RSAModelLL1_1simpleRSA4TrialsIterative: Cost function for one parameter optimization (iterative...

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

Description

Simple RSA

1 parameter optimization; The softness parameter is optimized.

The non-obedience parameter is fixed.

Usage

RSAModelLL1_1simpleRSA4TrialsIterative(params, data)

Arguments

params

One value vector, which specifies one of two parameters to be optimized: softPrefValue is optimized,i.e. The strength of "preferring one entity over others". (The larger the value the higher the tendency towards uniform liking) non-obedience (default = 0), i.e. The extent to which the instruction of the speaker is obeyed by the listener. (0 = full obedience, infinity = full instruction ignorance)

data

A Matrix with data rows. column structure: [1:OC1,OC2,OC3,4:UUFeat, 5:Q1Feat,6:Q2Feat] [7:Q1AnswerV1,V2,V3, 10:Q2AnswerV1,V2,V3] 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:UUFeat Uttered feature. A number between 1 and 3. (1: shape, 2: pattern, 3: color) 5:Q1Feat Questioned feature 1. A number between 1 and 3. (1: shape, 2: pattern, 3: color). Example: If you utter "blue" (feature: color), then you can learn something about shape and texture preferences. 6:Q2Feat Questioned feature 2. A number between 1 and 3. (1: shape, 2: pattern, 3: color). Example: If you utter "blue" (feature: color), then you can learn something about shape and texture preferences. 7:Q1AnswerV1, V2, V3 The columns 7-9 contain the participants' slider values for the first questioned feature. 10:Q2AnswerV1, V2, C3 The columns 10-12 contain the participants' slider values for the second questioned feature.

Details

This function uses RSAModelKLDiv3params_simpleRSA4TrialsIterative.

Value

Minimized Kullback-Leibler divergence and the optimal parameters.

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