Modeling accuracy as a function of response time with the generalized linear mixed effects model

Davidson, Douglas; Martin, Andrea E.

doi:10.1016/j.actpsy.2013.04.016

Cited by 27 publications

(33 citation statements)

References 36 publications

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“…However, this trick only means that the difficulty is merely transmogrified—now the cue integration process model is generating hypotheses about both psychological processing mechanisms and about the nature of representation. This is especially problematic because traditional dependent measures (e.g., performance on a task, brain responses, but especially reaction times) cannot discriminate between effects arising from differences in processing speed (a proxy for mechanism) and differences in representation strength or other aspect (Wickelgren, 1977 ; Davidson and Martin, 2013 ). This means that experimental designs will have to be careful not to conflate predictions about representation with predictions about mechanism itself.…”

Section: Capturing Multiple Distinctions In Parsingmentioning

confidence: 99%

Language Processing as Cue Integration: Grounding the Psychology of Language in Perception and Neurophysiology

2016

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I argue that cue integration, a psychophysiological mechanism from vision and multisensory perception, offers a computational linking hypothesis between psycholinguistic theory and neurobiological models of language. I propose that this mechanism, which incorporates probabilistic estimates of a cue's reliability, might function in language processing from the perception of a phoneme to the comprehension of a phrase structure. I briefly consider the implications of the cue integration hypothesis for an integrated theory of language that includes acquisition, production, dialogue and bilingualism, while grounding the hypothesis in canonical neural computation.

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Section: Capturing Multiple Distinctions In Parsingmentioning

confidence: 99%

Language Processing as Cue Integration: Grounding the Psychology of Language in Perception and Neurophysiology

2016

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“…The efficiency score shares the property with other recent suggestions for accounting for SATOs (Davidson & Martin, 2013;Seli, Jonker, Cheyne, & Smilek, 2013) that it assumes a linear relation between response time and accuracy. While such approaches may be better than focusing on a single behavioral variable, the assumption of linearity is at odds with work which has explicitly characterized the SATO (Fitts, 1966;Heitz, 2014;Wickelgren, 1977) and has shown a distinctly curvilinear relation between response time and accuracy.…”

Section: Speed-accuracy Trade-offsmentioning

confidence: 60%

Quantifying the benefits of using decision models with response time and accuracy data

et al. 2020

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Response time and accuracy are fundamental measures of behavioral science, but discerning participants' underlying abilities can be masked by speed-accuracy trade-offs (SATOs). SATOs are often inadequately addressed in experiment analyses which focus on a single variable or which involve a suboptimal analytic correction. Models of decision-making, such as the drift diffusion model (DDM), provide a principled account of the decision-making process, allowing the recovery of SATO-unconfounded decision parameters from observed behavioral variables. For plausible parameters of a typical between-groups experiment, we simulate experimental data, for both real and null group differences in participants' ability to discriminate stimuli (represented by differences in the drift rate parameter of the DDM used to generate the simulated data), for both systematic and null SATOs. We then use the DDM to fit the generated data. This allows the direct comparison of the specificity and sensitivity for testing of group differences of different measures (accuracy, reaction time, and the drift rate from the model fitting). Our purpose here is not to make a theoretical innovation in decision modeling, but to use established decision models to demonstrate and quantify the benefits of decision modeling for experimentalists. We show, in terms of reduction of required sample size, how decision modeling can allow dramatically more efficient data collection for set statistical power; we confirm and depict the non-linear speed-accuracy relation; and we show how accuracy can be a more sensitive measure than response time given decision parameters which reasonably reflect a typical experiment.

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“…Response Time Response time in this study is the time it takes to respond to each nonword. In many behavioural tasks response time is included as a control for the wellknown trade-off between speed and accuracy (e.g., Heitz, 2014;Davidson and Martin, 2013). This is not how we should interpret response time because there is not a correct or an incorrect answer to whether a given nonword belongs to the artificial language exposed to the participants.…”

Section: Trial Numbermentioning

confidence: 99%

Modelling L1 and the artificial language during artificial language learning

Tang¹,

Baer-Henney²

2020

Preprint

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Artificial language learning research (ALL) revealed that learners show bias towards certain artificial language patterns which they acquired with relative ease as a result of a universal mechanism that drives the learning process of a natural language. Building on insights from second language acquisition and psycholinguistic research in lexical processing, this study filled an important knowledge gap in ALL research by demonstrating the need to model the influence of both the L1 and the target language (L2) on learning bias. Specifically, native speakers of German (n = 232) and Mandarin (n = 219) were taught a phonological pattern using a set of non-words. In addition to replicating the impact of a consonant identity bias previously found in English (Linzen and Gallagher, 2017) for two very distinct languages, by training an analogical and a discriminative learning model over the lexicons of the L1 and the L2, we were able to capture their influences in the learning of the L2. Nonwords are more likely to be accepted as grammatical, if they are more similar to the L2 and more different from the L1. In addition, nonwords are less likely to be accepted as grammatical if the decision takes longer and if the nonword is judged at a later time. Our findings show that we are at risk to underestimate the role of the lexicons when examining learning biases with ALL. Some recommendations for conducting cross-linguistic ALL experiments were made.

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Modeling accuracy as a function of response time with the generalized linear mixed effects model

Cited by 27 publications

References 36 publications

Language Processing as Cue Integration: Grounding the Psychology of Language in Perception and Neurophysiology

Language Processing as Cue Integration: Grounding the Psychology of Language in Perception and Neurophysiology

Quantifying the benefits of using decision models with response time and accuracy data

Modelling L1 and the artificial language during artificial language learning

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