RSAModelUttKLDivParamACD4: Cost function for three parameter optimization for the...

In haniaelkersh/rsa-publish-test: Prior Inference Package

Description

Full-RSA

Softness, alpha and klValueFactor are optimized.

Non-obedience is fixed at 0.

Usage

RSAModelUttKLDivParamACD4(params, data)

Arguments

params

Three value vector specifying three of the four 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. 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.

Details

This function uses RSAModelUttKLDiv_4params.

Value

Minimized Kullback-Leibler divergence and the optimal parameters.

haniaelkersh/rsa-publish-test documentation built on Jan. 31, 2021, 2:02 a.m.