In this chapter the author addresses the following questions: What does it mean to say that a language is a creole? Do creoles constitute a separate global typological class apart from other language typological groupings? The author calls for research on creole languages that is free from linguistic feature bias, creole language list bias, and genealogical bias. He compares expansion languages from the Meso and South American indigenous language families, particularly the Quechuan family (focusing on Ecuador, the northern border), with the Arawakan, Tupian, Cariban, Jêan, Chibchan, Uto-Aztecan, Mayan, and Otomanguean families. The comparison highlights processes of ethnogenesis, morphological reduction, and sub- or adstrate influence. The findings help broaden the definition of “creole” to refer to a special lexifier– descendant relationship, making the notion of creole a relational one, to facilitate comparisons with other languages and with “linguistic areas.” Questions that remain include: Are there area-specific features? And can this approach shed light on the characterization of creoles as a particular group of languages? What role can language contact play in reconstructing language families?

A canonically agglutinative language or morphological pattern is traditionally analyzed as building words out of independent morphemes. Using data from Choguita Rarámuri (Uto-Aztecan), we attempt to quantify this notion by examining the extent to which meanings are predictable from their exponents without reference to context. We show that two-layer connectionist networks, computational models that map form onto meaning directly, can be used for this purpose. We also show that learning the meanings of morphemes can pose significant challenges to such models and constrains the design of the learning algorithm. In particular, models trained to equilibrium tend to focus on unreliable cues to the meanings they try to predict, especially when trained on a small corpus typical of underresourced languages. Some of these issues can be alleviated by a slow learning rate. However, one issue — which we call the problem of spurious excitement — is shown to be inherent to the learning algorithm, and always arises by the time the model achieves equilibrium. Spurious excitement means that a cue becomes associated with a meaning that it does not co-occur with, simply because of co-occurring with cues that disfavor the meaning. This case raises larger implications with respect to the type of learning mechanism involved in the acquisition of natural languages. Solutions to spurious excitement are discussed. The logistic activation function is shown to improve the performance of the model in detecting reliable cues to meanings that recur across many word types (i.e., cues of high type frequency), as well as eliminating spurious excitement.