# tools:::Rd_package_title("ndl") tools:::Rd_package_description("ndl") Naive discriminative learning implements classification models based on the Rescorla-Wagner equations and the equilibrium equations of the Rescorla-Wagner equations. This package provides three kinds of functionality: (1) discriminative learning based directly on the Rescorla-Wagner equations, (2) a function implementing the naive discriminative reader, and a model for silent (single-word) reading, and (3) a classifier based on the equilibrium equations. The functions and datasets for the naive discriminative reader model make it possible to replicate the simulation results for Experiment 1 of Baayen et al. (2011). The classifier is provided to allow for comparisons between machine learning (svm, TiMBL, glm, random forests, etc.) and discrimination learning. Compared to standard classification algorithms, naive discriminative learning may overfit the data, albeit gracefully. package ## Details The DESCRIPTION file: tools:::Rd_package_DESCRIPTION("ndl") tools:::Rd_package_indices("ndl") For more detailed information on the core Rescorla-Wagner equations, see the functions `RescorlaWagner` and `plot.RescorlaWagner`, as well as the data sets `danks`, `numbers` (data courtesy of Michael Ramscar), and `lexample` (an example discussed in Baayen et al. 2011). The functions for the naive discriminative learning (at the user level) are `estimateWeights` and `estimateActivations`. The relevant data sets are `serbian`, serbianUniCyr ,serbianUniLat , and `serbianLex`. The examples for `serbianLex` present the full simulation for Experiment 1 of Baayen et al. (2011). Key functionality for the user is provided by the functions `orthoCoding`, `estimateWeights`, and `estimateActivations`. `orthoCoding` calculates the letter n-grams for character strings, to be used as cues. It is assumed that meaning or meanings (separated by underscores if there are more then one) are available as outcomes. The frequency with which each (unique) combination of cues and outcomes occurs are required. For some example input data sets, see: `danks`, `plurals`, `serbian`, `serbianUniCyr` and `serbianUniLat`. The function `estimateWeights` estimates the association strengths of cues to outcomes, using the equilibrium equations presented in Danks (2003). The function `estimateActivations` estimates the activations of outcomes (meanings) given cues (n-grams). The Rcpp-based `learn` and `learnLegacy` functions use a C++ function to compute the conditional co-occurrence matrices required in the equilibrium equations. These are internally used by `estimateWeights` and should not be used directly by users of the package. The key function for naive discriminative classification is `ndlClassify`; see data sets `think` and `dative` for examples. ## Author(s) tools:::Rd_package_author("ndl") Maintainer: tools:::Rd_package_maintainer("ndl") Author Contributions: Initial concept by R. Harald Baayen with contributions from Petar Milin and Peter Hendrix. First R coding done by R. Harald Baayen. Initial R package development until version 0.1.6 by Antti Arppe. Initial documentation by Antti Arppe. Initial optimizations in C by Petar Milin and Antti Arppe. Classification functionality developed further by Antti Arppe. In version 0.2.14 to version 0.2.16, improvements to the NDL algorithm by Petar Milin and Cyrus Shaoul. In version 0.2.14 to version 0.2.16, improved performance optimizations (C++ and Rcpp) by Cyrus Shaoul. From version 0.2.17 onwards bug fixes and cran compliance by Tino Sering. ## References Baayen, R. H. and Milin, P. and Filipovic Durdevic, D. and Hendrix, P. and Marelli, M., An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review, 118, 438-482. Baayen, R. H. (2011) Corpus linguistics and naive discriminative learning. Brazilian Journal of Applied Linguistics, 11, 295-328. Arppe, A. and Baayen, R. H. (in prep.) Statistical classification and principles of human learning. ## Examples ```r ## Not run: # Rescorla-Wagner data(lexample) lexample$Cues <- orthoCoding(lexample$Word, grams=1) lexample.rw <- RescorlaWagner(lexample, nruns=25, traceCue="h", traceOutcome="hand") plot(lexample.rw) mtext("h - hand", 3, 1) data(numbers) traceCues <- c( "exactly1", "exactly2", "exactly3", "exactly4", "exactly5", "exactly6", "exactly7", "exactly10", "exactly15") traceOutcomes <- c("1", "2", "3", "4", "5", "6", "7", "10", "15") ylimit <- c(0,1) par(mfrow=c(3,3), mar=c(4,4,1,1)) for (i in 1:length(traceCues)) { numbers.rw <- RescorlaWagner(numbers, nruns=1, traceCue=traceCues[i], traceOutcome=traceOutcomes[i]) plot(numbers.rw, ylimit=ylimit) mtext(paste(traceCues[i], " - ", traceOutcomes[i], sep=""), side=3, line=-1, cex=0.7) } par(mfrow=c(1,1)) # naive discriminative learning (for complete example, see serbianLex) # This function uses a Unicode dataset. data(serbianUniCyr) serbianUniCyr$Cues <- orthoCoding(serbianUniCyr$WordForm, grams=2) serbianUniCyr$Outcomes <- serbianUniCyr$LemmaCase sw <- estimateWeights(cuesOutcomes=serbianUniCyr,hasUnicode=T) desiredItems <- unique(serbianUniCyr["Cues"]) desiredItems$Outcomes="" activations <- estimateActivations(desiredItems, sw)$activationMatrix rownames(activations) <- unique(serbianUniCyr[["WordForm"]]) syntax <- c("acc", "dat", "gen", "ins", "loc", "nom", "Pl", "Sg") activations2 <- activations[,!is.element(colnames(activations), syntax)] head(rownames(activations2),50) head(colnames(activations2),8) image(activations2, xlab="word forms", ylab="meanings", xaxt="n", yaxt="n") mtext(c("yena", "...", "zvuke"), side=1, line=1, at=c(0, 0.5, 1), adj=c(0,0,1)) mtext(c("yena", "...", "zvuk"), side=2, line=1, at=c(0, 0.5, 1), adj=c(0,0,1)) # naive discriminative classification data(think) think.ndl <- ndlClassify(Lexeme ~ Person + Number + Agent + Patient + Register, data=think) summary(think.ndl) plot(think.ndl, values="weights", type="hist", panes="multiple") plot(think.ndl, values="probabilities", type="density") ## End(Not run) ```

ndl.package function