Chapter 2 discusses the common belief that people different from us all look alike and act alike. The outgroup homogeneity effect, as it is called, is rooted in normal categorization processes that become oversimplified. Categorization produces a range of tendencies that contribute to prejudice such as stereotyping, inaccurate attributions, ingroup favoritism, outgroup derogation, dehumanization, even scapegoating and genocide. These phenomena are explained and connected to contemporary events such as anti-Asian hate crimes during the Covid 19 pandemic. Chapter 2 ends with strategies for change that include intergroup contact, creating more complex social identities, and cooperative learning.

This chapter introduces chief postulates common to usage-based (UB) approaches to language. The UB approach maintains that speakers’ experiences with language shape how language is stored. Experiences with specific words and word combinations in particular linguistic, discursive, and social contexts accrue in memory and subsequently contribute to patterns of variability evident in speech productions. Usage-based approaches regularly consider independent effects on lexical representations of decontextualized prior probabilities (e.g. phone/word/bigram frequencies, type frequencies), and, increasingly, contextually informed counts (e.g. lexical items’ cumulative exposure to conditioning effects of the production contexts, phone/word probabilities) are considered. This chapter offers an overview of studies exploring the connection between usage patterns and bilingual sound systems as well as studies exploring evidence of interlingual influence arising from bilingual lexical storage (schematic ties in memory). The chapter suggests potential avenues for future UB research into bilingual phonetics and phonology.

Chapter 3 introduces another cognitive mechanism called categorization. Categorization is an automatic and unconscious cognitive process that enables our limited cognitive capacities to understand and organize information, make predictions, and respond to new situations. Notable crosslinguistic variation can be found in conceptual categories. Such variation can be manifested in morphemes, words, grammar, phonology, and other levels of linguistic structure. The theory of linguistic relativism suggests that linguistic categories may implicitly affect how we categorize objects and events. Consequently, learning L2-specific categories generally entails a certain degree of conceptual recategorization.

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.

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.

Categorization is the process of assigning an object or event to a behaviorally relevant group. Before the 1990s, almost nothing was known about the neural networks and processes that mediate human categorization. As a result, theories of categorization were dominated by purely cognitive descriptions. The cognitive neuroscience revolution dramatically increased our understanding of the neural bases of human categorization. As a result, models grounded in neuroscience are becoming increasingly popular. Virtually all of these models assume that different neural systems mediate learning in different types of categorization tasks. Collectively, these models have already made profound contributions to our understanding of human categorization, by widening the empirical domain of categorization research, and by motivating experiments that might not otherwise have been run. Furthermore, this trend should increase in the future, as methods for studying the functioning human brain improve and the database of human brain function during categorization grows.

Statistical decision theory provides a general account of perceptual decision-making in a wide variety of tasks that range from simple target detection to complete identification. The fundamental assumptions are that all sensory representations are inherently noisy and that every behavior, no matter how trivial, requires a decision. Statistical decision theory is referred to as signal detection theory (SDT) when the stimuli vary on only one sensory dimension, and general recognition theory (GRT) when the stimuli vary on two or more sensory dimensions. SDT and GRT are both reviewed. The SDT review focuses on applications to the two-stimulus identification task and multiple-look experiments, and on response-time extensions of the model (e.g., the drift-diffusion model). The GRT review focuses on applications to identification and categorization experiments, and in the former case, especially on experiments in which the stimuli are constructed by factorially combining several levels of two stimulus dimensions. The basic GRT properties of perceptual separability, decisional separability, perceptual independence, and holism are described. In the case of identification experiments, the summary statistics method for testing perceptual interactions is described, and so is the model-fitting approach. Response time and neuroscience extensions of GRT are reviewed.

This chapter discusses psychological constructionist theories (PCTs). PCTs reject the existence of affect programs as special-purpose mechanisms for the phenomena called emotions. PCTs come in a two-factor version, endorsed by Schachter (1964) and Barrett (2017b), and a multi-factor version, endorsed by Russell (2009). Two-factor PCTs propose that emotions result from the combination of (a) diffuse bodily feelings and (b) a construction process that binds these felt bodily responses to external stimuli and produces a labeled feeling. The multi-factor PCT proposes that the phenomena that people call emotions are composed of many components. The categorization of these components does not result in an emotion per se, but an emotional self-ascription. Two-factor PCTs can deliver discrete emotions. The multi-factor theory does not deliver discrete emotions, but can nevertheless make sense of them. Empirical research that tests two-factor PCTs is discussed.

It is a traditional hope of comparative psychology that animal minds might be unitary, parsimonious, associative. In contrast, cognitive researchers acknowledge multiple learning systems, including humans’ capacity for explicit hypothesis testing and rule learning. The authors describe new paradigms that may dissociate the explicit from the associative and demonstrate animals’ explicit capabilities. These paradigms include matched tasks that foster explicit or associative category learning, and paradigms that disable crucial components of associative learning. Given this disabling, animals may adopt instead an alternative, more explicit learning system. The authors review this area, including research on humans, monkeys, rats, and pigeons. They also consider the evolutionary and fitness factors that might favor the development of complementary associative and explicit learning systems.

Chapter 2 presents a review of earlier studies that were mostly based on the assumption that general extenders are used with a referential function, are content-oriented, signal categorization and can be analyzed as set-marking tags or vague category identifiers. In the clearest cases, which are typically longer forms, they can be analyzed in terms of semantic features, shared with an antecedent, that identify the category involved. In other cases, there may be reference to an ad hoc category, also described as a non-lexicalized category, rather than one that is already lexicalized. The use of adjunctive forms as list completers is also normally interpreted in terms of set-marking.Adjunctive forms are also shown to be indicators of intertextuality. Long forms described as SKT tags incorporate reference to a "kind," indicating that classification or categorization is being signaled. A final section is dedicated to specific extenders, typically used with clear referential function.

This essay is a narrative of my work on Elizabeth Bishop, beginning with my Ph.D. dissertation (1976) and detailing my choices in the three landmark volumes I edited: Elizabeth Bishop and Her Art (University of Michigan Press, 1983) – the first collection of critical work on Bishop, which includes a section of her previously uncollected writing; Elizabeth Bishop: Poems, Prose, and Letters (Library of America, 2008), the first volume to include almost all of her published and major posthumously published and unpublished work; and Elizabeth Bishop: Prose (FSG, 2011), the first substantially complete independent collection of her prose works published to celebrate her centennial, which includes her significant correspondence with poet Anne Stevenson and the closest possible restoration of her book Brazil to what she originally intended, before the editors of Life rewrote it. The chapter ends with the “rescue” of one of her major unpublished poems.

This chapter reviews research on visual categorization in pigeons including (1) basic-level categories, members of which are perceptually similar to each other (e.g., car or chair), (2) subordinate-level categories representing a subclass of a basic level category (e.g.,office chair or sports car), and (3) superordinate-level categories comprising several basic-level categories (e.g., furniture or vehicle).Current research convincingly demonstrates pigeons’ ability to form these categories. Moreover, pigeons’ basic-level categories appear to be similar to those of humans. However, the extent of similarity between superordinate-level and subordinate-level categories in pigeons and humans is not yet clear.

In this chapter, we discuss the hypothesis people help to regulate each other’s bodies (for better or for worse), and this is a main mechanism through which culture wires a human brain. Cultural transmission prepares the developing brain and body to meet recurrent demands within a particular cultural context, thereby supporting the development of an internal model that is sufficiently tuned to specific environments. In this way, a human brain becomes wired to run a model of the world that will control the body in an efficient, predictive manner. Our approach provides an empirically inspired account of how a human brain becomes a cultural artifact.