kachergis2012_highlighting: Kachergis (2012) highlighting experiment data
In XSLmodels: Run and Optimize Cross-Situational Word Learning Models

kachergis2012_highlighting

R Documentation

Kachergis (2012) highlighting experiment data

Description

A list of two xslData objects ("words as cues" and "objects as cues") from the highlighting experiment in Kachergis (2012, CogSci), "Learning Nouns with Domain-General Associative Learning Mechanisms". Training trials (genuine simultaneous 2-cue trials, matching the paper's Figure 2: 2 words + 1 object, or 2 objects + 1 word, per trial) are read directly from the real per-trial ordering file for the real N=67 dataset behind the paper's published statistics – confirmed by reproducing its exact reported response proportions and chi-square values. See data-raw/add_kachergis2012_highlighting.R for the full construction rationale, including index relabeling needed to work around a uncfam_model() indexing limitation.

Usage

kachergis2012_highlighting

Format

An object of class list of length 2.

Details

Each condition replicates the classic 3-role highlighting structure (PE, PL, I) twice. Words-as-cues' PL items have their real target at a different index than their own word index, and its I items have two legitimate targets – neither fits xslData's one-correct-object-per- word accuracy convention, so only its two PE items get a real accuracy value (the rest are NA). Objects-as-cues has no such issue (every tested word's own index is among its trained objects after relabeling), so all 4 of its items have real accuracy. Because of the partial-NA accuracy vector, this dataset is kept standalone rather than appended to xsl_datasets: an NA would silently poison every other dataset's aggregate fit in get_group_model_fit()/get_crossvalidated_model_fit(), which sum SSE across all of xsl_datasets. See tests/bakeoff_comparison/kachergis2012_highlighting_fit.R for a custom scorer that fits a model against all four of the paper's reported proportions (PE-E, PL-L, I-E, I-L) directly off the association matrix, the way the paper's own (non-package) fitting procedure did.