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MILO Mobile: An iPad App to Measure Search Performance in Multi-Target Sequences

This article introduces a mobile app version of the Multi-Item Localization (MILO) task. The MILO task was designed to explore the temporal context of search through a sequence and has proven useful in both basic and applied research settings. Here, we describe the basic features of the app and how...

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Detalles Bibliográficos
Autores principales: Thornton, Ian M., Horowitz, Todd S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7307404/
https://www.ncbi.nlm.nih.gov/pubmed/32612800
http://dx.doi.org/10.1177/2041669520932587
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author Thornton, Ian M.
Horowitz, Todd S.
author_facet Thornton, Ian M.
Horowitz, Todd S.
author_sort Thornton, Ian M.
collection PubMed
description This article introduces a mobile app version of the Multi-Item Localization (MILO) task. The MILO task was designed to explore the temporal context of search through a sequence and has proven useful in both basic and applied research settings. Here, we describe the basic features of the app and how it can be obtained, installed, and modified. We also provide example data files and present two new sets of empirical data to verify that previous findings concerning prospective planning and retrospective memory (i.e., inhibitory tagging) are reproducible with the app. We conclude by discussing ongoing studies and future modifications that illustrate the flexibility and potential of the MILO Mobile app.
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spelling pubmed-73074042020-06-30 MILO Mobile: An iPad App to Measure Search Performance in Multi-Target Sequences Thornton, Ian M. Horowitz, Todd S. Iperception Methods This article introduces a mobile app version of the Multi-Item Localization (MILO) task. The MILO task was designed to explore the temporal context of search through a sequence and has proven useful in both basic and applied research settings. Here, we describe the basic features of the app and how it can be obtained, installed, and modified. We also provide example data files and present two new sets of empirical data to verify that previous findings concerning prospective planning and retrospective memory (i.e., inhibitory tagging) are reproducible with the app. We conclude by discussing ongoing studies and future modifications that illustrate the flexibility and potential of the MILO Mobile app. SAGE Publications 2020-06-20 /pmc/articles/PMC7307404/ /pubmed/32612800 http://dx.doi.org/10.1177/2041669520932587 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/ Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Methods
Thornton, Ian M.
Horowitz, Todd S.
MILO Mobile: An iPad App to Measure Search Performance in Multi-Target Sequences
title MILO Mobile: An iPad App to Measure Search Performance in Multi-Target Sequences
title_full MILO Mobile: An iPad App to Measure Search Performance in Multi-Target Sequences
title_fullStr MILO Mobile: An iPad App to Measure Search Performance in Multi-Target Sequences
title_full_unstemmed MILO Mobile: An iPad App to Measure Search Performance in Multi-Target Sequences
title_short MILO Mobile: An iPad App to Measure Search Performance in Multi-Target Sequences
title_sort milo mobile: an ipad app to measure search performance in multi-target sequences
topic Methods
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7307404/
https://www.ncbi.nlm.nih.gov/pubmed/32612800
http://dx.doi.org/10.1177/2041669520932587
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