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Kachergis, Yu & Shiffrin priming experiments: 4-block learning trajectories

Source: R/data.R
kachergis_priming.Rd

Three experiments run alongside Kachergis, Yu & Shiffrin's frequency and contextual-diversity studies. Participants studied the same cross-situational training sequence four times, taking an 18AFC test on all 18 words after each repetition. Each condition is therefore a multi-block xslData: train is one block, accuracy is a 4 x 18 matrix (one row per block), and response_matrix is a list of four 18 x 18 confusion matrices. xsl_run() scores block b on a model trained on b repetitions of the sequence, so a model's learning trajectory can be compared with people's.

Usage

kachergis_priming

Format

An object of class list of length 3.

Details

A named list of three conditions:

  • "4x4 6x": 4 words and 4 objects per trial, 27 trials, every pair 6 times (n = 54). The sequence is that of the original 4x4 condition, which differs from xsl_datasets condition 223 only in words 4 and 16 trading places between trials 11 and 24.

  • "4x4 3/6/9x": 4 words and 4 objects per trial, 27 trials, 6 pairs each 3, 6 and 9 times (n = 44); the training of 3_x8_369_4x4.

  • "3x3 3/6/9x": 3 words and 3 objects per trial, 36 trials, 6 pairs each 3, 6 and 9 times with low contextual diversity (n = 66); the training of freq369-3x3loCD.

The participants are different from those in xsl_datasets (until XSLmodels PR #30, their first block had been pooled into 3_x8_369_4x4 and freq369-3x3loCD). Participants whose accuracy across all four blocks was below 2/18 were excluded in the original analysis. Not included in xsl_datasets; see data-raw/add_kachergis_priming.R for construction details.

Examples

d <- kachergis_priming[["4x4 6x"]]
d
#> xslData object with label "priming 4x4 6x" and condition "4x4 6x"
#>   training trials: 27
#>       test trials: 0
#>             words: 18
#>           objects: 18
#>            blocks: 4 (training repeated, tested after each)
#>        accuracies: 72
#>          subjects: 54
rowMeans(d$accuracy)   # people's learning curve
#>    block1    block2    block3    block4 
#> 0.1841564 0.3652263 0.5514403 0.6965021 
fit <- xsl_run(uncfam(X = .2, B = 3, C = .97), d)
rowMeans(fit$fits[[1]]$perf)   # the model's
#> [1] 0.4364966 0.6848688 0.8194688 0.8945512