Heart failure (HF) is one of the main causes of morbidity, hospitalization, and death in the western world, and the economic burden associated with HF management is relevant and expected to increase in the future. We consider hospitalization data for HF in the most populated Italian Region, Lombardia. Data were extracted from the administrative data warehouse of the regional healthcare system. The main clinical outcome of interest is time to death and research focus is on investigating how recurrent hospitalizations affect the time to event. The main contribution of the article is to develop a joint model for gap times between consecutive rehospitalizations and survival time. The probability models for the gap times and for the survival outcome share a common patient specific frailty term. Using a flexible Dirichlet process model for %Bayesian nonparametric prior as the random-effects distribution accounts for patient heterogeneity in recurrent event trajectories. Moreover, the joint model allows for dependent censoring of gap times by death or administrative reasons and for the correlations between different gap times for the same individual. It is straightforward to include covariates in the survival and/or recurrence process through the specification of appropriate regression terms. The main advantages of the proposed methodology are wide applicability, ease of interpretation, and efficient computations. Posterior inference is implemented through Markov chain Monte Carlo methods.

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.

No abstract

Purpose We present functional logistic mixed-effects models (FLMEMs) for estimating population and individual-level learning curves in longitudinal experiments. Method Using functional analysis tools in a Bayesian hierarchical framework, the FLMEM captures nonlinear, smoothly varying learning curves, appropriately accommodating uncertainty in various aspects of the analysis while also borrowing information across different model layers. An R package implementing our method is available as part of the Supplemental Materials . Results Application to speech learning data from Reetzke, Xie, Llanos, and Chandrasekaran (2018) and a simulation study demonstrate the utility of FLMEM and its many advantages over linear and logistic mixed-effects models. Conclusion The FLMEM is highly flexible and efficient in improving upon the practical limitations of linear models and logistic linear mixed-effects models. We expect the FLMEM to be a useful addition to the speech, language, and hearing scientist's toolkit. Supplemental Material https://doi.org/10.23641/asha.7822568

The ability to learn novel speech category contrasts is an important skill in second language learning. There is substantial individual variability in the ability to learn perceptual categories. Prior research demonstrates that higher working memory capacity is associated with better initial category acquisition, typically assessed within a single session of learning. There is mixed evidence for the role of working memory in speech category learning and the underlying mechanisms are not well understood. To better understand the role of working memory in speech category learning beyond initial acquisition, we trained participants on non-native Mandarin tone speech categories across three sessions, separated by one and two months, respectively. Examining all participants together, we found that working memory was associated with better speech category learning from initial acquisition to learning months later. However, when considering only participants who performed at above-chance levels in the task, we found that working memory was positively related to performance only in initial sessions and not in later learning. Working memory was positively associated with generalization of category knowledge to novel talkers and was unrelated to maintenance of category knowledge across sessions. Using longitudinal drift diffusion mixed models, we found that higher working memory was associated with more efficient evidence accumulation rates throughout learning and more cautious responding in later learning sessions. These results indicate that better working memory is not a guarantee of enhanced speech category learning, but it may reflect quicker and more efficient initial learning, which may be less effortful for a learner. Similarly, lower working memory does not doom a learner to poor performance but may instead be linked to higher risk of task disengagement and slower initial learning.

No abstract

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 supervised category learning tasks in a single day—one with auditory stimuli and another with visual stimuli. We also replicated the results in 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 were better able to generalize their category knowledge to novel visual stimuli than novel auditory stimuli, with more categorical representations for visual 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.

We consider nonparametric inference for event time distributions based on current status data. We show that in this scenario conventional mixture priors, including the popular Dirichlet process mixture prior, lead to biologically uninterpretable results as they unnaturally skew the probability mass for the event times toward the extremes of the observed data. Simple assumptions on dependent censoring can fix the problem. We then extend the discussion to bivariate current status data with partial ordering of the two outcomes. In addition to dependent censoring, we also exploit some minimal known structure relating the two event times. We design a Markov chain Monte Carlo algorithm for posterior simulation. Applied to a recurrent infection study, the method provides novel insights into how symptoms-related hospital visits are affected by covariates.