Neural Mechanisms Underlying Diversification of Choice
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This repository contains all relevant data for the following paper: Couwenberg, L.E., Boksem, M.A.S., Sanfey, A.G., Smidts, A. (2020) Neural mechanisms underlying diversification of choice. Frontiers in Neuroscience, DOI: 10.3389/fnins.2020.00502 Files:Data_1-3.zip - the neural and behavioural data from all collected participants Task.zip - the experimental task used in the study, coded in Presentation Masterfile.xlsx – data file in long format, for all participants, for all trials, including: onsets of all screens in the task, responses made by the participant, stimulus type, current state of the ‘basket’, activity from the selected ROIs.Data_1-3.zip contain for each participant their preprocessed fMRI data, a structural image, the output from the experimental task, the onsets of all events (in the Masterfile), and the activity in the ROIs. Processing of fMRI data:Analyses on the brain data were performed using SPM12 (Statistical Parametric Mapping; Wellcome Department, London, UK). Prior to preprocessing, we combined and realigned the five read-outs acquired via the multi-echo sequence by using standard procedures described by Poser et al. (2006). Preprocessing consisted of realignment, slice-time correction to the middle slice, segmentation of the functional and anatomical image, co-registration of the functional images to the anatomical images, and normalization to the Montreal Neurological Institute (MNI) template using the segmentation parameters. Functional images were then smoothed with a Gaussian kernel of 8 mm full-width at half maximum (FWHM). The first 30 volumes, acquired prior to task initiation, were used to estimate the weighted echo time per voxel for optimal echo combination (Poser et al. 2006) including allowing T1 equilibration effects, and discarded from the analysis. The task consisted of a single run of approximately 45 minutes; a standard high-pass filter (cut-off 128 s) was used in the analyses to account for possible slow-frequency drifts. Participants: Forty-five participants completed the study. All provided written informed consent and were financially compensated via either a flat fee (30 Euro) or study credits for completion of the task. Exclusion criteria included self-reported claustrophobia, neurological or cardiovascular diseases, psychiatric disorders, regular use of marijuana, use of psychotropic drugs, metal parts in the body or any dietary restrictions (as many stimuli in the task were food items). Four participants were excluded due to excessive movement (> 3 mm) during fMRI data acquisition. Data is therefore reported from 41 participants (13 men and 28 women, M = 22.73 years, SD = 3.28, range = 18 to 34 years, all right-handed). Note that the dataset contains all (45) scanned participants. However, the Masterfile contains only the participants included in the analyses. Stimuli: We selected 40 product categories, each incorporating five different products, to present as choice sets in the task. The images of all these products are included in the Task.zip file. The majority of the product categories (i.e., 26 out of 40) consisted of food items (e.g., noodles, soup, or cereal). The remaining categories consisted of a variety of non-food items, such as socks, mugs, or hand soap. Within each category, the products were of the same brand and were priced similarly, but differed in terms of flavor, scent or color (e.g., five different flavors of instant noodles). Participants’ liking scores for each of the 200 products was assessed on an 11-point slider scale with decimal accuracy (0 = ‘I don’t like this product at all’, 10 = ‘I really like this product’) in an online survey before the scanning session. In this survey, the products were presented per category, such that the five products per category were rated on the same page, ordered randomly. These Liking scores are included in the Masterfile. Based on these liking ratings, we ranked the products within each category for each participant individually. We ranked equally liked items (i.e., up to the second decimal) in random order. These rankings are also included in the Masterfile. In order to select the most desirable set of stimuli for each participant, we excluded five product categories in which the most liked product had a liking rating lower than 4 on the 11-point scale. In case we were not able to exclude five product categories using this rule, we excluded categories with the greatest similarity in liking ratings. We used these excluded product categories in the filler trials. The remaining 35 product categories were presented in the trials of interest.
本仓库收录下述论文的全部相关研究数据：Couwenberg, L.E.、Boksem, M.A.S.、Sanfey, A.G.、Smidts, A.（2020）《决策多样化的神经机制》，刊载于《神经科学前沿》(Frontiers in Neuroscience)，DOI: 10.3389/fnins.2020.00502。 相关文件说明如下： 1. Data_1-3.zip：涵盖所有招募被试的神经影像与行为学实测数据； 2. Task.zip：本研究采用的实验任务代码，基于Presentation软件开发完成； 3. Masterfile.xlsx：面向全体被试所有试次的长格式数据文件，包含任务中各屏幕的呈现时序、被试作答记录、刺激类型、任务篮当前状态、选定感兴趣脑区（ROIs, Regions of Interest）的神经活动等信息。 Data_1-3.zip内包含每名被试的预处理功能磁共振成像（fMRI, functional Magnetic Resonance Imaging）数据、结构像、实验任务输出结果、所有事件的时序信息（详见Masterfile）以及ROIs的神经活动数据。 功能磁共振数据处理流程：脑影像数据分析采用SPM12（统计参数映射，Statistical Parametric Mapping；英国伦敦威康研究院）完成。预处理前，我们参照Poser等人（2006）提出的标准流程，对多回波序列采集的5次读出数据进行合并与重对齐。预处理流程涵盖：重对齐、以中间层面为基准的切片时间校正、功能像与结构像的分割、功能像到结构像的配准，以及基于分割参数将图像标准化至蒙特利尔神经研究所（MNI, Montreal Neurological Institute）模板。随后使用半高全宽（FWHM, full-width at half maximum）为8mm的高斯核对功能像进行平滑处理。任务启动前采集的前30个体积数据，用于估算每个体素的加权回波时间以实现最优回波组合（Poser et al. 2006），同时校正T1弛豫平衡效应，后续将该部分数据从正式分析中剔除。本研究仅设置一次时长约45分钟的扫描任务，分析中采用标准高通滤波器（截止频率128s）以消除低频信号漂移。 被试招募与筛选：共计45名被试完成本研究。所有被试均签署书面知情同意书，并可获得固定报酬（30欧元）或课程学分作为参与补偿。本研究的排除标准包括：自我报告的幽闭恐惧症、神经或心血管疾病、精神障碍、定期吸食大麻、使用精神类药物、体内存在金属植入物，以及任何饮食限制（因任务中多数刺激为食品类）。有4名被试因fMRI扫描期间头部运动幅度过大（>3mm）被排除。最终纳入统计分析的被试共41名（13名男性，28名女性，年龄均值M=22.73岁，标准差SD=3.28，年龄范围18~34岁，均为右利手）。需特别说明：本数据集包含全部45名完成扫描的被试数据，但Masterfile仅收录纳入最终分析的被试数据。 刺激材料设计：我们选取40个产品类别，每个类别包含5款不同产品，作为任务中的选择集。所有产品图片均收录于Task.zip文件中。其中26个类别为食品类（如面条、汤品或谷物食品），剩余14个类别为各类非食品类物品，如袜子、马克杯或洗手液。每个类别内的产品品牌一致、定价相近，但在风味、气味或颜色上存在差异（例如5种不同风味的速食面）。被试在扫描前通过在线问卷对200款产品分别进行喜好度评分，采用带小数精度的11点滑动量表（0表示“完全不喜欢该产品”，10表示“非常喜欢该产品”）。问卷中产品按类别呈现，每个类别的5款产品在同一页面随机排序展示。上述喜好度评分已收录于Masterfile中。基于这些评分，我们为每名被试单独对所属类别内的产品进行排序，对于评分相同的产品（精确到小数点后两位），将其随机排序。这些排序结果同样收录于Masterfile。为了为每名被试选取最合意的刺激集，我们排除了5个最受欢迎产品的喜好度评分低于4分（11点量表）的产品类别。若通过该规则无法排除5个类别，则进一步排除喜好度评分相似度最高的类别。被排除的产品类别将作为填充试次使用。剩余35个产品类别将用于正式实验试次。
