Cost function for two parameter optimization for the utterance choice experiments. Optimizing softness and klValueFactor. Non-obedience is fixed.

Full-RSA

Softness and klValueFactor are optimized.

Non-obedience is fixed at 0.

RSAModelUttKLDivParamAC(params, data)

Arguments

params

params	Two value vector specifying the two parameters to be optimized. softness: 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. klValueFactor: A parameter that can be negative, 0 or positive (Here it is set to = 1): 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
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.

Two value vector specifying the two parameters to be optimized.

softness: 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.
klValueFactor: A parameter that can be negative, 0 or positive (Here it is set to = 1):

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

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.

Minimized Kullback-Leibler divergence and the optimal parameters.