word_classification_data {AcousticNDLCodeR}

Data of PLoS ONE paper

Description

Dataset of a subject and modeling data for an auditory word identification task.

Usage

data(word_classification_data)

Format

Data from the four experiments and model estimates

ExperimentNumber

Experiment identifier

PresentationMethod

Method of presentation in the experiment: loudspeaker, headphones 3. Trial: Trial number in the experimental list

TrialScaled

scaled Trial

Subject

anonymized subject identifier

Item

word identifier -german umlaute and special character coded as 'ae' 'oe' 'ue' and 'ss'

Activation

NDL activation

LogActivation

log(activation+epsilon)

L1norm

L1-norm (lexicality)

LogL1norm

log of L1-norm

RecognitionDecision

recognition decision (yes/no)

RecognitionRT

latency for recognition decision

LogRecognitionRT

log recognition RT

DictationAccuracy

dictation accuracy (TRUE: correct word reported, FALSE otherwise) 15. DictationRT: response latency to typing onset

References

Denis Arnold, Fabian Tomaschek, Konstantin Sering, Florence Lopez, and R. Harald Baayen (2017). Words from spontaneous conversational speech can be recognized with human-like accuracy by an error-driven learning algorithm that discriminates between meanings straight from smart acoustic features, bypassing the phoneme as recognition unit PLoS ONE 12(4):e0174623. https://doi.org/10.1371/journal.pone.0174623