---
_id: '61119'
abstract:
- lang: eng
  text: '<p>The present article offers an assessment of intra-individual variability
    in visualattention using the Theory of Visual Attention, which provides a formal
    framework forquantifying attentional components. We specifically investigated
    overall attentionalcapacity – that is, the available processing speed – and its
    distribution, the relativeattentional weight.By reanalyzing a large existing dataset
    from Tünnermann and Scharlau (2021),we found that across multiple testing days,
    participants either remained stable within a20 Hz margin or showed consistent
    improvements in capacity – in some cases triplingtheir initial capacity. The weights
    in response to salient stimuli were remarkablyconsistent.To determine whether
    increases in capacity reflect pure test-retest effects or arefacilitated by consolidation
    between days, and to quantify within-day variability, weconducted a second study
    in which participants completed five self-administeredsessions within a single
    day. Capacities remained within the same magnitude and didnot show a consistent
    directional trend. The relative weights exhibited comparativelylittle variation
    in most participants, akin to the previously analyzed dataset. Further,estimation
    uncertainty increased with higher capacity values.These results suggest that capacity
    may be subject to training effects, but thatsuch improvements appear to depend
    on longer breaks between sessions. This hasimportant implications for individualized
    assessment: A personal prior could beestimated from a single session to accelerate
    future estimations, as long as subsequentsessions occur on the same day. Participants
    with higher capacities may require tailoredexperimentation methods when small
    to medium effects are of interest, due to increaseduncertainty.</p>'
author:
- first_name: Ngoc Chi
  full_name: Banh, Ngoc Chi
  last_name: Banh
- first_name: Ingrid
  full_name: Scharlau, Ingrid
  id: '451'
  last_name: Scharlau
  orcid: 0000-0003-2364-9489
citation:
  ama: 'Banh NC, Scharlau I. Intra-individual variability in TVA attentional capacity
    and weight distribution: A reanalysis across days and an experiment within-day.
    Published online 2025.'
  apa: 'Banh, N. C., &#38; Scharlau, I. (2025). <i>Intra-individual variability in
    TVA attentional capacity and weight distribution: A reanalysis across days and
    an experiment within-day</i>. Center for Open Science.'
  bibtex: '@article{Banh_Scharlau_2025, title={Intra-individual variability in TVA
    attentional capacity and weight distribution: A reanalysis across days and an
    experiment within-day}, publisher={Center for Open Science}, author={Banh, Ngoc
    Chi and Scharlau, Ingrid}, year={2025} }'
  chicago: 'Banh, Ngoc Chi, and Ingrid Scharlau. “Intra-Individual Variability in
    TVA Attentional Capacity and Weight Distribution: A Reanalysis across Days and
    an Experiment within-Day.” Center for Open Science, 2025.'
  ieee: 'N. C. Banh and I. Scharlau, “Intra-individual variability in TVA attentional
    capacity and weight distribution: A reanalysis across days and an experiment within-day.”
    Center for Open Science, 2025.'
  mla: 'Banh, Ngoc Chi, and Ingrid Scharlau. <i>Intra-Individual Variability in TVA
    Attentional Capacity and Weight Distribution: A Reanalysis across Days and an
    Experiment within-Day</i>. Center for Open Science, 2025.'
  short: N.C. Banh, I. Scharlau, (2025).
date_created: 2025-09-03T11:30:48Z
date_updated: 2025-09-09T12:04:43Z
department:
- _id: '424'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://osf.io/preprints/psyarxiv/fzvph
oa: '1'
project:
- _id: '115'
  name: 'TRR 318; TP A05: Echtzeitmessung der Aufmerksamkeit im Mensch-Roboter-Erklärdialog'
publication_status: published
publisher: Center for Open Science
status: public
title: 'Intra-individual variability in TVA attentional capacity and weight distribution:
  A reanalysis across days and an experiment within-day'
type: preprint
user_id: '38219'
year: '2025'
...