Probability Of Correct Response Research Articles

Enhancing touch sensibility with sensory electrical stimulation and sensory retraining

A large proportion of stroke survivors suffer from sensory loss, negatively impacting their independence, quality of life, and neurorehabilitation prognosis. Despite the high prevalence of somatosensory impairments, our understanding of somatosensory interventions such as sensory electrical stimulation (SES) in neurorehabilitation is limited. We aimed to study the effectiveness of SES combined with a sensory discrimination task in a well-controlled virtual environment in healthy participants, setting a foundation for its potential application in stroke rehabilitation. We employed electroencephalography (EEG) to gain a better understanding of the underlying neural mechanisms and dynamics associated with sensory training and SES. We conducted a single-session experiment with 26 healthy participants who explored a set of three visually identical virtual textures—haptically rendered by a robotic device and that differed in their spatial period—while physically guided by the robot to identify the odd texture. The experiment consisted of three phases: pre-intervention, intervention, and post-intervention. Half the participants received subthreshold whole-hand SES during the intervention, while the other half received sham stimulation. We evaluated changes in task performance—assessed by the probability of correct responses—before and after intervention and between groups. We also evaluated differences in the exploration behavior, e.g., scanning speed. EEG was employed to examine the effects of the intervention on brain activity, particularly in the alpha frequency band (8–13 Hz) associated with sensory processing. We found that participants in the SES group improved their task performance after intervention and their scanning speed during and after intervention, while the sham group did not improve their task performance. However, the differences in task performance improvements between groups only approached significance. Furthermore, we found that alpha power was sensitive to the effects of SES; participants in the stimulation group exhibited enhanced brain signals associated with improved touch sensitivity likely due to the effects of SES on the central nervous system, while the increase in alpha power for the sham group was less pronounced. Our findings suggest that SES enhances texture discrimination after training and has a positive effect on sensory-related brain areas. Further research involving brain-injured patients is needed to confirm the potential benefit of our solution in neurorehabilitation.

Supervised diagnostic classification of cognitive attributes using data augmentation.

Over recent decades, machine learning, an integral subfield of artificial intelligence, has revolutionized diverse sectors, enabling data-driven decisions with minimal human intervention. In particular, the field of educational assessment emerges as a promising area for machine learning applications, where students can be classified and diagnosed using their performance data. The objectives of Diagnostic Classification Models (DCMs), which provide a suite of methods for diagnosing students' cognitive states in relation to the mastery of necessary cognitive attributes for solving problems in a test, can be effectively addressed through machine learning techniques. However, the challenge lies in the latent nature of cognitive status, which makes it difficult to obtain labels for the training dataset. Consequently, the application of machine learning methods to DCMs often assumes smaller training sets with labels derived either from theoretical considerations or human experts. In this study, the authors propose a supervised diagnostic classification model with data augmentation (SDCM-DA). This method is designed to utilize the augmented data using a data generation model constructed by leveraging the probability of correct responses for each attribute mastery pattern derived from the expert-labeled dataset. To explore the benefits of data augmentation, a simulation study is carried out, contrasting it with classification methods that rely solely on the expert-labeled dataset for training. The findings reveal that utilizing data augmentation with the estimated probabilities of correct responses substantially enhances classification accuracy. This holds true even when the augmentation originates from a small labeled sample with occasional labeling errors, and when the tests contain lower-quality items that may inaccurately measure students' true cognitive status. Moreover, the study demonstrates that leveraging augmented data for learning can enable the successful classification of students, thereby eliminating the necessity for specifying an underlying response model.

Thurstonian model for the four-interval oddity task

The four-interval oddity task is an extension of the Triangle method, which is the three-interval oddity task and is one of the most broadly used conventional sensory discrimination methods. It is a special case of the m-interval oddity task or the m-alternative odd-man-out task where m = 4. It is also a special situation of the unspecified ‘M + N’ with M = 3 and N = 1. Some new forms of the analytical psychometric function for the four-interval oddity task based on a normal distribution function, a bivariate normal distribution function, and a non-central F-distribution function are provided originally in this paper. The performance of the four-interval oddity task in both difference testing and similarity/equivalence testing is explored and compared with some unspecified forced-choice sensory discrimination methods including the Triangle. Tables and R codes are presented and provided for estimations of the probability of correct response, Pc, Thurstonian discriminal distance δ or d´, and the B value for estimating the variance of d´ for the four-interval oddity task.

Thurstonian models for three variants (specified, unspecified, and unspecified with forgiveness) of the Two‐Out‐of‐Five method

AbstractThe Two‐Out‐of‐Five method is one of the basic sensory discrimination methods. It is a special case of the ‘M+N’ method with M = 3 and N = 2. Three variants of the method are discussed in the paper: specified, unspecified, and unspecified with forgiveness. Analytical and simulation‐derived psychometric functions for all three variants of the Two‐Out‐of‐Five method are derived and produced. The performance of the method in both difference and similarity/equivalence tests are explored. It is shown that both difference and similarity/equivalence testing powers for the specified Two‐Out‐of‐Five are larger than those for the 2‐AFC and 3‐AFC. Both difference and similarity/equivalence testing powers for the unspecified Two‐Out‐of‐Five are larger than those for the Triangle and Duo‐trio. Both difference and similarity/equivalence testing powers for the unspecified with forgiveness Two‐Out‐of‐Five are larger than those for the conventional unspecified Two‐Out‐of‐Five and unspecified Tetrads. Tables and R codes are presented and provided for estimations of the probability of correct response, Pc, Thurstonian discriminal distance or d', and the B value for estimating the variance of d' for the three variants of the Two‐Out‐of‐Five method.Practical ApplicationsThe Two‐Out‐of‐Five method is a powerful sensory discrimination method. However, this method has not been used widely in sensory and consumer research, partly since the method has not been explored adequately in the literature. This paper explores the Two‐Out‐of‐Five method and shows that the method has potential for wide application. The Two‐Out‐of‐Five method involves a larger number of samples (5) and sensory fatigue may be of concern. Hence, the method may not be suitable for taste and smell tests with strong stimuli. The method is particularly suitable for manual and visual inspection testing where sensory fatigue is of a lesser concern.

The methods of Tetrads, Hexads, and Octads: A type of more powerful sensory discrimination methods

The well-known sensory discrimination method of Tetrads is a special case of the ‘M + N’ with M = N = 2. This paper extends the method of Tetrads to a general situation, i.e., the ‘M + N’ with M = N =k≥2. The methods of Hexads and Octads are the special cases of the ‘M + N’ with M = N = k, where k = 3, and 4, respectively. The general analytical psychometric functions for the specified and unspecified ‘M + N’ with M = N = k≥2 are produced. Simulation-derived psychometric functions for the specified and unspecified methods of Hexads and Octads are also produced. The values of the simulation-derived psychometric functions are well-matched with the values of the analytical psychometric functions. The performances of the methods of Hexads and Octads in both difference and similarity/equivalence tests are explored and compared with the performances of the method of Tetrads. It is shown that both difference and similarity/equivalence testing powers for the unspecified methods of Hexads and Octads are larger than those for the unspecified method of Tetrads. It suggests that the unspecified methods of Tetrads, Hexads, and Octads are a type of more powerful unspecified sensory discrimination methods. Tables and R codes are presented and provided for estimations of probability of correct response, Pc, Thurstonian discriminal distance δ or d´, B value for estimating variance of d´ for the specified and unspecified methods of Hexads and Octads. Tables and R codes for critical values, testing powers, and sample sizes for difference and similarity/equivalence tests using the specified and unspecified methods of Hexads and Octads are also presented and provided. The methods of Hexads and Octads are appropriate for visual and manual evaluations of food or non-food products, where sensory fatigue, carryover, or adaption effects are not mainly concerned.

Mutual interference in working memory updating: A hierarchical Bayesian model

We propose a hierarchical Bayesian model for working memory updating. This model accounts for both the accuracy of the responses and the reaction times (RT) in the memory updating paradigm, which is a commonly used paradigm to measure working memory capacity. We adapt a mutual interference model from Oberauer and Kliegl (2006) to explain responses. Oberauer and Kliegl (2006) used a Boltzmann equation framework based on the activation levels of items stored in working memory to quantify the probability of correct response at the final recall step after memory updating. We expand the original framework with a Markov chain structure, so that the model accounts for the probabilities of all possible responses, correct or incorrect, at both the intermediate steps during memory updating and the final recall step after memory updating. We use a Wald diffusion process to characterize RT, where the drift rate parameters are associated with the activation levels of items in working memory. This model allows us to investigate the mechanisms underlying choices and RTs in the memory updating paradigm under a joint theoretical framework. A simulation study shows the effectiveness of this model, and posterior predictive distributions and out-of-sample validations show that this model gives a good account of empirical working memory updating findings. We apply the model to two published data sets. The first data set, from Oberauer and Kliegl (2001), examined age differences in working memory. Results from our model reveal an increased level of mutual interference, less use of memory trace information, and potentially less pre-activation of memorized items in older adults compared to younger adults. The second data set, from De Simoni and von Bastian (2018), investigated transfer effects of working memory training. Results from our model reveal a potential transfer effect in the speed of information accumulation, where training in one working memory task may improve the information processing speed in another.

Detecting Differential Item Functioning Using Cognitive Diagnosis Models: Applications of the Wald Test and Likelihood Ratio Test in a University Entrance Examination

ABSTRACT The present study aims to examine gender differential item functioning (DIF) in the reading comprehension section of a high stakes test using cognitive diagnosis models. Based on the multiple-group generalized deterministic, noisy “and” gate (MG G-DINA) model, the Wald test and likelihood ratio test are used to detect DIF. The flagged items are further inspected to find the attributes they measure, and the probabilities of correct response are checked across latent profiles to gain insights into the potential reasons for the occurrence of DIF. In addition, attribute and latent class prevalence are examined across males and females. The three items displaying large DIF involve three attributes, namely Vocabulary, Main Idea, and Details. The results indicate that females have lower probabilities of correct response across all latent profiles, and fewer females have mastered all the attributes. Moreover, the findings show that the same attribute mastery profiles are prevalent across genders. Finally, the results of the DIF analysis are used to select models that could replace the complex MG G-DINA without significant loss of information.

Mixture Model Method: A new method to handle aberrant responses in psychological and educational testing

<p id="p00005">Aberrant responses have been repeatedly reported in psychological and educational measurement. If traditional measurement models or methods (e.g., item response theory, IRT) are applied to data sets contaminated by aberrant responses, parameter estimates may be biased. Therefore, it is necessary to identify aberrant responses and to reduce their detrimental effects. <p id="p00005">In the literature, there are two traditional response time (RT)-based methods to detect aberrant responses: RT threshold method and RT residual method. The focus of these methods is to find a threshold of RT or RT residual. If a RT or RT residual is remarkably less than the threshold, this response should be regarded as an aberrant response with extremely short RT (e.g., speededness, rapid-guessing), and consequently does not provide information about the test taker’s latent trait. Afterwards, down-weighting strategy, which tries to limit the influence of aberrant responses on parameter estimation by reducing their weight in the sample, can be applied. <p id="p00005">The mixture model method (MMM), is a new method proposed to handle data contaminated by aberrant responses. This method applies the accommodating strategy, which is to extend a model in order to account for the contaminations directly. MMM shows more advantages in terms of: (1) detecting aberrant responses and obtaining parameter estimates simultaneously, instead of two steps (detecting and down-weighting); (2) precisely recovering the severity of aberrant responding. There are two categories of MMM. The first category of methods assumes that the classification (i.e., whether the item is answered normally or aberrantly) can be predicted by RT. While the second category is a natural extension of van der Linden’s (<xref xml:base="bibr" rid="b53">2007</xref>) hierarchical model, which models responses and RTs jointly. In this method, the observed RT, as well as the correct response probability of each item-by-person encounter can be decomposed to RT (or probability) caused by normal response and that caused by aberrant response according to the most important difference between the two distinct behaviors. This method leads to more precisely estimated item and person parameters, as well as excellent classification of aberrant/normal behavior. <p id="p00005">First, this article compares the basic logic of the two traditional RT-based methods and MMM. Aberrant responses are regarded as outliers in both RT threshold method and RT residual method. Therefore, they rely heavily on the severity of aberrance. If data set is contaminated by aberrant responses seriously, the observed RT (or RT residual) distribution will be different from the expected distribution, which in turn leads to low power and sometimes high false detection rate. On the other hand, MMM, which assumes that both observed RT and correct response probability follow a mixture distribution, treats aberrant and normal responses equally. In that way, it has little reliance on the severity of aberrance. In addition to that, MMM can apply to the situation when all the respondents actually respond regularly in theoretic. In that situation, all the responses are assumed to be classified into one category. Second, this article summarizes the disadvantages of the three methods. MMM has three primary limitations: (1) it usually relies heavily on strong assumptions, which means that it may not perform well if these assumptions are violated; (2) low proportion of aberrant response may lead to convergence problem and model identification problem; (3) it is quite complex and time-consuming. In all, practitioners should choose a proper method according to the characteristics of tests and categories of aberrant responses (e.g., rapid-guessing, item with preknowledge, cheating). In the end, this article suggests future researches may investigate the performance of MMM when its assumptions are violated or data consists of more types of aberrant response patterns. Fixing item parameter estimates, proposing some index to help choosing suitable methods, are encouraged to improve the efficiency of MMM.

Maximization of Some Types of Information for Unidentified Item Response Models with Guessing Parameters.

It is known that a family of fixed-effects item response models with equal discrimination and different guessing parameters has no model identifiability. For this family, some types of information including the Fisher information and a new one are maximized to have model identification. The conditions of monotonicity of these types of information with respect to a tuning parameter are given. In the case of the logistic model with guessing parameters, it is shown that maxima do not exist under some parametrization, where negative lower asymptote can be employed without changing the probabilities of correct responses by examinees.

A Gibbs sampler for the multidimensional four-parameter logistic item response model via a data augmentation scheme.

The four-parameter logistic (4PL) item response model, which includes an upper asymptote for the correct response probability, has drawn increasing interest due to its suitability for many practical scenarios. This paper proposes a new Gibbs sampling algorithm for estimation of the multidimensional 4PL model based on an efficient data augmentation scheme (DAGS). With the introduction of three continuous latent variables, the full conditional distributions are tractable, allowing easy implementation of a Gibbs sampler. Simulation studies are conducted to evaluate the proposed method and several popular alternatives. An empirical data set was analysed using the 4PL model to show its improved performance over the three-parameter and two-parameter logistic models. The proposed estimation scheme is easily accessible to practitioners through the open-source IRTlogit package.

Knowledge, Perceptions, and Preferred Information Sources Related to COVID-19 Among Central Pennsylvania Adults Early in the Pandemic: A Mixed Methods Cross-Sectional Survey.

To explore public knowledge, understanding of public health recommendations, perceptions, and trust in information sources related to COVID-19. A cross-sectional survey of central Pennsylvanian adults evaluated self-reported knowledge, and a convergent, mixed methods design was used to assess beliefs about recommendations, intended behaviors, perceptions, and concerns related to infectious disease risk, and trust of information sources. The survey was completed by 5,948 adults. The estimated probability of correct response for the basic knowledge score, weighted with confidence, was 0.79 (95% CI, 0.79-0.80). Knowledge was significantly higher in patients with higher education and nonminority race. While the majority of respondents reported that they believed following CDC recommendations would decrease the spread of COVID-19 in their community and intended to adhere to them, only 65.2% rated social isolation with the highest level of belief and adherence. The most trusted information source was federal public health websites (42.8%). Qualitative responses aligned with quantitative data and described concerns about illness, epidemiologic issues, economic and societal disruptions, and distrust of the executive branch's messaging. The survey was limited by a lack of minority representation, potential selection bias, and evolving COVID-19 information that may impact generalizability and interpretability. Knowledge about COVID-19 and intended adherence to behavioral recommendations were high. There was substantial distrust of the executive branch of the federal government, however, and concern about mixed messaging and information overload. These findings highlight the importance of consistent messaging from trusted sources that reaches diverse groups.

Automated Test Assembly for Multistage Testing With Cognitive Diagnosis.

Computer multistage adaptive test (MST) combines the advantages of paper and pencil-based test (P&P) and computer-adaptive test (CAT). As CAT, MST is adaptive based on modules; as P&P, MST can meet the need of test developers to manage test forms and keep test forms parallel. Cognitive diagnosis (CD) can accurately measure students’ knowledge states (KSs) and provide diagnostic information, which is conducive to student’s self-learning and teacher’s targeted teaching. Although MST and CD have a lot of advantages, many factors prevent MST from applying to CD. In this study, we first attempt to employ automated test assembly (ATA) to achieve the objectives of MST in the application of CD (called CD-MST) via heuristic algorithms. The mean correct response probability of all KSs for each item is used to describe the item difficulty of CD. The attribute reliability in CD is defined as the test quantitative target. A simulation study with the G-DINA model (generalized deterministic input noisy “and” gate model) was carried out to investigate the proposed CD-MST, and the results showed that the assembled panels of CD-MST satisfied the statistical and the non-statistical constraints.

A General Three-Parameter Logistic Model With Time Effect.

Within the framework of item response theory, a new and flexible general three-parameter logistic model with response time (G3PLT) is proposed. The advantage of this model is that it can combine time effect, ability, and item difficulty to influence the correct-response probability. In contrast to the traditional response time models used in educational psychology, the new model incorporates the influence of the time effect on the correct-response probability directly, rather than linking them through a hierarchical method via latent and speed parameters as in van der Linden's model. In addition, the Metropolis–Hastings within Gibbs sampling algorithm is employed to estimate the model parameters. Based on Markov chain Monte Carlo output, two Bayesian model assessment methods are used to assess the goodness of fit between models. Finally, two simulation studies and a real data analysis are performed to further illustrate the advantages of the new model over the traditional three-parameter logistic model.

Схожи или отличны? Применение Item Response Theory для анализа теста «Синонимы» на выборке взрослых с опытом и без опыта институционализации

The lack of valid and standardized instruments, directed on an assessment of the language domain in adolescents and adults in Russia postulates the urgent necessity of their development. To fi ll this gap, the language battery, ARFA-RUS, was created and applied in a large project investigating the long-term consequences of raring in institutional care settings on human development. In the current study, an Item Response Theory (IRT) approach was used to examine the psychometric properties of the Synonyms Subtest of ARFA-RUS as the fi rst step of validation of the battery. IRT results demonstrated the test is reliable for the low-to-moderate levels of the assessed ability; yet, to capture a wider ability range, more diffi cult items are needed. The ARFA-RUS Synonyms Subtest was less suitable for the postinstitutionalized group of adults; in this group, the latent ability estimate explained a lower percentage of variance in comparison to adults raised in biological families. With regard to item-specifi c analyses, two items demonstrated paradoxical patterns with decreased probability of correct response at increased ability. In addition, one item was eliminated from the fi nal version of the Synonyms Subtest due to its poor item fi t and low discrimination value.

Somatosensory perception sensitivity in voluntary postural sway movements: Age, gender and sway effect magnitudes.

1) to develop a reliable device for assessing somatosensory perception sensitivity in voluntary postural sway movement, specifically a sway discrimination apparatus (SwayDA) for testing voluntary lateral sway discrimination sensitivity (VLSDS); 2) to explore the relationship between mobility performance and VLSDS in older adults, and 3) to determine the effects of age, gender and sway magnitude on VLSDS. First, eighteen healthy young adults (8 males, 10 females, age ranging from 22 to 70) were recruited for a test-retest reliability study. During the SwayDA test, the participants were asked to discriminate between four possible medial-lateral sway extents when moving away from neutral standing. For Objective 2, twenty-five older participants (9 males, 16 females, mean age 70.1) undertook both the SwayDA and the mobility tests. The mobility testing battery consisted of single task and cognitive dual task timed-up and-go tests, and the 5 times sit-to-stand test. Pearson's correlation was calculated between SwayDA scores and mobility performance. For Objective 3, 20 community-dwelling adults over 65 years old (10 males, 10 females, mean age 71.3) and 20 young volunteers (10 males, 10 females, mean age 23.6) were recruited to study the effects of age, gender and sway magnitude on VLSDS. To obtain a bias-free measure of VLSDS, the probability of correct response was considered as the true-positive judgment, while the probability of incorrect response was considered as false-positive judgment, and these were cumulated across the response values. A receiver operating characteristic (ROC) curve was then generated and the Area Under the ROC Curve (AUC) was used to measure VLSDS. There was no significant difference in AUC scores between Day 1 and Day 8 (p > 0.05). ICC(3,1) reliability indices were 0.750 for sway to the left and 0.879 for sway to the right. Pearson's correlation revealed a significant correlation between the SwayDA sores and timed-up-and-go (TUG), cognitive dual task TUG, 5 times sit-to-stand test (r = -0.456, -0.522, and - 0.416 respectively, all p < 0.05). Factorial ANOVA showed age and gender main effects (F = 8.144, p < 0.01, and F = 8.806, p < 0.01, respectively), suggesting older adults and females had worse VLSDS. In addition, a significant difference was found between the young and older participants in the inner range of VLSDS (t = -2.875, p < 0.017), indicating that the decline of somatosensory perception of postural sway in older people may be magnitude-specific, and greatest for small deviations from upright stance. The SwayDA has good to excellent test-retest reliability. The finding that VLSDS score was significantly correlated with mobility performance in older adults highlights the importance of somatosensory perception in postural control. More importantly, the significantly worse VLSDS in older people observed in the inner lateral movement range may represent a unique characteristic of neuromuscular degeneration associated with aging, which should be monitored and addressed in rehabilitation programs.

Confidence and varieties of bias

We test the proposition that response bias can have two different bases; reflecting either differing beliefs about the a priori likelihood of competing response alternatives, or their relative utilities. In evidence accumulation models, these two types of bias are thought to manifest as variations in the starting point for accumulation and threshold for responding, respectively. Although these two mechanisms are indistinguishable for linear accumulators in terms of accuracy and RT, Vickers’ (1979) balance-of-evidence hypothesis predicts they have dissociable effects on confidence. We derived ten ordinal predictions from these models and confirmed them at the level of group averages using traditional ANOVA analyses of results from a new experiment that manipulated the probability of correct responses and the rewards associated with them. However, individual effects were more variable, particularly with respect to the reward manipulation. We then used Bamber’s (1979) state–trace analysis to test the predicted dissociations using Bayes factors developed by Prince, Brown and Heathcote (2012) and Davis-Stober, Morey and Heathcote (2016). Once again, we found support at the aggregate level but more equivocal results for individuals. We discuss why individual results are to be preferred over aggregate results in state–trace analysis and draw the lesson that tailored designs are needed to obtain clear results from individual state–trace analyses.

Understanding the Time-Varying Association between Prenatal Metal Exposure Mixtures and Neurodevelopment

Humans are exposed to chemical mixtures at each life stage and their effects can vary over time (i.e. critical windows). We extended weighted quantile sum (WQS) regression, a method designed to assess correlated exposures at a single time point, to estimate the effect of metal mixtures on neurobehavior over multiple time points, their critical windows and the contributions of each mixture component. We assessed neural function using the Rapid Visual Processing (RVP) task from the Cambridge Neuropsychological Test Automated Battery, focusing on latency to correct response and probability of correct response (POCR), in the Programming Research in Obesity, Growth, Environment and Social Stressors cohort in Mexico City. RVP data were gathered from 326 children aged 6-8 years and prenatal metal exposure was quantified using maternal blood at the 2nd and 3rd trimesters. Arsenic (As), cadmium (Cd), cesium (Cs), chromium (Cr), lead (Pb) and antimony (Sb) were selected based on their toxicological profile. Results are adjusted for age, gender, socio-economic status, maternal education and maternal age. In the overall model, the mixture was associated with latency [0.09; 95% CI[0.01, 0.17],p&amp;lt;0.04), driven largely by both 2nd and 3rd trimester Cs (combined weight&amp;#61;54.11%) and Cd (combined weight&amp;#61;20.53%). For POCR, the results were more variable by exposure timing. The overall mixture effect was -0.07 (95% CI[-0.15, 0.01], p&amp;lt;0.08), primarily driven by 3rd trimester Sb (weight&amp;#61;16.6%), Cs (weight&amp;#61;14.1%) and Pb (weight&amp;#61;13.2%), while 2nd trimester Cr contributed 16.7% of the weight. Overall, 3rd trimester metals contributed 58% of the weight and 2nd trimester metals contributed 42% These results indicate that metal mixtures have unique critical windows and that WQS can be used to assess mixture effects, the contributions of each component, as well as discern time-dependent effects.

Does the Visual Channel Improve the Perception of Consonants Produced by Speakers of French With Down Syndrome?

This work evaluates whether seeing the speaker's face could improve the speech intelligibility of adults with Down syndrome (DS). This is not straightforward because DS induces a number of anatomical and motor anomalies affecting the orofacial zone. A speech-in-noise perception test was used to evaluate the intelligibility of 16 consonants (Cs) produced in a vowel-consonant-vowel context (Vo = /a/) by 4 speakers with DS and 4 control speakers. Forty-eight naïve participants were asked to identify the stimuli in 3 modalities: auditory (A), visual (V), and auditory-visual (AV). The probability of correct responses was analyzed, as well as AV gain, confusions, and transmitted information as a function of modality and phonetic features. The probability of correct response follows the trend AV > A > V, with smaller values for the DS than the control speakers in A and AV but not in V. This trend depended on the C: the V information particularly improved the transmission of place of articulation and to a lesser extent of manner, whereas voicing remained specifically altered in DS. The results suggest that the V information is intact in the speech of people with DS and improves the perception of some phonetic features in Cs in a similar way as for control speakers. This result has implications for further studies, rehabilitation protocols, and specific training of caregivers. https://doi.org/10.23641/asha.6002267.

A Multilevel Longitudinal Nested Logit Model for Measuring Changes in Correct Response and Error Types.

This article presents a multilevel longitudinal nested logit model for analyzing correct response and error types in multilevel longitudinal intervention data collected under a pretest-posttest, cluster randomized trial design. The use of the model is illustrated with a real data analysis, including a model comparison study regarding model complexity and cluster bias. Two substantive research questions regarding the intervention effect on correct response probability and error patterns are investigated using the proposed model. The recovery of item parameters for the proposed model using two sample size conditions is examined via a simulation study. The accuracy of the parameter estimates is comparable with those found in previous studies for the same family of models, except for the intercept parameters of correct responses. Finally, the impact of ignoring cluster membership in the model on the parameter estimation is also studied by fitting a single-level model to multilevel data. Ignoring cluster membership in the model adversely affects the estimation of intercept parameters in correct and error responses.

Visual Analysis Among Novices: Training and Trend Lines as Graphic Aids

The current study evaluated the degree to which novice visual analysts could discern trends in simulated time-series data across differing levels of variability and extreme values. Forty-five novice visual analysts were trained in general principles of visual analysis. One group received brief training on how to identify and omit extreme values. Participants rated 72 continuous time-series graphs. Inferential analyses were used to estimate the probability of correct responses. Participants who received the additional training were more likely to correctly identify intervention effects across all conditions. Nevertheless, extreme values had a substantial impact on decision accuracy for all participants. The impact of extreme values was exacerbated by increases in overall variability. Results support the notion that automated trend lines are useful but not infallible when interpreting continuous time-series data. Implications for practice and avenues for future research are discussed.