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This chapter is an analysis of the frequency-power spectrum. It first discusses the probabilistic methods for detecting signatures of individual modes in the spectrum, including schemes that incorporate a Bayesian framework for the analysis. The chapter then turns to the automated detection of the ensemble signatures of the oscillations. Next, this chapter considers the detailed peak-bagging analysis, where a multiparameter model is fitted to the spectrum to provide robust estimates of parameters of the individual modes. It introduces the fundamentals, discusses the practical implementation and challenges, and finally considers the additional challenges posed by subgiants and red giants showing mixed modes.

This chapter discusses the importance of accounting information networks (AINs). More and more firms have refocused and vertically disaggregated, relying on small units which can involve both internal (within firm) and external (between firms) networks. The stability of such mixed mode structures requires the design of either AINs with a high degree of centrality or distributed AINs.

This chapter explores the fundamental categories of Locke's ontology. It argues that these are substance and mode. He conceives of substances as ontologically independent things or stuffs. His notion of mode is non-standard, including events as well as features of substances. When the idea of a feature contains different kinds of ideas as parts, Locke calls this the idea of a mixed mode. He sometimes seems to deny the reality of mixed modes, but this chapter makes the case that he does not really mean to do so. However, it does argue that he means to deny the existence of relations.

In this chapter, we develop theory and methodology to support mode adjustment and hindcasting/forecasting in the presence of different possible mode effect types, including additive effects and odds-multiplicative effects. Mode adjustment is particularly important if the ultimate goal is to report one aggregate estimate of response parameters, and also to allow for comparison to historical surveys performed with different modes. Effect type has important consequences for inferential validity when the baseline response changes over time (i.e. when there is a time trend or time effect). We present a methodology to provide inference for additive and odds-multiplicative effect types, and demonstrate its performance in a simulation study. We also show that if the wrong effect type is assumed, the resulting inference can be invalid as confidence interval coverage is greatly reduced and estimates can be biased.

In addition to reviewing the main findings and themes of the book, the final chapter grapples with a central issue in the design of mixed-mode surveys. Should researchers try to minimize differences across modes by avoiding mode-dependent features or should they try to take advantage of the strengths of each mode? These two approaches are sometimes called the unimodeand the best practicesapproaches, respectively. The chapter presents a mathematical model for mode effects. The model implies that when the purpose of the survey is to produce point estimates, particularly of factual phenomena, the best practices approach is generally most appropriate; however, when the point of the survey is to make comparisons across groups, especially for subjective phenomena, the unimode approach is generally the most appropriate. Finally, the chapter presents a list of recommendations for practice derived from the empirical findings discussed in the book.

This chapter discusses complex ideas, concentrating on those Locke calls modes, arguing for a non-idealist interpretation of those he calls relations, and introducing the one he calls substance. It analyzes the simple modes—space, time, and number—by untangling Locke’s language, which leaves unclear whether they are modes of the simple idea from which they are constructed (e.g., making 117 mode of 1) or of a general idea under which they fall (e.g., making 117 a mode of “number”), and by addressing questions about the simplicity of the ideas from which they are constructed and about the simple modes’ infinity. It contrasts them with mixed modes and discusses the latter’s construction. The chapter defends James Gibson’s view that for ideas of reflection, the relation between mode and simple idea isn’t that of whole to part, but of a universal to its determinations (e.g., making “remembrance” a mode of “perception, or thinking”).

This chapter examines the default mode network (DMN) and its role in delusion. It considers the nature of default processing, its neural substrate, cognitive properties, and role in the cognitive hierarchy. It explains how the DMN evolved to produce narrative fragments that enable mental time travel: the simulation of episodes of a subject's life to enable planning, deliberation, and reflection. The representations produced by the default system are essentially indexical: they provide a first-person perspective on information in virtue of their dependence on circuitry involved in motivational and emotional processes. It also discusses decontextualized processing and how it supervises default processing, allowing subjective narratives to be revised to fit reality. It shows that, in a delusional mind, this level of supervision is unavailable or compromised, leaving the subject at the mercy of his/her default system. Finally, the chapter explores delusion as a “mixed mode” of the DMN, along with the correlation between the first-person perspective and decontextualization.