uncfam_attention: Biased associative model with attention scaled to trial...

In XSLmodels: Run and Optimize Cross-Situational Word Learning Models

Biased associative model with attention scaled to trial uncertainty

Description

A variant of uncfam() (Kachergis et al. 2012's uncertainty- and familiarity-biased associative model) in which the associative weight distributed on a trial is additionally scaled by the ratio of that trial's mean object entropy to the mean entropy of all objects seen so far. This implements the "system-level" theory proposed by Fitneva & Christiansen (2015): that learners are more alert (i.e. allocate a higher learning rate) on trials with more uncertain items, such as in a Low Initial Accuracy condition where many items have been mis-paired.

Usage

uncfam_attention(X, B, C)

Arguments

X	Associative weight to distribute
B	Weighting of uncertainty vs. familiarity
C	Decay

Value

An object of class xslMod

Examples

mod <- uncfam_attention(X = .1, C = 1, B = .98)
xsl_run(mod, get_example_ambiguous_condition())

XSLmodels documentation built on Oct. 18, 2026, 5:08 p.m.