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Optimal control of eye-movements during visual search
We study the problem of optimal oculomotor control during the execution ...
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A Computational Model of Spatial Memory Anticipation during Visual Search
Some visual search tasks require to memorize the location of stimuli tha...
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Human-Piloted Drone Racing: Visual Processing and Control
Humans race drones faster than algorithms, despite being limited to a fi...
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An Exploratory Study on Visual Exploration of Model Simulations by Multiple Types of Experts
Experts in different domains rely increasingly on simulation models of c...
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Stability of defection, optimisation of strategies and the limits of memory in the Prisoner's Dilemma
Memory-one strategies are a set of Iterated Prisoner's Dilemma strategie...
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Active choice of teachers, learning strategies and goals for a socially guided intrinsic motivation learner
We present an active learning architecture that allows a robot to active...
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Understanding Humans' Strategies in Maze Solving
Navigating through a visual maze relies on the strategic use of eye movements to select and identify the route. When navigating the maze, there are trade-offs between exploring to the environment and relying on memory. This study examined strategies used to navigating through novel and familiar mazes that were viewed from above and traversed by a mouse cursor. Eye and mouse movements revealed two modes that almost never occurred concurrently: exploration and guidance. Analyses showed that people learned mazes and were able to devise and carry out complex, multi-faceted strategies that traded-off visual exploration against active motor performance. These strategies took into account available visual information, memory, confidence, the estimated cost in time for exploration, and idiosyncratic tolerance for error. Understanding the strategies humans used for maze solving is valuable for applications in cognitive neuroscience as well as in AI, robotics and human-robot interactions.
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