Cost function for two parameter optimization for the utterance choice experiments. (iterative setting) Optimizing softness and klValueFactor. Non-obedience is fixed. — SimpleRSAModelUttKLDivParamAK

Simple RSA

Softness and klValueFactor are optimized.

Non-obedience is fixed at 0.

SimpleRSAModelUttKLDivParamAK_iterative(params, data)

Arguments

params

params	Two value vector specifying the two parameters to be optimized. softPrefValue, i.e., the strength of preferring one entity over others. klValueFactor can be negative, zero or positive: zero Don't care about learning about feature preferences of the listener positive Care about learning about feature preferences of the listener negative Trying to pick non-ambiguous utterances
data	A matrix with data rows. column structure: `[1:OC1,OC2,OC3,4:numUttOptions,6: picked utterance]` 1:OC1 Object 1. A value between 1 and 27. 2:OC2 Object 2. A value between 1 and 27. 3:OC3 Object 3. A value between 1 and 27. 4:numUttOptions The number of valid utterances in the scene. 6: picked utterance The utterance picked by the participant.

Two value vector specifying the two parameters to be optimized.

data

A matrix with data rows.

column structure: [1:OC1,OC2,OC3,4:numUttOptions,6: picked utterance]

1:OC1 Object 1. A value between 1 and 27.

2:OC2 Object 2. A value between 1 and 27.

3:OC3 Object 3. A value between 1 and 27.

4:numUttOptions The number of valid utterances in the scene.

6: picked utterance The utterance picked by the participant.

Minimized Kullback-Leibler divergence and the optimal parameters.

This is the iterative version of SimpleRSAModelUttKLDivParamAK