Cost function for one parameter optimization for the utterance choice experiments. Optimizing klValueFactor. Softness, non-obedience and alpha are fixed. — RSAModelUttKLDivParamD4B.2 • priorinference

Full-RSA

klValueFactor is optimized.

Softness is fixed at 0.

Non-obedience is fixed at 0.2.

Alpha is fixed at 1.

RSAModelUttKLDivParamD4B.2(params, data)

Arguments

params

params	One value vector specifying the parameter to be optimized. 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,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 specifying the parameter to be optimized.

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.