<!DOCTYPE article PUBLIC '-//NLM//DTD Journal Publishing DTD v2.1 20050630//EN' 'http://uploads.ingentaconnect.com/docs/dtd/ingenta-journalpublishing.dtd'>
<article article-type="research-article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="aggregator">72010604</journal-id>
      <journal-title>Electronic Imaging</journal-title>
      <issn pub-type="ppub">2470-1173</issn><issn pub-type="epub"></issn>
      <publisher>
        <publisher-name>Society for Imaging Science and Technology</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.2352/ISSN.2470-1173.2018.14.HVEI-516</article-id>
      <article-id pub-id-type="sici">2470-1173(20180128)2018:14L.1;1-</article-id>
      <article-id pub-id-type="publisher-id">s15.phd</article-id>
      <article-id pub-id-type="other">/ist/ei/2018/00002018/00000014/art00015</article-id>
      <article-categories>
        <subj-group>
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Quantifying how humans trade off color and material in object identification</article-title>
      </title-group>
      <contrib-group>
        <contrib>
          <name>
            <surname>Radonjić</surname>
            <given-names>Ana</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Cottaris</surname>
            <given-names>Nicolas P.</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Brainard</surname>
            <given-names>David H.</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date>
        <day>28</day>
        <month>01</month>
        <year>2018</year>
      </pub-date>
      <volume>2018</volume>
      <issue>14</issue>
      <fpage>1</fpage>
      <lpage>6</lpage>
      <permissions>
        <copyright-year>2018</copyright-year>
      </permissions>
      <abstract>
        <p>How do different object properties combine for the purposes of object identification? We developed a paradigm that allows us measure the degree to which human observers rely on one object property (e.g., color) vs. another (e.g., material) when they make forced-choice similarity judgments.
 On each trial of our experiment, observers viewed a target object paired with two test objects: a material match, that differed from the target only in color (along a green-blue axis) and a color match, that differed from the target only in material (along a glossy-matte axis). Across trials,
 the target was paired with different combinations of material-match and color-match tests and observers selected the test that appeared more similar to the target. To analyze observer responses, we developed a model (a two-dimensional generalization of the maximum-likelihood difference scaling
 method) that allows us to recover (1) the color-material weight, reflecting the relative importance of color vs. material in object identification and (2) the underlying positions of the material-match and color-match tests in a perceptual color-material space. Our results reveal large individual
 differences in the relative weighting of color vs. material.</p>
      </abstract>
      <kwd-group>
        <kwd>VISUAL PERCEPTION</kwd>
        <kwd>OBJECT IDENTIFICATION</kwd>
        <kwd>COLOR PERCEPTION</kwd>
        <kwd>MATERIAL PERCEPTION</kwd>
        <kwd>MAXIMUM LIKELIHOOD DIFFERENCE SCALING</kwd>
        <kwd>LIGHTING</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
