Children's Divergent Thinking Improves When They Understand False Beliefs

This research utilized longitudinal and cross sectional methods to investigate the relation between the development of a representational theory of mind and children's growing ability to search their own minds for appropriate problem solutions. In the first experiment 59 pre-school children were given three false-belief tasks and a divergent thinking task. Those children who passed false-belief tasks produced significantly more items, as well as more original items, in response to divergent thinking questions than those children who failed. This significant association persisted even when chronological age, verbal and nonverbal general ability were partialed out. In a second study 20 children who failed the false-belief tasks in the first experiment were re-tested three months later. Again, those who now passed the false-belief tasks were significantly better at the divergent thinking task than those who continued to fail. The associations between measures of divergent thinking and understanding false-beliefs remained significant when controlling for the covariates. Earlier divergent thinking scores did not predict false-belief understanding three months later. Instead, children who passed false-belief tasks on the second measure improved significantly in relation to their own earlier performance and improved significantly more than children who continued to fail. False-belief task performance was significantly correlated to the amount of intra-individual improvement in divergent thinking even when age, verbal and nonverbal skills were partialed out. These findings suggest that developments in common underlying skills are responsible for the improvement in understanding other minds and searching one's own. Changes in representational and executive skills are discussed as potential causes for the improvement

Get nothing wrong:perspectives on the functions and fallibilities of professionals and algorithmic technologies in law and justice

peer reviewed38th EGOS Colloquium 2022 Sub-theme 31: Imperfect Knowledge: Re-examining the Role of Experts and Expertise 7-9th July 2022, Vienna, Austria “Get nothing wrong”: perspectives on the functions and fallibilities of professionals and algorithmic technologies in law and justice Christophe Dubois (University of Liège) [email protected] James Faulconbridge (Lancaster University) [email protected] Frida Pemer (Stockholm School of Economics) [email protected] Caroline Ruiner (University of Hohenheim) [email protected] Aline P. Seepma (University of Groningen) [email protected] Tale Skjølsvik (Oslo Metropolitan University) [email protected] Martin Spring (Lancaster University) [email protected] Introduction In this paper, we explore questions about the definition and constitution of expertise and experts as algorithmic technologies impact professional work. It is the aim of this paper to analyse the effects the implementation of Artificial Intelligence (AI) on professions, focusing on law and justice. We draw on Eyal’s (2019: 26) typology of different conceptions of expertise, and analyses that disaggregate expert work at the level of tasks (Sampson, 2020), to identify “what experts do”. This allows us to examine differing degrees and forms of expertise in different facets of expert work (Dreyfus and Dreyfus, 2005). Part of a professional logic is to “get nothing wrong”, yet the use of algorithmic technologies introduces new sources of imperfection, as well as revealing existing (human) ones. Based on the introduction of cases in law using algorithmic technologies, we propose a framework for understanding the different ways algorithmic technologies do and do not reconstitute the different roles and practices of professional experts