Jinjin Wu, George Farmer, Olivia Pready-James, Paul A. Warren
{"title":"The Robustness of Anchoring in a Naturalistic VR-Based Task","authors":"Jinjin Wu, George Farmer, Olivia Pready-James, Paul A. Warren","doi":"10.1111/cogs.70229","DOIUrl":"10.1111/cogs.70229","url":null,"abstract":"<p>Anchoring occurs when a quantitative estimate is biased toward an initially presented value (the anchor). Anchoring occurs both in high-level explicit estimation of numeric quantities and in lower-level perceptual tasks and persists even when reliable information about the quantity being estimated is directly available at the point of judgment. This suggests anchoring might derive from generic processing underpinning estimation. Such tasks, however, are almost exclusively lab-based, and the information required to complete the task is rarely available to the participant in a way that reflects how such information is sampled in real-life. To address this, across two experiments, we immersed participants in a virtual world on a platform that could be placed at any height. After an anchor was presented, participants subsequently estimated their height naturally, by interpreting sensory and cognitive cues from the 3D environment in which they were immersed. We manipulated the amount of information directly available to the participant for making their judgment as well as the offset between the anchor and the actual height. To modulate the extent to which task-relevant information was acquired naturally from the environment, we also contrasted anchoring effects in and out of VR. Although participants clearly used the available perceptual and cognitive information to make height estimates, anchoring effects were evident, displayed similar properties to those reported in previous lab-based studies, and were consistent both in and out of VR. Our design also allowed us to recover a novel subjective anchoring measure that facilitated a particularly parsimonious descriptive model of our anchoring data. We conclude that anchoring is a generic feature of all estimation tasks, even when task-relevant information is acquired naturally, as in real-life estimation. These results emphasize the potential for this cognitive bias to have a significant impact on performance in any real-world task requiring quantitative estimation.</p>","PeriodicalId":48349,"journal":{"name":"Cognitive Science","volume":"50 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cogs.70229","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148260172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
George Kachergis, Alvin Wei Ming Tan, Virginia A. Marchman, Philip S. Dale, Michael C. Frank
{"title":"Measuring Children's Early Vocabulary in Low-Resource Languages Using a Swadesh-Style Word List","authors":"George Kachergis, Alvin Wei Ming Tan, Virginia A. Marchman, Philip S. Dale, Michael C. Frank","doi":"10.1111/cogs.70228","DOIUrl":"https://doi.org/10.1111/cogs.70228","url":null,"abstract":"<p>Early language skill is predictive of many later life outcomes and is thus of great interest to developmental psychologists and clinicians. The MacArthur-Bates Communicative Development Inventories (CDIs), parent report instruments typically containing inventories of hundreds of children's vocabulary words, have proven to be valid and reliable instruments for measuring children's early language skill. The CDIs have been adapted to many dozens of languages, and cross-linguistic comparisons show both consistency and variability in language acquisition trajectories. However, thousands of languages do not yet have CDIs, nor the early language corpora needed to create them, posing a significant barrier to increasing the diversity of languages that are studied. Here, we propose a method for selecting candidate words to include on new CDIs through analyzing psychometric properties of the translation-equivalent concepts that are frequently included on existing CDIs. Leveraging 32 datasets from existing CDIs, we propose a list of 100 concepts that have low variability in their cross-linguistic learning difficulty. This pool of common concepts—analogous to the Swadesh lists, which are basic vocabulary lists used in glottochronology for cross-language comparison—can be used as a starting point for future CDI adaptations. We show that the proposed Swadesh-CDI list generalizes well to data from 10 additional languages.</p>","PeriodicalId":48349,"journal":{"name":"Cognitive Science","volume":"50 6","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148146990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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