simulateRTs: Generate simulated naming latencies

In PeterHendrix13/NDRa: Generate Simulated Naming Latencies and Pronunciations in the NDRa Model

Description

Generate simulated naming latencies for a set of words or nonwords.

Usage

simulateRTs(lexicon = lex, weightsSem = weights_sem,
  weightsPhon = weights_phon, parameters = list(wSem = 0.2, wPhon1 =
  0.05, wPhon2 = 0.098, wH = 0.152, wCompl = 1.27, backoff = 0.01, wlex =
  4.7, N = 20, wAct = 0.055, rtConst = 450), verbose = TRUE)

Arguments

lexicon	A dataframe with the colums "Word" and "Gestures". "Gestures" are demi-syllables (see Klatt, 1979) and can be generated using gestures().
weightsSem	An orthography-to-semantics weight matrix with letter unigrams and bigrams as cues and words as outcomes. The default, "weights_sem" uses the weight matrix from Hendrix et al. (2018).
weightsPhon	A phonology-to-semantic weight matrix with demi-syllables as cues and words as outcomes. The default, "weigths_phon" uses the weight matrix from Hendrix et al. (2018).
parameters	A list with the model parameters "wSem", "wPhon1", "wPhon2", "wH", "wCompl", "backoff", "wlex", "N", "wAct", and "rtConst". The default values are the values used by Hendrix (2018). For more information, also see Hendrix et al. (2018).

References

Hendrix, P, Ramscar, M., & Baayen, R. H. (2019). NDRa: a single route model of response times in the reading aloud task based on discriminative learning. Manuscript.

Klatt, D. H. (1979). Speech perception: a model of acoustic-phonetic analysis and lexical access. Journal of Phonetics, 7, 279-312.

Examples

# Load data for the ELP simulations in Hendrix (2018)
data(elp)

# Generate simulated naming latencies
elp$SimRT = simulateSimRTs(elp)

# Evaluate simulated naming latencies
cor(elp$SimRT, -1000/elp$RTnaming)

PeterHendrix13/NDRa documentation built on May 7, 2019, 6:05 a.m.