May 4, 2022 Dataset Open Access

Bruns, Patrick

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  <identifier identifierType="DOI">10.25592/uhhfdm.10183</identifier>
  <creators>
    <creator>
      <creatorName>Bruns, Patrick</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-2977-8874</nameIdentifier>
      <affiliation>Universität Hamburg</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Quantifying accuracy and precision from continuous response data in studies of spatial perception and crossmodal recalibration</title>
  </titles>
  <publisher>Universität Hamburg</publisher>
  <publicationYear>2022</publicationYear>
  <dates>
    <date dateType="Issued">2022-05-04</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://www.fdr.uni-hamburg.de/record/10183</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="References">10.25592/uhhfdm.948</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="References">https://osf.io/xcskn/</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsPartOf">10.25592/uhhfdm.10182</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This dataset contains data and code&amp;nbsp;associated with the study &amp;quot;Quantifying accuracy and precision from continuous response data in studies of spatial perception and crossmodal recalibration&amp;quot; by Patrick Bruns, Caroline Thun, and Brigitte R&amp;ouml;der.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;example_code.R&lt;/strong&gt; contains analysis code that can be used to&amp;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&amp;nbsp;inputs&amp;nbsp;a numeric vector containing the stimulus location (true value) in each trial and&amp;nbsp;a numeric vector containing the corresponding localization response (perceived value) in each trial.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;example_data.csv&lt;/strong&gt; contains the data used in the working example of the analysis code.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;localization.csv&lt;/strong&gt; 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).&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;recalibration.csv&lt;/strong&gt; 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&amp;deg; to the right of the sound source, were available. The file contains baseline performance (pre) and changes in performance after audiovisual exposure&amp;nbsp;relative to baseline (delta) in each of the localization performance metrics.&lt;/p&gt;

&lt;p&gt;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:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;bias:&lt;/strong&gt; 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&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;absolute constant error (aCE):&lt;/strong&gt; absolute value of bias (or CE), indicates the amount of bias irrespective of direction&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;mean absolute contant error (maCE):&lt;/strong&gt; mean of the aCE per target location, reflects over- or underestimation of peripheral target locations&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;variable error (VE): &lt;/strong&gt;mean of the standard deviations (&lt;em&gt;SD&lt;/em&gt;) of the single-trial localization errors at each target location&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;pooled variable error (pVE):&lt;/strong&gt;&amp;nbsp;&lt;em&gt;SD&lt;/em&gt; of the single-trial localization errors pooled across trials from all target locations&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;absolute error (AE):&lt;/strong&gt; mean of the absolute values of the single-trial localization errors, sensitive to both bias and variability of the localization responses&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;slope: &lt;/strong&gt;slope of the regression model function, indicates an overestimation (values &amp;gt; 1) or underestimation (values &amp;lt; 1) of peripheral target locations&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;em&gt;R&lt;/em&gt;&lt;sup&gt;2&lt;/sup&gt;: &lt;/strong&gt;coefficient of determination of the regression model, indicates the goodness of the fit of the localization&amp;nbsp;responses to the regression line&lt;/p&gt;</description>
  </descriptions>
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