---
res:
  bibo_abstract:
  - 'This study investigated how action histories – unfolding sequences of actions
    with objects – provide a context for both attentional allocation and linguistic
    repair strategies. Building on theories of enactive cognition and sensorimotor
    contingency theory, we experimentally manipulated action sequences (action history)
    to create either simple or rich “situational models,” and investigated how these
    models interact with attention and reflect in linguistic processes during human–robot
    interaction. Participants (N = 30) engaged in a controlled object placement task
    with a humanoid robot, where the action (manner) information was either provided
    or omitted. The omission elicited repair behaviors in participants that were in
    focus of our investigation. For rich models (competing action possibilities) participants
    demonstrated: a) increased attentional reorientation, reflecting active engagement
    with the situational model b) preference for restricted repairs, targeting the
    specific source of trouble in action selection. Conversely, a simple situational
    model led to more generalized attention patterns and open repair strategies, suggesting
    weaker constraints on internal processing. These findings highlight how situational
    structures emerge externally to scaffold internal cognitive processes, with action
    histories serving as a crucial context for the interface between perception, action,
    and language. We discuss how to implement such a tight loop in the assistance
    of a system.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Amit
      foaf_name: Singh, Amit
      foaf_surname: Singh
      foaf_workInfoHomepage: http://www.librecat.org/personId=91018
    orcid: 0000-0002-7789-1521
  - foaf_Person:
      foaf_givenName: Katharina J.
      foaf_name: Rohlfing, Katharina J.
      foaf_surname: Rohlfing
      foaf_workInfoHomepage: http://www.librecat.org/personId=50352
    orcid: 0000-0002-5676-8233
  bibo_doi: 10.31234/osf.io/yn2we_v1
  dct_date: 2025^xs_gYear
  dct_language: eng
  dct_subject:
  - Attention
  - Action
  - Repairs
  - Task model
  - HRI
  - Eyemovement
  dct_title: 'Manners Matter: Action history guides attention and repair choices during
    interaction@'
...