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The degree to which visuospatial working memory (VSWM) is separable from working memory in general is an open question. On one hand, the construct is often researched as a unitary, domain-specific system. On the other, there is evidence that VWSM shares a common processing component with verbal memory. One might interpret this shared component as domain-general attention. We used confirmatory factor analysis to demonstrate that VSWM shares a domain-general component with verbal memory tasks and has a domainspecific component that is independent of verbal memory. Furthermore, the domain-general component was found to correlate with reasoning ability in both the visuospatial and verbal domains. The domain-specific component only correlated with reasoning ability when the tests had a strong visuospatial component. We argue that theories of VSWM need to place greater emphasis on its multiply determined nature.

Resisting immediate temptations in favor of larger later rewards predicts academic success, socioemotional competence, and health. These links with delaying gratification appear from early childhood and have been explained by cognitive and social factors that help override tendencies toward immediate gratification. However, some tendencies may actually promote delaying gratification. We assessed children’s delaying gratification for different rewards across two cultures that differ in customs around waiting. Consistent with our preregistered prediction, results showed that children in Japan ( n = 80) delayed gratification longer for food than for gifts, whereas children in the United States ( n = 58) delayed longer for gifts than for food. This interaction may reflect cultural differences: Waiting to eat is emphasized more in Japan than in the United States, whereas waiting to open gifts is emphasized more in the United States than in Japan. These findings suggest that culturally specific habits support delaying gratification, providing a new way to understand why individuals delay gratification and why this behavior predicts life success.

Collaborative problem solving (CPS) is a crucial 21st century skill; however, current technologies fall short of effectively supporting CPS processes, especially for remote, computer-enabled interactions. In order to develop next-generation computer-supported collaborative systems that enhance CPS processes and outcomes by monitoring and responding to the unfolding collaboration, we investigate automated detection of three critical CPS process -construction of shared knowledge, negotiation/coordination, and maintaining team function -derived from a validated CPS framework. Our data consists of 32 triads who were tasked with collaboratively solving a challenging visual computer programming task for 20 minutes using commercial videoconferencing software. We used automatic speech recognition to generate transcripts of 11,163 utterances, which trained humans coded for evidence of the above three CPS processes using a set of behavioral indicators. We aimed to automate the trained human-raters' codes in a team-independent fashion (current study) in order to provide automatic real-time or offline feedback (future work). We used Random Forest classifiers trained on the words themselves (bag of n-grams) or with word categories (e.g., emotions, thinking styles, social constructs) from the Linguistic Inquiry Word Count (LIWC) tool. Despite imperfect automatic speech recognition, the n-gram models achieved AUROC (area under the receiver operating characteristic curve) scores of .85, .77, and .77 for construction of shared knowledge, negotiation/coordination, and maintaining team function, respectively; these reflect 70%, 54%, and 54% improvements over chance. The LIWC-category models achieved similar scores of .82, .74, and .73 (64%, 48%, and 46% improvement over chance). Further, the LIWC model-derived scores predicted CPS outcomes more similar to human codes, demonstrating predictive validity. We discuss embedding our models in collaborative interfaces for assessment and dynamic intervention aimed at improving CPS outcomes.  CCS Concepts: • Human-centered computing → Empirical studies in collaborative and social computing

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