simpleRSA4TrialsIterative_pr <- function(data, par1, par2, par3) {
  #  print(params)
  llRes <- 0
  for(i in c(1:nrow(data))) {
    if( (i-1)%%4 == 0) {
      preferencesPriorAll <- getPreferencesPrior(data[i,5]) # focussing on the feature type in question.
    }
    ## determining the object and utterance
    currentObjects <- c(data[i,1],data[i,2],data[i,3])
    uttFeat <- data[i,4]
    ##
    validUtterances <- determineValidUtterances(currentObjects)
    ## determining the model predictions
    prefPostAll <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_indep_pr(currentObjects, uttFeat, abs(par1), abs(par2), preferencesPriorAll, abs(par3))
    ## adding the KL Divergence terms of the relevant feature values for the two sets of answers.
    ##
    ## adding the negative log likelihoods
    for(j in c(1:3)) {
      llRes <- llRes + data[i, 5+j] *
        ( log(data[i, 5+j] + 1e-100) - log(prefPostAll[j + (data[i, 5]-1)*3] + 1e-100) )
    }
    preferencesPriorAll <- prefPostAll
  }
  return(llRes)
}

# ---- one parameter optimization ----
# optimizing 1st parameter in iterative model: softness

#' Cost function for one parameter optimization (iterative setting, independent of trial order).
#' Optimizing softness.
#' non-obedience is fixed at 0.
#' prior rate is fixed at 0.5.
#'
#' @description
#' Simple RSA
#'
#' 1 parameter optimization; The softness parameter is optimized. (1st)
#'
#' The non-obedience and prior rate parameter are fixed.
#' @param params One value vector, which specifies one of three parameters to be optimized:
#' \enumerate{
#'   \item{softPrefValue is optimized, i.e. The strength of "preferring one entity over others". (The larger the value the higher the tendency towards uniform liking)}
#'   \item{non-obedience is fixed at 0, i.e. The extent to which the instruction of the speaker is obeyed by the listener.
#'   (0 = full obedience, infinity = full instruction ignorance)}
#'   \item{priorRate is fixed to 0.5. This parameter specifies how much the prior information is weighed into the decision .}
#'}
#'
#' @param data A Matrix with data rows.
#'
#' column structure:
#'
#' [1:OC1,OC2,OC3,4:UUFeat, 5:Q1Feat,6:Q2Feat]
#'
#' [7:Q1AnswerV1,V2,V3, 10:Q2AnswerV1,V2,V3]
#'
#' \strong{1:OC1} Object 1. A value between 1 and 27.
#'
#' \strong{2:OC2} Object 2. A value between 1 and 27.
#'
#' \strong{3:OC3} Object 3. A value between 1 and 27.
#'
#' \strong{4:UUFeat} Uttered feature. A number between 1 and 3. (1: shape, 2: pattern, 3: color)
#'
#' \strong{5:Q1Feat} Questioned feature 1. A number between 1 and 3. (1: shape, 2: pattern, 3: color).
#'
#' Example: If you utter "blue" (feature: color), then you can learn something about shape and texture preferences.
#'
#' \strong{6:Q2Feat} Questioned feature 2. A number between 1 and 3. (1: shape, 2: pattern, 3: color).
#'
#' Example: If you utter "blue" (feature: color), then you can learn something about shape and texture preferences.
#'
#' \strong{7:Q1AnswerV1, V2, V3} The columns 7-9 contain the participants' slider values for the first questioned feature.
#'
#' \strong{10:Q2AnswerV1, V2, C3} The columns 10-12 contain the participants' slider values for the second questioned feature.
#'
#' @return Minimized Kullback-Leibler divergence and the optimal parameter values.
#'
#' @details
#' This function uses \code{\link{RSAModelKLDiv3params_simpleRSA4TrialsIterative_pr}}.
#' @export
LL1_1_Iterative_pr_notObey0_pr0.5 <- function(params,  data) {
  return(RSAModelKLDiv3params_simpleRSA4TrialsIterative_pr(data, abs(params[1]), 0, 0.5))
}

# optimizing 2nd parameter in iterative model: obedience

#' Cost function for one parameter optimization (iterative setting, independent of trial order).
#' Optimizing non-obedience.
#' softness is fixed at 0.
#' prior rate is fixed at 0.5.
#'
#' @description
#' Simple RSA
#'
#' 1 parameter optimization; The non-obedience parameter is optimized. (2nd)
#'
#' The non-obedience and prior rate parameter are fixed.
#' @param params One value vector, which specifies one of three parameters to be optimized:
#' \enumerate{
#'   \item{softPrefValue is fixed at 0, i.e. The strength of "preferring one entity over others". (The larger the value the higher the tendency towards uniform liking)}
#'   \item{non-obedience is optimized, i.e. The extent to which the instruction of the speaker is obeyed by the listener.
#'   (0 = full obedience, infinity = full instruction ignorance)}
#'   \item{priorRate is fixed to 0.5. This parameter specifies how much the prior information is weighed into the decision is weighed into the decision of the speaker regarding the feature preferences of the listener.}
#'}
#'
#' @param data A Matrix with data rows.
#'
#' column structure:
#'
#' [1:OC1,OC2,OC3,4:UUFeat, 5:Q1Feat,6:Q2Feat]
#'
#' [7:Q1AnswerV1,V2,V3, 10:Q2AnswerV1,V2,V3]
#'
#' \strong{1:OC1} Object 1. A value between 1 and 27.
#'
#' \strong{2:OC2} Object 2. A value between 1 and 27.
#'
#' \strong{3:OC3} Object 3. A value between 1 and 27.
#'
#' \strong{4:UUFeat} Uttered feature. A number between 1 and 3. (1: shape, 2: pattern, 3: color)
#'
#' \strong{5:Q1Feat} Questioned feature 1. A number between 1 and 3. (1: shape, 2: pattern, 3: color).
#'
#' Example: If you utter "blue" (feature: color), then you can learn something about shape and texture preferences.
#'
#' \strong{6:Q2Feat} Questioned feature 2. A number between 1 and 3. (1: shape, 2: pattern, 3: color).
#'
#' Example: If you utter "blue" (feature: color), then you can learn something about shape and texture preferences.
#'
#' \strong{7:Q1AnswerV1, V2, V3} The columns 7-9 contain the participants' slider values for the first questioned feature.
#'
#' \strong{10:Q2AnswerV1, V2, C3} The columns 10-12 contain the participants' slider values for the second questioned feature.
#'
#' @return Minimized Kullback-Leibler divergence and the optimal parameter values.
#'
#' @details
#' This function uses \code{\link{RSAModelKLDiv3params_simpleRSA4TrialsIterative_pr}}.
#'
#' @export
LL1_2_Iterative_pr_pref0_pr0.5 <- function(params, data) {
  return(RSAModelKLDiv3params_simpleRSA4TrialsIterative_pr(data, 0, abs(params[1]), 0.5))
}

# optimizing 3rd parameter in iterative model: prior rate

#' Cost function for one parameter optimization (iterative setting, independent of trial order).
#' Optimizing the prior rate.
#' softness and non-obedience are fixed at 0.
#'
#' @description
#' Simple RSA (iterative, independent of trial order)
#'
#' 1 parameter optimization; The prior rate parameter is optimized.
#'
#' The softness and non-obedience parameters are fixed.
#' @param params One value vector, which specifies one of three parameters to be optimized:
#' \enumerate{
#'   \item{softPrefValue is fixed at 0, i.e. The strength of "preferring one entity over others". (The larger the value the higher the tendency towards uniform liking)}
#'   \item{non-obedience is fixed at 0 i.e. The extent to which the instruction of the speaker is obeyed by the listener.
#'   (0 = full obedience, infinity = full instruction ignorance)}
#'   \item{priorRate is optimized. This parameter specifies how much the prior information is weighed into the decision.}
#'}
#'
#' @param data A Matrix with data rows.
#'
#' column structure:
#'
#' [1:OC1,OC2,OC3,4:UUFeat, 5:Q1Feat,6:Q2Feat]
#'
#' [7:Q1AnswerV1,V2,V3, 10:Q2AnswerV1,V2,V3]
#'
#' \strong{1:OC1} Object 1. A value between 1 and 27.
#'
#' \strong{2:OC2} Object 2. A value between 1 and 27.
#'
#' \strong{3:OC3} Object 3. A value between 1 and 27.
#'
#' \strong{4:UUFeat} Uttered feature. A number between 1 and 3. (1: shape, 2: pattern, 3: color)
#'
#' \strong{5:Q1Feat} Questioned feature 1. A number between 1 and 3. (1: shape, 2: pattern, 3: color).
#'
#' Example: If you utter "blue" (feature: color), then you can learn something about shape and texture preferences.
#'
#' \strong{6:Q2Feat} Questioned feature 2. A number between 1 and 3. (1: shape, 2: pattern, 3: color).
#'
#' Example: If you utter "blue" (feature: color), then you can learn something about shape and texture preferences.
#'
#' \strong{7:Q1AnswerV1, V2, V3} The columns 7-9 contain the participants' slider values for the first questioned feature.
#'
#' \strong{10:Q2AnswerV1, V2, C3} The columns 10-12 contain the participants' slider values for the second questioned feature.
#'
#' @return Minimized Kullback-Leibler divergence and the optimal parameter values.
#' @details
#' This function uses \code{\link{RSAModelKLDiv3params_simpleRSA4TrialsIterative_pr}}.
#' @export
LL1_3_Iterative_pr_pref0_notObey0 <- function(params,  data) {
  return(RSAModelKLDiv3params_simpleRSA4TrialsIterative_pr(data, 0, 0, abs(params[1])))
}

# ---- two parameter optimization ----
# optimizing 1st and 3rd parameters in iterative model: softness + prior rate
# obedience fixed at 0

#' Cost function for one parameter optimization (iterative setting, independent of trial order).
#' Optimizing the softness and prior rate.
#' non-obedience is fixed at 0.
#'
#' @description
#' Simple RSA (iterative, independent of trial order)
#'
#' 2 parameter optimization; The softness and prior rate parameter are optimized (1st and 3rd)
#'
#' The softness and prior rate parameters are fixed.
#' @param params One value vector, which specifies one of three parameters to be optimized:
#' \enumerate{
#'   \item{softPrefValue is optimized, i.e. The strength of "preferring one entity over others". (The larger the value the higher the tendency towards uniform liking)}
#'   \item{non-obedience is fixed at 0 i.e. The extent to which the instruction of the speaker is obeyed by the listener.
#'   (0 = full obedience, infinity = full instruction ignorance)}
#'   \item{priorRate is optimized. This parameter specifies how much the prior information is weighed into the decision.}
#'}
#'
#' @param data A Matrix with data rows.
#'
#' column structure:
#'
#' [1:OC1,OC2,OC3,4:UUFeat, 5:Q1Feat,6:Q2Feat]
#'
#' [7:Q1AnswerV1,V2,V3, 10:Q2AnswerV1,V2,V3]
#'
#' \strong{1:OC1} Object 1. A value between 1 and 27.
#'
#' \strong{2:OC2} Object 2. A value between 1 and 27.
#'
#' \strong{3:OC3} Object 3. A value between 1 and 27.
#'
#' \strong{4:UUFeat} Uttered feature. A number between 1 and 3. (1: shape, 2: pattern, 3: color)
#'
#' \strong{5:Q1Feat} Questioned feature 1. A number between 1 and 3. (1: shape, 2: pattern, 3: color).
#'
#' Example: If you utter "blue" (feature: color), then you can learn something about shape and texture preferences.
#'
#' \strong{6:Q2Feat} Questioned feature 2. A number between 1 and 3. (1: shape, 2: pattern, 3: color).
#'
#' Example: If you utter "blue" (feature: color), then you can learn something about shape and texture preferences.
#'
#' \strong{7:Q1AnswerV1, V2, V3} The columns 7-9 contain the participants' slider values for the first questioned feature.
#'
#' \strong{10:Q2AnswerV1, V2, C3} The columns 10-12 contain the participants' slider values for the second questioned feature.
#'
#' @return Minimized Kullback-Leibler divergence and the optimal parameter values.
#' @details
#' This function uses \code{\link{RSAModelKLDiv3params_simpleRSA4TrialsIterative_pr}}.
#' @export
LL2_13_Iterative_pr_notObey0 <- function(params,  data) {
  return(RSAModelKLDiv3params_simpleRSA4TrialsIterative_pr(data, abs(params[1]), 0, abs(params[2])))
}


# optimizing 1st and 3rd parameters in iterative model: softness + prior rate
# obedience fixed at 0

#' Cost function for one parameter optimization (iterative setting, independent of trial order).
#' Optimizing the softness and prior rate.
#' non-obedience is fixed at 0.1.
#'
#' @description
#' Simple RSA (iterative, independent of trial order)
#'
#' 2 parameter optimization; The softness and prior rate parameter are optimized (1st and 3rd)
#'
#' The softness and prior rate parameters are fixed.
#' @param params One value vector, which specifies one of three parameters to be optimized:
#' \enumerate{
#'   \item{softPrefValue is optimized, i.e. The strength of "preferring one entity over others". (The larger the value the higher the tendency towards uniform liking)}
#'   \item{non-obedience is fixed at 0 i.e. The extent to which the instruction of the speaker is obeyed by the listener.
#'   (0 = full obedience, infinity = full instruction ignorance)}
#'   \item{priorRate is optimized. This parameter specifies how much the prior information is weighed into the decision.}
#'}
#'
#' @param data A Matrix with data rows.
#'
#' column structure:
#'
#' [1:OC1,OC2,OC3,4:UUFeat, 5:Q1Feat,6:Q2Feat]
#'
#' [7:Q1AnswerV1,V2,V3, 10:Q2AnswerV1,V2,V3]
#'
#' \strong{1:OC1} Object 1. A value between 1 and 27.
#'
#' \strong{2:OC2} Object 2. A value between 1 and 27.
#'
#' \strong{3:OC3} Object 3. A value between 1 and 27.
#'
#' \strong{4:UUFeat} Uttered feature. A number between 1 and 3. (1: shape, 2: pattern, 3: color)
#'
#' \strong{5:Q1Feat} Questioned feature 1. A number between 1 and 3. (1: shape, 2: pattern, 3: color).
#'
#' Example: If you utter "blue" (feature: color), then you can learn something about shape and texture preferences.
#'
#' \strong{6:Q2Feat} Questioned feature 2. A number between 1 and 3. (1: shape, 2: pattern, 3: color).
#'
#' Example: If you utter "blue" (feature: color), then you can learn something about shape and texture preferences.
#'
#' \strong{7:Q1AnswerV1, V2, V3} The columns 7-9 contain the participants' slider values for the first questioned feature.
#'
#' \strong{10:Q2AnswerV1, V2, C3} The columns 10-12 contain the participants' slider values for the second questioned feature.
#'
#' @return Minimized Kullback-Leibler divergence and the optimal parameter values.
#' @details
#' This function uses \code{\link{RSAModelKLDiv3params_simpleRSA4TrialsIterative_pr}}.
#' @export
LL2_13_Iterative_pr_notObey0.1 <- function(params,  data) {
  return(RSAModelKLDiv3params_simpleRSA4TrialsIterative_pr(data, abs(params[1]), 0.1, abs(params[2])))
}

# optimizing 2nd and 3rd parameters in iterative model: obedience + prior rate
# softness fixed at 0


#' Cost function for one parameter optimization (iterative setting, independent of trial order).
#' \code{posterior = (1 - prior rate)  x  evidence + (prior rate)  x  prior}
#' Optimizing the non-obedience and prior rate.
#' softness is fixed at 0.
#'
#' @description
#' Simple RSA (iterative, independent of trial order)
#'
#' 2 parameter optimization; The softness and prior rate parameter are optimized (1st and 3rd)
#'
#' The softness and prior rate parameters are fixed.
#' @param params One value vector, which specifies one of three parameters to be optimized:
#' \enumerate{
#'   \item{softPrefValue is fixed at 0, i.e. The strength of "preferring one entity over others". (The larger the value the higher the tendency towards uniform liking)}
#'   \item{non-obedience is optimized, i.e. The extent to which the instruction of the speaker is obeyed by the listener.
#'   (0 = full obedience, infinity = full instruction ignorance)}
#'   \item{priorRate is optimized. This parameter specifies how much the prior information is weighed into the decision of the speaker regarding the feature preferences of the listener.}
#'}
#'
#' @param data A Matrix with data rows.
#'
#' column structure:
#'
#' [1:OC1,OC2,OC3,4:UUFeat, 5:Q1Feat,6:Q2Feat]
#'
#' [7:Q1AnswerV1,V2,V3, 10:Q2AnswerV1,V2,V3]
#'
#' \strong{1:OC1} Object 1. A value between 1 and 27.
#'
#' \strong{2:OC2} Object 2. A value between 1 and 27.
#'
#' \strong{3:OC3} Object 3. A value between 1 and 27.
#'
#' \strong{4:UUFeat} Uttered feature. A number between 1 and 3. (1: shape, 2: pattern, 3: color)
#'
#' \strong{5:Q1Feat} Questioned feature 1. A number between 1 and 3. (1: shape, 2: pattern, 3: color).
#'
#' Example: If you utter "blue" (feature: color), then you can learn something about shape and texture preferences.
#'
#' \strong{6:Q2Feat} Questioned feature 2. A number between 1 and 3. (1: shape, 2: pattern, 3: color).
#'
#' Example: If you utter "blue" (feature: color), then you can learn something about shape and texture preferences.
#'
#' \strong{7:Q1AnswerV1, V2, V3} The columns 7-9 contain the participants' slider values for the first questioned feature.
#'
#' \strong{10:Q2AnswerV1, V2, C3} The columns 10-12 contain the participants' slider values for the second questioned feature.
#'
#' @return Minimized Kullback-Leibler divergence and the optimal parameter values.
#' @details
#' This function uses \code{\link{RSAModelKLDiv3params_simpleRSA4TrialsIterative_pr}}.
#' @export
LL2_23_Iterative_pr_pref0 <- function(params,  data) {
  return(RSAModelKLDiv3params_simpleRSA4TrialsIterative_pr(data, 0, abs(params[1]), abs(params[2])))
}