Feature Type Focus: Cost function for one parameter optimization for the utterance choice experiments. Optimizing softness. Non-obedience and klValueFactor are fixed. — SimpleRSAModelUttKLDivFTFParamA • priorinference

Simple RSA

Softness is optimized.

Non-obedience is fixed at 0. klValueFactor is fixed at 1.

SimpleRSAModelUttKLDivFTFParamA(par, data)

Arguments

par

par	One value vector, which specifies one of three parameters to be optimized: softPrefValue: 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.
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.

One value vector, which specifies one of three parameters to be optimized:

softPrefValue: 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.

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.