Links to the institutional webpages associated with this project:

• https://apphatchery.org
• https://fabla.framer.website/

Visual similarity is an essential concept in vision science, and the methods used to quantify similarity have recently expanded in the areas of human-derived ratings and computer vision methodologies. Researchers who want to manipulate similarity between images (e.g., in a visual search, categorization, or memory task) often use the aforementioned methods, which require substantial, additional data collection prior to the primary task of interest. To alleviate this problem, we have developed an openly available database that uses multidimensional scaling (MDS) to model the similarity among 1200 items spread across 20 object categories, thereby allowing researchers to utilize similarity ratings within and between categories. In this article, we document the development of this database, including (1) collecting similarity ratings using the spatial arrangement method across two sites, (2) our computational approach with MDS, and (3) validation of the MDS space by comparing SpAM-derived distances to direct similarity ratings. The database and similarity data provided between items (and across categories) will be useful to researchers wanting to manipulate or control similarity in their studies. Link to dataset.

In the article, we present the Polish Vocabulary Size Test (PVST), a novel tool for assessing the receptive vocabulary size of both native and non-native Polish speakers. Based on item response theory and computerized adaptive testing, PVST dynamically adjusts to each test-taker’s proficiency level, ensuring high accuracy while keeping the test duration short. It is an up-to-date approach to vocabulary assessment and an alternative to LexTale (Lemhöfer & Broersma, 2012) and VST (Nation & Beglar, 2007) vocabulary tests. 

We validated PVST on a large sample of 656 individuals, with 64% native and 37% non-native speakers. As expected, native Polish speakers demonstrated significantly larger vocabularies (mean = 75.125 words; range = 19.556–122.693) compared to non-native speakers (mean = 7165 words; range = 646–23.394). For native speakers, vocabulary size showed a strong positive correlation with age (r = .496, p < .001), meaning that vocabulary grows lifelong, but after the age of 30 the speed of lexical growth significantly slows down. Non-native speakers’ vocabulary, in turn, does not depend on age but on language exposure—reading, speaking, and listening. Non-native vocabulary is unlikely to reach the size of an adult native speaker’s. PVST is one of the first attempts to construct an effective vocabulary size test in Polish. The PVST is available online at myvocab.info/pl. 

This paper presents a novel approach known as the cross estimation network (CEN) for fitting the datasets obtained from psychological or educational tests and estimating the parameters of item response theory (IRT) models. The CEN is comprised of two subnetworks: the person network (PN) and the item network (IN). The PN processes the response pattern of individual respondent and generates an estimate of the underlying ability, while the IN takes in the response pattern of individual item and outputs the estimates of the item parameters. Four simulation studies and an empirical study were comprehensively and rigorously conducted to investigate the performance of CEN on parameter estimation of the two-parameter logistic model under various testing scenarios. Results showed that CEN effectively fit the training data and produced accurate estimates of both person and item parameters. The trained PN and IN adhered to AI principles and acted as intelligent agents, delivering commendable evaluations for even unseen patterns of new respondents and items.

Code repository for implementing the CEN method: https://github.com/longfeizhangcode/CrossEstimationNetwor

Research questions regarding how, for whom, and where a treatment achieves its effect on an outcome have become increasingly valued in substantive research. Such questions can be answered by causal moderated mediation analysis, which assesses the heterogeneity of the mediation mechanism underlying the treatment effect across individual and contextual characteristics. Various moderated mediation analysis methods have been developed under the traditional path analysis/structural equation modeling framework. One challenge is that the definitions of moderated mediation effects depend on statistical models of the mediator and the outcome, and no solutions have been provided when either the mediator or the outcome is binary, or when the mediator or outcome model is nonlinear. In addition, it remains unclear to empirical researchers how to make causal arguments of moderated mediation effects due to a lack of clarifications of the underlying assumptions and methods for assessing the sensitivity to violations of the assumptions. This article overcomes the limitations by developing general definition, identification, estimation, and sensitivity analysis for causal moderated mediation effects under the potential outcomes framework. We also developed a user-friendly R package moderate.mediation (https://cran.r-project.org/web/packages/moderate.mediation/index.html) that allows applied researchers to easily implement the proposed methods and visualize the initial analysis results and sensitivity analysis results. We illustrated the application of the proposed methods and the package implementation with a re-analysis of the National Evaluation of Welfare-to-Work Strategies (NEWWS) Riverside data.

In this paper, we discuss key characteristics and typical experiment designs of the visual-world paradigm and compare different methods of analysing eye-movement data. We discuss the nature of the eye-movement data visual-world studies and provide data analysis tutorials on ANOVA, t-tests, linear mixed-effects model, growth curve analysis, cluster-based permutation analysis, bootstrapped differences of time-series, generalised additive modelling, and divergence point analysis. The goal of the analysis comparison and tutorials is to enable psycholinguists to apply each analysis method to their own data. We discuss the advantages and disadvantages of each method and offer recommendations about how to select an appropriate method depending on the research question and the experiment design.

Given the prevalence and increased interest to employ virtual reality technology in human behavioral research. An increase in the number of toolkits, toolbox, frameworks, and platforms to provide user’s a means to conduct human behavioral research in VR has been seen over the past decade. Despite this, numerous factors, such as limited fundings, personnel, time, and programming/art design proficiency continue to impede many users from efficiently carrying out behavioral experiments in VR. Hence, 5 years ago we began to conceptualize, create, and gradually develop and test a SkyrimVR-based behavioral experimental framework (SkyBXF) module that could fully utilize the rich resources provided by SkyrimVR and editable via Creation Kit. The goal was to provide researchers and students with minimal programming proficiency, 3D environment development expertise, time, and personnel; A ready-to-go behavioral experiment template to swiftly establish realistic virtual worlds that can be further customized for specific experimental needs. The initial thoughts for this project were something equivalent to Microsoft Powerpoint for creating slideshows, but for VR behavioral experiments. Where one could simply “install” SkyBXF and “load” a SkyBXF template and get right to conducting VR behavioral experiments! For more details on how to start a VR behavioral experiment using SkyBXF and the basic functions/options/controls of classic human behavior experiments, scan the QR code to visit our Github page.

Groundbreaking insights into the origins of the human mind have been garnered through the study of eye movements in preverbal subjects who are unable to explain their thought processes. Developmental research has largely relied on in-lab testing with trained experimenters. This constraint provides a narrow window into infant cognition and impedes large-scale data collection in families from diverse socioeconomic, geographic, and cultural backgrounds. Here we introduce a new open-source methodology for automatically analyzing infant eye-tracking data collected on personal devices in the home. Using algorithms from computer vision, machine learning, and ecological psychology, we develop an online webcam-linked eye tracker (OWLET) that provides robust estimation of infants’ point of gaze from smartphone and webcam recordings of infant assessments in the home. We validate OWLET in a large sample of 7-month-old infants (N = 127) tested remotely, using an established visual attention task. We show that this new method reliably estimates infants’ point-of-gaze across a variety of contexts, including testing on both computers and mobile devices, and exhibits excellent external validity with parental-report measures of attention. Our platform fills a significant gap in current tools available for rapid online data collection and large-scale assessments of cognitive processes in infants. Remote assessment addresses the need for greater diversity and accessibility in human studies and may support the ecological validity of behavioral experiments. This constitutes a critical and timely advance in a core domain of developmental research and in psychological science more broadly.