Team Robot
As technology improves and increases the likelihood of teams with humans and (semi-)autonomous artificial agents (e.g., virtual or robotic agents), studying potential agent capabilities becomes increasingly meaningful. Studies on organizational science focus on how members of teams communicate effectively. Team members must be able to understand each other’s goals, equipment necessary, and shared information about a given task. Shared mental models (SMMx) have been shown to efficiently expedite this information and allow team members to track each others’ progress.
Matthias Scheutz, Scott DeLoach and Julie A. Adam propose the first formal and computational frameworks for shared organizational mental models for human-robot (H-R) teams by monitoring team members physiological responses. The researchers broke down shared mental models between two key elements; data representations, capturing information and sharing between team members, and computational process, how data representations are shared and maintained. Data representations were broken down into five key component areas; agent capabilities, agent and task states, obligations, activity and equipment types, and functional agent roles. These were all given assigned algorithms to create a formal mathematical model to show how the data representation is maintained. Human Performance Factors (HPF) were also established to potentially provide a means for predicting human behavior and how their performances are affected by various internal, external, organizational and task factors. With the formal framework established, Scheutz, et al. established a computational framework for recording physiological measures to provide what they call “Workload Channel Estimation” that calculates estimates for workloads.
Researchers propose using BIOPAC’s wearable BioHarness monitor that would provide wireless physiological feedback to auditory, tactile, or motor stimuli. These measures would then inform the workload estimation and provide data for the computational framework on how physiological factors maintain the shared mental model. Scheutz, et al’s frameworks can provide new insight into what organizational strategies are most effective in communicating task information and potentially provide a measurement for team members’ most effective workload.
Matthias Scheutz, Scott DeLoach and Julie A. Adam propose the first formal and computational frameworks for shared organizational mental models for human-robot (H-R) teams by monitoring team members physiological responses. The researchers broke down shared mental models between two key elements; data representations, capturing information and sharing between team members, and computational process, how data representations are shared and maintained. Data representations were broken down into five key component areas; agent capabilities, agent and task states, obligations, activity and equipment types, and functional agent roles. These were all given assigned algorithms to create a formal mathematical model to show how the data representation is maintained. Human Performance Factors (HPF) were also established to potentially provide a means for predicting human behavior and how their performances are affected by various internal, external, organizational and task factors. With the formal framework established, Scheutz, et al. established a computational framework for recording physiological measures to provide what they call “Workload Channel Estimation” that calculates estimates for workloads.
Researchers propose using BIOPAC’s wearable BioHarness monitor that would provide wireless physiological feedback to auditory, tactile, or motor stimuli. These measures would then inform the workload estimation and provide data for the computational framework on how physiological factors maintain the shared mental model. Scheutz, et al’s frameworks can provide new insight into what organizational strategies are most effective in communicating task information and potentially provide a measurement for team members’ most effective workload.
Mobita Wireless EEG Takes a Glimpse Into the Future-Monitoring Prestimulus EEG Data
New studies examining physiological causality and electroencephalographic (EEG) correlations have provided insight on how the wearable and wireless Mobita Amplifier from BIOPAC is making an impact. A research team at the University of California, Santa Barbara, working with the American Institute of Physics, gathered data consistent with findings to conclude the psychological existence of retrocausality. The group focused on light and sound stimuli identification rates for frontal, central, and occipital parts of the brain.
The test data was divided into a pre-stimulus identification rate as well as a post-stimulus rate, to examine any correlation between standard physics and their hypothesis. No information about the stimulus should be expected to exist prior to stimulus selection. EEG recordings preceding each stimulus are analyzed to find correlations with the future selected stimulus.
During the case study, experimental subjects were fitted with a wireless EEG head cap (BIOPAC MB-32EEG-CAP-A) with 32 electrodes; the wearable BIOPAC Mobita Amplifier was utilized for its capacity to record 32 channels of high fidelity wireless EEG data. The head cap’s electrodes used paper-cotton strips soaked in water to make electrical connection with the scalp. Accompanied by BIOPAC’s AcqKnowledge research software, researchers implemented a modern and efficient process while exploring the validity of their test results and research findings.
The campus research group sought to measure electro cortical evoked potentials in the general population using random subjects with hopes of creating a basic means of measurement. Furthermore, “real-time analysis of EEG data may allow quantum events to be predicted in advance, which would affect interpretations of quantum mechanics and our notions of causality” (Baumgart et. al.). While conducting their experiment, the research group focused on keeping a furtive introduction of stimuli to test subjects equipped with the Mobita Amplifier. Stimuli were then chosen using a quantum random number generator (qRNG) and introduced to non-selected test samples.
Time durations for both light and sound stimuli vary in lengths to provide a way to differentiate the stimuli in the digital channel recording. Since sound stimuli contain longer durations than light, stimuli were recognized by digital signal length as well as being recorded by the stimulus control program. To quantify the interval of sound stimuli, the digital conduit also evaluated the voltage in parallel across the buzzer circuit to better monitor the reaction to sound stimuli. Furthermore, identification of stimulus type based on post-stimulus reaction was supported by the group’s current data for the frontal and central regions but not the occipital region.
The yielded results have lead to planning of future tests and have implicated that the existence of retrocausality is in fact measurable.
MEAP and its Implications for Cardiovascular Research
Ensemble analysis averages raw waveform signals through lining up their peaks, allowing researchers to mitigate noise or potential outside artifacts. Researchers Cieslak et al. (2017) from University of California, Santa Barbara, identified and assessed a new open source tool that conducts ensemble analysis of cardiovascular data. The moving ensemble analysis pipeline (MEAP) builds on classic collection and analysis tools; not only in detecting cardiovascular state during an experiment, but also in measuring how cardiovascular cycles change overtime.Cardiovascular measurements are typically averaged to reduce noise, but traditional measurement methods made capturing changes in cardiovascular cycles restricted to a select window of time. This makes it difficult to assess fast changes with traditional cardiovascular ICG data. With MEAP, variability is better analyzed, allowing it to become a more accurate dimension of assessment.
In assessing MEAP’s viability, researchers measured two participants as they completed four different tasks. The experiment began and ended with a random dot kinetogram task allowing for a baseline control of cardiovascular activity. This was followed by the “cold presser” and “Valsalva,” two tasks that were expected to induce strong physiological reactions. Another task included a video game, seen as having less predictive effects.
Two subjects were measured for ECG and other physiological signals as they completed the four physical and cognitive tasks. BIOPAC’s research solutions included ECG100C utilized for ECG, NICO100C-MRI to collect ICG signals, and NIBP100D CNAP Monitor 500 to record blood pressure. Data was gathered and measured with MP Research System with AcqKnowledge software.
The results pointed to changes typical cardiovascular measures wouldn’t be able to describe. This was seen during the Valsalva maneuver, where rapid baroflex changes occurred. It was also found cardiovascular data varied immensely while performing repetitive tasks.
The paper recognizes MEAP’s potential for rapidly advancing findings that use cardiovascular data. The authors point to this tool’s potential ability for exploring new areas of study that have been difficult to quantify in the past, such as linking cardiovascular reactivity to motivation. In acknowledging the benefits of MEAP, the authors stress the importance of not overstepping smaller aspects of acquisition, such as poorly attached electrodes or imbalanced experiment design. Overall, this paper recognizes, analyzes, and validates this exciting new development in the field of cardiovascular research.
Wireless | Personality Indicators for Flow State Susceptibility
Flow is described as almost complete immersion in a task or activity. Previous studies have identified that this intensive involvement leads to lower feelings of self consciousness, allowing concentration on a task to become effortless. Researchers Tain et al. (2017) sought to understand if there are precursors such as personality that would make individuals more susceptible to flow.Video games were the chosen task for inducing participants’ state of flow. Computer moderated environments (CME’s) can provide clear goals and instant feedback important for eliciting flow. It’s also easy to manipulate CME’s difficulty, which was an important variable for the study. The researchers hypothesized higher reported levels of task difficulty and shyness would be identifiable precursors for an individual’s ability at attaining flow state.
Out of 350 potential participants who applied for the study, those who had the 20 highest and lowest scores for self-reported shyness were chosen. Once selected, these participants were then asked to play a 3D Tetris-like game. The participants had to play at three different intervals lasting six minutes, with each interval varying the speed in which the pieces fell for the purpose of manipulating difficulty. While on the computer, ECG signals of each participant were acquired through BIOPAC’s BioNomadix wireless respiration and ECG amplifier. Participants were asked to complete a questionnaire asking if they realized how much time had passed. Awareness of time passing allowed for measurement of the amount of flow participants were experiencing. ECG signals and self-reported information were then analyzed, comparing differences between the shy and non-shy groups.
Researchers found significant physiological differences between the two groups. The shy group was seen as having a high heart rate when in flow state, and high levels when completing moderate and difficult tasks. Despite physiological differences, researchers weren’t able to identify shyness as a precursor of flow state. When in flow, participants were found to have increased and deeper respiration, while heart rate and variability stayed moderate. Instead of resulting in an increased amount of mental effort, researchers were able to conclude that flow only required a moderate amount of effort but lead to an increased state of parasympathetic activity.
Being that challenge in the task was induced for the purpose of eliciting anxiety in participants, the authors recommended future experiments should asses the amount of skill the user has before the task is administered. The authors identified that more research should be done in this field examining how different mental and physiological measurements could be telling of flow state.