CRA Metrics to Perceive Semantic Coordination in Team Communications: Michael Tristan Tolston

Michael Tristan Tolston
Michael Tristan Tolston

Working with a team and evaluating their perception is a crucial yet grueling venture. CRA or Conceptual recurrence analysis is an adaptable system, by which we can analyze the structure of semantic content in a group conversation. Michael Tristan Tolston is currently working on research about CRA. He illustrates how conceptual recurrence analysis (CRA) can be used to analyze and detect the pattern of semantic content in group/team communications. Hence, CRA is the process that provides insights into how the information is proceeded by any team. 

Comprehending this, Michael Tolston, a research psychologist based in Dayton(western Ohio), evaluates the comprehensive research on it. He completed his Bachelor’s and Master’s degree in Psychology from the University of Cincinnati. Tolston is skilled in evaluating the range of complex data from repetitive measures designs, analyzing, interpreting, and disseminating research findings. He uses advanced analytic techniques for summarizing and modeling physiological, behavioral, and linguistic data of human behavior.

Research for Semantic alignment in Conceptual recurrence analysis 

At present, he is working as a human factor professional at Ball Aerospace in support of the U.S., and as a research professional he is working in the fields of applied neuroscience, human-machine trust, interpersonal trust, individual and team training, human-human and human-machine teaming, development & application of advanced data analytic technique, and human performance and readiness assessment. While doing his research on CRA, he wrote a book named Beyond frequency counts: Novel conceptual recurrence analysis metrics to index semantic coordination in team communications.

Further, he explains that Semantic alignment is a type of language that determines and collects the data of relations between concepts. It detects how the conceptual recurrence analysis (CRA) can be used to detect conceptual structure in interpersonal communication. Micheal Tolston and his team are working on CRA metrics to analyze communication data, and the motive of his approach was to determine how the parameter is set in identified semantic spaces, and how those changes may subsequently affect the detection of group-level differences in the proposed CRA metrics.

The patient-caregiver interactions:

In several manners, Conceptual recurrence analysis (CRA) is very similar to categorical variations of recurrence quantification analysis that have shown, there are several relationships between semantic coordination and effectual patient-caregiver interactions. In such effective communication, the caregiver tends to align the conceptual content of his observation to his patient. And such interactions are typically defined by a well-built leader-follower relation. This relation shows the possibility of CRA metrics in the systematic evaluation of communicative content.

The relation between RQA and CRA:

CRA is an addition of recurrence quantification analysis (RQA), a nonlinear technique that is acclimated to calculate the structure in complex time-series data. RQA quantifies the pattern in terms of the recurrence or repetition of states of the system, and CRA calculates communication data, i.e., utterance and conversation, by measuring the scale on which a person’s pronouncements complete conversational turns. CRA provides a set of concepts over which similarity is calculated.

On the Final Note, This approach (as implemented in the program Discourses) has been used to quantify interpersonal communication data, and resultant words are called concepts. CRA offers a scalable framework (in Discourses), that users can adjust. Tolston and the team tested the effects of using bigger versus smaller semantic spaces on the sensitivity of the proposed CRA measures. In their extensive research, Michael Tristan Tolston and his team aim to provide a better basis for predicting communication effectiveness than general vocabulary alignment.