minerva2: MINERVA2 episodic memory model

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

MINERVA2 episodic memory model

Description

Hintzman's (1984, 1986) MINERVA2 adapted to cross-situational word learning: each trial is stored as a single episodic trace formed by concatenating that trial's word-vector sum and object-vector sum (words and objects are random Gaussian vectors, one per vocabulary item). At test, a word probe (word-half only, object-half zeroed) activates every trace by its similarity cubed; the activation-weighted sum of traces (the "echo") is compared, on its object half, against every candidate object to give a graded association – exponentiated by X into a Luce-choice-ready matrix rather than MINERVA2's usual hard best-match.

Usage

minerva2(X, D = 100)

Arguments

X	Choice-rule temperature: how sharply the echo/object cosine similarity is exponentiated into a choice weight (larger = more winner-take-all)
D	Dimensionality of the random word/object feature vectors (fixed hyperparameter, not a free cognitive parameter)

Details

Performance note: memory is exact (no forgetting/decay), so the model keeps one 2*D-length trace per trial and a test pass costs O(voc_sz * n_traces * D). D 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 <- minerva2(X = 5, D = 50)
xsl_run(mod, get_example_ambiguous_condition())

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