Distributed Cognition in Learning and Behavioral Change – Based on Human and Artificial Intelligence

Editors

Dietrich Albert

University of Graz

Tomoko Kojiri

Kansai University

Xiangen Hu

University of Memphis

Paul Seitlinger

Faculty of Psychology, University of Vienna

Impact

Original Research

07 February 2023

How systemic cognition enables epistemic engineering

Stephen J. Cowley

and

Rasmus Gahrn-Andersen

Epistemic engineering arises as systems and their parts develop functionality that is construed as valid knowledge. By hypothesis, epistemic engineering is a basic evolutionary principle. It ensures that not only living systems identify the differences that make differences but also ensure that distributed control enables them to construct epistemic change. In tracking such outcomes in human life, we stress that humans act within poly-centered, distributed systems. Similar to how people can act as inert parts of a system, they also actively bring forth intents and vicariant effects. Human cognitive agents use the systemic function to construct epistemic novelties. In the illustration, we used a published experimental study of a cyborg cockroach to consider how an evoneered system enables a human subject to perform as an adaptor with some “thought control” over the animal. Within a wide system, brains enable the techniques to arise ex novo as they attune to the dictates of a device. Human parts act as adaptors that simplify the task. In scaling up, we turn to a case of organizational cognition. We track how adaptor functions spread when drone-based data are brought to the maintenance department of a Danish utility company. While pivoting on how system operators combine experience with the use of software, their expertise sets off epistemically engineered results across the company and beyond. Vicariant effects emerge under the poly-centered control of brains, persons, equipment, and institutional wholes. As a part of culture, epistemic engineering works by reducing entropy.

2,237 views

10 citations

Human results in Experiment 3c. Blue: performance in the training phase, separated into 50-trial blocks. Orange: performance on the generalization tests. Error bars show ± one standard error of the mean.

Original Research

01 March 2023

Configural relations in humans and deep convolutional neural networks

Nicholas Baker

, 2 more and

Philip J. Kellman

2,142 views

0 citations

Original Research

23 November 2022

Explanatory machine learning for justified trust in human-AI collaboration: Experiments on file deletion recommendations

Kyra Göbel

, 2 more and

Ute Schmid

4,071 views

2 citations

Design thinking process by Fraunhofer FIT (FIT, 2019).

Conceptual Analysis

17 November 2022

Differences between remote and analog design thinking through the lens of distributed cognition

Daniel Wolferts

, 2 more and

René Reiners

2,522 views

3 citations

Original Research

25 August 2022

Distributed cognition for collaboration between human drivers and self-driving cars

Alice Plebe

, 2 more and

Mauro Da Lio

2,326 views

3 citations

Original Research

08 August 2022

Requirements and challenges for hybrid intelligence: A case-study in education

Bert Bredeweg

and

Marco Kragten

The potential for Artificial Intelligence is widely proclaimed. Yet, in everyday educational settings the use of this technology is limited. Particularly, if we consider smart systems that actually interact with learners in a knowledgeable way and as such support the learning process. It illustrates the fact that teaching professionally is a complex challenge that is beyond the capabilities of current autonomous robots. On the other hand, dedicated forms of Artificial Intelligence can be very good at certain things. For example, computers are excellent chess players and automated route planners easily outperform humans. To deploy this potential, experts argue for a hybrid approach in which humans and smart systems collaboratively accomplish goals. How to realize this for education? What does it entail in practice? In this contribution, we investigate the idea of a hybrid approach in secondary education. As a case-study, we focus on learners acquiring systems thinking skills and our recently for this purpose developed pedagogical approach. Particularly, we discuss the kind of Artificial Intelligence that is needed in this situation, as well as which tasks the software can perform well and which tasks are better, or necessarily, left with the teacher.

4,312 views

11 citations

Original Research

22 July 2022

Using the DiCoT framework for integrated multimodal analysis in mixed-reality training environments

Caleb Vatral

, 3 more and

Naveeduddin Mohammed

Simulation-based training (SBT) programs are commonly employed by organizations to train individuals and teams for effective workplace cognitive and psychomotor skills in a broad range of applications. Distributed cognition has become a popular cognitive framework for the design and evaluation of these SBT environments, with structured methodologies such as Distributed Cognition for Teamwork (DiCoT) used for analysis. However, the analysis and evaluations generated by such distributed cognition frameworks require extensive domain-knowledge and manual coding and interpretation, and the analysis is primarily qualitative. In this work, we propose and develop the application of multimodal learning analysis techniques to SBT scenarios. Using these analysis methods, we can use the rich multimodal data collected in SBT environments to generate more automated interpretations of trainee performance that supplement and extend traditional DiCoT analysis. To demonstrate the use of these methods, we present a case study of nurses training in a mixed-reality manikin-based (MRMB) training environment. We show how the combined analysis of the video, speech, and eye-tracking data collected as the nurses train in the MRMB environment supports and enhances traditional qualitative DiCoT analysis. By applying such quantitative data-driven analysis methods, we can better analyze trainee activities online in SBT and MRMB environments. With continued development, these analysis methods could be used to provide targeted feedback to learners, a detailed review of training performance to the instructors, and data-driven evidence for improving the environment to simulation designers.

3,394 views

9 citations

Original Research

19 July 2022

On Distributed Cognition While Designing an AI System for Adapted Learning

Magne V. Aarset

and

Leiv Kåre Johannessen

2,882 views

2 citations

Perspective

14 July 2022

Supporting Cognition With Modern Technology: Distributed Cognition Today and in an AI-Enhanced Future

Sandra Grinschgl

and

Aljoscha C. Neubauer

Correlations between sociodemographic data, Big 5 traits and facets, interpersonal trust, and general trust in AI.

In the present article, we explore prospects for using artificial intelligence (AI) to distribute cognition via cognitive offloading (i.e., to delegate thinking tasks to AI-technologies). Modern technologies for cognitive support are rapidly developing and increasingly popular. Today, many individuals heavily rely on their smartphones or other technical gadgets to support their daily life but also their learning and work. For instance, smartphones are used to track and analyze changes in the environment, and to store and continually update relevant information. Thus, individuals can offload (i.e., externalize) information to their smartphones and refresh their knowledge by accessing it. This implies that using modern technologies such as AI empowers users via offloading and enables them to function as always-updated knowledge professionals, so that they can deploy their insights strategically instead of relying on outdated and memorized facts. This AI-supported offloading of cognitive processes also saves individuals' internal cognitive resources by distributing the task demands into their environment. In this article, we provide (1) an overview of empirical findings on cognitive offloading and (2) an outlook on how individuals' offloading behavior might change in an AI-enhanced future. More specifically, we first discuss determinants of offloading such as the design of technical tools and links to metacognition. Furthermore, we discuss benefits and risks of cognitive offloading. While offloading improves immediate task performance, it might also be a threat for users' cognitive abilities. Following this, we provide a perspective on whether individuals will make heavier use of AI-technologies for offloading in the future and how this might affect their cognition. On one hand, individuals might heavily rely on easily accessible AI-technologies which in return might diminish their internal cognition/learning. On the other hand, individuals might aim at enhancing their cognition so that they can keep up with AI-technologies and will not be replaced by them. Finally, we present own data and findings from the literature on the assumption that individuals' personality is a predictor of trust in AI. Trust in modern AI-technologies might be a strong determinant for wider appropriation and dependence on these technologies to distribute cognition and should thus be considered in an AI-enhanced future.

10,303 views

7 citations