uncfam_predictive: Predictive biased associative model (item-level prediction...

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

Predictive biased associative model (item-level prediction error)

Description

A variant of uncfam() (Kachergis et al. 2012's uncertainty- and familiarity-biased associative model) that adds an item-level, Rescorla-Wagner-style prediction error term: on each trial, the amount learned about a word-object association is scaled by how much the word's predicted association strength (summed over the trial's objects) falls short of the maximum value, rather than normalizing to distribute a fixed amount of associative weight across the trial. This lets initially mis-paired ("surprising") items draw more learning than the un-normalized original model allows. Associations are bounded to ⁠[0, beta]⁠ (with beta = 1): without normalization, the update can otherwise overshoot below 0 (and diverge) or far above beta on a word's first exposure.

Usage

uncfam_predictive(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_predictive(X = .1, C = 1, B = .98)
xsl_run(mod, get_example_ambiguous_condition())

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