Editorial : High Performance Cognition: Information-Processing in Complex Skills, Expert Performance, and Flow

Non peer reviewe

Biocybernetic Adaptation Strategies: Machine Awareness of Human Engagement for Improved Operational Performance

Human operators interacting with machines or computers continually adapt to the needs of the system ideally resulting in optimal performance. In some cases, however, deteriorated performance is an outcome. Adaptation to the situation is a strength expected of the human operator which is often accomplished by the human through self-regulation of mental state. Adaptation is at the core of the human operator's activity, and research has demonstrated that the implementation of a feedback loop can enhance this natural skill to improve training and human/machine interaction. Biocybernetic adaptation involves a loop upon a loop, which may be visualized as a superimposed loop which senses a physiological signal and influences the operators task at some point. Biocybernetic adaptation in, for example, physiologically adaptive automation employs the steering sense of cybernetic, and serves a transitory adaptive purpose to better serve the human operator by more fully representing their responses to the sys- tem. The adaptation process usually makes use of an assessment of transient cog- nitive state to steer a functional aspect of a system that is external to the operators physiology from which the state assessment is derived. Therefore, the objective of this paper is to detail the structure of biocybernetic systems regarding the level of engagement of interest for adaptive systems, their processing pipeline, and the adaptation strategies employed for training purposes, in an effort to pave the way towards machine awareness of human state for self-regulation and improved operational performance

Considering agency and data granularity in the design of visualization tools

The Ecuadorian Government supports Gonzalo Gabriel Méndez through a SENESCYT scholarship.Previous research has identified trade-offs when it comes to designing visualization tools. While constructive “bottom-up” tools promote a hands-on, user-driven design process that enables a deep understanding and control of the visual mapping, automated tools are more efficient and allow people to rapidly explore complex alternative designs, often at the cost of transparency. We investigate how to design visualization tools that support a user-driven, transparent design process while enabling efficiency and automation, through a series of design workshops that looked at how both visualization experts and novices approach this problem. Participants produced a variety of solutions that range from example-based approaches expanding constructive visualization to solutions in which the visualization tool infers solutions on behalf of the designer, e.g., based on data attributes. On a higher level, these findings highlight agency and granularity as dimensions that can guide the design of visualization tools in this space.Postprin

The impact of expert visual guidance on trainee visual search strategy, visual attention and motor skills

Minimally invasive and robotic surgery changes the capacity for surgical mentors to guide their trainees with the control customary to open surgery. This neuroergonomic study aims to assess a "Collaborative Gaze Channel" (CGC); which detects trainer gazebehavior and displays the point of regard to the trainee. A randomized crossover study was conducted in which twenty subjects performed a simulated robotic surgical task necessitating collaboration either with verbal (control condition) or visual guidance with CGC (study condition). Trainee occipito-parietal (O-P) cortical function was assessed with optical topography (OT) and gaze-behavior was evaluated using video-oculography. Performance during gaze-assistance was significantly superior [biopsy number: (mean ± SD): control = 5.6 ± 1.8 vs. CGC = 6.6 ± 2.0; p < 0.05] and was associated with significantly lower O-P cortical activity [ HbO 2 mMol × cm [median (IQR)] control = 2.5 (12.0) vs. CGC 0.63 (11.2), p < 0.001]. A random effect model (REM) confirmed the association between guidance mode and O-P excitation. Network cost and global efficiency were not significantly influenced by guidance mode. A gaze channel enhances performance, modulates visual search, and alleviates the burden in brain centers subserving visual attention and does not induce changes in the trainee's O-P functional network observable with the current OT technique. The results imply that through visual guidance, attentional resources may be liberated, potentially improving the capability of trainees to attend to other safety critical events during the procedure

Multi-Brain Computing: BCI Monitoring and Real-Time Decision Making

In this chapter we survey recent research on multi-brain applications. That is, applications in which synchronized brain activity of multiple users is measured and integrated in order to use their joint brain activity to make real-time decisions about communication with and control of devices in smart environments. Interestingly, we can go back to early brain-computer interface research of 1970 to see many ideas and sometimes implementations of synchronized multi-brain "computing". Usually they can be found in the artistic domain. In this decade (2010-20) we see growing attention in this research area, partly because of the availability of affordable electroencephalograhic (EEG) devices and partly because of the interest of human-computer interaction researchers in affective computing. This additional interest is now responsible for a focus on brain-computer interface (BCI) research that has changed from clinical applications to applications that are of interest to industry, specific groups of professionals or to the general population. Traditional BCI researchers are not always open to these developments, in which rather than focusing on Amyotrophic Lateral Sclerosis (ALS) patients, this new research focuses on entertainment, games, art and playful applications in the domestic and public domains. Among the many applications of multibrain computing: (1) Joint decision-making in environments requiring high accuracy and/or rapid reactions, or feedback; (2) joint/shared control and movement planning of vehicles or robots; (3) assess team performance, stress-aware task allocation, rearrange tasks; (4) characterization of group emotions, preferences, appreciations; (5) social interaction research (two or more people), (6) arts, entertainment, games. We discuss some examples from multibrain computing and focus on possible ways of joint decision making (or otherwise using the measured brain activity of multiple users). We will also emphasize the possibilities that are offered by the multimodal context, that is, considering brain-computer interfacing as one of the many possible modalities to obtain information about a user's or a group of users' affective states, preferences, and decisions. How to fuse information coming from different modalities and from different users need to be discussed. For that, we can learn from multimodal interaction research in human-computer interaction, including observations on sequential and parallel multimodality

Moving Average Convergence Divergence filter preprocessing for real-time event-related peak activity onset detection : Application to fNIRS signals

International audienc

L'IA transporte dans le futur

National audienceLe transport est un des domaines où l'IA s'est fait remarquer, des voitures prototypes de Google ou d'Uber qui sillonnent villes et autoroutes américaines jusqu'aux avions avec un seul pilote....à veni