gangwani2011_category: Gangwani, Kachergis & Yu simultaneous category + object name...

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

Gangwani, Kachergis & Yu simultaneous category + object name learning

Description

Experiment 1 of Gangwani, Kachergis & Yu, "Simultaneous Cross-situational Learning of Category and Object Names" (an undergraduate-journal version is Gangwani & Kachergis, Indiana Undergraduate Journal of Cognitive Science 5, 2010). A hierarchical cross-situational design: on every training trial the learner sees 2 objects and hears 3 words – the two objects' own 1-to-1 names plus one 1-to-many "category" label that names those two objects and two further objects seen on other trials. Every object is thus part of a 2-to-1 word-referent mapping (a basic name and a category label), and the paper's finding is that learners violate mutual exclusivity: they learn both labels for the same object.

Usage

gangwani2011_category

Format

An object of class list of length 3.

Details

A named list of three xslData objects, one per training block – "block 1 (ad hoc)", "block 2 (natural)", "block 3 (ad hoc)" – run in that fixed order. All three share the same 36-trial co-occurrence schedule (only the stimuli differ); each carries its own block-specific human data.

Per object: train$words[[t]] is c(name_i, name_j, category_label) and train$objects[[t]] is c(i, j). Words 1-12 are object names (name i pairs with object i); words 13-15 are the category labels (13/14/15 name objects 1-4 / 5-8 / 9-12). accuracy is per-word human P(correct referent | word), length 15: positions 1-12 are 12-AFC name accuracy, positions 13-15 are 3-AFC category-membership accuracy (n = 34 canonical-block-order participants; the paper reports 33, dropping one more low performer). response_matrix (15 x 12, row-normalised) is the full human choice distribution including the category labels. test holds only the 12 name trials (12-AFC).

Not part of xsl_datasets, and not scorable by the built-in xsl_run() SSE: the association matrix is 15 words x 12 objects, so get_perf() / mafc_test() (which score via the diagonal m[w, w]) cover only words 1-12. Score the category labels – and the ME-violation measure that is the paper's point – with score_gangwani2011_category() in tests/bakeoff_comparison/gangwani2011_category_fit.R. The natural-category feature-similarity effect (block 2) and generalization to novel objects are out of scope for the package's amodal, index-based models and are not represented. See data-raw/add_gangwani2011_category.R for construction details.

Examples

block1 <- gangwani2011_category[["block 1 (ad hoc)"]]
# 15 x 12 matrix; suppressWarnings() because get_perf()'s diagonal rule
# warns on the non-square matrix (the built-in SSE is not meaningful here).
m <- suppressWarnings(xsl_run(baseline(), block1))$fits[[1]]$matrix
mafc_test(m, block1$test)                           # names (words 1-12) only

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