rem: REM (Retrieving Effectively from Memory) model

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

REM (Retrieving Effectively from Memory) model

Description

Shiffrin & Steyvers' (1997) REM adapted to cross-situational word learning. Word/object identity is a length-w feature vector drawn from a geometric base-rate distribution (g); each trial stores one episodic trace per candidate word-object pairing present, built by imperfectly copying the pair's true features (each feature independently stored with probability u, and if stored, correct with probability c, else a fresh random draw). At test, a candidate pairing's strength is the summed likelihood ratio of every trace given the true (fully-known) probe features, REM's odds-based alternative to minerva2()'s dot-product activation.

Usage

rem(g, u, c, w = 12)

Arguments

g	Base-rate feature-value distribution parameter (larger = more variable/distinctive feature values, in (0, 1))
u	Per-feature storage probability at study, in (0, 1)
c	Probability a stored feature is copied correctly (vs. replaced by a random, possibly-confusable value), in (0, 1)
w	Number of features per item (fixed hyperparameter, not a free cognitive parameter)

Details

Performance note: unlike minerva2() (one trace per trial) this model stores one trace per candidate (word, object) pair per trial – on a highly ambiguous corpus (many words/objects per utterance) memory size grows combinatorially rather than linearly with trial count, and both encoding and the voc_sz * ref_sz test pass scale with that trace count, making rem() the most expensive of this package's memory models on cluttered/naturalistic data. w is a fixed representational hyperparameter (not usually treated as a free cognitive parameter), not included by default in this package's DEoptim registry bounds – see xsl_model_registry().

Value

An object of class xslMod

Examples

mod <- rem(g = 0.4, u = 0.3, c = 0.7, w = 12)
xsl_run(mod, get_example_ambiguous_condition())

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