LL1_2_Iterative_pr_pref0_pr0.5: Cost function for one parameter optimization (iterative...

In CognitiveModeling/priorinference_iterative: Prior Inference Iterative

Description

Simple RSA

1 parameter optimization; The non-obedience parameter is optimized. (2nd)

The non-obedience and prior rate parameter are fixed.

Usage

LL1_2_Iterative_pr_pref0_pr0.5(params, data)

Arguments

params

One value vector, which specifies one of three parameters to be optimized: softPrefValue is fixed at 0, i.e. The strength of "preferring one entity over others". (The larger the value the higher the tendency towards uniform liking) non-obedience is optimized, i.e. The extent to which the instruction of the speaker is obeyed by the listener. (0 = full obedience, infinity = full instruction ignorance) priorRate is fixed to 0.5. This parameter specifies how much the prior information is weighed into the decision is weighed into the decision of the speaker regarding the feature preferences of the listener.

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_pr.

Value

Minimized Kullback-Leibler divergence and the optimal parameter values.

CognitiveModeling/priorinference_iterative documentation built on Dec. 17, 2021, 3:01 p.m.