Recursiveness is one of the features of the syntactic structure of any language, and morphology also shows recursiveness, even if it is strongly restricted, and the way in which it operates is not the same across all kinds of morphological structure. A new way of considering recursion in suffixation is proposed.

Chapter 4 introduces students to logical values, a simple data type that can only take values of one and zero. While simple, logical values are essential components of program flow (conditionals, loops) which they will learn next, so mastery of them is essential before tackling those more difficult tools. Logical values can also be used to subset arrays according to their values, making them critical for complex data management tasks. Students new to programming are often unfamiliar with operations that create logical values, or which operate on logical values, so this chapter provides detailed explanations and examples to familiarize students with this new and valuable data type.

A widely accepted principle in morphological studies is that inflectional affixes should not be found between a root and a derivational affix or internally in a compound. Many of the apparent exceptions to this general principle in English can be argued not to be genuinely exceptional, but some types, including an innovative type, appear to contradict the usual patterns, though it is not clear why this should be the case.

In summarizing the book, this chapter reconsiders some of the major recurrent issues that have been covered, issues such as the notion of a rule of word-formation, productivity and the difficulty in dealing with genuine examples from usage. There has also been a focus on understanding where the boundaries of categories lie. It is stressed that the questions that are discussed here could equally be discussed in books which focus on other aspects of language study, and that word-formation is just one area in which these issues can be tackled.

Chapter 12 presents the first application of MATLAB to behavioral sciences: modeling behavioral phenomena using MATLAB. Students learn basic computational modeling principles before applying their programming knowledge from Chapters 1 to 11 to model two types of behavior. First, classical conditioning is modeled using the Rescorla-Wagner model, which is used to make predictions about how an organism will react to multiple stimuli when presented together, such as in the classic case of Pavlov’s dog who was trained to salivate to the sound of a bell. Next, foraging behavior in animals is modeled, wherein agents forage for food on patches of resources, learning from experience when to exploit their current patch or explore in search of more food.

Although the term ‘lexeme’ is of increasing importance in linguistics, the term is often not defined in a way which allows for firm decisions about where its boundaries lie. Various points of contention are illustrated, and it is shown that French and anglophone traditions on the nature of the lexeme differ.

The effect of phonetics on word-formation in phonaesthemes, diminutives and the influence of rhyme are discussed. A new way of looking at phonaesthemes is proposed, to avoid some contradictory findings.

Although stipulating what category a particular phenomenon illustrates rather than presenting arguments for the conclusion does not seem like a good way to carry out science, stipulation is frequent in linguistics, not only for categories like inflection, derivation and compound, but also notably for word-classes.

This chapter provides an introduction to the book, defining the topic to be discussed, and looking at alternative ways of approaching it.

Chapter 3 introduces students to functions, which rapidly expands what they can do with MATLAB. For new programming students, this section begins with the underlying computer science principles of inputs and outputs, drawing connections to the same concepts in math. MATLAB functions are highly flexible in how they handle inputs and produce outputs, and this chapter explains those nuances in detail, using several often-used functions as examples. MATLAB also has special function features including multiple outputs and a unique syntax for functions which use text; as with input flexibility, the textbook explains their use and provides often-used example functions.

While borrowing from most languages does not affect the structure of English word-formation, the number of Greek and Latin loans and new words based on them is so overwhelming that new types of word-formation are created. Because loans and English (or other European) uses of the patterns do not always provide the same outputs, and because classical elements can be added to English elements, the neoclassical formations are difficult to describe.

Chapter 8 further develops students’ understanding of data structures by introducing several new ones. Higher-dimensional arrays are generalizations of the arrays they learned in Chapter 2, but can be hard to visualize because we live in a three-dimensional world, and this chapter includes several suggestions for managing higher-dimensional data. Cell arrays and structure arrays can store any data type, and students learn the unique syntaxes needed to manage this flexibility. Tables store spreadsheet-type data, which students are introduced to formally, while also learning the many indexing and display features that make this data type critical for data analysis. Multiple function outputs from Chapter 3 are introduced as their own data type with additional tricks for managing them. The chapter concludes with general tools for learning the structure of any MATLAB data type and the methods available for using it.

Although many new words occur in texts without attracting comment, there is evidence that people are aware of novelty in word-formation, and thus recognize new coinages

This chapter raises the question of why we need to study word-formation as well as other linguistic structures, why word-formation is different and what makes word-formation different.

Chapter 5 introduces perhaps the most difficult tools for new programmers to grasp, namely conditionals and loops, which control when, whether, and how often code executes. Presentation of conditionals and loops begins with descriptions of what they do: conditionals control whether code runs, or which piece of code runs from multiple options; while loops make code run repeatedly, either “while” some condition is met (“while” loops) or “for” each element of a set of values (“for” loops). This presentation makes clear to students how to translate these descriptions into MATLAB code, allowing them to see the correspondences between the broader programming task and the tools that accomplish them. Syntax is explored in detail, including common pitfalls and specification errors. Lastly, syntax for interrupting loops and changing their operation is presented, furthering students’ mastery of these critical tools.

Some words which might seem to be possible words are not used, because sometimes such words never become part of the norm. On the other hand, some words which do not appear to be possible words are used. Just how a possible word is to be defined is not clear, but the norm does appear to be a factor affecting the productivity of morphological processes.