Collective Motion with Anticipation: Flocking, Spinning, and Swarming

We investigate the collective dynamics of self-propelled particles able to probe and anticipate the orientation of their neighbors. We show that a simple anticipation strategy hinders the emergence of homogeneous flocking patterns. Yet, anticipation promotes two other forms of self-organization: collective spinning and swarming. In the spinning phase, all particles follow synchronous circular orbits, while in the swarming phase, the population condensates into a single compact swarm that cruises coherently without requiring any cohesive interactions. We quantitatively characterize and rationalize these phases of polar active matter and discuss potential applications to the design of swarming robots.Comment: 6 pages, 4 figure

Sensitivity to fine-grained and coarse visual information: The effect of blurring on anticipation skill

Copyright @ 2009 Edizione l PozziWe examined skilled tennis players’ ability to perceive fine and coarse information by assessing their ability to predict serve direction under three levels of visual blur. A temporal occlusion design was used in which skilled players viewed serves struck by two players that were occluded at one of four points relative to ball-racquet impact (-320ms, -160ms, 0ms, +160ms) and shown with one of three levels of blur (no blur, 20% blur, 40% blur). Using a within-task criterion to establish good and poor anticipators, the results revealed a significant interaction between anticipation skill and level of blur. Anticipation skill was significantly disrupted in the ‘20% blur’ condition; however, judgment accuracy of both groups then improved in the ‘40% blur’ condition while confidence in judgments declined. We conclude that there is evidence for processing of coarse configural information but that anticipation skill in this task was primarily driven by perception of fine-grained information.This research was supported by a University of Hong Kong Seed Funding for Basic Research grant awarded to the second author

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

The Cat Is On the Mat. Or Is It a Dog? Dynamic Competition in Perceptual Decision Making

Recent neurobiological findings suggest that the brain solves simple perceptual decision-making tasks by means of a dynamic competition in which evidence is accumulated in favor of the alternatives. However, it is unclear if and how the same process applies in more complex, real-world tasks, such as the categorization of ambiguous visual scenes and what elements are considered as evidence in this case. Furthermore, dynamic decision models typically consider evidence accumulation as a passive process disregarding the role of active perception strategies. In this paper, we adopt the principles of dynamic competition and active vision for the realization of a biologically- motivated computational model, which we test in a visual catego- rization task. Moreover, our system uses predictive power of the features as the main dimension for both evidence accumulation and the guidance of active vision. Comparison of human and synthetic data in a common experimental setup suggests that the proposed model captures essential aspects of how the brain solves perceptual ambiguities in time. Our results point to the importance of the proposed principles of dynamic competi- tion, parallel specification, and selection of multiple alternatives through prediction, as well as active guidance of perceptual strategies for perceptual decision-making and the resolution of perceptual ambiguities. These principles could apply to both the simple perceptual decision problems studied in neuroscience and the more complex ones addressed by vision research.Peer reviewe