This chapter discusses the Ontogeny Phylogeny Model (OPM), which focuses on the formation and development of second language phonological systems. It proposes an interrelationship between L2 native-like productions, L1 transfer, and universal factors. The model argues that chronologically, and as style becomes increasingly formal, L2 native-like processes increase, L1 transfer processes decrease, and universal processes increase and then decrease. It further claims that the roles of universals and L1 transfer are mediated by markedness and similarity, both of which slow L2 acquisition. Specifically, in similar phenomena L1 transfer processes persist, while in marked phenomena universal processes persist. The OPM also argues that these same principles obtain for learners acquiring more than one L2, monolingual and bilingual acquisition, and L1 attrition. In addition to the chronological stages and variation of the individual learner, the model claims that these relationships hold true for language variation and change, including pidgins and creoles.

Experience is the cornerstone of Epicurean philosophy and nowhere is this more apparent than in the Epicurean views about the nature, formation, and application of concepts. ‘The Epicureans on Preconceptions and Other Concepts’ by Gábor Betegh and Voula Tsouna aims to piece together the approach to concepts suggested by Epicurus and his early associates, trace its historical development over a period of approximately five centuries, compare it with competing views, and highlight the philosophical value of the Epicurean account on that subject. It is not clear whether, properly speaking, the Epicureans can be claimed to have a theory about concepts. However, an in-depth discussion of the relevant questions will show that the Epicureans advance a coherent if elliptical explanation of the nature and formation of concepts and of their epistemological and ethical role. Also, the chapter establishes that, although the core of the Epicurean account remains fundamentally unaffected, there are shifts of emphasis and new developments marking the passage from one generation of Epicureans to another and from one era to the next.

In Chapter 5, we discuss the processing components that underlie the perspective-taking analogy that we articulated in Chapter 2. This analysis makes it clear that the retrieval of personal knowledge and experience is critical, and we review some of what is known about episodic retrieval and how it can be used in this context. In forming an analogy, one must be able to identify how elements of the story world are related to corresponding elements in one’s own experience. To understand this process, we discuss how readers must construct similarity relations. Finally, we discuss the mechanics of analogy formation per se and describe the notion of a structural mapping between the reader and the character that underlies the perspective-taking analogy. We close out Chapter 5 with a discussion of perspective-taking dynamics. This includes an illustration of how perspective taking can be driven by the events of the story world or evaluations of the character. As we make clear, perspective taking is an ongoing process that can unfold in a variety of ways over the course of reading a narrative.

This chapter explores interpersonal attraction, the subjective appeal of another person, which is often accompanied by a positive emotional reaction and an affiliative motivation for greater closeness to that person. This chapter organizes the many specific traits that enhance attraction in terms of characteristics that offer domain-general rewards (e.g., pleasure, self-esteem, belonging) and characteristics that advance specific evolutionary goals (e.g., survival, reproduction). The chapter then reviews the characteristics that are most consistently desirable, including physical attractiveness, social status, warmth/kindness, intelligence, proximity, familiarity, similarity, and reciprocity by reviewing relevant research findings, as well as exceptions and boundary conditions. The chapter ends with a review of how sociocultural factors, including the immediate situation, women’s reproductive cycles, and the broader relationship trajectories provide context for understanding romantic attraction.

Chapter 2: Linearly independent lists of vectors that span a vector space are of special importance. They provide a bridge between the abstract world of vector spaces and the concrete world of matrices. They permit us to define the dimension of a vector space and motivate the concept of matrix similarity.

Inductive reasoning involves using existing knowledge to make predictions about novel cases. This chapter reviews and evaluates computational models of this fundamental aspect of cognition, with a focus on work involving property induction. The review includes early induction models such as similarity coverage, and the feature-based induction model, as well as a detailed coverage of more recent Bayesian and connectionist approaches. Each model is examined against benchmark empirical phenomena. Model limitations are also identified. The chapter highlights the major advances that have been made in our understanding of the mechanisms that drive induction, as well as identifying challenges for future modeling. These include accounting for individual and developmental differences and applying induction models to explain other forms of reasoning.

Analogy is a core cognitive capacity encompassing basic similarity (“this is like that”), relational similarity (proportional analogies of the form A:B::C:x), and complex system mappings, in which the elements of one situation are structurally aligned with the elements of another. The latter permits complex inferences from a known source situation to a less familiar target situation. Because of its centrality in human thinking, analogy has been the subject of numerous computational modeling efforts. Models of similarity come from multiple traditions in cognitive science, including associationist approaches (such as connectionist models), “traditional” symbolic approaches (such as graph matching and production systems), and hybrid symbolic/connectionist approaches. This chapter reviews and evaluates several models from these various approaches in terms of their ability to simulate basic similarity, relational similarity, and system mapping.

This chapter provides an overview of approaches to formal modeling in the domain of categorization. The core psychological processes addressed by models are: generating a classification decision in response to a stimulus and constructing category representations based on supervised experience. A taxonomy is provided that organizes the formal models in terms of their use of a fixed, combined, or constructed approach to predicting categories under either a cue-based or item-based framework. The chapter gives in-depth coverage of a leading approach (exemplar models) as well as an emerging alternative: a constructed cue-based model (DIVA) that differs from competing accounts by learning to reconstruct the input features via sets of category-specific weights and using the degree of reconstructive success (i.e., goodness-of-fit to the category) to determine the likelihood of membership.

This chapter introduces the basic patterns of Chinese comparison sentences, emphasizing that there are no comparative adjectives in Chinese. Attention is drawn to the features of the special constructions of 比 bǐ and 跟 gēn constructions and their negation forms.

Sign systems help to create descriptive and depictive representations. Descriptive representations operate on symbols. They are based on conceptual analyses identifying objects or events as well as attributes and interrelations. Attributes and relations are ascribed by predications to entities according to syntactic rules resulting in so-called propositions (“idea units”). These propositions can be integrated into coherent semantic networks. Propositional representations are considered as mental structures which can be externalized in the form of spoken or written texts. Despite their informational incompleteness, descriptions have high representational power. Depictive representations are based on inherent commonalities between a representing object and the represented subject matter. The inherent commonalities can be based on similarity or analogy. These representations are complete with regard to a certain class of information. Due to their completeness and consistency and because information can be read off directly, depictive representations have high computational efficiency.

Chapter 5 examines how considerations of coherence manifest in the use of analogical reasoning by investor-state tribunals. In particular, it demonstrates through concrete examples and case studies that the persuasiveness and correctness of an arbitral award based on analogical reasoning depends on the degree of its internal coherence. It is argued that coherence in an analogical inference manifests in two ways. Firstly, in a methodological sense, coherence manifests itself in the way the adjudicator frames the legal question at issue and in the degree to which the analogy, as drawn, satisfies the elements of similarity, structural parallels, and purposiveness. Secondly, in a substantive sense, coherence manifests itself in the normative contextualisation of the legal question and in the moral appeal of the proposed interpretation derived from the analogy.

The similarity principle is based on the acknowledgment that a river, if left alone for a sufficiently long time with fixed values of water and sediment discharge loads, will adjust its width, depth, slope, and meandering pattern in a certain manner. If the values of water and sediment discharge loads imposed on the river change are different, the adjustment will be made similarly. This chapter derives the hydraulic geometry using this principle of similarity.

This chapter gets into the techniques of data analytics, focusing on the three pillars of data mining, namely clustering, classification, and association rule mining, and how each can be used for cybersecurity. This chapter can be seen as a crash course in data mining. It begins with an understanding of the overall knowledge discovery and data mining process models and follows the elements of the data life cycle. This chapter outlines foundational elements such as measures of similarity and measures of evaluation. It outlines the landscape of various algorithms in clustering, classification, and frequent and rare patterns.