Behavior Research Methods最新文献

Metacognitive monitoring is an extremely important ability that predicts a wide range of outcomes. However, do people have insight into their own metacognitive monitoring capacity? This study measured participants' perceived metacognitive monitoring abilities using a novel psychometrically validated questionnaire (Study 1) and then examined how well survey responses aligned with online measures of metacognitive monitoring (resolution, discrimination, sensitivity, efficiency) taken from confidence ratings participants made while performing a perceptual decision-making task and Raven's Progressive Matrices (Study 2). We found a negative correlation between the questionnaire responses and many of the online measures of metacognitive monitoring - those who reported being better at metacognitive monitoring, in fact tended to be worse according to the online metacognitive ratings. This occurred because, in general, high self-perceptions of monitoring ability were, in fact, related to higher confidence and lower cognitive performance. These findings suggest that we may have inaccurate insights into our own metacognitive monitoring capacity and questionnaire-based measures of metacognitive abilities may be problematic as they may represent unrealistic self-perceptions.

Network science tools are becoming increasingly important to psycholinguistics, but few open-access data sets exist for exploring network properties of even well-studied languages like English. We constructed several phonological similarity networks (neighbors differ in exactly one consonant or vowel phoneme) using words from a lexicon based on the SUBTLEX-US English corpus, distinguishing networks by size and word representation (i.e., lemma vs. word form). The resulting networks are shown to exhibit many familiar characteristics, including small-world properties, broad degree distributions, and robustness to node removal, regardless of network size and word representation. We also validated the SUBTLEX phonological networks by showing that they exhibit contrasts in degree and clustering coefficient comparable to the same contrasts found in prior studies and exhibit familiar trends after extraction of a backbone network of nodes important to network centrality. The data release ( https://github.com/aldo-git-bit/phonological-similarity-networks-SUBTLEX ) includes 17 adjacency lists that can be further explored using the networkX package in Python, a package of files for building new adjacency lists from scratch, and several scripts that allow users to analyze and extend these results.

Parent report measures have proven to be a valuable research tool for studying early language development. Caregivers are given a list of words and are asked which of them their child has already used. However, most available measures are not suited for children beyond infancy, come with substantial licensing costs or lack a clear psychometric foundation. Here, we present the PREVIC (Parent Report of Expressive Vocabulary in Children), an open-access, high-quality vocabulary checklist for German-speaking children between 3 and 8 years of age. The PREVIC was constructed leveraging the advantages of item response theory: we designed a large initial item pool of 379 words and collected data from 1190 caregivers of children between 3 and 8 years of age. Based on these data, we computed a range of fit indices for each item (word) and used an automated item selection algorithm to compile a final pool that contains items that (a) vary in difficulty and (b) fit the Rasch (one-parameter logistic) model. The resulting task is highly reliable and shows convergent validity. The IRT-based construction allowed us to design an adaptive version of the task that substantially reduces the task duration while retaining measurement precision. The task - including the adaptive version - was implemented as a website and is freely accessible online ( https://ccp-odc.eva.mpg.de/previc-demo/ ). The PREVIC fills an important gap in the toolkit of researchers interested in language development and provides an ideal starting point for developing converging measures in other languages.

Social interactions are a fundamental aspect of human life, yet, their objective and naturalistic measurement remains challenging for scientific research. This challenge can be addressed using digital communication data. To this end, we have developed Dona, an open-source platform for donating messaging data from WhatsApp, Facebook, and Instagram. Given the highly sensitive nature of messaging data, we ensure participant privacy through rigorous data minimization. Dona removes all sensitive information on the user side prior to donation, retaining only de-identified meta-data such as message length and timestamps. This paper presents an overview of the platform, a deployment guide, and example use cases. In addition, we evaluate the informativeness of minimized messaging data for studying social interactions with two approaches. First, we conducted a user study in which 85 participants donated their data, received visualizations of their messaging behavior and evaluated the informativeness of this visual feedback. Second, we performed a quantitative analysis using over 1500 donated chats to confirm whether minimized messaging data captures known aspects of human interactions, such as interaction balance, heterogeneity, and burstiness. The results demonstrate that minimized, de-identified messaging data reflects informative interaction features as assessed by both self-reports and objective metrics. In conclusion, Dona is a donation platform well suited for sensitive contexts in which researchers aim to balance participant privacy with the acquisition of objective and informative data on social interactions.

Scale scores in psychology studies are commonly accompanied by a reliability coefficient like alpha. Coefficient alpha is an index that summarizes reliability across the entire score distribution, implying equal precision for all scores. However, an underappreciated fact is that reliability can be conditional such that scores in certain parts of the score distribution may be more reliable than others. This conditional perspective of reliability is common in item response theory (IRT), but psychologists are generally not well versed in IRT. Correspondingly, the representativeness of a single summary index like alpha across the entire score distribution can be unclear but is rarely considered. If conditional reliability is fairly homogeneous across the score distribution, coefficient alpha may be sufficiently representative and a useful summary. But, if conditional reliability is heterogeneous across the score distribution, alpha may be unrepresentative and may not align with the reliability of a typical score in the data or with a particularly important score like a cut point where decisions are made. This paper proposes a method, R package, and Shiny application to quantify the potential differences between coefficient alpha and conditional reliability across the score distribution. The goal is to facilitate comparisons between conditional reliability and reliability summary indices so that psychologists can contextualize the reliability of their scores more clearly and comprehensively.

Interaction effects are very common in the psychological literature. However, interaction effects are typically very small and often fail to replicate. In this study, we conducted a simulation comparing the generalizability and estimability of two linear regression models: one correctly specified to account for interaction effects and one misspecified including simple effects only. We manipulated noise levels, predictor variable correlations, and different sets of regression weights, resulting in 9216 different conditions. From each dataset, we drew 1000 samples of N = 25, 50, 100, 250, 500, and 1000, resulting in a total of 55,296,000 analyses for each model. Our results show that misspecification can drastically bias regression estimates, sometimes leading to zero or reversed simple effects. Furthermore, we found that when models are generalized to the entire population, the difference between the explained variance in the sample and in the population is often smaller for the misspecified model than for the correctly specified model. However, the comparison between models shows that the correctly specified model explains the data at the population level better overall. These results emphasize the importance of theory in modeling choices and show that it is important to provide a rationale for why interactions are included or excluded in an analysis.

Animal behavior analysis software has become an essential tool in the life sciences. However, the currently available tools have some significant shortcomings that limit their use by individuals without programming skills, access to higher informatics resources, or the capability to invest large sums of money. We have developed and validated an easy-to-use and straightforward tracking system named ESTraS to address this issue. This freeware software allows researchers to track and analyze rodent behaviors, offering additional options such as trajectory and angle analysis. Through ESTraS, researchers can utilize unsupervised clustering techniques, such as k-means or hierarchical clustering, to further explore the obtained results. This clustering enables the classification of results based on observed similarities among subjects. The data of this manuscript shows that ESTraS can prove to be extremely valuable, not only by providing essential behavioral analysis tools but also by offering specific data analysis options with just one click.

Interpersonal judgments play a central role in human social interactions, influencing decisions ranging from friendships to presidential elections. Despite extensive research on the accuracy of these judgments, an overreliance on broad personality traits and subjective judgments as criteria for accuracy has hindered progress in this area. Further, most individuals involved in past studies (either as judges or targets) came from ad-hoc student samples which hampers generalizability. This paper introduces Who Knows ( https://whoknows.uni-muenster.de ), an innovative smartphone application designed to address these limitations. Who Knows was developed with the aim to create a comprehensive and reliable database for examining first impressions. It utilizes a gamified approach where users judge personality-related characteristics of strangers based on short video introductions. The project incorporates multifaceted criteria to evaluate judgments, going beyond traditional self-other agreement. Additionally, the app draws on a large pool of highly specific and heterogenous items and allows users to judge a diverse array of targets on their smartphones. The app's design prioritizes user engagement through a responsive interface, feedback mechanisms, and gamification elements, enhancing their motivation to provide judgments. The Who Knows project is ongoing and promises to shed new light on interpersonal perception by offering a vast dataset with diverse items and a large number of participants (as of fall 2024, N = 9,671 users). Researchers are encouraged to access this resource for a wide range of empirical inquiries and to contribute to the project by submitting items or software features to be included in future versions of the app.

Traditional methods of displaying stimuli in eyewitness memory research, such as mock crime videos, are often criticised for lacking ecological validity. To overcome this, researchers have suggested using virtual reality (VR) technology to display the stimuli as it can promote a sense of presence, leading to real-world responses. However, little research has compared VR with traditional methods to demonstrate this enhanced validity. In Study 1, 54 participants viewed a mock crime video on screen or in VR while their heart rate was recorded, then completed measures of presence and emotion, and had their recall tested after 10 min. In Study 2, 74 participants' recall was tested after a 7-day delay and included a more in-depth exploration of emotional experience. In both studies, participants in the VR group reported a statistically significant increase in their sense of general presence, spatial presence, and involvement in the scene; however, there was no statistically significant difference in recall between the groups. Participants in the VR group had a statistically significant increase in heart rate in Study 1 only, and emotional experience in Study 2 only. The findings of this research suggest that VR may provide a more ecologically valid eyewitness experience than videos, without impacting participant memory or wellbeing. The findings of the current research are discussed in relation to previous literature and implications for experimental eyewitness memory research.