Cost function for two parameter optimization for the utterance choice experiments. Optimizing non-obedience and klValueFactor. Softness and alpha are fixed. — RSAModelUttKLDivParamBD4A.2 • priorinference

Full-RSA

Non-obedience and klValueFactor are optimized.

Softness is fixed at 0.2.

Alpha is fixed at 1.

RSAModelUttKLDivParamBD4A.2(params, data)

Arguments

params

params	Two value vector specifying two of the four parameters to be optimized: non-obedience: 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". 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:

non-obedience: 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".
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.