This chapter brings to light the main conclusions of this work, while also presenting generalizable principles for future research to expand on. This part of the book contributes to the theoretical study of mediation and aims to encourage further first-level analysis approaches to the study of mediation and mediators. Most importantly, it clarifies the actual input of Kofi Annan, Lakhdar Brahimi, and Staffan de Mistura to the mediation efforts in Syria. It leaves readers with a detailed record of what Kofi Annan, Lakhdar Brahimi, and Staffan de Mistura have done in their roles as mediators in Syria and presents a comprehensive analysis of the dynamics that shaped their decision-making. Outside the cases of these three mediators, it introduces a method by which to forensically identify a mediator’s fingerprints on the mediation process and charts a map to guide investigations into the decision-making processes of mediators. In doing so, it hopefully serves to encourage steps toward developing institutional mechanisms to evaluate the performance of these mediators – to hold them accountable for their successes and their failures.

The Introduction outlines the main objectives of the book: to pinpoint the agency of UN mediators as decision-makers and explain the strategic-thinking behind their decision-making. To do so, it bridges the literature surrounding levels of analysis in international relations and foreign policy analysis with scholarship focused on mediation. By bridging knowledge from scholars who have focused on first-level analysis, or the role of the individual in shaping policy, this chapter finds that mediators are an opportune case-study for such a method. Ultimately, by exploring the world of the mediator, with the tools developed in foreign policy analysis, the political behavior of these individuals and the phenomena of mediation can be better explained. Given the focus on UN mediators and the Syrian conflict, the chapter ends with an institutional analysis of the margin of maneuver UN mediators have as well as a detailed discussion on why the UN’s mediation process in Syria is linked to its raison d’être.

This introductory chapter delves into the inception of developmental cognitive neuroscience, a field shaped by historical inquiries into brain development, childhood learning, and the nature–nurture debate. We trace the origins of this interdisciplinary endeavor, revealing how it has emerged as a pioneering approach to comprehending human development. In this chapter, we dissect the core components of developmental cognitive neuroscience: development, cognition, and neuroscience. We elucidate their interconnectedness, underpinning theories, and evolving methodologies, spotlighting the transformative impact of recent technological strides. Throughout the book, our emphasis remains on the synthesis of these elements, illustrating their collective role in advancing our comprehension of human development. This chapter establishes the groundwork for an engaging exploration of the intricate interplay between brain maturation, cognitive processes, and the unfolding of human potential.

Developmental Cognitive Neuroscience (DCN) has made significant strides since its inception in the late 1980s. In this concluding chapter, we celebrate the progress made in understanding brain development from prenatal stages to adulthood, exploring genetics, epigenetics, neural foundations, and their connections to cognitive and socioemotional growth. While DCN research has gained public and policy attention, there are still theoretical, methodological, and practical challenges ahead, with the field being relatively young and open to exploration. We highlight key takeaways, emphasizing the intricate relationships between brain development, cognition, and the environment. The chapter discusses ongoing limitations and emerging research areas, aiming to inspire future researchers, particularly graduate students, to explore these promising directions. Lastly, we explore the broader societal impact of DCN research, showcasing its potential to deepen our understanding of human development and learning, bridging the gaps between genes, brain structure and function, and environmental influences. DCNs evolution promises to enrich our knowledge of human development and learning, offering insights that can benefit society.

The first, introductory, chapter explains the poor state of Chadic comparative linguistics and motivates the present study against the backdrop of previous research. It outlines the frames of reference and limitations of the study and justifies the double approach of multilateral comparative and micro-level dialectological analysis and comparison of data. It illustrates the spread of lexical innovations by reference to a set of schematic maps of isoglosses. Different formats of data representation are related to different levels of analysis. The structure of the book is outlined.

This book is about how to construct and use computational models of specific parts of the nervous system, such as a neuron, a part of a neuron or a network of neurons, as well as their measurable signals. It is designed to be read by people from a wide range of backgrounds from the neurobiological, physical and computational sciences. The word ‘model’ can mean different things in different disciplines, and even researchers in the same field may disagree on the nuances of its meaning. For example, to biologists, this term can mean ‘animal model’. In particle physics, the ‘standard model’ is a step towards a complete theory of fundamental particles and interactions. We therefore attempt to clarify what we mean by modelling and computational models in the context of neuroscience. We discuss what might be called the philosophy of modelling: general issues in computational modelling that recur throughout the book.

We present an overview of the role, benefits, and drawbacks of theory in scientific research, particularly in the social and behavioral sciences. We discuss what theory is and what it is not. We also focus on some key elements of theory such as its ability to explain phenomena at multiple parallel levels of analysis. Evolutionary theory is offered as an example that illustrates the importance of conceptual integration across different disciplines. We further describe the key characteristics of good theories, such as parsimony, depth, breadth, and coherence (both internal and external), and we encourage the use of “coherence stress-tests” to help refine theory. We then discuss 4 advantages and 10 disadvantages of using theory in social and behavioral science research. Finally, we suggest conceptual tools and provide a list of recommendations for theory-driven research. We hope this chapter will help in the complex pursuit of improving research practices in the social and behavioral sciences.

A research design is the sequence of things done in order to collect the information needed to answer a research question. The design states which data will be sought, from which sources, at which times, and in which ways. This chapter describes the influences that shape the decisions researchers must make when constructing a research design. Research designs differ in the source of information used, whether data used are naturally occurring or a result of intervention, and the way data are elicited, recorded, and analyzed. Typically, the nature of the research question, assumptions on which it is based, and ethical considerations drive the design construction. I describe seven major influences on design choice in this chapter: research question novelty; levels of analysis and explanation used; epistemological and ontological assumptions; characteristics of data sources; data analyzability; piloting results; and various practicalities. Understanding these influences will improve research design decisions.

Like Chapter 7, this chapter also discusses the sequencing of case study designs. Here we particularly focus on the deductive–inductive sequencing. Using an exemplar case study, we discuss how prediction outliers (deviant cases) identified during the initial study can guide the sequencing of designs in further stages. In the context of the example, we discuss the research question, theoretical sampling, cases, levels of analysis, and the potential requirement for additional data collection. Furthermore, we discuss the issue of omitted variable bias and internal validity in the context of sequenced case study designs. We end the chapter with a discussion on how to report sequenced case studies following deductive–inductive reasoning.

In this chapter, we move on from the archetypical to the sequenced case study designs. First of all, we discuss what sequencing case study designs entail. Using an illustrative example, we discuss one type of sequencing in detail (i.e. the inductive-deductive sequencing). In the context of the example, we discuss the research question, theoretical sampling, controls, cases, embedded units, and the levels of analysis involved in the sequenced design. Lastly, we briefly discuss how to report the sequenced case study design.

This chapter presents an idealized path towards theory development (for all kinds of theories), called the “demarcation-explanation” cycle. The cycle comprised four stages: (a) the provisional demarcation of the explanandum in a working definition, (b) the proposal of different types of explanations, (c) the validation of explanations in empirical research, (d) and the proposal of a scientific definition flowing from these explanations. The chapter also takes a closer look at (a) different types of definitions (working vs. scientific, intensional vs. extensional/diviso) and ways to evaluate the adequacy of scientific definitions, (b) different types of explanations (constitutive, causal, mechanistic, teleological) and levels of analysis, and (c) ingredients of mechanistic explanations such as representations, operations, operating conditions (related to automaticity), and related notions of dual-process/system models, rationality, and cognition.

This chapter describes the intellectual foundations that have influenced the learning sciences (LS) from its beginning, and identifies the core elements that unify many chapters of this handbook. Its theoretical influences include pragmatism, constructivism, sociocultural theory, situated learning, and distributed cognition. The chapter organizes LS research into two levels of analysis: the individual or elemental, and the sociocultural or systemic. The chapter reviews the methodologies that have been used to study each level of analysis and summarizes research findings at each level. LS research bridges research and practice and combines elemental and systemic perspectives on learning across a range of timescales of human behavior.

Much social science still fails to employ a now standard philosophical and scientific conceptualization of levels of analysis.In so doing, it still largely avoids the multilevel reasoning appropriate for social science, instead maintaining dogmatic attachments to one or another level of analysis, especially an individual-level one.

Discusses empirical examples showing how the preference for individual-level explanation – that is, for psychological, social-psychological, and microeconomic explanation – has limited the explanation of a wide range of macrosocial outcomes: of European economic history, of postindustrial development, of world economic development and globalization generally.

Works in detail through important empirical examples – including a canonical one from Max Weber – showing how supra-individual social causal processes often provide more sensible depictions of historical processes than do cumbersome ones imagining highly autonomous individual and organizational actors.

Mechanistic explanations of personality functioning span a broad range, as is evidenced in the thoughtful commentary provided by Crowell et al. Mechanistic explanations of behavior and experience are complicated, however, by vague boundaries between potential causes and effects, across different levels of analysis as well as within the same level. This vagueness creates challenges in developing theories of how factors at lower levels of analysis are related to those at higher levels of analysis, as well as in disentangling causes and effects in general. Personality pathology is further complicated by relatively rare or even unique psychological and behavioral patterns of interest, and the long timescales over which development occurs. These challenges are important targets for future research on mechanistic explanations of personality.

Psychopathologists have been arguing forever about whether psychiatric syndromes are some variety of “biological,” “genetic,” or “brain disorders.”This argument has been hobbled by the lack of a coherent theory of what it means for a complex behavioral entity to be biological, or what the alternative to being biological might be. In the absence of a well-articulated theory, there has been a tendency to assume that the question at issue is whether or not behavioral entities are correlated with chemical, neurological, or genetic variables; and since the answer to this question is always affirmative, the consensus has generally been to move psychiatric syndromes farther and farther down the hierarchy of the sciences. I make the case that questions of whether complex behavioral categories correlate with physical ones is misplaced. Instead, I suggest that questions about the proper location of entities involve determining the level at which the entity is most clearly defined; at other levels the entity is out of focus: detectible but blurry. Blurry entities are poor candidates for successful scientific analysis.