{"ID":5675938,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-04T18:11:06.935443673Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01359","arxiv_id":"2607.01359","title":"Mitigating Confirmation Bias through Hand-Drawing Videos","abstract":"Understanding data visualizations is essential for informed decision-making, yet interpretation is often shaped and even distorted by prior beliefs. We investigate whether an embodied pedagogical approach, in which viewers observe the dynamic hand-drawing of a visualization, can mitigate confirmation bias and improve interpretation accuracy. We conducted a study comparing static bar charts to videos in which charts are constructed through hand-drawing, across contexts that either align with or challenge participants' prior beliefs. The results indicate that hand-drawn videos helped participants accurately interpret data, even when the data conflicted with their prior beliefs. This approach also reduced belief-consistent errors and increased belief-overriding responses. These findings suggest that exposing the construction process of a visualization supports more accurate reasoning and mitigates the influence of confirmation bias. Consequently, this work introduces a promising design space for bias-mitigating data interfaces.","short_abstract":"Understanding data visualizations is essential for informed decision-making, yet interpretation is often shaped and even distorted by prior beliefs. We investigate whether an embodied pedagogical approach, in which viewers observe the dynamic hand-drawing of a visualization, can mitigate confirmation bias and improve i...","url_abs":"https://arxiv.org/abs/2607.01359","url_pdf":"https://arxiv.org/pdf/2607.01359v1","authors":"[\"Chenyu Lin\",\"Cindy Xiong\",\"Icy Zhang\"]","published":"2026-07-01T18:19:06Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[]","has_code":false}