scholarly journals Finding love: The materialities of love-locks and geocaches

2020 ◽  
pp. 135918352095937
Author(s):  
Ceri Houlbrook ◽  
Adam Parker
Keyword(s):  
Real World ◽  
Integral Component ◽  
Treasure Hunting ◽  
Human Participants

This article is the product of a collaboration between a folklorist researching the global phenomenon of love-locks (padlocks attached to public structures in declaration of romantic commitment) and an archaeologist who also happens to be a player of ‘Geocaching’ (a real-world, outdoor treasure hunting game using GPS-enabled devices). A chance discussion between the two revealed significant overlaps between love-locking and geocaching, despite the two practices being divergent in function and intention. Some overlaps are tangible, with love-locks forming an integral component of a number of geocaches worldwide. Other overlaps are theoretical, with both practices resulting in contemporary assemblages, or ‘serial collaborative creations’, fundamentally driven by the relationships between objects, places and human participants. The question driving this article is: what can we learn about these two seemingly different customs by considering where they overlap?

scholarly journals Mobile EEG identifies the re-allocation of attention during real-world activity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Simon Ladouce ◽  
David I. Donaldson ◽  
Paul A. Dudchenko ◽  
Magdalena Ietswaart
Keyword(s):  
Cognitive Processing ◽  
Sensory Processing ◽  
Real World ◽  
Event Related Potential ◽  
Limited Resources ◽  
Attentional Resources ◽  
Processing Demands ◽  
Neural Marker ◽  
Per Se ◽  
Human Participants

Abstract The distribution of attention between competing processing demands can have dramatic real-world consequences, however little is known about how limited attentional resources are distributed during real-world behaviour. Here we employ mobile EEG to characterise the allocation of attention across multiple sensory-cognitive processing demands during naturalistic movement. We used a neural marker of attention, the Event-Related Potential (ERP) P300 effect, to show that attention to targets is reduced when human participants walk compared to when they stand still. In a second experiment, we show that this reduction in attention is not caused by the act of walking per se. A third experiment identified the independent processing demands driving reduced attention to target stimuli during motion. ERP data reveals that the reduction in attention seen during walking reflects the linear and additive sum of the processing demands produced by visual and inertial stimulation. The mobile cognition approach used here shows how limited resources are precisely re-allocated according to the sensory processing demands that occur during real-world behaviour.

scholarly journals Learning an Optimization Algorithm Through Human Design Iterations

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Thurston Sexton ◽  
Max Yi Ren
Keyword(s):  
Optimization Algorithm ◽  
Real World ◽  
Bayesian Optimization ◽  
Search Performance ◽  
Human Beings ◽  
Domain Specific ◽  
Human Participants ◽  
The One ◽  
Lack Of Commitment ◽  
And Control

Solving optimal design problems through crowdsourcing faces a dilemma: On the one hand, human beings have been shown to be more effective than algorithms at searching for good solutions of certain real-world problems with high-dimensional or discrete solution spaces; on the other hand, the cost of setting up crowdsourcing environments, the uncertainty in the crowd's domain-specific competence, and the lack of commitment of the crowd contribute to the lack of real-world application of design crowdsourcing. We are thus motivated to investigate a solution-searching mechanism where an optimization algorithm is tuned based on human demonstrations on solution searching, so that the search can be continued after human participants abandon the problem. To do so, we model the iterative search process as a Bayesian optimization (BO) algorithm and propose an inverse BO (IBO) algorithm to find the maximum likelihood estimators (MLEs) of the BO parameters based on human solutions. We show through a vehicle design and control problem that the search performance of BO can be improved by recovering its parameters based on an effective human search. Thus, IBO has the potential to improve the success rate of design crowdsourcing activities, by requiring only good search strategies instead of good solutions from the crowd.

scholarly journals Objective (Real-world) Motion Responses in Scene Responsive Regions

2017 ◽  
Author(s):  
Didem Korkmaz Hacialihafiz ◽  
Andreas Bartels
Keyword(s):  
Real World ◽  
Reference Frames ◽  
Retrosplenial Cortex ◽  
Natural Scenes ◽  
Special Role ◽  
Perceptual Stability ◽  
Retinal Motion ◽  
Motion Responses ◽  
Human Participants

AbstractWe perceive scenes as stable even when eye movements induce retinal motion, for example during pursuit of a moving object. Mechanisms mediating perceptual stability have primarily been examined in motion regions of the dorsal visual pathway. Here we examined whether motion responses in human scene regions are encoded in eye- or world centered reference frames. We recorded brain responses in human participants using fMRI while they performed a well-controlled visual pursuit paradigm previously used to examine dorsal motion regions. In addition, we examined effects of content by using either natural scenes or their Fourier scrambles. We found that parahippocampal place area (PPA) responded to motion only in world- but not in eye-centered coordinates, regardless of scene content. The occipital place area (OPA) responded to both, objective and retinal motion equally, and retrosplenial cortex (RSC) had no motion responses but responded to pursuit. Only PPA’s objective motion responses were higher during scenes than scrambled images, although there was a similar trend in OPA. These results indicate a special role of PPA in representing its content in real-world coordinates. Our results question a strict subdivision of dorsal “what” and ventral “where” streams, and suggest a role of PPA in contributing to perceptual stability.

scholarly journals Internship alternatives: Solutions for the COVID-19 pandemic and beyond

2021 ◽  
pp. 014473942110428
Author(s):  
Darren A Wheeler ◽  
Brandon C Waite
Keyword(s):  
Public Administration ◽  
Real World ◽  
Practical Experience ◽  
Learning Opportunities ◽  
The Real ◽  
Integral Component ◽  
Workplace Setting ◽  
Administration Programs

Internships are an integral component of most undergraduate and graduate public administration programs. These learning opportunities allow students to get practical experience in a workplace setting before graduation and provide them with an opportunity to apply knowledge gained in the classroom to the “real world.” But what are students, departments, and employers to do when circumstances—including major disruptions like the COVID-19 pandemic as well as situations unique to specific students—complicate or even prevent on-site internship experiences? This article outlines a variety of approaches to finding a solution to this problem, weighing the benefits and drawbacks of each.

An Integrated Framework for Robust Human-Robot Interaction

2013 ◽  
pp. 281-301
Author(s):  
Mohan Sridharan
Keyword(s):  
Mobile Robots ◽  
Real World ◽  
Learning Algorithm ◽  
Human Robot Interaction ◽  
Sensory Cues ◽  
Robot Interaction ◽  
Integrated Framework ◽  
Real World Application ◽  
Human Participants ◽  
High Level

Developments in sensor technology and sensory input processing algorithms have enabled the use of mobile robots in real-world domains. As they are increasingly deployed to interact with humans in our homes and offices, robots need the ability to operate autonomously based on sensory cues and high-level feedback from non-expert human participants. Towards this objective, this chapter describes an integrated framework that jointly addresses the learning, adaptation, and interaction challenges associated with robust human-robot interaction in real-world application domains. The novel probabilistic framework consists of: (a) a bootstrap learning algorithm that enables a robot to learn layered graphical models of environmental objects and adapt to unforeseen dynamic changes; (b) a hierarchical planning algorithm based on partially observable Markov decision processes (POMDPs) that enables the robot to reliably and efficiently tailor learning, sensing, and processing to the task at hand; and (c) an augmented reinforcement learning algorithm that enables the robot to acquire limited high-level feedback from non-expert human participants, and merge human feedback with the information extracted from sensory cues. Instances of these algorithms are implemented and fully evaluated on mobile robots and in simulated domains using vision as the primary source of information in conjunction with range data and simplistic verbal inputs. Furthermore, a strategy is outlined to integrate these components to achieve robust human-robot interaction in real-world application domains.

scholarly journals An Integrated Framework for Robust Human-Robot Interaction

Robotics ◽  
2013 ◽  
pp. 1255-1275
Author(s):  
Mohan Sridharan
Keyword(s):  
Mobile Robots ◽  
Real World ◽  
Learning Algorithm ◽  
Human Robot Interaction ◽  
Sensory Cues ◽  
Robot Interaction ◽  
Integrated Framework ◽  
Real World Application ◽  
Human Participants ◽  
High Level

Developments in sensor technology and sensory input processing algorithms have enabled the use of mobile robots in real-world domains. As they are increasingly deployed to interact with humans in our homes and offices, robots need the ability to operate autonomously based on sensory cues and high-level feedback from non-expert human participants. Towards this objective, this chapter describes an integrated framework that jointly addresses the learning, adaptation, and interaction challenges associated with robust human-robot interaction in real-world application domains. The novel probabilistic framework consists of: (a) a bootstrap learning algorithm that enables a robot to learn layered graphical models of environmental objects and adapt to unforeseen dynamic changes; (b) a hierarchical planning algorithm based on partially observable Markov decision processes (POMDPs) that enables the robot to reliably and efficiently tailor learning, sensing, and processing to the task at hand; and (c) an augmented reinforcement learning algorithm that enables the robot to acquire limited high-level feedback from non-expert human participants, and merge human feedback with the information extracted from sensory cues. Instances of these algorithms are implemented and fully evaluated on mobile robots and in simulated domains using vision as the primary source of information in conjunction with range data and simplistic verbal inputs. Furthermore, a strategy is outlined to integrate these components to achieve robust human-robot interaction in real-world application domains.

scholarly journals Preparatory attention incorporates contextual expectations

2021 ◽  
Author(s):  
Surya Gayet ◽  
Marius V. Peelen
Keyword(s):  
Real World ◽  
Visual Processing ◽  
Retinal Image ◽  
Activity Patterns ◽  
Target Object ◽  
Attentional Selection ◽  
Preparatory Attention ◽  
World Vision ◽  
Preparatory Activity ◽  
Human Participants

Humans are remarkably proficient at finding objects within a complex visual world. Current theories of attentional selection propose that this ability is mediated by target-specific preparatory activity in visual cortex, biasing visual processing in favor of the target object. In real-world situations, however, the retinal image that any object will produce is unknown in advance; its size, for instance, varies dramatically with the object's distance from the observer. Using fMRI, we show that preparatory activity is systematically modulated by expectations derived from scene context. Human participants searched for objects at different distances in scenes. Activity patterns in object-selective cortex during search preparation (while no objects were presented), resembled activity patterns evoked by viewing targets object in isolation. Crucially, this preparatory activity was modulated by distance, reflecting the predicted retinal image of the object at each distance. These findings reconcile current theories of attentional selection with the challenges of real-world vision.

Replications can cause distorted belief in scientific progress

2018 ◽  
Vol 41 ◽  
Author(s):  
Michał Białek
Keyword(s):  
Real World ◽  
Scientific Progress ◽  
The Public ◽  
Psychological Science

AbstractIf we want psychological science to have a meaningful real-world impact, it has to be trusted by the public. Scientific progress is noisy; accordingly, replications sometimes fail even for true findings. We need to communicate the acceptability of uncertainty to the public and our peers, to prevent psychology from being perceived as having nothing to say about reality.

Flagellar Attachment in Selected Trypanosomes

1973 ◽  
Vol 31 ◽  
pp. 496-497
Author(s):  
J. J. Paulin
Keyword(s):  
Cell Membrane ◽  
Lateral Surface ◽  
The Body ◽  
Flagellar Pocket ◽  
Integral Component ◽  
Free Flagellum ◽  
Flagellar Axoneme

Movement in epimastigote and trypomastigote stages of trypanosomes is accomplished by planar sinusoidal beating of the anteriorly directed flagellum and associated undulating membrane. The flagellum emerges from a bottle-shaped depression, the flagellar pocket, opening on the lateral surface of the cell. The limiting cell membrane envelopes not only the body of the trypanosome but is continuous with and insheathes the flagellar axoneme forming the undulating membrane. In some species a paraxial rod parallels the axoneme from its point of emergence at the flagellar pocket and is an integral component of the undulating membrane. A portion of the flagellum may extend beyond the anterior apex of the cell as a free flagellum; the length is variable in different species of trypanosomes.

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