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The Adolescent Brain Cognitive Development (ABCD) Study is a longitudinal neuroimaging study of unprecedented scale that is in the process of following over 11,000 youth from middle childhood though age 20. However, a design feature of the study's stop-signal task violates ''context independence'', an assumption critical to current non-parametric methods for estimating stop-signal reaction time (SSRT), a key measure of inhibitory ability in the study. This has led some experts to call for the task to be changed and for previously collected data to be used with caution. We present a cognitive process modeling framework, the RDEX-ABCD model, that provides a parsimonious explanation for the impact of this design feature on “go” stimulus processing and successfully accounts for key behavioral trends in the ABCD data. Simulation studies using this model suggest that failing to account for the context independence violations in the ABCD design can lead to erroneous inferences in several realistic scenarios. However, we demonstrate that RDEX-ABCD effectively addresses these violations and can be used to accurately measure SSRT along with an array of additional mechanistic parameters of interest (e.g., attention to the stop signal, cognitive efficiency), advancing investigators’ ability to draw valid and nuanced inferences from ABCD data. Availability of data and materials: Data from the ABCD Study are available through the NIH Data Archive (NDA): nda.nih.gov/abcd. Code for all analyses featured in this study is openly available on the Open Science Framework (OSF): osf.io/2h8a7/.

Probability theory is the “logic of science” (Jaynes, 2003) andBayesian data analysis (BDA) is the glue that brings that logicto data. BDA is a general, flexible alternative to standard statis-tical approaches (e.g., NHST) that provides the scientist withclarity and ease to address their personal scientific questions.Doing BDA in a probabilistic programming language (PPL) af-fords several additional advantages: a compositional approachto writing models, separation of model specification from al-gorithmic implementation (a la lm() in R), and continuity fromarticulating data analytic models to Bayesian cognitive mod-els. Furthermore, specifying one’s model and data analysisin a PPL allows you to search for “optimal experiments” forfree. This tutorial will walk the participant through the basicsof BDA to state-of-the-art applications, using an interactive on-line web-book and tools for integrating BDA into their existingworkflow.

In prior research, signal detection theory (SDT) has been widely utilized to assess metacognitive ability. However, the SDT metacognitive model requires the use of a two-alternative forced-choice task, while confidence must also be measured discretely. In our model, participants' cognitive ability and their confidence in the cognitive task were used to estimate their metacognitive abilities. Therefore, in this study, a metacognitive model that can be applied to various cognitive tasks was developed. This model implements the item response theory (IRT) and Q -learning models to estimate cognitive ability; participants' metacognitive ability is defined as the discrepancy between their confidence in their cognitive ability and their actual cognitive ability. The entire procedure was divided into two experiments. In experiment 1, two different cognitive tasks were used to estimate metacognitive ability and to examine overall discriminative and convergent validity. Notably, the parameters representing metacognitive ability did not correlate with cognitive ability but were positively correlated between the two tasks. In experiment 2, we performed a similar analysis using a different task to test the replicability of experiment 1. The results for experiment 2 were replicated for discriminative and convergent validity, albeit with weak results. Our metacognitive model exhibited high interpretability and versatility. [ABSTRACT FROM AUTHOR]

In prior research, signal detection theory (SDT) has been widely utilized to assess metacognitive ability. However, the SDT metacognitive model requires the use of a two-alternative forced-choice task, while confidence must also be measured discretely. In our model, participants’ cognitive ability and their confidence in the cognitive task were used to estimate their metacognitive abilities. Therefore, in this study, a metacognitive model that can be applied to various cognitive tasks was developed. This model implements the item response theory (IRT) and Q-learning models to estimate cognitive ability; participants’ metacognitive ability is defined as the discrepancy between their confidence in their cognitive ability and their actual cognitive ability. The entire procedure was divided into two experiments. In experiment 1, two different cognitive tasks were used to estimate metacognitive ability and to examine overall discriminative and convergent validity. Notably, the parameters representing metacognitive ability did not correlate with cognitive ability but were positively correlated between the two tasks. In experiment 2, we performed a similar analysis using a different task to test the replicability of experiment 1. The results for experiment 2 were replicated for discriminative and convergent validity, albeit with weak results. Our metacognitive model exhibited high interpretability and versatility.

Evidence accumulation models are a useful tool to allow researchers to investigate the latent cognitive variables that underlie response time and response accuracy. However, applying evidence accumulation models can be difficult because they lack easily computable forms. Numerical methods are required to determine the parameters of evidence accumulation that best correspond to the fitted data. When applied to complex cognitive models, such numerical methods can require substantial computational power which can lead to infeasibly long compute times. In this paper, we provide efficient, practical software and a step-by-step guide to fit evidence accumulation models with Bayesian methods. The software, written in C++, is provided in an R package: 'ggdmc'. The software incorporates three important ingredients of Bayesian computation, (1) the likelihood functions of two common response time models, (2) the Markov chain Monte Carlo (MCMC) algorithm (3) a population-based MCMC sampling method. The software has gone through stringent checks to be hosted on the Comprehensive R Archive Network (CRAN) and is free to download. We illustrate its basic use and an example of fitting complex hierarchical Wiener diffusion models to four shooting-decision data sets.

Decoding others' intentions accurately in order to adapt one's own behavior is pivotal throughout life. In this study, we asked how younger and older adults deal with uncertainty in dynamic social environments. We used an advice-taking paradigm together with Bayesian modeling to characterize effects of aging on learning about others' time-varying intentions. We observed age differences when comparing learning on two levels of social uncertainty: the fidelity of the adviser and the volatility of intentions. Older adults expected the adviser to change his/her intentions more frequently (i.e., a higher volatility of the adviser). They also showed higher confidence (i.e., precision) in their volatility beliefs and were less willing to change their beliefs about volatility over the course of the experiment. This led them to update their predictions about the fidelity of the adviser more quickly. Potentially indicative of stereotype effects, we observed that older advisers were perceived as more volatile, but also more faithful than younger advisers. This offers new insights into adult age differences in response to social uncertainty. • We used an advice-taking paradigm together with Bayesian modelling to characterize effects and mechanisms of aging on learning about others' time-varying intentions. • Both a person's age and their counterpart's age affect how we learn about others in changing social contexts. • Older adults expected their social partner to change intentions more frequently. • Older adults showed higher precision, i.e., confidence in their own beliefs and changed them less. • Older advisers were perceived as more volatile, but also more faithful than younger advisers. [ABSTRACT FROM AUTHOR]

Can sentence comprehension impairments in aphasia be explained by difficulties arising from dependency completion processes in parsing? Two distinct models of dependency completion difficulty are investigated, the Lewis and Vasishth (2005) activation‐based model and the direct‐access model (DA; McElree, 2000). These models' predictive performance is compared using data from individuals with aphasia (IWAs) and control participants. The data are from a self‐paced listening task involving subject and object relative clauses. The relative predictive performance of the models is evaluated using k‐fold cross‐validation. For both IWAs and controls, the activation‐based model furnishes a somewhat better quantitative fit to the data than the DA. Model comparisons using Bayes factors show that, assuming an activation‐based model, intermittent deficiencies may be the best explanation for the cause of impairments in IWAs, although slowed syntax and lexical delayed access may also play a role. This is the first computational evaluation of different models of dependency completion using data from impaired and unimpaired individuals. This evaluation develops a systematic approach that can be used to quantitatively compare the predictions of competing models of language processing. [ABSTRACT FROM AUTHOR]

The Adolescent Brain Cognitive Development (ABCD) Study is a longitudinal neuroimaging study of unprecedented scale that is in the process of following over 11,000 youth from middle childhood though age 20. However, a design feature of the study's stop-signal task violates "context independence", an assumption critical to current non-parametric methods for estimating stop-signal reaction time (SSRT), a key measure of inhibitory ability in the study. This has led some experts to call for the task to be changed and for previously collected data to be used with caution. We present a cognitive process modeling framework, the RDEX-ABCD model, that provides a parsimonious explanation for the impact of this design feature on "go" stimulus processing and successfully accounts for key behavioral trends in the ABCD data. Simulation studies using this model suggest that failing to account for the context independence violations in the ABCD design can lead to erroneous inferences in several realistic scenarios. However, we demonstrate that RDEX-ABCD effectively addresses these violations and can be used to accurately measure SSRT along with an array of additional mechanistic parameters of interest (e.g., attention to the stop signal, cognitive efficiency), advancing investigators' ability to draw valid and nuanced inferences from ABCD data. Data from the ABCD Study are available through the NIH Data Archive (NDA): nda.nih.gov/abcd. Code for all analyses featured in this study is openly available on the Open Science Framework (OSF): osf.io/2h8a7/. • Formal models of the stop-signal task can be used to measure mechanistic processes. • A feature of the ABCD Study stop-signal paradigm violates a key model assumption. • We created a novel model that addresses the impact of this violation. • The new model can be used to measure several mechanistic processes in ABCD. [ABSTRACT FROM AUTHOR]

People deciding between alternatives have at their disposal a toolbox containing both compensatory strategies, which take into account all available attributes of those alternatives, and noncompensatory strategies, which consider only some of the attributes. It is commonly assumed that noncompensatory strategies play only a minor role in decisions from givens , where attribute information is openly presented, because all attributes can be processed automatically "at a glance." Based on a literature review, however, I establish that previous studies on strategy selection in decisions from givens have yielded highly heterogeneous findings, including evidence of widespread use of noncompensatory strategies. Drawing on insights from visual attention research on subitizing, I argue that this heterogeneity might be due to differences across studies in the number of attributes and in whether the same or different symbols are used to represent high/low attribute values across attributes. I tested the impact of these factors in two experiments with decisions from givens in which both the number of attributes shown for each alternative and the coding of attribute values was manipulated. An analysis of participants' strategy use with a Bayesian multimethod approach (taking into account both decisions and response-time patterns) showed that a noncompensatory strategy was more frequently selected in conditions with a higher number of attributes; the type of attribute coding scheme did not affect strategy selection. Using a compensatory strategy in the conditions with eight (vs. four) attributes was associated with rather long response times and a high rate of strategy execution errors. The results suggest that decisions from givens can incur cognitive costs that prohibit reliance on automatic compensatory decision making and that can favor the adaptive selection of a noncompensatory strategy. [ABSTRACT FROM AUTHOR]

Number-knower levels are a series of stages of number concept development in early childhood. A child's number-knower level is typically assessed using the give- N task. Although the task procedure has been highly refined, the standard ways of analyzing give- N data remain somewhat crude. Lee and Sarnecka (Cogn Sci 34:51-67, , in press) have developed a Bayesian model of children's performance on the give- N task that allows knower level to be inferred in a more principled way. However, this model requires considerable expertise and computational effort to implement and apply to data. Here, we present an approximation to the model's inference that can be computed with Microsoft Excel. We demonstrate the accuracy of the approximation and provide instructions for its use. This makes the powerful inferential capabilities of the Bayesian model accessible to developmental researchers interested in estimating knower levels from give- N data. [ABSTRACT FROM AUTHOR]

Pragmatic theories of utterance interpretation share the assumption that listeners reason about alternative utterances that a speaker could have produced, but didn't. For such reasoning to be successful, listeners must have precise expectations about a speaker's production choices. This is at odds with the considerable variability across speakers that exists at all levels of linguistic representation. This tension can be reconciled by listeners adapting to the statistics of individual speakers. While linguistic adaptation is increasingly widely attested, semantic/pragmatic adaptation is underexplored. Moreover, what kind of representations listeners update during semantic/pragmatic adaptation - estimates of the speaker's lexicon, or estimates of the speaker's utterance preferences - remains poorly understood. In this work, we investigate semantic/pragmatic adaptation in the domain of uncertainty expressions like might and probably. In a series of web-based experiments, we find 1) that listeners vary in their expectations about a generic speaker's use of uncertainty expressions; 2) that listeners rapidly update their expectations about the use of uncertainty expressions after brief exposure to a speaker with a specific usage of uncertainty expressions; and 3) that listeners' interpretations of uncertainty expressions change after being exposed to a specific speaker. We present a novel computational model of semantic/pragmatic adaptation based on Bayesian belief updating and show, through a series of model comparisons, that semantic/pragmatic adaptation is best captured by listeners updating their beliefs both about the speaker's lexicon and their utterance preferences. This work has implications for both semantic theories of uncertainty expressions and psycholinguistic theories of adaptation: it highlights the need for dynamic semantic representations and suggests that listeners integrate their general linguistic knowledge with speaker-specific experiences to arrive at more precise interpretations. [ABSTRACT FROM AUTHOR]