Efficient reuse of computations in planning
Solving complex planning problems efficiently and flexibly requires reusing expensive previous computations. The brain can do this, but how? I present a new theory that addresses this question and connects planning to hitherto distinct areas within cognitive neuroscience, such as entorhinal representation of cognitive maps and cognitive control.
Assessing the potential for learning analogy problem-solving: does EF play a role?
Analogical reasoning is related to everyday learning and scholastic learning and is a robust predictor of g. Therefore, children's ability to reason by analogy is often measured in a school context to gain insight into children's cognitive and intellectual functioning. Often, the ability to reason by analogy is measured by means of conventional, static instruments. Static tests are criticised by researchers and practitioners to provide an overview of what individuals have learned in the past and for this reason are assumed not to tap into the potential for learning, based on Vygotsky's zone of proximal development. This seminar will focus on children's potential for reasoning by analogy, as measured by means of a dynamic test, which has a test-training-test design. In so doing, the potential relationship between dynamic test outcomes and executive functioning will be explored.
Analogical Reasoning Plus: Why Dissimilarities Matter
Analogical reasoning remains foundational to the human ability to forge meaningful patterns within the sea of information that continually inundates the senses. Yet, meaningful patterns rely not only on the recognition of attributional similarities but also dissimilarities. Just as the perception of images rests on the juxtaposition of lightness and darkness, reasoning relationally requires systematic attention to both similarities and dissimilarities. With that awareness, my colleagues and I have expanded the study of relational reasoning beyond analogous reasoning and attributional similarities to highlight forms based on the nature of core dissimilarities: anomalous, antinomous, and antithetical reasoning. In this presentation, I will delineate the character of these relational reasoning forms; summarize procedures and measures used to assess them; overview key research findings; and describe how the forms of relational reasoning work together in the performance of complex problem solving. Finally, I will share critical next steps for research which has implications for instructional practice.
Abstraction and Analogy in Natural and Artificial Intelligence
In 1955, John McCarthy and colleagues proposed an AI summer research project with the following aim: “An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” More than six decades later, all of these research topics remain open and actively investigated in the AI community. While AI has made dramatic progress over the last decade in areas such as vision, natural language processing, and robotics, current AI systems still almost entirely lack the ability to form humanlike concepts and abstractions. Some cognitive scientists have proposed that analogy-making is a central mechanism for conceptual abstraction and understanding in humans. Douglas Hofstadter called analogy-making “the core of cognition”, and Hofstadter and co-author Emmanuel Sander noted, “Without concepts there can be no thought, and without analogies there can be no concepts.” In this talk I will reflect on the role played by analogy-making at all levels of intelligence, and on prospects for developing AI systems with humanlike abilities for abstraction and analogy.