Cost function for two parameter optimization for the utterance choice experiments. Optimizing alpha and klValueFactor. Softness and non-obedience are fixed. — RSAModelUttKLDivParamCD4 • priorinference

Full-RSA

Alpha and klValueFactor are optimized.

Softness and non-obedience are fixed at 0.

RSAModelUttKLDivParamCD4(params, data)

Arguments

params

params	Two value vector specifying two of the four parameters to be optimized: alpha: A parameter value between 0 and 1.Exponential scaling of the speaker choosing the utterance that maximizes the chance of the listener getting the target object right. 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.

Two value vector specifying two of the four parameters to be optimized:

alpha: A parameter value between 0 and 1.Exponential scaling of the speaker choosing the utterance that maximizes the chance of the listener getting the target object right.
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.

Minimized Kullback-Leibler divergence and the optimal parameters.