Package index • XSLmodels

Models

Cross-situational word learning models

baseline(): Baseline model
decay(): Decay model
uncfam(): Kachergis 2012
uncfam_general(): General biased associative model (nests the uncfam family)
uncfam_gamma(): Kachergis 2012 with a free familiarity exponent
uncfam_elimination(): Biased associative model with learning by elimination
uncfam_attention(): Biased associative model with attention scaled to trial uncertainty
uncfam_predictive(): Predictive biased associative model (item-level prediction error)
uncfam_sampling(): Kachergis 2012
multi_sampling(): Multi-sampling associative model
propose_but_verify(): Trueswell et al. 2013 propose-but-verify model
pursuit(): Stevens et al. 2014 pursuit model
fazly(): Fazly et al. 2010 probablistic associative model
guess_and_test(): Guess and test model
rescorla_wagner(): Rescorla-Wagner (1972) error-driven associative model
tilles(): Tilles and Fontanari (2013) reinforcement model
bayesian_decay(): Bayesian decay model
kalman_filter(): Kalman filter associative model
softmax_rl(): Softmax reinforcement learning model
fgt2009(): Frank, Goodman & Tenenbaum (2009) intentional Bayesian word-learning model
fgt2009_rsa(): Frank, Goodman & Tenenbaum word-learning model with pragmatic (RSA) reasoning
minerva2(): MINERVA2 episodic memory model
todam(): TODAM holographic reduced representation model
rem(): REM (Retrieving Effectively from Memory) model

xsl_run(): Run XSL model
xsl_fit(): Fit XSL model using differential evolution
fgt2009_sweep_alpha(): Sweep the lexicon-size prior alpha for an FGT model
update_params(): Update parameters of xslMod
get_group_model_fit(): Get group model fit for a specific model
get_crossvalidated_model_fit(): Get cross-validated model fit
cross_validated_group_fits(): Perform Cross-Validation on Group Fits of a Model
get_train_test_split(): Split Data into Training and Testing Sets Based on Indices

xsl_datasets: Cross-situational word learning experiment data
kachergis2012_highlighting: Kachergis (2012) highlighting experiment data
kachergis_initial_accuracy: Kachergis, Grimmick, & Gureckis initial accuracy experiment data
gangwani2011_category: Gangwani, Kachergis & Yu simultaneous category + object name learning
vlach_debrock2017: Vlach & DeBrock (2017) spaced/massed repetition experiment data
benitez2020: Benitez et al. (2020) temporal-structure-of-naming experiment data
suanda2014: Suanda, Mugwanya, & Namy (2014) cross-situational learning in children
koehne2013: Koehne, Trueswell, & Gleitman (2013) multiple-meaning word learning
xsl_holdout_datasets: Registry of datasets held out from xsl_datasets
rollins_corpus: CHILDES/Rollins naturalistic word-learning corpus (Frank et al. 2009)
fm_corpus: Frank, Tenenbaum & Fernald naturalistic word-learning corpus
xslData() new_xslData(): xslData S3 class
new_xslFit() xslMod() new_xslMod(): Constructor for xsl_model S3 class
xslControl() new_xslControl(): xslControl S3 class
xslFit(): xslFit S3 class
get_example_ambiguous_condition(): Get example ambiguous condition
get_example_unambiguous_condition(): Get example unambiguous condition
show_datasets(): Show available datasets in the package
show_models(): Show available models in the package
create_cooc_matrix(): Creates co-occurrence matrix from training trials

get_perf(): Calculate Luce choice (proportion correct) for each item in a model knowledge matrix
mafc_test(): Evaluate m-alternative forced choice test
predict_referent(): Predict referent selection for a single trial from an association matrix
rsa_listener() rsa_speaker(): Rational Speech Act pragmatic listener and speaker
get_tp(): Get true positives (TP), given a knowledge matrix and a gold-standard lexicon
get_fscore(): Calculate F-score, precision, recall, and specificity for a knowledge matrix at a given threshold
get_roc() plot_roc() get_roc_max(): Calculate receiver operating characteristic (ROC) scores for a model association Matrix
plot_training_trials(): Create Animated Plot of Co-Occurrence Matrix from Training Trials