Cross-timescale experience evaluation framework for productive teaming

This paper presents the initial concept for an evaluation framework to systematically evaluate productive teaming (PT). We consider PT as adaptive human-machine interactions between human users and augmented technical production systems. Also, human-to-human communication as part of a hybrid team with multiple human actors is considered, as well as human-human and human-machine communication for remote and mixed remote- and co-located teams. The evaluation comprises objective, performance-related success indicators, behavioral metadata, and measures of human experience. In particular, it considers affective, attentional and intentional states of human team members, their influence on interaction dynamics in the team, and researches appropriate strategies to satisfyingly adjust dysfunctional dynamics, using concepts of companion technology. The timescales under consideration span from seconds to several minutes, with selected studies targeting hour-long interactions and longer-term effects such as effort and fatigue. Two example PT scenarios will be discussed in more detail. To enable generalization and a systematic evaluation, the scenarios’ use cases will be decomposed into more general modules of interaction

Going one step further: towards cognitively enhanced problem-solving teaming agents

Operating current advanced production systems, including Cyber-Physical Systems, often requires profound programming skills and configuration knowledge, creating a disconnect between human cognition and system operations. To address this, we suggest developing cognitive algorithms that can simulate and anticipate teaming partners' cognitive processes, enhancing and smoothing collaboration in problem-solving processes. Our proposed solution entails creating a cognitive system that minimizes human cognitive load and stress by developing models reflecting humans individual problem-solving capabilities and potential cognitive states. Further, we aim to devise algorithms that simulate individual decision processes and virtual bargaining procedures that anticipate actions, adjusting the system’s behavior towards efficient goal-oriented outcomes. Future steps include the development of benchmark sets tailored for specific use cases and human-system interactions. We plan to refine and test algorithms for detecting and inferring cognitive states of human partners. This process requires incorporating theoretical approaches and adapting existing algorithms to simulate and predict human cognitive processes of problem-solving with regards to cognitive states. The objective is to develop cognitive and computational models that enable production systems to become equal team members alongside humans in diverse scenarios, paving the way for more efficient, effective goal-oriented solutions

Hesitation Processing Analysis Using Continuous Mouse-Tracking and Gamification

Betz S, Székély E, Zarrieß S, Schröer M, Schade L, Wagner P. Hesitation Processing Analysis Using Continuous Mouse-Tracking and Gamification. In: Wendemuth A, Böck R, Siegert I, eds. Elektronische Sprachsignalverarbeitung 2020. Tagungsband der 31. Konferenz. Studientexte zur Sprachkommunikation. Vol 95. Dresden: TUD Press; 2020: 85-92

Temporal context and conditional associative learning

<p>Abstract</p> <p>Background</p> <p>We investigated how temporal context affects the learning of arbitrary visuo-motor associations. Human observers viewed highly distinguishable, fractal objects and learned to choose for each object the one motor response (of four) that was rewarded. Some objects were consistently preceded by specific other objects, while other objects lacked this task-irrelevant but predictive context.</p> <p>Results</p> <p>The results of five experiments showed that predictive context consistently and significantly accelerated associative learning. A simple model of reinforcement learning, in which three successive objects informed response selection, reproduced our behavioral results.</p> <p>Conclusions</p> <p>Our results imply that not just the representation of a current event, but also the representations of past events, are reinforced during conditional associative learning. In addition, these findings are broadly consistent with the prediction of attractor network models of associative learning and their prophecy of a persistent representation of past objects.</p