{"id":1245,"date":"2026-06-29T16:30:23","date_gmt":"2026-06-29T16:30:23","guid":{"rendered":"https:\/\/laikateam.com\/l\/estudios\/which-elements-of-the-serp-are-viewed-but-not-clicked-on-eye-tracking-study\/"},"modified":"2026-07-01T14:42:24","modified_gmt":"2026-07-01T14:42:24","slug":"ai-overviews-eye-tracking-attention-gap","status":"publish","type":"estudios","link":"https:\/\/laikateam.com\/l\/en\/studies\/ai-overviews-eye-tracking-attention-gap\/","title":{"rendered":"Which elements of the SERP are viewed but not clicked on [Eye-tracking study]"},"content":{"rendered":"

[et_pb_section fb_built=”1″ custom_padding_last_edited=”on|phone” _builder_version=”4.16″ _module_preset=”default” background_color=”#f7d857″ background_image=”https:\/\/laikateam.com\/l\/wp-content\/uploads\/2024\/01\/Google-update-report.jpg” background_blend=”overlay” custom_padding=”150px||||false|false” custom_padding_tablet=”20px||||false|false” custom_padding_phone=”20px||||false|false” global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ _module_preset=”default” custom_padding=”74px|||||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ _module_preset=”default” text_text_color=”#1e488d” header_font_size=”60px” text_orientation=”center” global_colors_info=”{}”]<\/p>\n

The mirror of attention: which elements of the SERP are viewed but not clicked on<\/h1>\n

[EYE TRACKING STUDY]<\/h1>\n

 <\/p>\n

The mirror of attention: Attention is a vanity metric. Just because a SERP element is looked at does not mean it works. In this eye-tracking study, we measured the attention-to-action gap for each element: the difference between how many people look at it and how many click on it. The results paint a clear picture of the elements that convert attention into clicks and the \u2018black holes\u2019 that absorb it without giving anything back. SERP elements are looked at but not clicked on <\/strong><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.16″ _module_preset=”default” background_color=”#1e488d” global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ _module_preset=”default” text_text_color=”#FFFFFF” header_text_color=”#FFFFFF” header_2_text_color=”#FFFFFF” header_2_font_size=”42px” header_3_text_color=”#FFFFFF” global_colors_info=”{}”]<\/p>\n

Table of Contents<\/h2>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_blurb title=”Methodology, Objectives, Limitations, and Data Used” url=”#metodologia” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#FFFFFF” icon_placement=”left” _builder_version=”4.16″ _module_preset=”default” header_level=”h3″ header_font=”||||on||||” header_text_color=”#FFFFFF” header_line_height=”1.2em” body_font=”|600|||||||” body_text_color=”#FFFFFF” animation=”off” link_option_url=”#general” border_radii_image=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Is Looking the same as clicking? The great disconnect between attention and action ” url=”#general” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#FFFFFF” icon_placement=”left” _builder_version=”4.27.4″ _module_preset=”default” header_level=”h3″ header_font=”||||on||||” header_text_color=”#FFFFFF” header_line_height=”1.2em” body_font=”|600|||||||” body_text_color=”#FFFFFF” animation=”off” link_option_url=”#general” border_radii_image=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Black holes of attention: you look at them but don't click on them” url=”#industrias” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#FFFFFF” icon_placement=”left” _builder_version=”4.27.4″ _module_preset=”default” header_level=”h3″ header_font=”||||on||||” header_text_color=”#FFFFFF” header_line_height=”1.2em” body_font=”|600|||||||” body_text_color=”#FFFFFF” animation=”off” link_option_url=”#industrias” border_radii_image=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Efficient converters: when viewing does lead to a click” url=”#actualizaciones” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#FFFFFF” icon_placement=”left” _builder_version=”4.16″ _module_preset=”default” header_level=”h3″ header_font=”||||on||||” header_text_color=”#FFFFFF” header_line_height=”1.2em” body_font=”|600|||||||” body_text_color=”#FFFFFF” animation=”off” link_option_url=”#actualizaciones” border_radii_image=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Inside AI Overviews: A conversion core and passive satellites” url=”#exito” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#FFFFFF” icon_placement=”left” _builder_version=”4.16″ _module_preset=”default” header_level=”h3″ header_font=”||||on||||” header_text_color=”#FFFFFF” header_line_height=”1.2em” body_font=”|600|||||||” body_text_color=”#FFFFFF” animation=”off” link_option_url=”#exito” border_radii_image=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”Authors and Next Steps” url=”#autores” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#FFFFFF” icon_placement=”left” _builder_version=”4.16″ _module_preset=”default” header_level=”h3″ header_font=”||||on||||” header_text_color=”#FFFFFF” header_line_height=”1.2em” body_font=”|600|||||||” body_text_color=”#FFFFFF” animation=”off” link_option_url=”#exito” border_radii_image=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_blurb][et_pb_blurb title=”How to cite the study” url=”#full-estudio” use_icon=”on” font_icon=”$||divi||400″ icon_color=”#FFFFFF” icon_placement=”left” _builder_version=”4.16″ _module_preset=”default” header_level=”h3″ header_font=”||||on||||” header_text_color=”#FFFFFF” header_line_height=”1.2em” body_font=”|600|||||||” body_text_color=”#FFFFFF” animation=”off” link_option_url=”#exito” border_radii_image=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”methodology” _builder_version=”4.16″ _module_preset=”default” background_color=”#f5f5f5″ global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ _module_preset=”default” text_font=”||||on||||” header_3_text_color=”#1e488d” link_option_url=”#conclusiones” global_colors_info=”{}”]<\/p>\n

TL;DR: Click here to read the conclusions<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row module_id=”#metodologia” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_divider divider_position=”bottom” divider_weight=”6px” _builder_version=”4.16″ _module_preset=”default” width=”12%” module_alignment=”left” custom_margin=”||||false|false” global_colors_info=”{}”][\/et_pb_divider][et_pb_text _builder_version=”4.16″ _module_preset=”default” header_2_font_size=”42px” global_colors_info=”{}”]<\/p>\n

Methodology<\/h2>\n

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Every element on a results page competes for two different user resources: their attention and their click. We usually measure only the latter through CTR or infer it from the position. <\/p>\n

Eye-tracking allows us to measure both separately and, above all, the difference between them: how much attention an element captures that never translates into action.<\/p>\n

We define the key metric for this study:<\/p>\n

Attention-Action Gap = Engagement (% of viewers) \u2212 CTR (% of clicks)<\/strong>.<\/p>\n

A high gap indicates passive engagement, which means that the element is viewed frequently but rarely clicked.<\/p>\n

A low gap indicates an effective element: almost all of the user’s attention results in a click.<\/p>\n

 <\/p>\n

Objectives<\/h2>\n

 <\/p>\n

    \n
  1. Quantify engagement and CTR separately for each category of SERP element.<\/li>\n
  2. Calculate the attention-action gap and rank the items from most to least “passive.”<\/li>\n
  3. Characterize the depth of processing (average fixation, first glance) of the elements with the largest gaps.<\/li>\n
  4. Within the AI Overview, identify which subcomponents perform conversions and which ones are merely decorative.<\/li>\n<\/ol>\n
      <\/ol>\n

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      Limitations<\/h2>\n

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        \n
      1. Limited sample size<\/strong>(22 participants): The percentages for categories with small sample sizes (Images N=6, PAS N=10) should be interpreted with caution.<\/li>\n
      2. Free navigation<\/strong>: Not all participants saw the same elements; all metrics are calculated only for those who had the element displayed (Shown = 1).<\/li>\n
      3. Clicks without prior fixation<\/strong>: Some buttons (e.g., \u201cShow More\u201d in the AIO) register reflexive clicks without prior fixation on their AOI, which can result in an apparent negative gap. We note this wherever it occurs. <\/li>\n
      4. Observational, non-experimental design<\/strong>: identifies associations, not causation.<\/li>\n
      5. Depth metrics<\/strong> are calculated only for participants who looked at the element.<\/li>\n<\/ol>\n

        The conclusions should be interpreted with these limitations in mind<\/strong>.<\/span><\/p>\n

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        Tools and Data<\/h3>\n

         <\/p>\n