The purpose of this chapter is to give a brief overview of some of the clinical features of dementia. A level of knowledge is assumed and there are other texts that comprehensively examine general aspects of dementia diagnosis, neurobiology, current interventions, etc. We will provide an overview of the range of cognitive systems and how impairments in these systems relate to the clinical presentation. While decision-making capacity and cognitive abilities are related, they are not the same thing. The first refers to the patient’s ability to make a particular decision (at a particular point in time) and the latter encompasses a broad range of processes, including a person’s memory, problem-solving, language, attention and visuospatial abilities. Understanding different cognitive domains and how they interact is necessary for accurate clinical assessment and in determining disease severity. Also, in relation to legal considerations, characterising specific deficits may also help to explain why decision-making capacity is impaired or, just as importantly, in identifying how to apply measures to support an individual’s decision-making.

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.

To conceptualize the communicative role of working memory (WM), the Ease-of-Language Understanding (ELU) model was proposed (e.g., Rönnberg, 2003; Rönnberg et al., 2008, 2013, 2019, 2020). The model states that ease of language understanding is determined by the speed and accuracy with which the signal is matched to existing multimodal language representations. When matching is fast and complete, language understanding is effortless; this process may be facilitated by predictions based on the contents of WM. However, when the contents of the language signal mismatches with existing representations, WM is triggered to access knowledge in semantic long-term memory (SLTM) and personal experience from episodic long-term memory (ELTM) – promoting inference-making and postdictions in WM. The interplay between WM and LTM is fundamental to language understanding; its efficiency becomes apparent in adverse conditions and its breakdown may explain cognitive decline and dementia. Empirical support, limitations, and future studies will be discussed.

Metacognition, or thinking about thinking, can adaptively modulate cognitive processing. For example, a student preparing for an exam may introspectively evaluate what she knows well already so that she can allocate more time to studying material she does not know as well. Such metacognition involves feedback between metacognitive monitoring, which assesses the current state of cognition, and metacognitive control that effects changes in cognitive processing. Some interesting and complex forms of cognition likely involve metacognition. Metacognition is also linked to explicit memory, executive control, theory of mind, consciousness, and other phenomena central to cognitive science. Like learning, memory, and cognition, metacognition is likely present in at least rudimentary forms in some animals other than humans. Information about the extent to which metacognition occurs in animals other than humans informs our understanding about the evolution of cognition. Metacognitive monitoring likely evolved because it supports effective metacognitive control.

In this chapter, I focus on the origins of memory, posing the question of what were the very initial forces that led to the evolution of learning. Although the common answer is that the function of memory is to allow future behavior to benefit from past experiences, I argue that future benefit is too small to overcome the large energetic costs associated with the neural mechanisms that support memory formation. Instead, I advance the hypothesis that memory evolved to solve an immediate problem, the identification of novel biologically significant objects. Although such identification is often attributed to innate recognition, reliance on genetic encoding would require enormous genomic space and would be unreliable when phylogenetically novel but important objects were encountered. Some often-unappreciated features of Pavlovian conditioning make it an ideal mechanism for immediate perceptual identification of biologically important objects. Although episodic memory is most clearly identified with this recognition process, immediate perceptual identification may be a general function of several memory systems.

Several decades of basic research support the neural basis of multiple memory systems. These systems are highly relevant to all health behaviors, since behaviors are learned from experience and require some form of memory process to retain learning and affect subsequent action. Research on the neuroscience of appetitive behaviors has rigorously studied motivational processes involved in behaviors such as drug use, diet, and sex. However, very little of this otherwise stellar research has attempted to integrate its findings with multiple memory system views that acknowledge the wide range of memory effects uncovered in several highly relevant basic research areas. Further, good explanatory theories of multiple memory systems studied mostly in addiction and in animal research have not yet been integrated with the vast knowledge base from human cognitive science. Moreover, most research on the epidemiology, prevention, or treatment of problems in appetitive behavior has not taken into account these basic research findings and has instead focused on theories and methods derived primarily from survey research. Yet, basic research areas from neuroscience and cognitive science are highly relevant to all areas of study of appetitive behavior, and the prevailing focus in prevention science on concepts derived from survey research may be channeled mostly by the training of investigators and disciplinary history. This chapter provides one example of how these disparate literatures from basic research might be integrated to advance our understanding of this class of behavior and derive new possibilities for intervention. It highlights examples of key findings supporting the need for a greater translational effort but also highlights large gaps in knowledge. Future research filling these gaps and others in the void between compelling research domains could substantially change and advance the study of addictions and all appetitive or habit-forming behaviors.