Exploration of Sensemaking in the Education of Novices to the Complex Cognitive Work Domain of Air Traffic Control

Many current complex business and industry jobs consist primarily of cognitive work; however, current approaches to training may be inadequate for this type of work (Hoffman, Feltovich, Fiore, Klein, & Ziebell, 2009). To try and improve training and education for cognitive work, Klein and Baxter (2006) have proposed cognitive transformation theory (CTT), a learning theory that claims that sensemaking activities are essential for acquiring expertise that is adaptive and thus well suited for cognitive work domains. In the present research, cognitive task analysis methods were used to identify and assess sensemaking support in the instruction and learning of complex concepts by two experienced air traffic control professors and seven of their students. The goal of this research was to compare instructional strategies used in an academic setting with the predictions of CTT to gain insight into strategies for the application of CTT. Cognitive task analysis methods employed included course observation, artifact examination, and knowledge elicitation sessions with two professors and seven of their students. Knowledge elicitation transcriptions were coded using categories derived from CTT and the data/frame theory of sensemaking (e.g. Klein, Moon, & Hoffman, 2006; Sieck, Klein, Peluso, Smith, & Harris-Thompson, 2007) to assess theoretical and applied implications for learning and instruction in a complex domain. Findings are represented by synthesizing theory driven predictions with grounded training strategies and technologies. In addition, recommendations are advanced for applying CTT to training and educational systems in order to provide sensemaking support during early phases of learning from which expertise may be developed

Capturing coordination and intentionality in joint musical improvisation.

Humans collaborate with each other on a wide variety of tasks that are often largely improvised and unscripted. In this study, we investigated the dynamics of coordination in a joint musical improvisation task, what the effect of intentions is on coordination, and how musicians propagate these intentions. To quantify coordination within musical trios, we derived per-musician time series of acoustic features to which we applied effective transfer entropy (ETE) and empirical dynamic modeling (EDM), two methods derived from complex systems science. Using ETE allowed us to investigate coordination as directional information flow between musicians, whereas through EDM we conceptualized coordination as the predictability of a complex system. We found that both techniques, when applied to root-mean-square (RMS) amplitude time series, could be used to distinguish coordinating from noncoordinating musicians. Various other feature-technique combinations, such as fractal dimension-ETE and Tonnetz distance-EDM, were also viable. Our results further suggest that coordination improves as an intention gets more shared, that is, as more musicians in the joint improvisation have the same intention. Lastly, we found evidence suggesting that musicians increase the predictability of their playing when seeking to end a performance, though our results did not provide an indication that this was done with the intention of improving coordination with partners

Collaborative Creativity:Information-Driven Coordination Dynamics and Prediction in Movement and Musical Improvisation

Humans collaborate with a large number of people in order to create and accomplish incredible feats. We argue that rich coordination dynamics underpin our capacity for collaborative creativity. These dynamics characterize the ways in which people are able to covary their thoughts, actions, behavior, etc. for functional purposes. We investigated the coordination dynamics of improvisation as a special case of collaborative creativity using two openly available data sets: a movement-based mirror game and jazz piano improvisation. By focusing on improvisation, the tasks elicit the need for real-time adaptation and mutual prediction based on information exchange between interacting individuals, with the creative ‘product’ being the behavioral performance itself. For each data set, we performed a transfer entropy analysis as well as an estimate of prediction decay. The combination of these two methods allows us to understand the dynamics as information-driven coordination flow and to differentiate unidirectional influence from mutual influence as well as the predictability of signals exhibited during collaborative creativity. We observed that for the mirror game, experts and novices exhibited unidirectional and bidirectional influence on each other’s movements largely independent of their improvisational experience level. Further, movement improvisation signals generated by experts were generally more predictable than those of novices. In terms of the jazz improvisation, our results showed evidence of bidirectional influence between the onset densities of coupled and one-way improvisational dyads, and the predictability of the signal did not vary systematically across these conditions. We discuss these findings in terms of differences between improvisational contexts, methodical challenges, and future directions

Collaborative Creativity:Information-Driven Coordination Dynamics and Prediction in Movement and Musical Improvisation

Humans collaborate with a large number of people in order to create and accomplish incredible feats. We argue that rich coordination dynamics underpin our capacity for collaborative creativity. These dynamics characterize the ways in which people are able to covary their thoughts, actions, behavior, etc. for functional purposes. We investigated the coordination dynamics of improvisation as a special case of collaborative creativity using two openly available data sets: a movement-based mirror game and jazz piano improvisation. By focusing on improvisation, the tasks elicit the need for real-time adaptation and mutual prediction based on information exchange between interacting individuals, with the creative ‘product’ being the behavioral performance itself. For each data set, we performed a transfer entropy analysis as well as an estimate of prediction decay. The combination of these two methods allows us to understand the dynamics as information-driven coordination flow and to differentiate unidirectional influence from mutual influence as well as the predictability of signals exhibited during collaborative creativity. We observed that for the mirror game, experts and novices exhibited unidirectional and bidirectional influence on each other’s movements largely independent of their improvisational experience level. Further, movement improvisation signals generated by experts were generally more predictable than those of novices. In terms of the jazz improvisation, our results showed evidence of bidirectional influence between the onset densities of coupled and one-way improvisational dyads, and the predictability of the signal did not vary systematically across these conditions. We discuss these findings in terms of differences between improvisational contexts, methodical challenges, and future directions

Team Interaction Dynamics during Collaborative Problem Solving

The need for better understanding collaborative problem solving (CPS) is rising in prominence as many organizations are increasingly addressing complex problems requiring the combination of diverse sets of individual expertise to address novel situations. This research draws from theoretical and empirical work that describes the knowledge coordination arising from team communications during CPS and builds from this by incorporating methods to study interaction dynamics. Interaction between team members in such contexts is inherently dynamic and exhibits nonlinear patterns not accounted for by extant research methods. To redress this gap, the present study draws from methods designed to study social and team interaction as a nonlinear dynamical system. CPS was examined by studying knowledge building and interaction processes of 43 dyads working to solve NASA’s Moonbase Alpha simulation. Specifically, frame-differencing, an automated video analysis technique, was used to capture the bodily movements of participants and content coding was applied to the teams’ communications to characterize their CPS processes. A combination of linear and nonlinear analytic and modeling techniques were applied to quantify and predict CPS performance based on the observed interaction dynamics and other individual differences. We hypothesized that teams exhibiting synchronization in their bodily movements and complementarity in their communications would produce better problem solving outcomes. The present research advances theory and empirical knowledge on effective team interaction during CPS and provides practical guidance on methods that can be used to observe and quantify interaction dynamics during CPS in complex work domains

Interpersonal Coordination Dynamics in Psychotherapy

Supplementary material for: Wiltshire, T.J., Philipsen, J.S., Trasmundi, S.B. et al. Interpersonal Coordination Dynamics in Psychotherapy: A Systematic Review. Cognitive Therapy and Research (2020). https://doi-org.tilburguniversity.idm.oclc.org/10.1007/s10608-020-10106-

A Prospective Framework For The Design Of Ideal Artificial Moral Agents: Insights From The Science Of Heroism In Humans

The growing field of machine morality has becoming increasingly concerned with how to develop artificial moral agents. However, there is little consensus on what constitutes an ideal moral agent let alone an artificial one. Leveraging a recent account of heroism in humans, the aim of this paper is to provide a prospective framework for conceptualizing, and in turn designing ideal artificial moral agents, namely those that would be considered heroic robots. First, an overview of what it means to be an artificial moral agent is provided. Then, an overview of a recent account of heroism that seeks to define the construct as the dynamic and interactive integration of character strengths (e.g., bravery and integrity) and situational constraints that afford the opportunity for moral behavior (i.e., moral affordances). With this as a foundation, a discussion is provided for what it might mean for a robot to be an ideal moral agent by proposing a dynamic and interactive connectionist model of robotic heroism. Given the limited accounts of robots engaging in moral behavior, a case for extending robotic moral capacities beyond just being a moral agent to the level of heroism is supported by drawing from exemplar situations where robots demonstrate heroism in popular film and fiction