Self-directedness, integration and higher cognition

In this paper I discuss connections between self-directedness, integration and higher cognition. I present a model of self-directedness as a basis for approaching higher cognition from a situated cognition perspective. According to this model increases in sensorimotor complexity create pressure for integrative higher order control and learning processes for acquiring information about the context in which action occurs. This generates complex articulated abstractive information processing, which forms the major basis for higher cognition. I present evidence that indicates that the same integrative characteristics found in lower cognitive process such as motor adaptation are present in a range of higher cognitive process, including conceptual learning. This account helps explain situated cognition phenomena in humans because the integrative processes by which the brain adapts to control interaction are relatively agnostic concerning the source of the structure participating in the process. Thus, from the perspective of the motor control system using a tool is not fundamentally different to simply controlling an arm

Modelling dynamic decision making with the ACT-R cognitive architecture

This paper describes a model of dynamic decision making in the Dynamic Stocks and Flows (DSF) task, developed using the ACT-R cognitive architecture. This task is a simple simulation of a water tank in which the water level must be kept constant whilst the inflow and outflow changes at varying rates. The basic functions of the model are based around three steps. Firstly, the model predicts the water level in the next cycle by adding the current water level to the predicted net inflow of water. Secondly, based on this projection, the net outflow of the water is adjusted to bring the water level back to the target. Thirdly, the predicted net inflow of water is adjusted to improve its accuracy in the future. If the prediction has overestimated net inflow then it is reduced, if it has underestimated net inflow it is increased. The model was entered into a model comparison competition-the Dynamic Stocks and Flows Challenge-to model human performance on four conditions of the DSF task and then subject the model to testing on five unseen transfer conditions. The model reproduced the main features of the development data reasonably well but did not reproduce human performance well under the transfer conditions. This suggests that the principles underlying human performance across the different conditions differ considerably despite their apparent similarity. Further lessons for the future development of our model and model comparison challenges are considered

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Fostering shared knowledge with active graphical representation in different collaboration scenarios

This study investigated how two types of graphical representation tools influence the way in which learners use shared and unshared knowledge resources in two different collaboration scenarios, and how learners represent and transfer shared knowledge under these different conditions. Moreover, the relation between the use of knowledge resources, representation, and the transfer of shared knowledge was analyzed. The type of graphical representation (content-specific vs. content-unspecific) and the collaboration scenario (video conferencing vs. face-to-face) were varied. 64 university students participated. Results show that the learning partners converged in their profiles of resource use. With the content-specific graphical representation, learners used more appropriate knowledge resources. Learners in the computer-mediated scenarios showed a greater bandwidth in their profiles of resource use. A relation between discourse and outcomes could be shown for the transfer but not for the knowledge representation aspectIn dieser Studie werden die Wirkungen von verschiedenen Arten graphischer Repräsentation auf die Nutzung geteilter und ungeteilter Wissensressourcen in zwei verschiedenen Kooperationsszenarien untersucht. Des Weiteren wird analysiert, wie Lernende geteiltes und ungeteiltes Wissen unter diesen verschiedenen Bedingungen repräsentieren und transferieren. Schließlich wird die Beziehung zwischen der Nutzung von Wissensressourcen auf der einen Seite sowie der Repräsentation und dem Transfer geteilten Wissens auf der anderen Seite geprüft. Mit der Art der graphischen Repräsentation (inhaltsspezifisch vs. inhaltsunspezifisch) und dem Kooperationsszenario (Videokonferenz vs. face-to-face) werden zwei Faktoren experimentell variiert. 64 Studierende nahmen an der Studie teil. Ergebnisse zeigen, dass die Lernpartner in ihren Profilen der Ressourcennutzung konvergierten. Lernende, die durch die inhaltsspezifische graphische Repräsentation unterstützt wurden, verwendeten angemessenere Wissensressourcen. Lernende in den computervermittelten Szenarien weisen eine größere Bandbreite in ihren Profilen der Ressourcennutzung auf. Eine direkte Wirkung vom Diskurs der Lernenden auf die Entwicklung geteilten Wissens konnte für den Transfer, aber nicht für die Wissensrepräsentation gezeigt werde

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings

How Do Gestures Influence Thinking and Speaking? The Gesture-for-Conceptualization Hypothesis.

Peer reviewedPostprin

Solving Tree Problems with Category Theory

Artificial Intelligence (AI) has long pursued models, theories, and techniques to imbue machines with human-like general intelligence. Yet even the currently predominant data-driven approaches in AI seem to be lacking humans' unique ability to solve wide ranges of problems. This situation begs the question of the existence of principles that underlie general problem-solving capabilities. We approach this question through the mathematical formulation of analogies across different problems and solutions. We focus in particular on problems that could be represented as tree-like structures. Most importantly, we adopt a category-theoretic approach in formalising tree problems as categories, and in proving the existence of equivalences across apparently unrelated problem domains. We prove the existence of a functor between the category of tree problems and the category of solutions. We also provide a weaker version of the functor by quantifying equivalences of problem categories using a metric on tree problems.Comment: 10 pages, 4 figures, International Conference on Artificial General Intelligence (AGI) 201