This chapter provides an overview of the processes that are commonly used for analyzing data. Our intention is to explain what these processes achieve and why they are done. Analyzing data goes through four stages. For each stage, we explain the most important concept and then explain the practical steps that are involved. This begins with the data themselves as variables. Next, we move on to describing the data, their variance and covariance, with linear models. Next, we cover interpreting effects and focus on effect sizes. We end with a discussion of inferences about the population and how the presence of uncertainty has to be taken into account in reaching conclusions.

A research design is the sequence of things done in order to collect the information needed to answer a research question. The design states which data will be sought, from which sources, at which times, and in which ways. This chapter describes the influences that shape the decisions researchers must make when constructing a research design. Research designs differ in the source of information used, whether data used are naturally occurring or a result of intervention, and the way data are elicited, recorded, and analyzed. Typically, the nature of the research question, assumptions on which it is based, and ethical considerations drive the design construction. I describe seven major influences on design choice in this chapter: research question novelty; levels of analysis and explanation used; epistemological and ontological assumptions; characteristics of data sources; data analyzability; piloting results; and various practicalities. Understanding these influences will improve research design decisions.

In this chapter, we review the work that has been done on children’s temperament and prosocial behavior in childhood and adolescence, highlighting the importance of taking a nuanced and multidimensional approach to examining the links between temperament and children’s prosocial behavior. Thus, in addition to examining the higher-order temperamental factors (such as negative emotionality), we also examine how the specific dimensions of temperament (e.g., anger proneness) predict different types of prosocial behaviors (e.g., sharing). Finally, we consider how the links between prosocial behaviors and temperament are likely complicated by the fact that temperamental variables interact both with each other and with environmental factors, such as parenting, to predict different types of prosocial behaviors.

Research presentations offer personal, interpersonal, and professional benefits to students and more senior researchers. Through presentations, students gain important skills (e.g., analytic thinking), are able to meet potential mentors and/or employers, and develop their identities as scholars in a given field. Senior researchers may see increases in motivation, productivity, and collaborative opportunities. Various avenues for presenting one’s work include institutional based, regional, national, and international conferences. Readers are encouraged to reflect on logistics and personal and professional goals when deciding on which conference is right for them. Descriptions of poster presentations, oral presentations, and job talks are provided. Subsequently, this chapter offers practical guidance on “best practices” for presenting one’s research in each respective modality. Readers are encouraged to reflect on the composition of their audience, the goals of their presentation, and the visual organization of material to craft the most effective presentation possible.

Cognitive engineering is the application of cognitive science to engineering. While the majority of the cognitive models and architectures commonly associated with cognitive engineering were created to understand human behavior, their use in engineering has been carried out with the purpose of realizing better systems. As such, cognitive engineering model fidelity varies, based on application goals. This chapter provides readers with a history of cognitive modeling in cognitive engineering and its diverse contributions by reviewing the seminal work of Card, Moran, and Newell, which laid the foundations for many developments. It then examines the use of cognitive models in complex systems engineering. The chapter concludes with a summary and a discussion of potential threats and future advances.

Quantum cognition is a new field in cognitive science, which is characterized by the application of quantum probability theory, quantum dynamics, and quantum information processing to account for human behavior in cognitive tasks. This chapter provides an introduction to the basic principles and a review of applications of these principles to a wide range of cognitive tasks. The power of quantum cognition comes from using the same principles to coherently link together a wide range of phenomena that have never been previously connected together.

Computer modeling of specific psychological processes began over fifty years ago. Cognitive scientists do not use computers merely as tools, but also as inspiration about the nature of mental processes. Computational cognitive science has a long way to go. There are many unanswered questions.However, cognitive scientists believe that the mind/brain is in principle intelligible in terms of whatever turns out to be the best theory of what computers can do. The overview of cognitive science given in this chapter should suffice to show that significant progress has been made.

Cognitive control, the ability to flexibly and selectively process information in the service of higher-level goals, is essential to daily functioning. However, despite the burgeoning research in this domain, much remains to be understood regarding its underlying neurocomputational mechanisms. This chapter highlights several prominent models that have made significant progress towards understanding the core principles of neural information processing and computation that are central to cognitive control. Neural network models are reviewed that characterize: (1) how tasks are represented, updated, and learned (e.g., attentional control, task-switching, structure learning); and (2) how cognitive control is evaluated and allocated based on assessments of demand (e.g., conflict monitoring, outcome prediction, and expected value of control). This brief survey of influential theoretical models provides an important foundational introduction into the primary mechanisms of cognitive control, and concludes with key open questions and future directions aimed at developing a fuller understanding of this domain.

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 introduction and an overview of computational cognitive sciences. Computational cognitive sciences explore the essence of cognition and various cognitive functionalities through developing mechanistic, process-based understanding by specifying corresponding computational models. These models impute computational processes onto cognitive functions and thereby produce runnable programs. Detailed simulations and other operations can then be conducted. Understanding the human mind strictly from observations of, and experiments with, human behavior is ultimately untenable. Computational modeling is therefore both useful and necessary. Computational cognitive models are theoretically important because they represent detailed cognitive theories in a unique, indispensable way. Computational cognitive modeling has thus far deepened the understanding of the processes and the mechanisms of the mind in a variety of ways.

The focus of this chapter is on neurobiologically informed and constrained models of working memory as defined by Miller, Galanter, and Pribram (1960): the holding of goals and subgoals in mind in service of planning and executing complex behaviors. In particular, the chapter focuses on models specifically addressing critical challenges and mechanisms following from the need for rapid and selective gating of working memory contents. To start, the important computational challenges posed by the tradeoff between maintaining vs. updating are discussed, providing motivation for the rest of the chapter.After that, several seminal models that have contributed to current thinking are reviewed, including the authors’ own PBWM framework that has proven influential. Finally, several recent developments from the deep learning and neurophysiology literatures are addressed and critical questions and some directions for future progress are discussed.

In the framework of computational cognitive modeling, natural language understanding and generation must be integrated with other cognitive capabilities, such as reasoning and learning. The language understanding component of an intelligent agent extracts and formally represents the meaning of texts and dialog turns. The output of language understanding must reflect the speaker’s intended meaning and be sufficiently detailed to serve as input to reasoning and action in artificial intelligent agents. One kind of agent action is verbal, so agents must include a language generation capability. This chapter describes a particular language understanding system that meets the requirements for the above language capabilities and also puts forward methodological arguments about the interplay between theories, models, and computational systems.

Computational work on morality has emerged from two major sources – empirical moral science and philosophical ethics.Moral science has revealed a diversity of moral phenomena: moral behavior (including moral decision making), moral judgments, moral emotions, moral sanctions, moral communication.Philosophical ethics has long focused on moral decision making, and this is where most of the computational work has emerged. Much of it uses rule-based systems rooted in formal logic but is complemented by connectionist, case-based, and other approaches, and more recently by reinforcement learning models.Computational work on moral judgments is sparser, in part because moral judgments build on numerous complex mental capacities, such as causal and counterfactual reasoning and theory of mind. Nonetheless, some models of blame judgments have emerged that draw on information processing approaches from empirical moral science. Even less work has tackled moral emotions, sanctions, and communication – phenomena that present vast challenges and opportunities for future work.

Vision is one of the most complex proficiencies we possess, but its underpinnings are still shrouded in mystery. Many great scientific minds have been engaged in the enterprise of modeling vision. This chapter takes a look at some of the history of this effort, stretching from the times of the ancient Greeks to recent developments in neural networks, and discusses how current techniques may play a role in furthering our understanding of vision.