---
_id: '67620'
abstract:
- lang: eng
  text: Visual attention selects task-relevant elements from the visual environment,
    linking perception and action. Because of this intermediary role, it provides
    a basis for comparing performance in different tasks. In this work, we study whether
    model-based estimates of visual attention resources – concretely, processing speed
    derived from a formal theory of visual attention – are comparable across tasks.
    To our best knowledge, such a comparison on the process level has not been conducted
    yet. Thirty participants completed two tasks in a within-subject design. For each
    task, attention resources are estimated using the same theoretical framework,
    allowing a direct task-to-task comparison. The results show that model-based estimates
    of attentional processing speed differ between tasks. At the same time, the estimates
    show different patterns of variability, which suggests that systematic adaptations
    of one or both models could, in principle, align the parameters across tasks.
    These findings indicate that current model estimates are task-dependent, but not
    necessarily incompatible. Identifying adaptation points would move the study of
    attention beyond task-specific phenomenology toward a more general characterization
    of attentional mechanisms. Although this paper does not provide a solution to
    this problem, it formulates the challenge and outlines concrete directions for
    future research.
article_number: '29499'
author:
- first_name: Kai
  full_name: Biermeier, Kai
  id: '55908'
  last_name: Biermeier
  orcid: 0000-0002-2879-2359
- first_name: Ingrid
  full_name: Scharlau, Ingrid
  id: '451'
  last_name: Scharlau
  orcid: 0000-0003-2364-9489
citation:
  ama: Biermeier K, Scharlau I. Comparing attention estimates across two tasks through
    formalization. <i>Scientific Reports</i>. 2026;16(1). doi:<a href="https://doi.org/10.1038/s41598-026-71598-9">10.1038/s41598-026-71598-9</a>
  apa: Biermeier, K., &#38; Scharlau, I. (2026). Comparing attention estimates across
    two tasks through formalization. <i>Scientific Reports</i>, <i>16</i>(1), Article
    29499. <a href="https://doi.org/10.1038/s41598-026-71598-9">https://doi.org/10.1038/s41598-026-71598-9</a>
  bibtex: '@article{Biermeier_Scharlau_2026, title={Comparing attention estimates
    across two tasks through formalization}, volume={16}, DOI={<a href="https://doi.org/10.1038/s41598-026-71598-9">10.1038/s41598-026-71598-9</a>},
    number={129499}, journal={Scientific Reports}, publisher={Springer Nature}, author={Biermeier,
    Kai and Scharlau, Ingrid}, year={2026} }'
  chicago: Biermeier, Kai, and Ingrid Scharlau. “Comparing Attention Estimates across
    Two Tasks through Formalization.” <i>Scientific Reports</i> 16, no. 1 (2026).
    <a href="https://doi.org/10.1038/s41598-026-71598-9">https://doi.org/10.1038/s41598-026-71598-9</a>.
  ieee: 'K. Biermeier and I. Scharlau, “Comparing attention estimates across two tasks
    through formalization,” <i>Scientific Reports</i>, vol. 16, no. 1, Art. no. 29499,
    2026, doi: <a href="https://doi.org/10.1038/s41598-026-71598-9">10.1038/s41598-026-71598-9</a>.'
  mla: Biermeier, Kai, and Ingrid Scharlau. “Comparing Attention Estimates across
    Two Tasks through Formalization.” <i>Scientific Reports</i>, vol. 16, no. 1, 29499,
    Springer Nature, 2026, doi:<a href="https://doi.org/10.1038/s41598-026-71598-9">10.1038/s41598-026-71598-9</a>.
  short: K. Biermeier, I. Scharlau, Scientific Reports 16 (2026).
date_created: 2026-10-22T13:35:43Z
date_updated: 2026-10-22T13:42:05Z
department:
- _id: '424'
doi: 10.1038/s41598-026-71598-9
intvolume: '        16'
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://www.nature.com/articles/s41598-026-71598-9
oa: '1'
publication: Scientific Reports
publication_identifier:
  issn:
  - 2045-2322
publication_status: published
publisher: Springer Nature
status: public
title: Comparing attention estimates across two tasks through formalization
type: journal_article
user_id: '55908'
volume: 16
year: '2026'
...