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35 Research products

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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Geoff Musick; Thomas A. O'Neill; Beau G. Schelble; Nathan J. McNeese; +1 Authors

    Abstract As artificial intelligence (AI) continues to grow in proficiency, the potential for AI to be used as team members rather than tools is becoming closer to realization. This advancement is driving new research investigations into the applicability of human-human teamwork knowledge to the context of human-autonomy teaming. In the current study, we apply qualitative methods to explore how the perceived composition of a team (how many humans and how many agents on the team) affects sentiments toward teammates, team processes, cognitive states, and the emergence of a system of team cognition. A total of 46 teams completed a teamwork simulation task and were interviewed afterwards regarding their teamwork experience. All of the teams were comprised of only humans; however, two conditions were led to believe that their teammate(s) were autonomous agents. Interviews were analyzed using grounded theory and the Gioia methodology, which revealed thematic differences between the team compositions. In light of our results, we offer a new model that describes how early-stage action teams achieve effective team processes and emergent cognitive states.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Computers in Human B...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Computers in Human Behavior
    Article . 2021
    License: Elsevier TDM
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Computers in Human B...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Computers in Human Behavior
      Article . 2021
      License: Elsevier TDM
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Wortha, Franz; Azevedo, Roger; Taub, Michelle; Narciss, Susanne;

    Emotions are a core factor of learning. Studies have shown that multiple emotions are co-experienced during learning and have a significant impact on learning outcomes. The present study investigated the importance of multiple, co-occurring emotions during learning about human biology with MetaTutor, a hypermedia-based tutoring system. Person-centered as well as variable-centered approaches of cluster analyses were used to identify emotion clusters. The person-centered clustering analyses indicated three emotion profiles: a positive, negative and neutral profile. Students with a negative profile learned less than those with other profiles and also reported less usage of emotion regulation strategies. Emotion patterns identified through spectral co-clustering confirmed these results. Throughout the learning activity, emotions built a stable correlational structure of a positive, a negative, a neutral and a boredom emotion pattern. Positive emotion pattern scores before the learning activity and negative emotion pattern scores during the learning activity predicted learning, but not consistently. These results reveal the importance of negative emotions during learning with MetaTutor. Potential moderating factors and implications for the design and development of educational interventions that target emotions and emotion regulation with digital learning environments are discussed.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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    Europe PubMed Central
    Article . 2019
    Data sources: PubMed Central
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Europe PubMed Central
      Article . 2019
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Jean-Paul Noel; Ryan A. Stevenson; Mark T. Wallace;

    AbstractBinding across sensory modalities yields substantial perceptual benefits, including enhanced speech intelligibility. The coincidence of sensory inputs across time is a fundamental cue for this integration process. Recent work has suggested that individuals with diagnoses of schizophrenia (SZ) and autism spectrum disorder (ASD) will characterize auditory and visual events as synchronous over larger temporal disparities than their neurotypical counterparts. Namely, these clinical populations possess an enlarged temporal binding window (TBW). Although patients with SZ and ASD share aspects of their symptomatology, phenotypic similarities may result from distinct etiologies. To examine similarities and variances in audiovisual temporal function in these two populations, individuals diagnosed with ASD (n = 46; controls n = 40) and SZ (n = 16, controls = 16) completed an audiovisual simultaneity judgment task. In addition to standard psychometric analyses, synchrony judgments were assessed using Bayesian causal inference modeling. This approach permits distinguishing between distinct causes of an enlarged TBW: an a priori bias to bind sensory information and poor fidelity in the sensory representation. Findings indicate that both ASD and SZ populations show deficits in multisensory temporal acuity. Importantly, results suggest that while the wider TBWs in ASD most prominently results from atypical priors, the wider TBWs in SZ results from a trend toward changes in prior and weaknesses in the sensory representations. Results are discussed in light of current ASD and SZ theories and highlight that different perceptual training paradigms focused on improving multisensory integration may be most effective in these two clinical populations and emphasize that similar phenotypes may emanate from distinct mechanistic causes.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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    Europe PubMed Central
    Other literature type . 2018
    Data sources: PubMed Central
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    European Journal of Neuroscience
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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    European Journal of Neuroscience
    Article . 2018
    License: Wiley Online Library User Agreement
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      Europe PubMed Central
      Other literature type . 2018
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      European Journal of Neuroscience
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      European Journal of Neuroscience
      Article . 2018
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  • A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2008 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted in 2008 when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full pregnancy and fertility histories from both men and women, an educational history of dates of degrees and school attendance, contact with the criminal justice system, military service, and various employment events, including the date of first and current jobs, with respective information on occupation, industry, wages, hours, and benefits. Finally, physical measurements and biospecimens were also collected at Wave IV, and included anthropometric measures of weight, height and waist circumference, cardiovascular measures such as systolic blood pressure, diastolic blood pressure, and pulse, metabolic measures from dried blood spots assayed for lipids, glucose, and glycosylated hemoglobin (HbA1c), measures of inflammation and immune function, including High sensitivity C-reactive protein (hsCRP) and Epstein-Barr virus (EBV). Datasets: DS0: Study-Level Files DS1: Wave I: In-Home Questionnaire, Public Use Sample DS2: Wave I: Public Use Contextual Database DS3: Wave I: Network Variables DS4: Wave I: Public Use Grand Sample Weights DS5: Wave II: In-Home Questionnaire, Public Use Sample DS6: Wave II: Public Use Contextual Database DS7: Wave II: Public Use Grand Sample Weights DS8: Wave III: In-Home Questionnaire, Public Use Sample DS9: Wave III: In-Home Questionnaire, Public Use Sample (Section 17: Relationships) DS10: Wave III: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancies) DS11: Wave III: In-Home Questionnaire, Public Use Sample (Section 19: Relationships in Detail) DS12: Wave III: In-Home Questionnaire, Public Use Sample (Section 22: Completed Pregnancies) DS13: Wave III: In-Home Questionnaire, Public Use Sample (Section 23: Current Pregnancies) DS14: Wave III: In-Home Questionnaire, Public Use Sample (Section 24: Live Births) DS15: Wave III: In-Home Questionnaire, Public Use Sample (Section 25: Children and Parenting) DS16: Wave III: Public Use Education Data DS17: Wave III: Public Use Graduation Data DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS20: Wave III: Peabody Picture Vocabulary Test (PVT), Public Use DS21: Wave III: Public In-Home Weights DS22: Wave IV: In-Home Questionnaire, Public Use Sample DS23: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16B: Relationships) DS24: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16C: Relationships) DS25: Wave IV: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancy Table) DS26: Wave IV: In-Home Questionnaire, Public Use Sample (Section 19: Live Births) DS27: Wave IV: In-Home Questionnaire, Public Use Sample (Section 20A: Children and Parenting) DS28: Wave IV: Biomarkers, Measures of Inflammation and Immune Function DS29: Wave IV: Biomarkers, Measures of Glucose Homeostasis DS30: Wave IV: Biomarkers, Lipids DS31: Wave IV: Public Use Weights Wave I: The Stage 1 in-school sample was a stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school -- a school that sent graduates to the high school and that included a 7th grade -- was also recruited from the community. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12. The Stage 2 in-home sample of 27,000 adolescents consisted of a core sample from each community, plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample.; Wave II: The Wave II in-home interview surveyed almost 15,000 of the same students one year after Wave I.; Wave III: The in-home Wave III sample consists of over 15,000 Wave I respondents who could be located and re-interviewed six years later.; Wave IV: All original Wave I in-home respondents were eligible for in-home interviews at Wave IV. At Wave IV, the Add Health sample was dispersed across the nation with respondents living in all 50 states. Administrators were able to locate 92.5% of the Wave IV sample and interviewed 80.3% of eligible sample members. ; For additional information on sampling, including detailed information on special oversamples, please see the Add Health Study Design page. Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health. Waves I and II focused on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants aged into adulthood, the scientific goals of the study expanded and evolved. Wave III explored adolescent experiences and behaviors related to decisions, behavior, and health outcomes in the transition to adulthood. Wave IV expanded to examine developmental and health trajectories across the life course of adolescence into young adulthood, using an integrative study design which combined social, behavioral, and biomedical measures data collection. Response Rates: Response rates for each wave were as follows: Wave I: 79 percent; Wave II: 88.6 percent; Wave III: 77.4 percent; Wave IV: 80.3 percent; Adolescents in grades 7 through 12 during the 1994-1995 school year. Respondents were geographically located in the United States. audio computer-assisted self interview (ACASI) computer-assisted personal interview (CAPI) computer-assisted self interview (CASI) paper and pencil interview (PAPI) face-to-face interview

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Brett Laursen; Robert L. Altman; William M. Bukowski; Li Wei;

    AbstractObjectiveThe present study concerns an overlooked trait indicator of childhood peer status: Being fun. The study is designed to identify the degree to which being fun is uniquely associated with the peer status variables of likeability and popularity.MethodTwo studies of children in grades 4 to 6 (ages 9 to 12) are reported. The first involved 306 girls and 305 boys attending school in northern Colombia. The second involved 363 girls and 299 boys attending school in southern Florida. Students completed similar peer nomination inventories, once in the first study and twice (8 weeks apart) in the second.ResultsIn both studies, being fun was positively correlated with likeability and popularity. In the second study, being fun predicted subsequent changes in likeability and popularity, after controlling for factors known to be related to each. Initial likeability and popularity also predicted subsequent changes in perceptions of being fun.ConclusionsAnecdotal evidence suggests that children are intensely focused on having fun. The findings indicate that this focus extends beyond the immediate rewards that fun experiences provide; some portion of peer status is uniquely derived from the perception that one is fun to be around.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Journal of Personali...arrow_drop_down
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    Journal of Personality
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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Journal of Personality
    Article . 2020
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Journal of Personali...arrow_drop_down
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      Journal of Personality
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Journal of Personality
      Article . 2020
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Fernández-Verdú, Ceneida (ed.); Llinares, Salvador (ed.); Gutiérrez, Ángel (ed.); Planas, Núria (ed.);
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Recolector de Cienci...arrow_drop_down
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    Authors: Lily Tsoi; Kiley Hamlin; Adam Waytz; Andrew Scott Baron; +1 Authors

    There is a debate regarding the function of theory of mind (ToM), the capacity to infer, attribute, and reason about mental states. On the one hand are evolutionary and psychological work suggesting that ToM is greater for competition than cooperation. On the other hand are findings and theories promoting greater ToM for cooperation than competition. We investigate the question of whether ToM is greater for competition than cooperation or vice versa by examining the period of development during which explicit ToM comes online. In two studies, we examined preschool children’s abilities to explicitly express an understanding of false beliefs—a key marker of ToM—and ability to apply that understanding in first-person social interactions in competitive and cooperative contexts. Our findings reveal that preschool children are better at understanding false beliefs and applying that understanding in competitive contexts than in cooperative contexts.

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    https://doi.org/10.31234/osf.i...
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  • Authors: Harley, Jason, M; Bouchet, François; Azevedo, Roger;

    International audience; We help advance the research on emotions with a preliminary investigation of differences between 116 students' typical studying emotions and those they experienced while studying with an ITS. Results revealed that students reported significantly lower levels of negative emotions while studying with an ITS compared to their typical emotional dispositions toward studying. 1 Introduction Achievement emotions are critical because of the impact they have on our success and failure in important and influential domains such as learning and success in school [1]. Emotions can support achievement by fostering motivation, focusing attention and limited cognitive resources on achievement-related activities and promoting adaptive information processing and self-regulation strategies [1]. While research has focused on the emotions learners tend to experience while interacting with these systems, little is known about how students general academic emotional tendencies compare with those experienced during these, often novel, interactions [2]. Understanding how students typically feel while studying is valuable because of its potential to inform user models and design more adaptive ITSs [3]. Moreover, comparisons provide an affective benchmark to help researchers appreciate affective benefits or shortcomings that systems have when compared to students' academic status quo. In this study, we investigated the effect of administering the achievement emotions questionnaire (AEQ [1]) prior to learners' interaction with MetaTutor and halfway through their interaction with it on the negative emotions they reported experiencing. We were particularly interested in learners' negative emotions because of the deleterious impact they can have on learners' experience with the system, self-regulated learning skill use, and achievement. Our hypothesis was that learners would report lower intensity levels of these emotions while studying with MetaTutor on account of lower appraisals of instrumental task value [4, 5]. In other words, because MetaTutor is a low stakes studying environment, like many ITSs, students can focus on content and process practice and mastery without concern for grades [3].

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    Authors: Amy C. Hartl; Brett Laursen; Stéphane Cantin; Frank Vitaro;

    Resource Control Theory (Hawley, 1999) posits a group of bistrategic popular youth who attain status through coercive strategies while mitigating fallout via prosociality. This study identifies and distinguishes this bistrategic popular group from other popularity types, tracing the adjustment correlates of each. Adolescent participants (288 girls, 280 boys; Mage = 12.50 years) completed peer nominations in the Fall and Spring of the seventh and eighth grades. Longitudinal latent profile analyses classified adolescents into groups based on physical and relational aggression, prosocial behavior, and popularity. Distinct bistrategic, aggressive, and prosocial popularity types emerged. Bistrategic popular adolescents had the highest popularity and above average aggression and prosocial behavior; they were viewed by peers as disruptive and angry but were otherwise well‐adjusted.

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    Child Development
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    Child Development
    Article . 2019
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      Child Development
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    Authors: Elisabeth Marchand; Shirlene Wade; Jessica Sullivan; David Barner;

    Abstract We tested 5- to 7-year-old bilingual learners of French and English (N = 91) to investigate how language-specific knowledge of verbal numerals affects numerical estimation. Participants made verbal estimates for rapidly presented random dot arrays in each of their two languages. Estimation accuracy differed across children’s two languages, an effect that remained when controlling for children’s familiarity with number words across their two languages. In addition, children’s estimates were equivalently well ordered in their two languages, suggesting that differences in accuracy were due to how children represented the relative distance between number words in each language. Overall, these results suggest that bilingual children have different mappings between their verbal and nonverbal counting systems across their two languages and that those differences in mappings are likely driven by an asymmetry in their knowledge of the structure of the count list across their languages. Implications for bilingual math education are discussed.

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    https://doi.org/10.31234/osf.i...
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    Journal of Experimental Child Psychology
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      Journal of Experimental Child Psychology
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35 Research products
  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Geoff Musick; Thomas A. O'Neill; Beau G. Schelble; Nathan J. McNeese; +1 Authors

    Abstract As artificial intelligence (AI) continues to grow in proficiency, the potential for AI to be used as team members rather than tools is becoming closer to realization. This advancement is driving new research investigations into the applicability of human-human teamwork knowledge to the context of human-autonomy teaming. In the current study, we apply qualitative methods to explore how the perceived composition of a team (how many humans and how many agents on the team) affects sentiments toward teammates, team processes, cognitive states, and the emergence of a system of team cognition. A total of 46 teams completed a teamwork simulation task and were interviewed afterwards regarding their teamwork experience. All of the teams were comprised of only humans; however, two conditions were led to believe that their teammate(s) were autonomous agents. Interviews were analyzed using grounded theory and the Gioia methodology, which revealed thematic differences between the team compositions. In light of our results, we offer a new model that describes how early-stage action teams achieve effective team processes and emergent cognitive states.

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    Computers in Human Behavior
    Article . 2021
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      Computers in Human Behavior
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Wortha, Franz; Azevedo, Roger; Taub, Michelle; Narciss, Susanne;

    Emotions are a core factor of learning. Studies have shown that multiple emotions are co-experienced during learning and have a significant impact on learning outcomes. The present study investigated the importance of multiple, co-occurring emotions during learning about human biology with MetaTutor, a hypermedia-based tutoring system. Person-centered as well as variable-centered approaches of cluster analyses were used to identify emotion clusters. The person-centered clustering analyses indicated three emotion profiles: a positive, negative and neutral profile. Students with a negative profile learned less than those with other profiles and also reported less usage of emotion regulation strategies. Emotion patterns identified through spectral co-clustering confirmed these results. Throughout the learning activity, emotions built a stable correlational structure of a positive, a negative, a neutral and a boredom emotion pattern. Positive emotion pattern scores before the learning activity and negative emotion pattern scores during the learning activity predicted learning, but not consistently. These results reveal the importance of negative emotions during learning with MetaTutor. Potential moderating factors and implications for the design and development of educational interventions that target emotions and emotion regulation with digital learning environments are discussed.

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    Europe PubMed Central
    Article . 2019
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    Authors: Jean-Paul Noel; Ryan A. Stevenson; Mark T. Wallace;

    AbstractBinding across sensory modalities yields substantial perceptual benefits, including enhanced speech intelligibility. The coincidence of sensory inputs across time is a fundamental cue for this integration process. Recent work has suggested that individuals with diagnoses of schizophrenia (SZ) and autism spectrum disorder (ASD) will characterize auditory and visual events as synchronous over larger temporal disparities than their neurotypical counterparts. Namely, these clinical populations possess an enlarged temporal binding window (TBW). Although patients with SZ and ASD share aspects of their symptomatology, phenotypic similarities may result from distinct etiologies. To examine similarities and variances in audiovisual temporal function in these two populations, individuals diagnosed with ASD (n = 46; controls n = 40) and SZ (n = 16, controls = 16) completed an audiovisual simultaneity judgment task. In addition to standard psychometric analyses, synchrony judgments were assessed using Bayesian causal inference modeling. This approach permits distinguishing between distinct causes of an enlarged TBW: an a priori bias to bind sensory information and poor fidelity in the sensory representation. Findings indicate that both ASD and SZ populations show deficits in multisensory temporal acuity. Importantly, results suggest that while the wider TBWs in ASD most prominently results from atypical priors, the wider TBWs in SZ results from a trend toward changes in prior and weaknesses in the sensory representations. Results are discussed in light of current ASD and SZ theories and highlight that different perceptual training paradigms focused on improving multisensory integration may be most effective in these two clinical populations and emphasize that similar phenotypes may emanate from distinct mechanistic causes.

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    European Journal of Neuroscience
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    European Journal of Neuroscience
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  • A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2008 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted in 2008 when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full pregnancy and fertility histories from both men and women, an educational history of dates of degrees and school attendance, contact with the criminal justice system, military service, and various employment events, including the date of first and current jobs, with respective information on occupation, industry, wages, hours, and benefits. Finally, physical measurements and biospecimens were also collected at Wave IV, and included anthropometric measures of weight, height and waist circumference, cardiovascular measures such as systolic blood pressure, diastolic blood pressure, and pulse, metabolic measures from dried blood spots assayed for lipids, glucose, and glycosylated hemoglobin (HbA1c), measures of inflammation and immune function, including High sensitivity C-reactive protein (hsCRP) and Epstein-Barr virus (EBV). Datasets: DS0: Study-Level Files DS1: Wave I: In-Home Questionnaire, Public Use Sample DS2: Wave I: Public Use Contextual Database DS3: Wave I: Network Variables DS4: Wave I: Public Use Grand Sample Weights DS5: Wave II: In-Home Questionnaire, Public Use Sample DS6: Wave II: Public Use Contextual Database DS7: Wave II: Public Use Grand Sample Weights DS8: Wave III: In-Home Questionnaire, Public Use Sample DS9: Wave III: In-Home Questionnaire, Public Use Sample (Section 17: Relationships) DS10: Wave III: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancies) DS11: Wave III: In-Home Questionnaire, Public Use Sample (Section 19: Relationships in Detail) DS12: Wave III: In-Home Questionnaire, Public Use Sample (Section 22: Completed Pregnancies) DS13: Wave III: In-Home Questionnaire, Public Use Sample (Section 23: Current Pregnancies) DS14: Wave III: In-Home Questionnaire, Public Use Sample (Section 24: Live Births) DS15: Wave III: In-Home Questionnaire, Public Use Sample (Section 25: Children and Parenting) DS16: Wave III: Public Use Education Data DS17: Wave III: Public Use Graduation Data DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS20: Wave III: Peabody Picture Vocabulary Test (PVT), Public Use DS21: Wave III: Public In-Home Weights DS22: Wave IV: In-Home Questionnaire, Public Use Sample DS23: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16B: Relationships) DS24: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16C: Relationships) DS25: Wave IV: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancy Table) DS26: Wave IV: In-Home Questionnaire, Public Use Sample (Section 19: Live Births) DS27: Wave IV: In-Home Questionnaire, Public Use Sample (Section 20A: Children and Parenting) DS28: Wave IV: Biomarkers, Measures of Inflammation and Immune Function DS29: Wave IV: Biomarkers, Measures of Glucose Homeostasis DS30: Wave IV: Biomarkers, Lipids DS31: Wave IV: Public Use Weights Wave I: The Stage 1 in-school sample was a stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school -- a school that sent graduates to the high school and that included a 7th grade -- was also recruited from the community. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12. The Stage 2 in-home sample of 27,000 adolescents consisted of a core sample from each community, plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample.; Wave II: The Wave II in-home interview surveyed almost 15,000 of the same students one year after Wave I.; Wave III: The in-home Wave III sample consists of over 15,000 Wave I respondents who could be located and re-interviewed six years later.; Wave IV: All original Wave I in-home respondents were eligible for in-home interviews at Wave IV. At Wave IV, the Add Health sample was dispersed across the nation with respondents living in all 50 states. Administrators were able to locate 92.5% of the Wave IV sample and interviewed 80.3% of eligible sample members. ; For additional information on sampling, including detailed information on special oversamples, please see the Add Health Study Design page. Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health. Waves I and II focused on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants aged into adulthood, the scientific goals of the study expanded and evolved. Wave III explored adolescent experiences and behaviors related to decisions, behavior, and health outcomes in the transition to adulthood. Wave IV expanded to examine developmental and health trajectories across the life course of adolescence into young adulthood, using an integrative study design which combined social, behavioral, and biomedical measures data collection. Response Rates: Response rates for each wave were as follows: Wave I: 79 percent; Wave II: 88.6 percent; Wave III: 77.4 percent; Wave IV: 80.3 percent; Adolescents in grades 7 through 12 during the 1994-1995 school year. Respondents were geographically located in the United States. audio computer-assisted self interview (ACASI) computer-assisted personal interview (CAPI) computer-assisted self interview (CASI) paper and pencil interview (PAPI) face-to-face interview

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    Authors: Brett Laursen; Robert L. Altman; William M. Bukowski; Li Wei;

    AbstractObjectiveThe present study concerns an overlooked trait indicator of childhood peer status: Being fun. The study is designed to identify the degree to which being fun is uniquely associated with the peer status variables of likeability and popularity.MethodTwo studies of children in grades 4 to 6 (ages 9 to 12) are reported. The first involved 306 girls and 305 boys attending school in northern Colombia. The second involved 363 girls and 299 boys attending school in southern Florida. Students completed similar peer nomination inventories, once in the first study and twice (8 weeks apart) in the second.ResultsIn both studies, being fun was positively correlated with likeability and popularity. In the second study, being fun predicted subsequent changes in likeability and popularity, after controlling for factors known to be related to each. Initial likeability and popularity also predicted subsequent changes in perceptions of being fun.ConclusionsAnecdotal evidence suggests that children are intensely focused on having fun. The findings indicate that this focus extends beyond the immediate rewards that fun experiences provide; some portion of peer status is uniquely derived from the perception that one is fun to be around.

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    Journal of Personality
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    Journal of Personality
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    Authors: Fernández-Verdú, Ceneida (ed.); Llinares, Salvador (ed.); Gutiérrez, Ángel (ed.); Planas, Núria (ed.);
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    Authors: Lily Tsoi; Kiley Hamlin; Adam Waytz; Andrew Scott Baron; +1 Authors

    There is a debate regarding the function of theory of mind (ToM), the capacity to infer, attribute, and reason about mental states. On the one hand are evolutionary and psychological work suggesting that ToM is greater for competition than cooperation. On the other hand are findings and theories promoting greater ToM for cooperation than competition. We investigate the question of whether ToM is greater for competition than cooperation or vice versa by examining the period of development during which explicit ToM comes online. In two studies, we examined preschool children’s abilities to explicitly express an understanding of false beliefs—a key marker of ToM—and ability to apply that understanding in first-person social interactions in competitive and cooperative contexts. Our findings reveal that preschool children are better at understanding false beliefs and applying that understanding in competitive contexts than in cooperative contexts.

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  • Authors: Harley, Jason, M; Bouchet, François; Azevedo, Roger;

    International audience; We help advance the research on emotions with a preliminary investigation of differences between 116 students' typical studying emotions and those they experienced while studying with an ITS. Results revealed that students reported significantly lower levels of negative emotions while studying with an ITS compared to their typical emotional dispositions toward studying. 1 Introduction Achievement emotions are critical because of the impact they have on our success and failure in important and influential domains such as learning and success in school [1]. Emotions can support achievement by fostering motivation, focusing attention and limited cognitive resources on achievement-related activities and promoting adaptive information processing and self-regulation strategies [1]. While research has focused on the emotions learners tend to experience while interacting with these systems, little is known about how students general academic emotional tendencies compare with those experienced during these, often novel, interactions [2]. Understanding how students typically feel while studying is valuable because of its potential to inform user models and design more adaptive ITSs [3]. Moreover, comparisons provide an affective benchmark to help researchers appreciate affective benefits or shortcomings that systems have when compared to students' academic status quo. In this study, we investigated the effect of administering the achievement emotions questionnaire (AEQ [1]) prior to learners' interaction with MetaTutor and halfway through their interaction with it on the negative emotions they reported experiencing. We were particularly interested in learners' negative emotions because of the deleterious impact they can have on learners' experience with the system, self-regulated learning skill use, and achievement. Our hypothesis was that learners would report lower intensity levels of these emotions while studying with MetaTutor on account of lower appraisals of instrumental task value [4, 5]. In other words, because MetaTutor is a low stakes studying environment, like many ITSs, students can focus on content and process practice and mastery without concern for grades [3].

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    Authors: Amy C. Hartl; Brett Laursen; Stéphane Cantin; Frank Vitaro;

    Resource Control Theory (Hawley, 1999) posits a group of bistrategic popular youth who attain status through coercive strategies while mitigating fallout via prosociality. This study identifies and distinguishes this bistrategic popular group from other popularity types, tracing the adjustment correlates of each. Adolescent participants (288 girls, 280 boys; Mage = 12.50 years) completed peer nominations in the Fall and Spring of the seventh and eighth grades. Longitudinal latent profile analyses classified adolescents into groups based on physical and relational aggression, prosocial behavior, and popularity. Distinct bistrategic, aggressive, and prosocial popularity types emerged. Bistrategic popular adolescents had the highest popularity and above average aggression and prosocial behavior; they were viewed by peers as disruptive and angry but were otherwise well‐adjusted.

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    Child Development
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    Authors: Elisabeth Marchand; Shirlene Wade; Jessica Sullivan; David Barner;

    Abstract We tested 5- to 7-year-old bilingual learners of French and English (N = 91) to investigate how language-specific knowledge of verbal numerals affects numerical estimation. Participants made verbal estimates for rapidly presented random dot arrays in each of their two languages. Estimation accuracy differed across children’s two languages, an effect that remained when controlling for children’s familiarity with number words across their two languages. In addition, children’s estimates were equivalently well ordered in their two languages, suggesting that differences in accuracy were due to how children represented the relative distance between number words in each language. Overall, these results suggest that bilingual children have different mappings between their verbal and nonverbal counting systems across their two languages and that those differences in mappings are likely driven by an asymmetry in their knowledge of the structure of the count list across their languages. Implications for bilingual math education are discussed.

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    https://doi.org/10.31234/osf.i...
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    Journal of Experimental Child Psychology
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Journal of Experimen...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      https://doi.org/10.31234/osf.i...
      Preprint . 2019
      License: CC BY
      Data sources: Crossref
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Journal of Experimental Child Psychology
      Article . 2020
      License: Elsevier TDM
      Data sources: Crossref
      PsyArXiv
      Preprint . 2019
      Data sources: PsyArXiv
      addClaim

      This Research product is the result of merged Research products in OpenAIRE.

      You have already added works in your ORCID record related to the merged Research product.