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Cited by 26Social Robot Interaction and HRIPsychiatry, Mental Health, NeuroscienceSpeech and dialogue systems

The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.

Personalizing Human-Agent Interaction Through Cognitive Models

Open access
Schürmann, Tim, Beckerle, Philipp·2020·Frontiers in Psychology
DOI 10.3389/fpsyg.2020.561510PMC PMC7541964
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Abstract

Cognitive modeling of human behavior has advanced the understanding of underlying processes in several domains of psychology and cognitive science. In this article, we outline how we expect cognitive modeling to improve comprehension of individual cognitive processes in human-agent interaction and, particularly, human-robot interaction (HRI). We argue that cognitive models offer advantages compared to data-analytical models, specifically for research questions with expressed interest in theories of cognitive functions. However, the implementation of cognitive models is arguably more complex than common statistical procedures. Additionally, cognitive modeling paradigms typically have an explicit commitment to an underlying computational theory. We propose a conceptual framework for designing cognitive models that aims to identify whether the use of cognitive modeling is applicable to a given research question. The framework consists of five external and internal aspects related to the modeling process: research question, level of analysis, modeling paradigms, computational properties, and iterative model development. In addition to deriving our framework from a concise literature analysis, we discuss challenges and potentials of cognitive modeling. We expect cognitive models to leverage personalized human behavior prediction, agent behavior generation, and interaction pretraining as well as adaptation, which we outline with application examples from personalized HRI.

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Crossref · 10.3389/fpsyg.2020.561510

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Figures

Figure 1 Cognitive human-robot interaction (HRI) as presented and discussed by Mutlu et al. (2016) as an example of human-agent interaction (HAI) (blue and gray). Various interaction challenges might be tackled applying cognitive models and exhibit strong potential for personalization (magenta). For instance, human behavior prediction (models of humans), interaction pretraining and adaptation (models of coordination), and generating agent behavior from human models (models of agents).
Figure 1 Cognitive human-robot interaction (HRI) as presented and discussed by Mutlu et al. (2016) as an example of human-agent interaction (HAI) (blue and gray). Various interaction challenges might be tackled applying cognitive models and exhibit strong potential for personalization (magenta). For instance, human behavior prediction (models of humans), interaction pretraining and adaptation (models of coordination), and generating agent behavior from human models (models of agents).CC BY · PubMed Central
Figure 2 Conceptual framework for cognitive model design. External aspects are displayed in gray, and pink indicates initial considerations that inform the specific design decisions, which are themselves colored blue. The framework is used by evaluating domain suitability and available resources to reach a decision on whether to initiate the modeling process. Then, model-related aspects are defined and the resulting model is empirically evaluated. If necessary, an iterative process of model improvement is started.
Figure 2 Conceptual framework for cognitive model design. External aspects are displayed in gray, and pink indicates initial considerations that inform the specific design decisions, which are themselves colored blue. The framework is used by evaluating domain suitability and available resources to reach a decision on whether to initiate the modeling process. Then, model-related aspects are defined and the resulting model is empirically evaluated. If necessary, an iterative process of model improvement is started.CC BY · PubMed Central

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Citations

Cites

Research works printed but not linked: 2

  1. Griffiths, 2008, Bayesian models of cognition, 59
  2. Sun, Introduction to computational cognitive modeling, 3

Cited by

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Metadata

Authors
Schürmann, T., & Beckerle, P.
Affiliations
  • Technische Universität Darmstadt (DE)
  • TU Dortmund University (DE)
Corresponding author
Beckerle, Philipp
ORCID
Venue
Frontiers in Psychology
Publisher
Frontiers Media SA
Published
2020-09-24
Volume
11
Pages
561510-561510
ISSN
1664-1078 (ISSN-L)
DOI
10.3389/fpsyg.2020.561510
PMC
PMC7541964
OpenAlex
W3089172315
Cited by
24
References
45
Citations by year
2026: 2 · 2025: 5 · 2024: 4 · 2023: 4 · 2022: 3 · 2021: 6
FWCI
3.35
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Psychology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Technische Universität Dortmund Eldorado (Technische Universität Dortmund) (submittedVersion, cc-by)
  • Technischen Universität Darmstadt (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • TUbilio (Technical University of Darmstadt) (submittedVersion, cc-by)
  • TUbilio (Technical University of Darmstadt) (submittedVersion)
  • Technischen Universität Darmstadt (submittedVersion)
  • Technische Universität Dortmund Eldorado (Technische Universität Dortmund)
  • Technischen Universität Darmstadt (cc-by)
Funders
Deutsche Forschungsgemeinschaft
Topics
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
  • Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
Keywords
cognitive modeling, human-agent interaction, human-robot interaction, personalized interaction, individual differences, agent adaptation, human behavior prediction

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