Deficits in spatial and navigation abilities are among the earliest signs of dementia. Yet, traditional neuropsychological tests primarily target memory and attention. The Spatial Performance Assessment for Cognitive Evaluation (SPACE) is a novel gamified digital assessment for iPads that uses various spatial tasks to detect early deficits in spatial navigation abilities indicative of cognitive impairment. In this study, 348 participants aged 21-76 completed the Montreal Cognitive Assessment (MoCA), SPACE, and a sociodemographic and health questionnaire. We investigated whether SPACE could predict scores on the MoCA beyond known risk factors for cognitive impairment. Using a factor analysis, we then assessed whether SPACE could complement the MoCA by capturing latent variables independent of MoCA scores that represent additional spatial aspects of cognitive functioning. Results from a hierarchical regression revealed that the pointing and perspective-taking tasks in SPACE significantly predicted MoCA scores beyond age and gender. Surprisingly, none of the risk factors predicted MoCA scores. The factor analysis revealed that the MoCA and perspective-taking contributed to a separate factor from the other navigation tasks in SPACE. We also provide normative data on age and gender for each task in SPACE, which can serve as benchmarks for future studies to identify individuals at risk.

Attention control, the ability to maintain focus while resisting distractions, is critical for executing goal-directed behaviors. Attention control has been measured via tasks and self-reports, and some studies have started to examine the degree to which these different measurement types correspond. We investigated the relationship between task-based and self-reported attention control measures in two studies. In Study 1 (online), participants completed an anti-saccade task follow by self-reported measures of attention control. Anti-saccade task performance was not associated with self-reported attention control shifting or distractibility. In Study 2 (in-person), we incorporated additional task-based measures of attention control and found results consistent with Study 1 (i.e., no association between self-reported and task-based measures of attention control). Bayes factors provided evidence in support of the null hypothesis, or the absence of a relationship between self-reported and task-based measures of attention control. Factor analyses revealed self-reported and task-based measures of attention control separated into two uncorrelated factors. Taken together, our findings suggest that task-based and self-reported attention control measures may not reflect the same underlying construct, which has implications for study design and research on individual differences in attention control.

We propose and implement an approach for deriving multidimensional scaling (MDS) solutions for objects from diverse everyday-object categories. The goal is for the MDS solutions to capture relative similarities between pairs of objects both within and across the categories. For example, if the members of the category apples are more similar to one another than are the members of the category lamps, then the MDS solution for the apples will be more compressed overall than the MDS solution for lamps. To achieve this goal, the key idea is that, rather than collecting similarity-judgment data one category at a time, we alternate in random fashion across trials the category from which the similarity-judgment data are collected. We hypothesize that if similarity-judgment data are collected one category at a time, observers may recalibrate their judgment scale with respect to each individual category, which could cause loss of information of overall discriminability relations across the different categories. By using the alternating-category approach, observers may be able to maintain a more nearly constant judgment scale across the different categories. We combine the alternating-category procedure with the use of metric forms of MDS that produce MDS solutions in which differences in overall discriminability relations across categories are maintained. We provide preliminary evidence of the success of the approach by showing that, when used as input to a simple computational model of recognition memory, the derived MDS solutions predict reasonably well the false-alarm rates associated with the different categories observed in an old-new recognition experiment.

Many intensive longitudinal studies are interested in topics that are not always amenable to direct physical measurement and instead are often theorized as latent constructs (e.g., affect, emotion, mood, behavior, personality). However, several recent reviews suggest that few empirical studies using scales to measure these constructs consider or report psychometrics when working with intensive longitudinal data (only 15-50%). Furthermore, a recent survey reported that many researchers working with intensive longitudinal data do not consider psychometrics because they either do not know how or are unaware that such methods exist. This is potentially problematic because it can be unclear whether momentary shifts in the variables represent true, construct-relevant changes or merely measurement error. Therefore, the goals of the current paper are to (a) provide an overview of the existing intensive longitudinal psychometric methods, (b) demonstrate how these methods can be applied, and (c) show how including psychometric methods can supplement and fortify intensive longitudinal analyses. Specifically, the paper covers four main topics: reliability, construct validation, measurement invariance, and measurement error modeling. The paper is also accompanied by a Shiny application to facilitate access and implementation of these methods. Supplemental materials provide in-depth walkthroughs of software applications in R and Mplus with annotated code for the empirical examples in the paper.

The explanatory item response model (EIRM) is a common tool in psychometrics to model person and item characteristics as functions of covariates. Existing tutorials demonstrate how to model dichotomous or polytomous item responses. In this tutorial, we show how to fit the extended two-parameter logistic (E2PL) item response model for continuous item responses using the brms package in R. Using two worked examples with visual analog scale data, we demonstrate data exploration, model building, and interpretation strategies. By following this tutorial, researchers will be able to fit and interpret the EIRM for continuous item response data.

Computer-based interactive tasks generate rich process data that capture respondents' problem-solving behaviors, particularly sequences of actions that trigger transitions between problem states. In recent years, the process-based measurement models analyzing transition sequences have emerged as a promising approach for estimating latent problem-solving ability. A fundamental step in developing these models is the predefinition of the transition effectiveness. However, existing effectiveness indicators are often limited to restricted value ranges (e.g., dichotomous or polytomous scales) and theoretical perspective of expert evaluation, thereby constraining the flexibility of process-based models. To address these limitations, this study introduces two probability-based indicators: state effectiveness and transition effectiveness . When validated using empirical data from the PISA 2012 problem-solving assessment, the probability-based effectiveness indicators exhibited a broader range of numerical values, enabling finer-grained discrimination among states and transitions. Subsequently, we developed the transition evaluation model (TEM), a process-based model that incorporates the transition effectiveness to better differentiate transition characteristics. Simulation study demonstrated TEM's robust parameter estimation, satisfactory model-data fit, and high estimation accuracy across diverse conditions. In an empirical study, TEM outperformed three models, including the Sequential Response Model (SRM), the State Response Measurement Model (SRMM), and SRM with Polytomous Effectiveness Indicators (SRM-PEI) in terms of model-data fit, and yields more nuanced transition characteristic curves and interpretable ability estimates. These findings underscore the value of probability-based effectiveness indicators and TEM as advanced tools for analyzing complex problem-solving assessments.

The FAIR (Findable, Accessible, Interoperable, and Reusable) data principles form the foundation of the open data movement. However, while many current practices ensure data are findable and accessible, true interoperability and reusability remain limited. This paper introduces the Truth Effect Database (TED), a large-scale, trial-level, open database harmonizing data from illusory truth effect studies designed to enhance interoperability and reusability. TED currently integrates data from 59 studies in 29 publications, spanning 12,249 participants and 808,231 trials, accounting for a wide range of dispositional and contextual variables. To promote usability, TED focuses on user-friendly data submission using a custom entry website and data extraction using the R package acdcquery. These tools guide researchers through both data entry and retrieval, eliminating the need for direct interaction with the database's internal structure. We illustrated the utility of TED through Bayesian multilevel analyses, highlighting substantial variance in the illusory truth effect at the subject level, moderated by the delay between exposure and judgment phases in truth effect paradigms. Beyond this first demonstration, TED provides the foundation for a wide range of future research. These include (living) meta-analyses, simulation-based power analyses, rigorous replication and reanalysis of existing studies, and the validation and development of formal cognitive models. As an open and extensible infrastructure, TED serves as a blueprint for sustainable, community-driven database development in psychological science.

Imagining performing a movement without executing it (movement imagery) is a covert and multidimensional skill. Leading models propose that the sub-processes of imagery generation, maintenance and manipulation are critical to movement imagery. However, measuring these individual processes, and the potential relationships between them, remains challenging. Here we developed and validated a combination of online tests to assess these different processes. A total of 180 healthy individuals completed the Movement Imagery Questionnaire-Revised, Second Edition (MIQ-RS; generation), the Imagined Finger Sequence Task (iFST; maintenance) and the Hand Laterality Judgement Task (HLJT; manipulation). MIQ-RS showed a bifactorial structure (visual and kinaesthetic modalities) according to confirmatory factor analysis, with good internal consistency. In the iFST, internal validity analyses showed a clear effect of sequence complexity, in both execution and imagery. Reliability, estimated via signal-to-noise ratios (SNRs), was also adequate (SNR ≥ 1.6). In the HLJT, expected effects of rotation angle, hand view and their interaction were found, consistent with biomechanical constraints. Reliability was also adequate (SNR ≥ 1.75). Test-retest reliability (123 participants reassessed after 6-8days) was generally adequate (intraclass correlation coefficients (ICCs) ≥ 0.67). Notably, criterion validity across tests, assessed using Bayesian Spearman's correlations, showed that correlations were generally absent (BF01 ≥ 3) or of small magnitude (r ≤ 0.27). We conclude that the online versions of these tests showed adequate structural/internal validity and (test-retest) reliability. Importantly, weak criterion validity across tests suggests that the differing sub-processes underlying movement imagery are largely dissociable, underscoring the need for comprehensive, multidimensional assessment of movement imagery ability.

Prediction is a core feature of language, which is widely studied across research domains. The Munich Sentence (MuSe) database enhances reproducibility by providing sentence completion norms for 619 German sentences, including cloze probabilities and entropy estimates from up to 232 participants. Sentence completions were collected in two online studies in which participants completed sentence beginnings with a single-word response after either hearing (auditory sample, N = 133) or reading (visual sample, N = 98) the sentence beginning. All responses were manually preprocessed to correct typos and spelling mistakes and to label grammatical errors, proper nouns, and singular and plural variants of the same response. In addition to the sentence norms, we provide trial-level data with participant-level demographic information and subclinical autistic and schizotypal trait measures. Together with open-access R scripts or our web tool, this allows tailoring the cleaning and norming steps to integrate individual-difference measures. For a subset of 479 sentence beginnings, the database also includes professional audio recordings of sentence beginnings, which can be flexibly combined with 531 recordings of unique sentence-final words and implemented in auditory language paradigms. All material is freely accessible via the Open Science Framework ( https://osf.io/ktnze/overview ) and the MuSe webtool ( https://munichsentencedatabase.franziskaknolle.com/ ).