How to Understand Belief Drift? Externalization of Variables Considering Different Background Knowledge

<table class="fixed-width table-group" id="tab4"><tr><td><table class="table"><colgroup><col style="width:7.83em"/><col style="width:6.21em"/><col style="width:6.39em"/></colgroup><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_left">Commonality</td><td class="align_center">Terms</td><td class="align_center">VLs</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left">Mean</td><td class="align_center">0.116 (0.037)</td><td class="align_center">0.055 (0.041)</td></tr><tr><td class="align_left">Median</td><td class="align_center">0.112</td><td class="align_center">0.045</td></tr><tr><td class="align_left">Maximum value</td><td class="align_center">0.208</td><td class="align_center">0.200</td></tr><tr><td class="align_left">Minimum value</td><td class="align_center">0.037</td><td class="align_center">0.000</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Commonalities of terms and VLs using test data at random (parentheses represent the standard deviation).</div>

Advances in Human-Computer Interaction

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Table 4

Table 4: How to Understand Belief Drift? Externalization of Variables Considering Different Background Knowledge 