Cited by 37Personal Information Management and User BehaviorCognitive Functions and MemoryInnovative Human-Technology Interaction

How to Remember What to Remember: Exploring Possibilities for Digital Reminder Systems

Brewer, R. N., Morris, M. R., Lindley, S. E.·2017·Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Abstract

Digital reminder systems typically use time and place as triggers to remind people to perform activities. In this paper, we investigate how digital reminder systems could better support the process of remembering in a wider range of situations. We report findings from a survey and one-week diary study, which reveal that people want to remember to perform a broad spectrum of activities in the future, many of which cannot be supported by simple time- and location-based reminders. In addition to these examples of prospective memory, or ‘remembering intentions’ [53], we also find that people want support in ‘retrieving’ [53] information and details, especially those encountered through social interactions or intended for use in conversations with others. Drawing on our analysis of what people want to remember and how they try to support this, we draw implications for the design of intelligent reminder systems such as digital assistants (e.g. Microsoft’s Cortana) and smart speaker systems (e.g. Amazon Echo), and highlight the possibilities afforded by drawing on conversation and giving material form to digital reminders.

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Crossref · 10.1145/3130903

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4 of 4 references verified0 citation errors · 0 not checkable

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  • Algorithmically triggered autobiographical remembering can be a welcome opportunity for reminiscing about one's recent past and is already used by several social media platforms in combination with algorithmic curation.

    “Although algorithmically triggered autobiographical remembering can be a welcome opportunity for reminiscing about one's recent past and is-as mentioned above-already used by several social media platforms in combination with algorithmic curation (Jacobsen, 2020; see also Brewer et al., 2017), it is also faced with several challenges.”

    From Understanding Autobiographical Memory in the Digital Age: The AMEDIA-Model (2024) · cites this paper as Brewer et al., 2017

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Authors
Brewer, R. N., Morris, M. R., & Lindley, S. E.
Affiliations
  • Northwestern University (US)
  • Microsoft (United States) (US)
Venue
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Published
2017-09-11
Volume
1
Issue
3
Pages
1-20
ISSN
2474-9567 (ISSN-L)
OpenAlex
W2740697003
Cited by
37
References
4
Citations by year
2026: 2 · 2025: 1 · 2024: 3 · 2023: 9 · 2022: 1 · 2021: 7 · 2020: 6 · 2019: 6 · 2018: 2
FWCI
6.16
Type
journal article
Language
en
Open access
closed
Locations
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (publishedVersion)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Personal Information Management and User Behavior
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Functions and Memory
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
Keywords
prospective memory, information retrieval, conversation, virtual assistants, smart speakers, user needs, social interaction

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