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

Bayesian Semiparametric Longitudinal Inverse-Probit Mixed Models for Category Learning

Understanding how the adult human brain learns novel categories is an important problem in neuroscience. Drift-diffusion models are popular in such contexts for their ability to mimic the underlying neural mechanisms. One such model for gradual longitudinal learning was recently developed in Paulon et al. (J Am Stat Assoc 116:1114-1127, 2021). In practice, category response accuracies are often the only reliable measure recorded by behavioral scientists to describe human learning. Category response accuracies are, however, often the only reliable measure recorded by behavioral scientists to describe human learning. To our knowledge, however, drift-diffusion models for such scenarios have never been considered in the literature before. To address this gap, in this article, we build carefully on Paulon et al. (J Am Stat Assoc 116:1114-1127, 2021), but now with latent response times integrated out, to derive a novel biologically interpretable class of 'inverse-probit' categorical probability models for observed categories alone. However, this new marginal model presents significant identifiability and inferential challenges not encountered originally for the joint model in Paulon et al. (J Am Stat Assoc 116:1114-1127, 2021). We address these new challenges using a novel projection-based approach with a symmetry-preserving identifiability constraint that allows us to work with conjugate priors in an unconstrained space. We adapt the model for group and individual-level inference in longitudinal settings. Building again on the model's latent variable representation, we design an efficient Markov chain Monte Carlo algorithm for posterior computation. We evaluate the empirical performance of the method through simulation experiments. The practical efficacy of the method is illustrated in applications to longitudinal tone learning studies.

Auditory and visual category learning in children and adults

Categories are fundamental to everyday life and the ability to learn new categories is relevant across the lifespan. Categories are ubiquitous across modalities, supporting complex processes such as object recognition and speech perception. Prior work has proposed that different categories may engage learning systems with unique developmental trajectories. There is a limited understanding of how perceptual and cognitive development influences learning as prior studies have examined separate participants in a single modality. The current study presents a comprehensive assessment of category learning in 8-12-year-old children (12 female; 34 white, 1 Asian, 1 more than one race; M household income $85-$100 K) and 18-61-year-old adults (13 female; 32 white, 10 Black or African American, 4 Asian, 2 more than one race, 1 other; M household income $40-55 K) in a broad sample collected online from the United States. Across multiple sessions, participants learned categories across modalities (auditory, visual) that engage different learning systems (explicit, procedural). Unsurprisingly, adults outperformed children across all tasks. However, this enhanced performance was asymmetrical across categories and modalities. Adults far outperformed children in learning visual explicit categories and auditory procedural categories, with fewer differences across development for other types of categories. Adults' general benefit over children was due to enhanced information processing, while their superior performance for visual explicit and auditory procedural categories was associated with less cautious correct responses. These results demonstrate an interaction between perceptual and cognitive development that influences learning of categories that may correspond to the development of real-world skills such as speech perception and reading. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Arousal States as a Key Source of Variability in Speech Perception and Learning

The human brain exhibits the remarkable ability to categorize speech sounds into distinct, meaningful percepts, even in challenging tasks like learning non-native speech categories in adulthood and hearing speech in noisy listening conditions. In these scenarios, there is substantial variability in perception and behavior, both across individual listeners and individual trials. While there has been extensive work characterizing stimulus-related and contextual factors that contribute to variability, recent advances in neuroscience are beginning to shed light on another potential source of variability that has not been explored in speech processing. Specifically, there are task-independent, moment-to-moment variations in neural activity in broadly-distributed cortical and subcortical networks that affect how a stimulus is perceived on a trial-by-trial basis. In this review, we discuss factors that affect speech sound learning and moment-to-moment variability in perception, particularly arousal states—neurotransmitter-dependent modulations of cortical activity. We propose that a more complete model of speech perception and learning should incorporate subcortically-mediated arousal states that alter behavior in ways that are distinct from, yet complementary to, top-down cognitive modulations. Finally, we discuss a novel neuromodulation technique, transcutaneous auricular vagus nerve stimulation (taVNS), which is particularly well-suited to investigating causal relationships between arousal mechanisms and performance in a variety of perceptual tasks. Together, these approaches provide novel testable hypotheses for explaining variability in classically challenging tasks, including non-native speech sound learning.

Comparing perceptual category learning across modalities in the same individuals

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 drift-diffusion 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.

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

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.