authors: - firstname: "Stefan" lastname: "Appelhoff" affiliation: "Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany" id: "ORCID:0000-0001-8002-0877" - firstname: "Ralph" lastname: "Hertwig" affiliation: "Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany" id: "ORCID:0000-0002-9908-9556" - firstname: "Bernhard" lastname: "Spitzer" affiliation: "Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany" id: "ORCID:0000-0001-9752-932X" title: "The mpib_ecomp_sourcedata dataset" description: | When judging the average value of sample stimuli (e.g., numbers) people tend to either over- or underweight extreme sample values, depending on task context. In a context of overweighting, recent work has shown that extreme sample values were overly represented also in neural signals, in terms of an anti-compressed geometry of number samples in multivariate electroencephalography (EEG) patterns. Here, we asked whether neural representational geometries may also reflect underweighting of extreme values (i.e., compression) which has been observed behaviorally in a great variety of tasks. keywords: - cognitive neuroscience - decision-making - numerical cognition - sequential sampling - value distortions - compression - anti-compression - EEG - electroencephalography - eyetracking - representational similarity analysis - RSA - multivariate pattern analysis - MVPA - computational modeling # "reftype": IsSupplementTo, IsDescribedBy, or IsReferencedBy # "id": doi:, arxiv:, or pmid: # "citation": any string references: - reftype: "IsSupplementTo" id: doi:10.1101/2022.03.31.486560 citation: "Appelhoff, S., Hertwig, R. & Spitzer, B. EEG-representational geometries and psychometric distortions in approximate numerical judgment. (2022) doi:10.1101/2022.03.31.486560" - reftype: "IsDescribedBy" id: doi:10.5281/zenodo.6411313 citation: "Appelhoff, Stefan. (2022). eComp Experiment Code (2022.1.0). Zenodo. https://doi.org/10.5281/zenodo.6411319" - reftype: "IsReferencedBy" id: doi:10.5281/zenodo.6411287 citation: "Appelhoff, Stefan. (2022). eComp Analysis Code (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6411288" - reftype: "IsReferencedBy" citation: "https://gin.g-node.org/sappelhoff/mpib_ecomp_dataset" - reftype: "IsReferencedBy" citation: "https://gin.g-node.org/sappelhoff/mpib_ecomp_derivatives" license: name: "Open Data Commons Public Domain Dedication and License (PDDL) v1.0" url: "https://opendatacommons.org/licenses/pddl/1-0/" funding: - "Max Planck Institute for Human Development" resourcetype: Dataset templateversion: 1.2