XSLmodels

New Features

• Added uncfam_elimination(), a variant of uncfam() with learning by elimination (mutual exclusivity). Each on-trial pair's mutual confidence P(o|w) * P(w|o) is computed from the current associations, and a pair gets *elimination support* to the extent that the trial's other words and objects are already accounted for by each other. That support, weighted by a free parameter eps, is added to uncfam()'s familiarity-and-uncertainty share of the trial's associative weight before renormalizing; eps = 0 reproduces uncfam() exactly. In uncfam(), an already-known pair on a trial draws weight *away* from the trial's other pairs; with elimination, knowing one word directs learning to the remaining word-object pairing. Motivated by the initial-accuracy experiment, where learners were more accurate on an initially mis-paired word the more of its study trials it shared with an initially accurate word. Registered in show_models()/xsl_model_registry()
• Added uncfam_gamma(), a variant of uncfam() that decouples familiarity's contribution to a trial's attentional allocation from uncertainty's. uncfam()'s allocation weight is exp(B * entropy) * assocs -- entropy gets a free exponent (B), but familiarity (assocs) enters linearly with an implicit weight of 1 and no free parameter of its own. uncfam_gamma() adds one parameter, gamma, raising familiarity to a free power instead (assocs^gamma); gamma = 1 reproduces uncfam() exactly. Cross-validated against all of xsl_datasets, it beats plain uncfam() in every fold of a 5-fold split (mean test SSE 0.48 vs. 0.56), with a best-fit gamma around 0.3 (familiarity's pull *dampens* with diminishing returns). A separate analysis fitting this same free-gamma substrate to active cross-situational word learning (learners choosing which items to see named next, rather than a fixed passive sequence) instead found a best-fit gamma around 2 (amplified, not dampened) -- see ?uncfam_gamma for the full comparison
• Added minerva2(), todam(), and rem(), three distributed/episodic memory models ported from standalone prototypes that predated this package (Hintzman's MINERVA2, 1984/1986; Murdock's TODAM, 1982; Shiffrin & Steyvers' REM, 1997) and were never wired into it or tested. All three represent words/objects as random feature vectors rather than tallying a single word-by-object association, and turn their native similarity/odds score into a Luce-choice-ready matrix via an exponentiated (X-temperature) or likelihood-ratio-based choice rule so they plug into get_perf()/mafc_test() like every other model here. Porting surfaced real bugs in the prototypes, fixed along the way (see each model's @details/source comments): minerva2()'s prototype compared a 2D echo-content vector against D-dim object vectors (silent recycling, not an error) instead of just the echo's object half; todam()'s prototype computed trial-1 attention weights as cos_sim(item, 0) = NaN, poisoning memory from the first trial on, and both todam() prototypes called the long-removed as.real(); rem()'s prototype never actually implemented REM at all (it declared a geometric feature-vector generator but its model/test functions were an unmodified copy of the MINERVA2 prototype's continuous-Gaussian code) -- rem() here is a fresh adaptation of Shiffrin & Steyvers' storage/odds equations to this package's associative task. Benchmarking (see ?minerva2/?todam/?rem performance notes) found rem()'s raw per-simulation likelihood-ratio odds span several orders of magnitude (a lucky match on one rare feature value can dominate), which would otherwise let a single lucky simulation hijack xsl_run()'s sum-across-simulations aggregation; rem() now row-normalizes each simulation's choice weights before returning them. All three are registered in show_models()/xsl_model_registry(), fitting only their choice-rule temperature (X) or storage parameters (g, u, c) by default -- D/w (feature-vector dimensionality) are fixed representational hyperparameters, not free cognitive parameters. Speed varies widely: on the full xsl_datasets (53 conditions) at n_sim = 500, minerva2() takes ~30s, todam() ~8 min, and rem() ~20 min -- todam()/rem() are impractical to xsl_fit() at full DEoptim settings (NP = 100, itermax = 100 would be tens of thousands of evaluations) without sharply reducing n_sim/NP/itermax, as the package's own fitting examples already do for slow models
• Added 3 more temporal-contiguity conditions to xsl_datasets from Kachergis, Yu, & Shiffrin (2009) -- the same paper behind the existing 1_max_temp_spat_cont_orig/4_no_spat_orig_max_tc conditions. data-raw/XSL-dataset-fields.csv documented 6 "overlap" conditions (3 matched pairs of a control and non-control order at the same overlap degree) that were never pulled in; only the 3 control-order ones (1olap3tr_contr, temp_cont_1olap2tr_contr, temp_cont_2olap2tr_contr) have real subject-level accuracy data findable anywhere in the repo (in data-raw/agg_data/x13_1-21.txt, per export_data.R's own comments mapping its generic condition labels to these named orders). The other 3 have order files but no discoverable human data, and are deliberately left out rather than guessed. See data-raw/add_temporal_contiguity_overlap.R
• Added a fifth standalone dataset, benitez2020 (a named list of 7 xslData objects, one per condition x age-group combination) from Benitez et al. (2020), "The temporal structure of naming events differentially affects children's and adults' cross-situational word learning" (data: osf.io/2hmxr). data-raw/Benitez2020-rep/Benitez2020_XSLdata_extraction.R already extracted training orders, 2AFC test trials, and response-count matrices from the raw OSF data but stopped short of building xslData objects (a literal # ToDo in the file); data-raw/add_benitez2020.R picks up from there. 8 symmetric word-object pairs, 12 training trials, 8-item 2AFC test; unlike the other standalone datasets, real per-word accuracy is recoverable directly from the provided response matrices (not just an overall mean) -- structurally it could join xsl_datasets directly, but is kept standalone by choice, as held-out generalization-test data (see xsl_holdout_datasets)
• Added xsl_holdout_datasets, a registry of every dataset in the package that is *not* part of xsl_datasets and therefore never seen by get_group_model_fit()/get_crossvalidated_model_fit() -- i.e. everything available for testing a fitted model's generalization to genuinely held-out data, along with how to score each one (most need a different scoring approach than xsl_datasets's standard diagonal convention, which is exactly why they're standalone; benitez2020 and kachergis_initial_accuracy are the two directly-scorable exceptions, kept out of the fitting pool specifically to serve as held-out data)
• Added a fourth standalone dataset, vlach_debrock2017 (Vlach & DeBrock, 2017, *Journal of Memory and Language*, "Remember dax? Relations between children's cross-situational word learning, memory, and language abilities"). 12 symmetric word-object pairs, 2 pairs per training trial, 36 trials, with pairs varying in whether their 6 repetitions are massed or spaced (n = 47 children, ages 2-5). The source only reports an overall mean/SD across all pairs and participants, not a per-word breakdown, so accuracy is NA throughout rather than fabricated -- kept standalone (not part of xsl_datasets) for the same reason as kachergis2012_highlighting/gangwani2011_category. See ?vlach_debrock2017 and data-raw/add_vlach_debrock2017.R
• Added a third standalone dataset, gangwani2011_category (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 design: each training trial shows 2 objects and names 3 words -- the two objects' own 1-to-1 names plus one 1-to-many "category" label naming those two objects and two others seen on other trials -- so every object carries both a basic name and a category label, and the paper's finding is that learners violate mutual exclusivity by learning both. A named list of three xslData objects (one per training block: ad hoc / natural / ad hoc), sharing one 36-trial co-occurrence schedule. Its 15 x 12 (word x object) association matrix breaks xslData's diagonal-scoring convention the same way kachergis2012_highlighting does, so it is kept standalone; score it with score_gangwani2011_category() in the new tests/bakeoff_comparison/gangwani2011_category_fit.R, which reads 12-AFC name accuracy, 3-AFC category accuracy, and the paper's headline ME-violation measure straight off any model's matrix. See ?gangwani2011_category and data-raw/add_gangwani2011_category.R

Other changes

• control$start_matrix is no longer silently ignored. Only some models initialized from it; the rest dropped it without warning (so e.g. a familiarization phase passed as a start matrix simply vanished for them). Models now declare supports_start_matrix in xslMod(), and xsl_run() errors if a start matrix is given to one that doesn't, or if it doesn't match the training data's dimensions. baseline(), decay(), rescorla_wagner(), softmax_rl(), uncfam_sampling(), and multi_sampling() now support it, joining the uncfam() family, propose_but_verify(), and pursuit(). The remaining models (fazly(), guess_and_test(), tilles(), bayesian_decay(), kalman_filter(), fgt2009()/fgt2009_rsa(), minerva2()/todam()/rem()) have no single association matrix a start matrix maps onto (or would need a model-specific convention, e.g. a variance as well as a mean), so prior learning should be passed to them as training trials, as kachergis_initial_accuracy does. A custom model built with xslMod() must set supports_start_matrix = TRUE to receive one. Runs without a start matrix are unchanged

Documentation