Social Dual Task Developmental Dataset and Analyses

Project abstract: Multitasking is part of the everyday lives of both adolescents and adults. We often multitask during social interactions by simultaneously keeping track of other, non-social information. Here, we examined how keeping track of non-social information impacts the ability to navigate social interactions in adolescents and adults. Participants aged 11-17 and 22-30 years old were instructed to carry out two tasks, one social and one non-social, within each trial. The social task involved referential communication, requiring participants to use social cues to guide their decisions, which sometimes required taking a different perspective. The non-social task manipulated cognitive load by requiring participants to remember non-social information in the form of one two-digit number (low load) or three two-digit numbers (high load) presented before each social task stimulus. Participants showed performance deficits when under high cognitive load and when the social task involved taking a different perspective, and individual differences in both trait perspective taking and working memory capacity predicted performance. Overall, adolescents were less adept at multitasking than adults when under high cognitive load. These results suggest that multitasking during social interactions incurs performance deficits, and that adolescents, compared to adults, are more sensitive to the effects of cognitive load while multitasking. Data: These files include our dataset, as well as the scripts used to analyze the data. You will need to download R (http://www.r-project.org/) to use these files. Data are from 37 adolescents (11-17 years) and 30 adults (22-30 years). Ages are not provided to preserve anonymity. Participants completed an adapted version of the “Director Task” (Dumontheil, Hillebrandt, Apperly, & Blakemore, 2012) with an embedded working memory (WM) Task component. Afterwards, participants completed a verbal reverse digit-span task as a measure of WM capacity and the Interpersonal Reactivity Index questionnaire to assess individual differences in trait perspective taking (Davis, 1980). We have also included information about the errors made by individuals, as well as the Director Task stimuli. Data Analysis: We used mixed effects modelling to determine what factors best predicted multitasking performance. Accuracy was determined on a trial-by-trial basis, where a trial was considered accurate only if participants correctly performed both the Director Task and embedded WM Task. As our main interest was performance during social interactions, and not recall of non-social information, we analysed Director Task RT (correct trials only). We used the lme4 package in R (Bates, Maechler, & Bolker, 2013) to perform a linear mixed effects analysis on the relationship between our factors of interest and multitasking performance (accuracy and RT). Our factors of interest included three from the task: cognitive load (low vs. high), condition (DA vs. DP), perspective (same vs. different), and two individual traits: WM capacity and trait perspective taking (TPT). As we hypothesised an interaction between WM capacity and TPT would relate to our task, we included a combined measure of these two individual traits by calculating and summing the ratios of TPT and WM capacity (Combined Traits). We determined which factors best predicted performance for our measures of interest by testing global models including our factors of interest as fixed effects. Each model included a random intercept for each participant. Because of computational limitations, we performed a two-step procedure that involved five global models. First, all possible combinations of the variables within each of the five global models were tested using an automated model selection procedure (MuMIn1.9.0; Barton, 2013). Models were ranked using Second-order Akaike Information Criterion (AICc; Burnham & Anderson, 2002). Second, the best fitting model for each of the five global models were compared and ranked using AIC and likelihood ratio tests. The object_positions.xls file shows the object positions (slot number) for each of the 48 Director Task stimuli. For example, in a trial using picture number 1, the participant would need to select the object in slot number 3 during 3-object trials in which the Director shared the same perspective as the participant. However, the participant would need to select the object in slot number 11 during 3-object trials in which the Director had a different perspective than the participant (i.e., the Director is behind the shelf array). Finally, on 1-object trials, the participant would need to select the object in show number 6. You can use this file to do error analyses with the provided dataset to see what kind of errors participants made in the Director Task.

项目摘要：多任务处理是青少年与成年人日常生活的组成部分。我们常在社交互动中同时处理多项任务，例如在与他人交流的同时追踪非社交类信息。本研究旨在探究追踪非社交信息如何影响青少年与成年人的社交互动能力。

招募了11-17岁的青少年与22-30岁的成年人作为被试，每名被试需在每次试次中完成两项任务：一项社交任务与一项非社交任务。其中社交任务为指称沟通任务，要求被试利用社交线索辅助决策，有时还需切换视角。非社交任务通过记忆负载操纵认知负荷：在每次社交任务刺激呈现前，向被试呈现1个两位数（低负载）或3个两位数（高负载）的非社交信息，要求被试完成记忆。

结果显示，当处于高认知负载条件下，或是社交任务需要切换视角时，被试的任务表现均出现缺损；同时，特质视角采择能力与工作记忆容量的个体差异均可预测任务表现。整体而言，在高认知负载条件下，青少年的多任务处理能力逊于成年人。本研究结果表明，社交互动中的多任务处理会导致任务表现缺损，且相较于成年人，青少年在多任务处理时对认知负载的影响更为敏感。

数据：本数据集包含本次研究的原始数据与分析脚本，使用前需下载R语言软件（http://www.r-project.org/）。本次研究共纳入37名青少年（11-17岁）与30名成年人（22-30岁），未公开具体年龄以保护被试匿名性。被试需完成改编版的指导者任务（Director Task），并嵌入了工作记忆（WM）任务组件（改编自Dumontheil、Hillebrandt、Apperly与Blakemore，2012年的研究）。随后，被试需完成言语反向数字广度任务以评估工作记忆容量，并填写人际反应指数量表（Interpersonal Reactivity Index）以测量特质视角采择能力的个体差异（Davis，1980年）。本数据集同时包含被试的错误作答记录与指导者任务的刺激材料信息。

数据分析：本研究采用混合效应建模来确定哪些因素可最优预测多任务处理表现。试次正确率的判定标准为：仅当被试同时正确完成指导者任务与嵌入的工作记忆任务时，该试次才被计为正确。由于本研究的核心关注点为社交互动中的任务表现，而非非社交信息的记忆成绩，因此仅分析正确试次下的指导者任务反应时（RT）。我们使用R语言的lme4包（Bates、Maechler与Bolker，2013年）对核心自变量与多任务表现（正确率与反应时）之间的关系进行线性混合效应分析。

核心自变量包括三类任务相关因素：认知负载（低vs.高）、任务条件（DA vs. DP）、视角类型（相同vs.不同），以及两类个体特质因素：工作记忆容量与特质视角采择能力（TPT）。基于“工作记忆容量与特质视角采择能力的交互作用会影响任务表现”的研究假设，我们通过计算特质视角采择能力与工作记忆容量的比值并求和，生成了二者的联合特质指标（Combined Traits）。

为确定最优预测任务表现的因素，我们构建了包含上述核心自变量作为固定效应的全局模型，并采用自动化模型选择流程（MuMIn 1.9.0；Barton，2013年）对五个全局模型内的所有变量组合进行测试。模型排序依据为二阶赤池信息准则（AICc；Burnham与Anderson，2002年）。随后，我们通过AIC值与似然比检验，对五个全局模型中的最优拟合模型进行比较与排序。

object_positions.xls文件记录了48个指导者任务刺激的物体位置（插槽编号）。例如，在使用图片1的试次中，当指导者与被试视角一致的3物体试次中，被试需选择插槽3中的物体；而当指导者与被试视角不同（即指导者位于货架阵列后方）的3物体试次中，被试需选择插槽11中的物体。在单物体试次中，被试则需选择编号为6的展示物体。您可利用该文件结合本数据集开展错误分析，以探究被试在指导者任务中出现的错误类型。