DOI: 10.34894/jv4knu
title: Neural Mechanisms Underlying Diversification of Choice

description: &lt;div&gt;This repository contains all relevant data for the following paper:&lt;br&gt;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&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;u&gt;Files:&lt;/u&gt;&lt;/div&gt;&lt;div&gt;Data_1-3.zip - the neural and behavioural data from all collected participants&lt;br&gt;Task.zip - the experimental task used in the study, coded in Presentation&lt;br&gt;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.&lt;/div&gt;&lt;div&gt;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.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;u&gt;Processing of fMRI data:&lt;/u&gt;&lt;/div&gt;&lt;div&gt;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. &lt;/div&gt;&lt;div&gt;&lt;u&gt;&lt;br&gt;Participants:&lt;/u&gt; &lt;/div&gt;&lt;div&gt;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 (&gt; 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.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;u&gt;Stimuli: &lt;/u&gt;&lt;br&gt;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. &lt;br&gt;&lt;/div&gt;
description: This entry is a seventeen thousand, two hundred and ninety-six-file data package totaling 3.9 GB, containing files in .txt, .pcl, .nii, .log, .sce and .exp formats.If you use this dataset, please cite: Boksem, Maarten; Couwenberg, Linda; Smidts, Ale (2020). Neural Mechanisms Underlying Diversification of Choice. Erasmus University Rotterdam (EUR). Dataset. https://doi.org/10.25397/eur.12229211