Social Dual Task Developmental Dataset and Analyses

Project abstract: Many situations involve processing social and non-social information simultaneously. However, is not known how performance is affected in such situations. Here, we examined how our ability to process social information is affected by the need to keep track of non-social information. Both adolescent and adult participants were instructed to carry out two tasks within each trial. The social task involved referential communication – requiring participants to use social cues to guide their decisions. At the same time, cognitive load was manipulated by requiring participants to remember non-social information in the form of either one or three two-digit numbers visually presented before each social task stimulus. 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 35 adolescents (11-17 years, mean age=14.5±1.7) and 29 adults (22-30 years, mean age=25.1±2.3). 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 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 accuracy and RT for both tasks. As we hypothesised main effects and interactions between many of our variables, we determined which factors best predicted performance for our measures of interest by creating global models including cognitive load, condition, perspective, WM Capacity, TPT, as well as a combined metric of TPT and WM capacity (Combined Traits), as fixed effects. Random intercepts for each participant were included in all models. We compared all possible combinations of the variables within our global model using an automated model selection procedure (MuMIn1.10.0; Barton, 2013). Models were ranked using Second-order Akaike Information Criterion (AICc; Burnham & Anderson, 2002). We first tested for group effects by including all participants and including group (adolescents or adults) as a fixed effect. If there was a group difference on a performance measure we then checked to see if the best fitting model differed between groups. 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：诸多情境需同时加工社会信息与非社会信息，但目前尚不明确此类情境下任务表现会受到何种影响。本研究探讨了个体加工社会信息的能力会如何受到追踪非社会信息的需求的影响。研究招募青少年与成年被试，要求其在每轮试次中同时完成两项任务。其中社会任务为指称沟通任务，需被试利用社会线索指导决策；与此同时，通过要求被试记忆非社会信息操控认知负荷：在每个社会任务刺激呈现前，视觉呈现1个或3个两位数数字，以此设置不同认知负荷水平。

数据：本数据集包含本次研究的全部数据与数据分析所用脚本，使用这些文件需先下载R语言环境（http://www.r-project.org/）。本数据来自35名青少年（年龄11-17岁，平均年龄=14.5±1.7）与29名成年人（年龄22-30岁，平均年龄=25.1±2.3），未提供具体年龄信息以保护被试匿名性。被试需完成改编版的“主管任务（Director Task）”（Dumontheil、Hillebrandt、Apperly & Blakemore, 2012），并内嵌了工作记忆（working memory, WM）任务组件。后续，被试还完成了言语反向数字广度任务以评估工作记忆容量，以及人际反应指针量表（Interpersonal Reactivity Index）以测量特质视角采择的个体差异（Davis, 1980）。本数据集还包含被试所犯错误的相关信息，以及主管任务的刺激材料信息。

数据分析：本研究使用R语言的lme4包（Bates、Maechler & Bolker, 2013），针对两项任务的准确率与反应时，就核心自变量与二者的关系开展线性混合效应分析。基于对多个变量主效应与交互效应的研究假设，我们构建了包含认知负荷、任务条件、视角采择、工作记忆容量、特质视角采择得分（trait perspective taking, TPT），以及特质视角采择与工作记忆容量的联合指标（Combined Traits）作为固定效应的全局模型，以确定哪些因素可最佳预测核心表现指标。所有模型均纳入了每个被试的随机截距项。我们通过自动化模型选择程序（MuMIn 1.10.0; Barton, 2013），对全局模型内所有变量的可能组合进行了比较。模型排序采用二阶赤池信息准则（Second-order Akaike Information Criterion, AICc; Burnham & Anderson, 2002）。我们首先纳入所有被试，并将组别（青少年或成年人）作为固定效应，以检验组别效应。若某一表现指标存在组间差异，则进一步检验最优拟合模型在两组间是否存在差异。

object_positions.xls文件展示了48个主管任务刺激各自对应的物体位置（槽位编号）。例如，在使用图片1的试次中，当主管与被试视角一致的3物体试次中，被试需选择槽位3中的物体；而当主管与被试视角不同（即主管位于货架阵列后方）的3物体试次中，被试需选择槽位11中的物体。最后，在1物体试次中，被试需选择编号为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，以明确被试在主管任务中所犯错误的类型。