FeatureTypeFocus: Main cost function for three parameter optimization for the utterance choice experiments. Optimizing softness, non-obedience and klValueFactor. — SimpleRSAModelUttKLDiv

Simple RSA

Actual RSA model Kullback-Leibler divergence determination for the utterance choice experiments.

Softness, non-obedience and klValueFactor are optimized.

SimpleRSAModelUttKLDiv_3paramsFTF(data, par1, par2, par3)

Arguments

data	A matrix with data rows. column structure: `[1:OC1,OC2,OC3,4:numUttOptions,7-X:TurkerSliderValues]` 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. 7-X:TurkerSliderValues These columns contain the participants' slider values.
par1	softness parameter A parameter value between `[0,infinity)` (The larger the value the higher the tendency towards uniform liking).Value reflects how categorical the listener's preferences are:0: The listener always picks her preferred object.If the listener prefers red objects, she will always pick the red object in the scene.infinity: It is as likely for the listener to pick green, blue or red objects.
par2	non-obedience parameter Determines the extent to which the instruction of the speaker is obeyed by the listener.(0 = full obedience, infinity = full instruction ignorance).Example:0: Listener always picks red objects following the utterance "red".infinity: Listener as likely to pick green, blue or red objects even if the utterance is "red".
par3	klValueFactor A parameter that can be negative, 0 or positive: zero Don't care about learning about the feature preferences of the listener positive Care about learning about the feature preferences of the listener negative Trying to pick non-ambiguous utterances

Minimized Kullback-Leibler divergence and the optimal parameters.