Consciousness in mixed systems: merging artificial and biological minds via Brain-Machine Interface

The rapidly developing field of Brain-Machine Interface (BMI) technology seeks to establish a direct communication-and-control channel between human brain and machines. Practical applications for BMI include restoration of lost vision and motor functions, and even extending normal human capabilities.\ud But unfortunately current BMI systems are far too poor to achieve even a level of performance that is comparable to what humans are normally capable of, let alone improving it. And this situation holds on for quite a while. The possible solution for coming out is to move research focus to those aspects of brain-machine interaction that usually do not receive much attention.\ud The study of consciousness is one of such important aspects, as this poster seeks to prove, that could eventually allow us to bring BMI technology to the advanced stages, making its capabilities closer to capabilities of those BMI devices that appear in science fiction. Understanding consciousness and how it arises from the brain is crucial for achieving that goal.\ud And BMI technology itself provides a lot of new questions and opportunities for consciousness research. BMI can progress far enough to allow such levels of integration between artificial devices and biological neural networks that they could work as a single system, not just separate entities communicating between each other. But how consciousness can then be represented in this mixed system? Will consciousness be privilege of living part only? Can the artificial part add something to conscious experience or even expand it? Furthermore, it would be possible to integrate neural systems of different living organisms by interfacing them to single artificial network. Will their consciousness be integrated then too? And how can such integrated mind be experienced?\ud This poster explores ways in which Brain-Machine Interfaces can contribute to consciousness research, and discusses how better understanding of consciousness in context of brain-machine interaction will allow us to build BMI systems with extended capabilities

Influence of a hybrid digital toolset on the creative behaviors of designers in early-stage design

The purpose of this research was to investigate how diversification of the repertoire of digital design techniques affects the creative behaviors of designers in the early design phases. The principal results of practice-based pilot experiments on the subject indicate three key properties of the hybrid digital tooling strategy. The strategy features intelligent human-machine integration, facilitating three different types of synergies between the designer and the digital media: human-dominated, machine-dominated, and a balanced human-machine collaboration. This strategy also boosts the cognitive behaviors of the designer by triggering divergent, transformative and convergent design activities and allowing for work on various abstraction levels. In addition, the strategy stimulates the explorative behaviors of the designer by encouraging the production of and interaction with a wide range of design representations, including physical and digital, dynamic and static objects. Thus, working with a broader range of digital modeling techniques can positively influence the creativity of designers in the early conception stages

Virtual Meeting Rooms: From Observation to Simulation

Virtual meeting rooms are used for simulation of real meeting behavior and can show how people behave, how they gesture, move their heads, bodies, their gaze behavior during conversations. They are used for visualising models of meeting behavior, and they can be used for the evaluation of these models. They are also used to show the effects of controlling certain parameters on the behavior and in experiments to see what the effect is on communication when various channels of information - speech, gaze, gesture, posture - are switched off or manipulated in other ways. The paper presents the various stages in the development of a virtual meeting room as well and illustrates its uses by presenting some results of experiments to see whether human judges can induce conversational roles in a virtual meeting situation when they only see the head movements of participants in the meeting

A human factors methodology for real-time support applications

A general approach to the human factors (HF) analysis of new or existing projects at NASA/Goddard is delineated. Because the methodology evolved from HF evaluations of the Mission Planning Terminal (MPT) and the Earth Radiation Budget Satellite Mission Operations Room (ERBS MOR), it is directed specifically to the HF analysis of real-time support applications. Major topics included for discussion are the process of establishing a working relationship between the Human Factors Group (HFG) and the project, orientation of HF analysts to the project, human factors analysis and review, and coordination with major cycles of system development. Sub-topics include specific areas for analysis and appropriate HF tools. Management support functions are outlined. References provide a guide to sources of further information

Error by design: Methods for predicting device usability

This paper introduces the idea of predicting ‘designer error’ by evaluating devices using Human Error Identification (HEI) techniques. This is demonstrated using Systematic Human Error Reduction and Prediction Approach (SHERPA) and Task Analysis For Error Identification (TAFEI) to evaluate a vending machine. Appraisal criteria which rely upon user opinion, face validity and utilisation are questioned. Instead a quantitative approach, based upon signal detection theory, is recommended. The performance of people using SHERPA and TAFEI are compared with heuristic judgement and each other. The results of these studies show that both SHERPA and TAFEI are better at predicting errors than the heuristic technique. The performance of SHERPA and TAFEI are comparable, giving some confidence in the use of these approaches. It is suggested that using HEI techniques as part of the design and evaluation process could help to make devices easier to use

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