Bayesian Semiparametric Longitudinal Drift-Diffusion Mixed Models for Tone Learning in Adults

Paulon, Giorgio; Llanos, Fernando; Chandrasekaran, Bharath; Sarkar, Abhra

doi:10.1080/01621459.2020.1801448

Cited by 11 publications

(14 citation statements)

References 68 publications

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“…Theoretically, drift-diffusion models assume that during decision making, evidence for multiple decision options (in our case, categories) is accumulated at varying rates in a single accumulator (Nosofsky & Palmeri, 1997) and a decision is made when this evidence reaches a particular threshold. These models have recently been extended to multi-alternative, longitudinal, mixed model setting specifically in the context of category learning by considering multiple simultaneous accumulators of evidence (Paulon et al, 2020).…”

Section: Drift-diffusion Modelingmentioning

confidence: 99%

“…Importantly, the DDM of Paulon et al (2020) allows the rates and thresholds to evolve longitudinally as the participants become more experienced in their decision tasks. Moreover, drifts and the boundaries are allowed to differ between individuals, capturing the heterogeneity in category learning performance across different participants.…”

Section: Drift-diffusion Modelingmentioning

confidence: 99%

“…We defined 1 We allowed the evidence accumulation and decision threshold parameters to vary flexibly across learning. As a supplementary analysis, we also compared this flexible model of Paulon et al (2020) with other sub-cases in which one parameter is fixed but the other is allowed to vary to understand the relative importance of each parameter for auditory and visual learning. We compared the models using the Watanabe-Akaike information criterion (WAIC), a popular approach for assessing predictive performances of competing methods.…”

Section: Generalization Testmentioning

confidence: 99%

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Comparing perceptual category learning across modalities in the same individuals

et al. 2021

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Category learning is a fundamental process in human cognition that spans the senses. However, much still remains unknown about the mechanisms supporting learning in different modalities.In the current study, we directly compared auditory and visual category learning in the same individuals. Thirty participants (22 F; 18-32 years old) completed two unidimensional rule-based category learning tasks in a single day-one with auditory stimuli and another with visual stimuli. We replicated the results in a second experiment with a larger online sample (N = 99, 45 F, 18-35 years old). The categories were identically structured in the two modalities to facilitate comparison. We compared categorization accuracy, decision processes as assessed through driftdiffusion models, and the generalizability of resulting category representation through a generalization test. We found that individuals learned auditory and visual categories to similar extents and that accuracies were highly correlated across the two tasks. Participants had similar evidence accumulation rates in later learning, but early on had slower rates for visual than auditory learning. Participants also demonstrated differences in the decision thresholds across modalities. Participants had more categorical generalizable representations for visual than auditory categories. These results suggest that some modality-general cognitive processes support category learning but also suggest that the modality of the stimuli may also affect category learning behavior and outcomes.

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Section: Drift-diffusion Modelingmentioning

confidence: 99%

Section: Drift-diffusion Modelingmentioning

confidence: 99%

Section: Generalization Testmentioning

confidence: 99%

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Comparing perceptual category learning across modalities in the same individuals

et al. 2021

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“…The diffusion model, also known as the Ratcliff Diffusion Model or Drift Diffusion Model, is a classic and well-validated sequential sampling model that can account for the full relationship between speed and accuracy during decisionmaking. 1,26 While there have been various extensions and related sequential sampling approaches, [27][28][29][30][31] the standard diffusion model uses accuracy and response time distributions to estimate individual components of the decision-making process in simple two-choice tasks. One such task that has been studied extensively using this model is lexical decision, 26 where an individual is instructed to make a yes/no decision about whether a string of letters (e.g., "tuble") spells a real word in English.…”

Section: Description Of the Diffusion Modelmentioning

confidence: 99%

Understanding Speed–Accuracy Processing Dynamics in Aphasia Using Response Time Modeling

Evans

Quique

2021

Semin Speech Lang

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People with aphasia demonstrate language impairments evident in both performance accuracy and processing speed, but the direct relationship between accuracy and speed requires further consideration. This article describes two recent attempts to make quantitative progress in this domain using response time modeling: the diffusion model (Ratcliff, 1978) applied to two-choice tasks and a multinomial ex-Gaussian model applied to picture naming. The diffusion model may be used to characterize core linguistic processing efficiency and speed–accuracy tradeoffs independently, and research suggests that maladaptive speed–accuracy tradeoffs lead to performance impairments in at least some people with aphasia. The multinomial ex-Gaussian response time model of picture naming provides a simple and straightforward way to estimate the optimal response time cutoffs for individual people with aphasia (i.e., the cutoff where additional time is unlikely to lead to a correct response). While response time modeling applied to aphasia research is at an early stage of development, both the diffusion model and multinomial ex-Gaussian response time model of picture naming show promise and should be further developed in future work. This article also provides preliminary recommendations for clinicians regarding how to conceptualize, identify, and potentially address maladaptive speed–accuracy tradeoffs for people with aphasia.

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“…The methodology presented here is highly generic and broadly adaptable to diverse problems. For instance, Paulon et al (2020) developed a similar local clustering method in the presence of a single categorical predictor x with a small number of levels for a specific application with a complex drift-diffusion likelihood function. The focus of this article, however, is on developing a general methodology with an emphasis on the multivariate case (x 1 , .…”

Section: Introductionmentioning

confidence: 99%

Bayesian Semiparametric Hidden Markov Tensor Partition Models for Longitudinal Data with Local Variable Selection

Paulon¹,

Müller²,

Sarkar³

2021

Preprint

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We present a flexible Bayesian semiparametric mixed model for longitudinal data analysis in the presence of potentially high-dimensional categorical covariates. Building on a novel hidden Markov tensor decomposition technique, our proposed method allows the fixed effects components to vary between dependent random partitions of the covariate space at different time points. The mechanism not only allows different sets of covariates to be included in the model at different time points but also allows the selected predictors' influences to vary flexibly over time. Smooth time-varying additive random effects are used to capture subject specific heterogeneity. We establish posterior convergence guarantees for both function estimation and variable selection. We design a Markov chain Monte Carlo algorithm for posterior computation. We evaluate the method's empirical performances through synthetic experiments and demonstrate its practical utility through real world applications.

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Bayesian Semiparametric Longitudinal Drift-Diffusion Mixed Models for Tone Learning in Adults

Cited by 11 publications

References 68 publications

Comparing perceptual category learning across modalities in the same individuals

Comparing perceptual category learning across modalities in the same individuals

Understanding Speed–Accuracy Processing Dynamics in Aphasia Using Response Time Modeling

Bayesian Semiparametric Hidden Markov Tensor Partition Models for Longitudinal Data with Local Variable Selection

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