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Auflistung nach Schlagwort "ACT-R"

1 - 3 von 3
  • Zeitschriftenartikel
    Extending ACT-R for modeling dynamics and timing for operating human-machine systems
    (MMI Interaktiv - Modellierung und Simulation in Mensch: Vol. 1, No. 07, 2004) Leuchter, Sandro; Urbas, Leon
    Der Artikel zeigt das Potential architekturgebundener kognitiver Modelle am Beispiel von ACT-R für die Gestaltung von Mensch-Maschine-Systemen in verschiedenen Domänen auf und identifiziert Entwicklungsbedarf für diesen Einsatzbereich. Mit den kognitionswissenschaftlich fundierten Elementen Gedächtnisabruf, subsymbolische Aktivierung, Perzeption und Motorik stellen kognitive Architekturen eine elaborierte und insbesondere aufgrund aktueller Entwicklungen in der Modellkompilation aus höheren Abstraktionsebenen inzwischen auch effizient einsetzbare und solide Basis dar. Für zukünftige Anwendung für Mensch-Maschine-Systemen leiten die Autoren aus einer Betrachtung verschiedener Modellierungsanstrengungen mit dem System ACT-R in verschiedenen Domänen die Notwendigkeit der Erweiterung auf Architekturebene mit Elementen zur einheitlichen Behandlung des Abrufs zeit- und dauerbezogener Informationen, dem Management paralleler Ziele und einer mehrschichtigen Anbindung an Modelle der technischen Teilsysteme ab.
  • Konferenzbeitrag
    The Impact of Time on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach
    (INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, 2019) Lex, Elisabeth; Kowald, Dominik
    In our work [KPL17], we study temporal usage patterns of Twitter hashtags, and we use the Base-Level Learning (BLL) equation from the cognitive architecture ACT-R [An04] to model how a person reuses her own, individual hashtags as well as hashtags from her social network. The BLL equation accounts for the time-dependent decay of item exposure in human memory. According to BLL, the usefulness of a piece of information (e.g., a hashtag) is defined by how frequently and how recently it was used in the past, following a time-dependent decay that is best modeled with a power-law distribution. We used the BLL equation in our previous work to recommend tags in social bookmarking systems [KL16]. Here [KPL17], we adopt the BLL equation to model temporal reuse patterns of individual (i.e., reusing own hashtags) and social hashtags (i.e., reusing hashtags, which has been previously used by a followee) and to build a cognitive-inspired hashtag recommendation algorithm. We demonstrate the efficacy of our approach in two empirical social networks crawled from Twitter, i.e., CompSci and Random (for details about the datasets, see [KPL17]). Our results show that our approach can outperform current state-of-the-art hashtag recommendation approaches.
  • Zeitschriftenartikel
    Modeling Interruption and Resumption in a Smartphone Task: An ACT-R Approach
    (i-com: Vol. 14, No. 2, 2015) Wirzberger, Maria; Russwinkel, Dr.-Ing. Nele
    This research aims to inspect human cognition when being interrupted while performing a smartphone task with varying levels of mental demand. Due to its benefits especially in the early stages of interface development, a cognitive modeling approach is used. It applies the cognitive architecture ACT-R to shed light on task-related cognitive processing. The inspected task setting involves a shopping scenario, manipulating interruption via product advertisements and mental demands by the respective number of people shopping is done for. Model predictions are validated through a corresponding experimental setting with 62 human participants. Comparing model and human data in a defined set of performance-related parameters displays mixed results that indicate an acceptable fit – at least in some cases. Potential explanations for the observed differences are discussed at the end.