Describes the process of civil commitment. Defines competence to stand trial. Compares the laws regarding the “insanity defense.” Explains how mental health practitioners protect patients’ rights, including the right to treatment and the right to refuse treatment.

Distinguishes between malingering, factitious, and somatic symptom disorders. Discusses the various models and treatments for somatic symptom disorders. Describes the architecture of sleep. Discusses the various ways psychologists measure and track sleep. Describes the types of sleep--wake disorders. Discusses the various models and treatments for sleep disorders. Describes some of the techniques psychologists use as primary and adjunctive treatments for medical conditions. Lists some common medical conditions that are treated by psychological means.

We indicated in the concluding remarks of the previous chapter that feedforward neural networks have powerful modeling capabilities, as reflected by the universal approximation theorem. In one of its versions, the theorem asserts that networks with a single hidden layer are rich enough to model almost any arbitrary function.

Describes the symptoms of adjustment disorders. Identifies the symptoms of post-traumatic stress disorder and acute stress disorder. Describes the essential nature of dissociative disorders. Discusses the various treatments for the trauma- and stressor-related disorders. Identifies the symptoms of dissociative identity disorder, dissociative amnesia, and depersonalization/derealization disorder. Discusses the treatment of dissociative disorders.

In the last lecture we showed how “what is said” or conveyed in-and-by discourse indexically presumes upon or “indexically presupposes” certain conditions in the context of an emerging entextualization, revealed in the emerging metrical patterns of denotational information organized by deixis, repetition, parallelism (repetition with semantic substitutions), and on occasion by metricalizing discourse markers (Schiffrin 1987) like so, well, yeah, et cetera.

As social scientists we study the events of communication so as to reveal something about the way the order of social formations is experienced and sometimes revealed to those within. We study both the way these formations are drawn into events in which those within attempt to coordinate one with another and how such events are part and parcel of how such social formations come into being, persist over historical time, and are transformed. We are devoted to the goal of giving an adequate account of how we “do things with words” and with other modalities of interpersonal behavior, looking “upward” and “outward” from particular events to the framing sociocultural structures that give meaning and value to event-bound particulars. The received wisdom for how to go about the study of such coordination is to start with individual communicative events, senders and receivers, and the informational messages that they transmit to each other. We, too, will begin there, though we will quickly see the narrowness and insufficiency of such an approach.

We encountered one instance of Bayesian inference in Chapter 50, based on the quadratic loss in the context of mean-square-error (MSE) estimation. We explained there that the optimal solution for inferring a hidden zero-mean random variable $x$ from observations of another zero-mean random variable $y$ is given by the conditional estimator, $\mathbb{E}\left(x|y\right)$, whose computation requires knowledge of the conditional distribution, $f_{x|y}\left(x|y\right)$.

In supervised methods, learning is attained by training on a sufficient amount of labeled data in order to deliver reliable levels of classification. However, there are important situations in practice where data is scarce because it is either difficult or expensive to collect. This scenario leads to few-shot learning, where it is desired to train a classifier by using only a few training samples for each class.

Summarizes how psychologists define psychopathology. Discusses ways in which abnormality was viewed historically, and modern mental health care. Identifies the research methods that psychologists utilize.

We illustrated in Example 63.2 one limitation of linear separation surfaces by considering the XOR mapping (63.11). The example showed that certain feature spaces are not linearly separable and cannot be resolved by the perceptron algorithm. The result in the example was used to motivate one powerful approach to nonlinear separation surfaces by means of kernel methods.

In the immediate past chapters we developed several techniques for the design of linear classifiers, such as logistic regression, perceptron, and support vector machines (SVM). These algorithms are suitable for data that are linearly separable; otherwise, their performance degrades significantly. In this chapter we explain how the methods can be adjusted to determine nonlinear separation surfaces.

In most multistage decision problems, we are interested in determining the optimal strategy, ${\pi }^{\star }\left(a\right|s)$ (i.e., the optimal actions to follow in the state–action space). Most of the algorithms described in the previous chapters focused on evaluating the state and state–action value functions, ${\upsilon }^{\pi }\left(s\right)$ and $q^{\pi }(s,a)$, for a given policy $\pi \left(a\right|s)$. More is needed to learn the optimal policy.

Describes the differences between gender, sex, and sexual orientation. Outlines the sexual response cycle. Identifies the basic features and diagnostic criteria for dysphoria. Discusses the various treatments for gender dysphoria. Describes the sexual dysfunctions. Discusses the treatment approaches for the sexual dysfunctions. Describes the paraphilic disorders. Discusses the treatment approaches for paraphilic disorders.