BALDEY: A database of auditory lexical decisions

Ernestus, Mirjam; Cutler, Anne

doi:10.1080/17470218.2014.984730

Cited by 57 publications

(69 citation statements)

References 45 publications

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“…Our second example addresses the analysis of the response latencies elicited in the auditory lexical decision megastudy of Ernestus and Cutler (2015) (data available at http://www.mirjamernestus. nl/Ernestus/Baldey/baldey_data.zip).…”

Section: The Baldey Datasetmentioning

confidence: 99%

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The cave of shadows: Addressing the human factor with generalized additive mixed models

Baayen

Vasishth

Kliegl

et al. 2017

Journal of Memory and Language

201

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Generalized additive mixed models are introduced as an extension of the generalized linear mixed model which makes it possible to deal with temporal autocorrelational structure in experimental data. This autocorrelational structure is likely to be a consequence of learning, fatigue, or the ebb and flow of attention within an experiment (the 'human factor'). Unlike molecules or plots of barley, subjects in psycholinguistic experiments are intelligent beings that depend for their survival on constant adaptation to their environment, including the environment of an experiment. Three data sets illustrate that the human factor may interact with predictors of interest, both factorial and metric. We also show that, especially within the framework of the generalized additive model, in the nonlinear world, fitting maximally complex models that take every possible contingency into account is ill-advised as a modeling strategy. Alternative modeling strategies are discussed for both confirmatory and exploratory data analysis.

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Section: The Baldey Datasetmentioning

confidence: 99%

“…We then consider a mega-study with auditory lexical decision (Ernestus and Cutler, 2015) using a regression design with crossed random effects of subject and item. The third analysis concerns a self-paced reading study in which subjects were reading Dutch poems, following up on earlier analyses presented in Baayen and Milin (2010).…”

Section: Introductionmentioning

confidence: 99%

The cave of shadows: Addressing the human factor with generalized additive mixed models

Baayen

Vasishth

Kliegl

et al. 2017

Journal of Memory and Language

201

View full text Add to dashboard Cite

show abstract

“…The first approach is based on statistical modeling of reaction times (RTs). This analysis is based on reaction time data on compounds in BALDEY [9], by using competing risks survival analysis by Generalized Additive Models [10,11]. In risk analysis, the regression is not explaining the RTs themselves; instead, the parameters of the underlying decision process are modeled from which the RTs are a consequence.…”

Section: Introductionmentioning

confidence: 99%

The Recognition of Compounds: A Computational Account

Bosch¹,

Boves

Ernestus

2017

Interspeech 2017

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This paper investigates the processes in comprehending spoken noun-noun compounds, using data from the BALDEY database. BALDEY contains lexicality judgments and reaction times (RTs) for Dutch stimuli for which also linguistic information is included. Two different approaches are combined. The first is based on regression by Dynamic Survival Analysis, which models decisions and RTs as a consequence of the fact that a cumulative density function exceeds some threshold. The parameters of that function are estimated from the observed RT data. The second approach is based on DIANA, a processoriented computational model of human word comprehension, which simulates the comprehension process with the acoustic stimulus as input. DIANA gives the identity and the number of the word candidates that are activated at each 10 ms time step.Both approaches show how the processes involved in comprehending compounds change during a stimulus. Survival Analysis shows that the impact of word duration varies during the course of a stimulus. The density of word and non-word hypotheses in DIANA shows a corresponding pattern with different regimes. We show how the approaches complement each other, and discuss additional ways in which data and process models can be combined.

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“…DIANA has accurately simulated lexicality judgments and corresponding reaction times for several different lexical decision experiments (Dutch database BALDEY, [13,5,6]; the Massive Auditory Lexical Decision (MALD) North American English 1 ([7]). We compare this approach with the results from a regression analysis based on linear fixed effects model (lmer) in the R package ( [4]).…”

Section: Introductionmentioning

confidence: 99%

Combining Data-Oriented and Process-Oriented Approaches to Modeling Reaction Time Data

Bosch¹,

Boves²,

Ernestus

2016

Interspeech 2016

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This paper combines two different approaches to modeling reaction time data from lexical decision experiments, viz. a dataoriented statistical analysis by means of a linear mixed effects model, and a process-oriented computational model of human speech comprehension.The linear mixed effect model is implemented by lmer in R. As computational model we apply DIANA, an end-to-end computational model which aims at modeling the cognitive processes underlying speech comprehension. DIANA takes as input the speech signal, and provides as output the orthographic transcription of the stimulus, a word/non-word judgment and the associated reaction time. Previous studies have shown that DIANA shows good results for large-scale lexical decision experiments in Dutch and North-American English.We investigate whether predictors that appear significant in an lmer analysis and processes implemented in DIANA can be related and inform both approaches. Predictors such as 'previous reaction time' can be related to a process description; other predictors, such as 'lexical neighborhood' are hard-coded in lmer and emergent in DIANA. The analysis focuses on the interaction between subject variables and task variables in lmer, and the ways in which these interactions can be implemented in DIANA.

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BALDEY: A database of auditory lexical decisions

Cited by 57 publications

References 45 publications

The cave of shadows: Addressing the human factor with generalized additive mixed models

The cave of shadows: Addressing the human factor with generalized additive mixed models

The Recognition of Compounds: A Computational Account

Combining Data-Oriented and Process-Oriented Approaches to Modeling Reaction Time Data

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