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Open Access Publications from the University of California

Scholarly Works (3 results)

  • Article
  • Peer Reviewed
Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 44 (2022)
The dual mechanisms of control framework describes two modes of goal-directed behavior: proactive control (goal maintenance) and reactive control (goal activation on task demands). Shifts between these modes may explain variations in user performance in computing tasks. Although these mechanisms are relevant to the design of interactive systems, and particularly educational systems, their relation to human-computer interaction (HCI) is under-researched. We propose a manipulation to induce proactive or reactive control in the context of mathematical problem solving on an online tutoring system. We present two experiments where students solved problems using either proactive or reactive control. Study 1 validates the manipulation by investigating behavioral measures that reflect usage of the intended strategy and assesses whether either cognitive control mode impacted learning. Study 2 investigates whether alternating between control modes during problem solving affects student performance.
    Cover page: Eliciting Proactive and Reactive Control during Use of an Interactive Learning EnvironmentCreative Commons 'BY' version 4.0 license
    • Article
    • Peer Reviewed
    Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 45 (2023)
    Intelligent tutoring systems (ITS) afford complex learning environments in which inductive reasoning is engaged, but where sparse log data complicates the classification of cognitive states. Using a simple rule learning task, we explore the feasibility of verbal think aloud protocols to contribute to machine learning algorithms designed to classify states of rule search, rule discovery, and rule following. Domain-general versus domain-specific contributions to classification are considered through the use of isomorphic rule learning tasks with numeric and spatial stimuli. We trained and tested models both within and across task domains. Models including verbal data outperform models based only on log data with particular improvement in classifying rule discovery. These results provide a foundation for real-time classification of cognitive states during ITS use.
      Cover page: Classification of Rule Learning Phases in Inductive ReasoningCreative Commons 'BY' version 4.0 license
      • Article
      • Peer Reviewed
      ICTS Publications (2011)

      How environmental factors combine with genetic risk at the molecular level to promote complex trait diseases such as multiple sclerosis (MS) is largely unknown. In mice, N-glycan branching by the Golgi enzymes Mgat1 and/or Mgat5 prevents T cell hyperactivity, cytotoxic T-lymphocyte antigen 4 (CTLA-4) endocytosis, spontaneous inflammatory demyelination and neurodegeneration, the latter pathologies characteristic of MS. Here we show that MS risk modulators converge to alter N-glycosylation and/or CTLA-4 surface retention conditional on metabolism and vitamin D(3), including genetic variants in interleukin-7 receptor-α (IL7RA*C), interleukin-2 receptor-α (IL2RA*T), MGAT1 (IV(A)V(T-T)) and CTLA-4 (Thr17Ala). Downregulation of Mgat1 by IL7RA*C and IL2RA*T is opposed by MGAT1 (IV(A)V(T-T)) and vitamin D(3), optimizing branching and mitigating MS risk when combined with enhanced CTLA-4 N-glycosylation by CTLA-4 Thr17. Our data suggest a molecular mechanism in MS whereby multiple environmental and genetic inputs lead to dysregulation of a final common pathway, namely N-glycosylation.

        Cover page: Genetics and the environment converge to dysregulate N-glycosylation in multiple sclerosis.
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