May 4, 2022 Dataset Open Access

Bruns, Patrick

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{"@context":"https://schema.org/","@id":"http://doi.org/10.25592/uhhfdm.10183","@type":"Dataset","citation":[{"@id":"http://doi.org/10.25592/uhhfdm.948","@type":"CreativeWork"},{"@id":"https://osf.io/xcskn/","@type":"CreativeWork"}],"creator":[{"@id":"https://orcid.org/0000-0002-2977-8874","@type":"Person","affiliation":"Universit\u00e4t Hamburg","name":"Bruns, Patrick"}],"datePublished":"2022-05-04","description":"<p>This dataset contains data and code&nbsp;associated with the study &quot;Quantifying accuracy and precision from continuous response data in studies of spatial perception and crossmodal recalibration&quot; by Patrick Bruns, Caroline Thun, and Brigitte R&ouml;der.</p>\n\n<p><strong>example_code.R</strong> contains analysis code that can be used to&nbsp;to calculate error-based and regression-based localization performance metrics from single-subject response data with a working example in R. It requires as&nbsp;inputs&nbsp;a numeric vector containing the stimulus location (true value) in each trial and&nbsp;a numeric vector containing the corresponding localization response (perceived value) in each trial.</p>\n\n<p><strong>example_data.csv</strong> contains the data used in the working example of the analysis code.</p>\n\n<p><strong>localization.csv</strong> contains extracted localization performance metrics from 188 subjects which were analyzed in the study to assess the agreement between error-based and regression-based measures of accuracy and precision. The subjects had all naively performed an azimuthal sound localization task (see related identifiers for the underlying raw data).</p>\n\n<p><strong>recalibration.csv</strong> contains extracted localization performance metrics from a subsample of 57 subjects in whom data from a second sound localization test, performed after exposure to audiovisual stimuli in which the visual stimulus was consistently presented 13.5&deg; to the right of the sound source, were available. The file contains baseline performance (pre) and changes in performance after audiovisual exposure&nbsp;relative to baseline (delta) in each of the localization performance metrics.</p>\n\n<p>Localization performance metrics were either derived from the single-trial localization errors (error-based approach) or from a linear regression of localization responses on the actual target locations (regression-based approach).The following localization performance metrics were included in the study:</p>\n\n<p><strong>bias:</strong> overall bias of localization responses to the left (negative values) or to the right (positive values), equivalent to constant error (CE) in error-based approaches and intercept in regression-based approaches</p>\n\n<p><strong>absolute constant error (aCE):</strong> absolute value of bias (or CE), indicates the amount of bias irrespective of direction</p>\n\n<p><strong>mean absolute contant error (maCE):</strong> mean of the aCE per target location, reflects over- or underestimation of peripheral target locations</p>\n\n<p><strong>variable error (VE): </strong>mean of the standard deviations (<em>SD</em>) of the single-trial localization errors at each target location</p>\n\n<p><strong>pooled variable error (pVE):</strong>&nbsp;<em>SD</em> of the single-trial localization errors pooled across trials from all target locations</p>\n\n<p><strong>absolute error (AE):</strong> mean of the absolute values of the single-trial localization errors, sensitive to both bias and variability of the localization responses</p>\n\n<p><strong>slope: </strong>slope of the regression model function, indicates an overestimation (values &gt; 1) or underestimation (values &lt; 1) of peripheral target locations</p>\n\n<p><strong><em>R</em><sup>2</sup>: </strong>coefficient of determination of the regression model, indicates the goodness of the fit of the localization&nbsp;responses to the regression line</p>","distribution":[{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/4c7c1b80-1741-4aa8-99a1-9173f43a3d99/example_code.R","encodingFormat":"r"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/4c7c1b80-1741-4aa8-99a1-9173f43a3d99/example_data.csv","encodingFormat":"csv"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/4c7c1b80-1741-4aa8-99a1-9173f43a3d99/localization.csv","encodingFormat":"csv"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/4c7c1b80-1741-4aa8-99a1-9173f43a3d99/recalibration.csv","encodingFormat":"csv"}],"identifier":"http://doi.org/10.25592/uhhfdm.10183","inLanguage":{"@type":"Language","alternateName":"eng","name":"English"},"license":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Quantifying accuracy and precision from continuous response data in studies of spatial perception and crossmodal recalibration","url":"https://www.fdr.uni-hamburg.de/record/10183"}