Jane Chandlee and Jeffrey Heinz
Computational phonology studies the nature of the computations necessary and sufficient for characterizing phonological knowledge. As a field it is informed by the theories of computation and phonology.
The computational nature of phonological knowledge is important because at a fundamental level it is about the psychological nature of memory as it pertains to phonological knowledge. Different types of phonological knowledge can be characterized as computational problems, and the solutions to these problems reveal their computational nature. In contrast to syntactic knowledge, there is clear evidence that phonological knowledge is computationally bounded to the so-called regular classes of sets and relations. These classes have multiple mathematical characterizations in terms of logic, automata, and algebra with significant implications for the nature of memory. In fact, there is evidence that phonological knowledge is bounded by particular subregular classes, with more restrictive logical, automata-theoretic, and algebraic characterizations, and thus by weaker models of memory.
Connectionism is an important theoretical framework for the study of human cognition and behavior. Also known as Parallel Distributed Processing (PDP) or Artificial Neural Networks (ANN), connectionism advocates that learning, representation, and processing of information in mind are parallel, distributed, and interactive in nature. It argues for the emergence of human cognition as the outcome of large networks of interactive processing units operating simultaneously. Inspired by findings from neural science and artificial intelligence, connectionism is a powerful computational tool, and it has had profound impact on many areas of research, including linguistics. Since the beginning of connectionism, many connectionist models have been developed to account for a wide range of important linguistic phenomena observed in monolingual research, such as speech perception, speech production, semantic representation, and early lexical development in children. Recently, the application of connectionism to bilingual research has also gathered momentum. Connectionist models are often precise in the specification of modeling parameters and flexible in the manipulation of relevant variables in the model to address relevant theoretical questions, therefore they can provide significant advantages in testing mechanisms underlying language processes.
Language is a system that maps meanings to forms, but the mapping is not always one-to-one. Variation means that one meaning corresponds to multiple forms, for example faster ~ more fast. The choice is not uniquely determined by the rules of the language, but is made by the individual at the time of performance (speaking, writing). Such choices abound in human language. They are usually not just a matter of free will, but involve preferences that depend on the context, including the phonological context. Phonological variation is a situation where the choice among expressions is phonologically conditioned, sometimes statistically, sometimes categorically. In this overview, we take a look at three studies of variable vowel harmony in three languages (Finnish, Hungarian, and Tommo So) formulated in three frameworks (Partial Order Optimality Theory, Stochastic Optimality Theory, and Maximum Entropy Grammar). For example, both Finnish and Hungarian have Backness Harmony: vowels must be all [+back] or all [−back] within a single word, with the exception of neutral vowels that are compatible with either. Surprisingly, some stems allow both [+back] and [−back] suffixes in free variation, for example, analyysi-na ~ analyysi-nä ‘analysis-