todam: TODAM holographic reduced representation model

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

TODAM holographic reduced representation model

Description

Murdock's (1982) TODAM adapted to cross-situational word learning: word and object vectors (random Gaussian, one per vocabulary item) are bound via circular convolution into a single memory vector M, attention- weighted each trial by how familiar (cosine-similar to the current M) each word/object already is. At test, a candidate object is circularly correlated back out of M and compared (cosine) against each word; similarities are shifted non-negative, row-normalized, and exponentiated by X into a graded (Luce-choice-ready) matrix rather than a hard best-match.

Usage

todam(X, D = 500)

Arguments

X	Choice-rule temperature: how sharply the retrieved-object/word 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: every encoding step and every test comparison requires an FFT-based circular convolution/correlation over a D-length vector, so cost scales with ⁠D log(D)⁠; unlike minerva2()'s per-trial trace, todam() holds a single fixed-size memory vector regardless of corpus length. 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 <- todam(X = 5, D = 200)
xsl_run(mod, get_example_ambiguous_condition())

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