predict_referent: Predict referent selection for a single trial from an...

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

Predict referent selection for a single trial from an association matrix

Description

Given a model's word-object association matrix, returns the probability distribution over a trial's candidate objects for one heard word – the quantity to compare against a participant's choice on a referent-selection (m-alternative forced choice) trial. This is model-agnostic: it works on the matrix of any xslFit (or any word-by-object matrix), reading each entry as an association strength / meaning weight.

Usage

predict_referent(
  m,
  word,
  objects,
  prior = NULL,
  pragmatic = FALSE,
  threshold = NULL,
  rsa_alpha = 3,
  depth = 1
)

Arguments

m	A word-by-object association matrix (e.g. xsl_run(mod, data)$fits[[1]]$matrix). Assumed non-negative; negative entries are clamped to 0. dimnames are used to resolve character word/objects.
word	The heard word: a row name of m, or a positive integer row index. A value not present in m is treated as a novel word.
objects	The candidate objects present on the trial: column names of m, or positive integer column indices. An entry not present in m is treated as an unnamed (novel) object.
prior	Optional prior over objects (length length(objects), non-negative). Defaults to uniform. Set an entry to 0 to remove that object from consideration entirely (e.g. an object the speaker has no epistemic access to).
pragmatic	If TRUE, use the RSA pragmatic listener instead of the literal one.
threshold	Optional; binarize m at this value (m >= threshold) before predicting. The RSA layer is sharpest on a binary lexicon.
rsa_alpha, depth	Speaker rationality and recursion depth for the RSA listener (only used when pragmatic = TRUE).

Details

The literal rule is Bayes' rule on the word's row, ⁠P(object | word) proportional to m[word, object] * prior(object)⁠, normalized over the objects present. With pragmatic = TRUE the objects are instead resolved by a one-step Rational Speech Act pragmatic listener (rsa_listener()), which additionally reasons that the speaker could have used a different word – this is what yields strong mutual exclusivity when a heard word is lexically ambiguous and a competitor word names one of the candidates. A truly novel (unseen) word carries no lexical evidence and returns the prior in either mode.

Value

A numeric vector of probabilities over objects, in the given order, summing to 1.

Examples

m <- xsl_run(fgt2009(alpha = 1), get_example_unambiguous_condition())$fits[[1]]$matrix
predict_referent(m, 1, c(1, 2, 3))
predict_referent(m, 1, c(1, 2, 3), prior = c(1, 0, 1)) # object 2 unavailable

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