Topic: human cognition

Since the introduction of the term explainable artificial intelligence, many contrasting definitions and methods have been proposed. A key issue is that most of the extant explanations are not sufficiently clear either to practitioners or to users. While some researchers use interpretation algorithms as post-hoc explanations (Samek et al., 2021; Ribeiro, 2016), others argue that we should use models which are interpretable in the first place (Rudin, 2019). Although the latter is important, developers are not always willing to sacrifice accuracy by choosing a less complex interpretable model. H

Machine learning researchers frequently focus on human-level performance, in particular in games. However, in these applications human (or human-level) behavior is commonly reduced to a simple dot on a performance graph. Cognitive science, in particular theories of learning and decision making, could hold the key to unlock what is behind this dot, thereby gaining further insights into human cognition and the design principles of intelligent algorithms. However, cognitive experiments commonly focus on relatively simple paradigms such as restricted multi-armed bandit tasks. In this talk, I will

It is generally acknowledged that analogy is a core mechanism of human cognition, but paradoxically, analogies based on structural similarities would rarely be implemented spontaneously (e.g. without an explicit invitation to compare two representations). The scarcity of deep spontaneous analogies is at odds with the demonstration that familiar concepts from our daily-life are spontaneously used to encode the structure of our experiences. Based on this idea, we will present experimental works highlighting the predominant role of structural similarities in analogical retrieval. The educational

Cognitive maps have been proposed as an essential relational structure for human memory and thought (Tolman, 1948; Constantinescu et al., 2016). In this format, remembered entities are embedded in space and their relationships are encoded via concrete angles and Euclidean distances. Although much of higher human cognition may be spatial, cognitive maps are not the only possible relational structure. With a behavioral experiment we aim to challenge the extent of the cognitive map hypothesis and to investigate when and how humans represent knowledge via the relational structure of cognitive grap