Metacognition and Reflection by Interdisciplinary Experts: Insights from Cognitive Science and Philosophy

Interdisciplinary understanding requires integration of insights from different perspectives, yet it appears questionable whether disciplinary experts are well prepared for this. Indeed, psychological and cognitive scientific studies suggest that expertise can be disadvantageous because experts are often more biased than non-experts, for example, or fixed on certain approaches, and less flexible in novel situations or situations outside their domain of expertise. An explanation is that experts’ conscious and unconscious cognition and behavior depend upon their learning and acquisition of a set of mental representations or knowledge structures. Compared to beginners in a field, experts have assembled a much larger set of representations that are also more complex, facilitating fast and adequate perception in responding to relevant situations. This article argues how metacognition should be employed in order to mitigate such disadvantages of expertise: By metacognitively monitoring and regulating their own cognitive processes and representations, experts can prepare themselves for interdisciplinary understanding. Interdisciplinary collaboration is further facilitated by team metacognition about the team, tasks, process, goals, and representations developed in the team. Drawing attention to the need for metacognition, the article explains how philosophical reflection on the assumptions involved in different disciplinary perspectives must also be considered in a process complementary to metacognition and not completely overlapping with it. (Disciplinary assumptions are here understood as determining and constraining how the complex mental representations of experts are chunked and structured.) The article concludes with a brief reflection on how the process of Reflective Equilibrium should be added to the processes of metacognition and philosophical reflection in order for experts involved in interdisciplinary collaboration to reach a justifiable and coherent form of interdisciplinary integration. An Appendix of “Prompts or Questions for Metacognition” that can elicit metacognitive knowledge, monitoring, or regulation in individuals or teams is included at the end of the article

[How] Can Pluralist Approaches to Computational Cognitive Modeling of Human Needs and Values Save our Democracies?

In our increasingly digital societies, many companies have business models that perceive users’ (or customers’) personal data as a siloed resource, owned and controlled by the data controller rather than the data subjects. Collecting and processing such a massive amount of personal data could have many negative technical, social and economic consequences, including invading people’s privacy and autonomy. As a result, regulations such as the European General Data Protection Regulation (GDPR) have tried to take steps towards a better implementation of the right to digital privacy. This paper proposes that such legal acts should be accompanied by the development of complementary technical solutions such as Cognitive Personal Assistant Systems to support people to effectively manage their personal data processing on the Internet. Considering the importance and sensitivity of personal data processing, such assistant systems should not only consider their owner’s needs and values, but also be transparent, accountable and controllable. Pluralist approaches in computational cognitive modelling of human needs and values which are not bound to traditional paradigmatic borders such as cognitivism, connectionism, or enactivism, we argue, can create a balance between practicality and usefulness, on the one hand, and transparency, accountability, and controllability, on the other, while supporting and empowering humans in the digital world. Considering the threat to digital privacy as significant to contemporary democracies, the future implementation of such pluralist models could contribute to power-balance, fairness and inclusion in our societies

Why critical realism fails to justify critical social research

Many social scientists have argued that research should be designed to perform a ‘critical’ function, in the sense of challenging the socio-political status quo. However, very often, the relationship between the political value judgements underpinning this commitment and the values intrinsic to inquiry, as a distinct form of activity has been left obscure. Furthermore, the validity of those judgements has usually been treated either as obvious or as a matter of personal commitment. But there is an influential tradition of work that claims to derive evaluative and prescriptive conclusions about current society directly from factual investigation of its history and character. In the nineteenth century, Hegel and Marx were distinctive in treating the force of ethical and political ideals as stemming from the process of social development itself, rather than as coming from a separate realm, in the manner of Kant. Of course, the weaknesses of teleological meta-narratives of this kind soon came to be widely recognised, and ‘critical’ researchers rarely appeal to them explicitly today. It is therefore of some significance that, under the banner of critical realism, Bhaskar and others have put forward arguments that are designed to serve a similar function, while avoiding the problems associated with teleological justification. The claim is that it is possible to derive negative evaluations of actions and institutions, along with prescriptions for change, solely from the premise that these promote false ideas, or that they frustrate the meeting of needs. In this article I assess these arguments, but conclude that they fail to provide effective support for a 'critical' sociology

Measuring cognitive load and cognition: metrics for technology-enhanced learning

This critical and reflective literature review examines international research published over the last decade to summarise the different kinds of measures that have been used to explore cognitive load and critiques the strengths and limitations of those focussed on the development of direct empirical approaches. Over the last 40 years, cognitive load theory has become established as one of the most successful and influential theoretical explanations of cognitive processing during learning. Despite this success, attempts to obtain direct objective measures of the theory's central theoretical construct – cognitive load – have proved elusive. This obstacle represents the most significant outstanding challenge for successfully embedding the theoretical and experimental work on cognitive load in empirical data from authentic learning situations. Progress to date on the theoretical and practical approaches to cognitive load are discussed along with the influences of individual differences on cognitive load in order to assess the prospects for the development and application of direct empirical measures of cognitive load especially in technology-rich contexts

Innate talents: reality or myth?

Talents that selectively facilitate the acquisition of high levels of skill are said to be present in some children but not others. The evidence for this includes biological correlates of specific abilities, certain rare abilities in autistic savants, and the seemingly spontaneous emergence of exceptional abilities in young children, but there is also contrary evidence indicating an absence of early precursors of high skill levels. An analysis of positive and negative evidence and arguments suggests that differences in early experiences, preferences, opportunities, habits, training, and practice are the real determinants of excellence

Towards an alternative to Benner’s theory of expert intuition in nursing: A discussion paper

Several authors have highlighted the role of intuition in expertise. In particular, a large amount of data has been collected about intuition in expert nursing, and intuition plays an important role in the influential theory of nursing expertise developed by Benner (1984). We discuss this theory, and highlight both data that support it and data that challenge it. Based on this assessment, we propose a new theory of nursing expertise and intuition, which emphasizes how perception and conscious problem solving are intimately related. In the discussion, we propose that this theory opens new avenues of enquiry for research into nursing expertise

Science democratised = expertise decommissioned

Science and expertise have been antithetical forms of knowledge in both the ancient and the modern world, but they appear identical in today’s postmodern world, especially in Science & Technology Studies (STS) literature. The ancient Athenians associated science (epistemé) with the contemplative life afforded to those who lived from inherited wealth. Expertise (techné) was for those lacking property, and hence citizenship. Such people were regularly forced to justify their usefulness to Athenian society. Some foreign merchants, collectively demonised in Plato’s Dialogues as ‘sophists’, appeared so insulting to citizen Socrates, because they dared to alienate aspects of this leisured existence (e.g. the capacity for articulate reasoning) and repackage them as techniques that might be purchased on demand from an expert – that is, a sophist. In effect, the sophists cleverly tried to universalise their own alien status, taking full advantage of the strong analogy that Athenians saw between the governance of the self and the polis. Unfortunately, Plato, the original spin doctor, immortalised Socrates’ laboured and hyperbolic rearguard response to these sly and partially successful attempts at dislodging hereditary privilege..

The Pragmatic Turn in Explainable Artificial Intelligence (XAI)

In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post hoc interpretability that seems to be predominant in most recent literature

Critical thinking and systems thinking: towards a critical literacy for systems thinking in practice

About the book: In reflective problem solving and thoughtful decision making using critical thinking one considers evidence, the context of judgment, the relevant criteria for making the judgment well, the applicable methods or techniques for forming the judgment, and the applicable theoretical constructs for understanding the problem and the question at hand. In this book, the authors present topical research in the study of critical thinking. Topics discussed include developing critical thinking through probability models; the promotion of critical thinking skills through argument mapping; an instructional model for teacher training in critical thinking; advanced academic literacy and critical thinking and critical thinking and higher education