uncfam_elimination: Biased associative model with learning by elimination

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

Biased associative model with learning by elimination

Description

A variant of uncfam() (Kachergis et al. 2012's uncertainty- and familiarity-biased associative model) that adds inference by elimination (mutual exclusivity) to how a trial's associative weight is allocated. If the other words and objects on a trial are already confidently paired with each other, the remaining word and object probably go together, so their pairing should draw attention even though it is still weak. In uncfam(), by contrast, an already-strong pair on the trial draws weight away from such a pairing.

Usage

uncfam_elimination(X, B, C, eps)

Arguments

X	Associative weight to distribute
B	Weighting of uncertainty vs. familiarity
C	Decay
eps	Weight of elimination inference (0 = identical to uncfam())

Details

On each trial, each on-trial pair (w', o') has a mutual confidence q(w', o') = P(o'|w') P(w'|o'), from the row- and column-normalized associations. A pair (w, o) gets elimination support s(w, o) = \sum_{w' \ne w, o' \ne o} q(w', o'): how much of the rest of the trial is accounted for by other pairs. The share of the trial's weight X given to (w, o) is then

\frac{b(w, o) + \epsilon s(w, o)}{1 + \epsilon \sum s}

where b(w, o) is uncfam()'s normalized familiarity-and-uncertainty share, so eps = 0 reproduces uncfam() exactly.

Motivated by the initial-accuracy experiment (kachergis_initial_accuracy), where learners were more accurate on an initially mis-paired word the more of its study trials it shared with a word they already knew.

Value

An object of class xslMod

Examples

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

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