356 research outputs found
CodeDataCharSynchPatternsPBV.tar.gz
No full textCode and data accompanying the manuscript: Characterizing synchrony patterns across cognitive task stages of associative recognition memory. By Oscar Portoles, Jelmer Borst, and Marieke van Vugt. European Journal of Neuroscience, 201
CodeDataCharSynchPatternsPBV.tar.gz
No full textCode and data accompanying the manuscript: Characterizing synchrony patterns across cognitive task stages of associative recognition memory. By Oscar Portoles, Jelmer Borst, and Marieke van Vugt. European Journal of Neuroscience, 201
A Model of Distraction using new Architectural Mechanisms to Manage Multiple Goals
Get PDFCognitive models assume a one-to-one correspondencebetween task and goals. We argue that modeling a task bycombining multiple goals has several advantages: a task canbe constructed from components that are reused from othertasks, and it enables modeling thought processes that competewith or support regular task performance. To achieve this, weupdated the PRIMs architecture (a derivative of ACT-R) withthe capacity for parallel goals that have different activationlevels. We use this extension to model visual distraction intwo experiments. The model provides explanations for thefinding that distraction increases with task difficulty in amemory task, but decreases with task difficulty in a visualsearch task
AndersonSupplementalMaterial_rev – Supplemental material for The Common Time Course of Memory Processes Revealed
No full textSupplemental material, AndersonSupplementalMaterial_rev for The Common Time Course of Memory Processes Revealed by John R. Anderson, Jelmer P. Borst, Jon M. Fincham, Avniel Singh Ghuman, Caitlin Tenison and Qiong Zhang in Psychological Science</p
A Model of Distraction using new Architectural Mechanisms to Manage Multiple Goals
Get PDFCognitive models assume a one-to-one correspondencebetween task and goals. We argue that modeling a task bycombining multiple goals has several advantages: a task canbe constructed from components that are reused from othertasks, and it enables modeling thought processes that competewith or support regular task performance. To achieve this, weupdated the PRIMs architecture (a derivative of ACT-R) withthe capacity for parallel goals that have different activationlevels. We use this extension to model visual distraction intwo experiments. The model provides explanations for thefinding that distraction increases with task difficulty in amemory task, but decreases with task difficulty in a visualsearch task
Anticipatory Human-Machine Interaction (Dagstuhl Seminar 22202)
No full textEven after three decades of research on human-machine interaction (HMI), current systems still lack the ability to predict mental states of their users, i.e., they fail to understand users' intentions, goals, and needs and therefore cannot anticipate their actions. This lack of anticipation drastically restricts their capabilities to interact and collaborate effectively with humans. The goal of this Dagstuhl Seminar was to discuss the scientific foundations of a new generation of human-machine systems that anticipate, and proactively adapt to, human actions by monitoring their attention, behavior, and predicting their mental states. Anticipation might be realized by using mental models of tasks, specific situations and systems to build up expectations about intentions, goals, and mental states that gathered evidence can be tested against.
The seminar provided an inter-disciplinary forum to discuss this emerging topic by bringing together - for the first time - researchers from a range of fields that are directly relevant but hitherto haven't met on this topic so far. This includes human-computer interaction, cognitive-inspired AI, machine learning, computational cognitive science, and social and decision sciences. We discussed theoretical foundations, key research challenges and opportunities, new computational methods, and future applications of anticipatory human-machine interaction
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