Purposive Communication CAS101 PDF

PURPOSIVE COMMUNICATION WMSU BRYAN B. MARCIAL College of Liberal Arts, Department of English 1  COURSE DESCRIPTION This course aims to develop students' communicative competence and enh...

PURPOSIVE COMMUNICATION WMSU BRYAN B. MARCIAL College of Liberal Arts, Department of English 1  COURSE DESCRIPTION This course aims to develop students' communicative competence and enhances their cultural and intercultural awareness through multimodal tasks that provide them opportunities for communicating effectively and appropriately to a multicultural audience in a local and global context. It equips students with tools for critical evaluation of a variety of texts and focuses on the power of language with the introduction of language acquisition and development and the impact of images to emphasize the importance of conveying messages responsibly. The knowledge, skills, and insights that students gain from this course may be used in their other other academic endeavors, their chosen disciplines, and their future careers as they compose and produce relevant oral, written, audio-visual and/or web-based output for various purposes. 2  Topics under lesson one: American and British English Language and Communication (Kottak, 2008) Macro skills Outline: What exactly is language? (Madrunio & Martin, 2018) Speech Community Language Acquisition Mother tongues/First Languages Second Languages 3  Language Learning Language Change Cultural Hybridization Topics under Nonhuman Primate Communication lesson one: Call systems Sign Language Nonverbal Communication Language Contrasted with Call Systems 4  The physical adaptation source (Yule, 2006) Teeth, lips, mouth, larynx and pharynx The Human Brain Topics under The Genetic Source lesson one: Animals and Human Language Types of Signals in Communication Properties of Human Language Productivity 5  Cultural Transmission Topics under Duality lesson one: Chimpanzees and Language The barest rudiments of Language 6  Types of Communication Communication Topics under lesson two: Types of Communication According to Mode (Verbal, Non-verbal, and Visual Communication) Types of Communication According to Context (Intrapersonal, Interpersonal, Extended, Organizational, and Intercultural Communication) Types of Communication According to Purpose and Style 7  Communication Models (Aristotle, Laswell, Shannon-Weaver, and Berlo) General Principles of Effective Topics under Communication, Oral Communication, and Written Communication & Ethics of lesson three: Communication Public Speaking (Test II of Midterm Examination) 8 Communication Modes (face to face, video, audio, and text-based) Communication and Technology Topics under lesson four: Intercultural Communication Local and Global Communication in Multicultural Settings Varieties and Registers of Spoken and Written Language Exploring Texts Reflecting Different Cultures Coping with the Challenges of Intercultural Communication 9 Topics under lesson five: The Debate Literature as Communication Communication Across Professions The Job Interview Study the following words. Which spelling is correct? Which spelling is incorrect? Tick the appropriate box. Correct? Incorrect? 1. Aeroplane 2. Airplane 3. Colonise 4. Colonize 5. Defence 6. Defense 7. Enrolment 8. Enrollment 9. Honour 10. Honor 11  Language and Communication Macro Receptive Productive Skills Skills Skills Speaking Reading Listenin g Writing 12  Together with the Communica Animals are said to creation of human Language communicate with each life is the creation other. tion of a wonderful and dynamic human capacity called language. 13 Is there a difference between Language and Communication? If yes, what is the difference between the two? 14 What exactly is language? (Madrunio & Martin, 2018) Linguists agree that a language can only be called a language if it has the following: i.) System of rules (also known as grammar ii.) A sound system (phonology) iii.) A vocabulary (lexicon). These are the requirements for identifying a means of communication as a language. 15 Language Acquisition in Theoretical Context This topic is about one of the great mysteries of the human experience — how we go from being small, cute, noisy blobs that don’t understand or produce language to eloquent kindergartners who not only know the meanings of several thousand words but can rattle off stories (some true, some maybe not), commentaries, opinions, and questions seemingly without end. One parent came home from work one day to an exhausted partner who complained that their 3-year-old daughter had not stopped talking since eight o’clock that morning. How do children, who can’t tie their shoes, who may or may not be potty-trained, and who can’t compute basic mathematical operations, perform this feat? We’ll walk through many of the stages that children go through on their way to gaining eloquence and provide some answers to the question of how they do it. One of the main answers we start with is that babies are not just passively experiencing their world. Instead, their brains are designed to anticipate that human language will have certain properties —for instance, that sentences have a hierarchical structure—and this predisposition allows them to rapidly assimilate important information about language from their environment.  This answer (that children are born predisposed to learn language) eases the problem, but doesn’t lessen the mystery. We still want to explain how children begin to tease apart the fluid stream of sounds coming at them from different speakers and possibly from different languages. We still need to unravel the process by which children take an individual word (once they are able to isolate individual words) and figure out which of the infinite possible meanings or concepts in the universe that one little word is supposed to label. The question of how children figure out the rules of their particular language is still a puzzle to be solved. This topic won’t answer all of these mysteries—linguists still have a lot of work to do—but we’ll answer many of them. So, let’s get started! The Logical Problem of Language Acquisition A driving idea in the field of language acquisition is known as the Logical Problem of Language Acquisition (LPLA). Simplifying (for now), the LPLA notes that the manner in which children acquire language is not predicted by the kind of language that they hear. There is a gap between what children hear and experience and what they are eventually able to do with language. A key issue in the field, and one that this book is centered around, is how children bridge this gap. How do they go from nothing to everything in the manner that they do?  As we will see, children learn language universally (all normal children do so regardless of which language they are born into), quickly (by kindergarten, typically), easily, and relatively uniformly. Moreover, they do all this without any meaningful correction or instruction, and in the face of information that is ambiguous at best, and misleading at worst. This is a genuine puzzle, one that was instrumental in the formation of the field of modern linguistics, and one that continues to guide all modern linguistic and language acquisition theory. What do you think is necessary to acquire language? One very obvious ingredient is language itself—children need to be exposed to language. We call this input. We’ll talk much later about what happens if language input is not available to children in the early years of life, but for now we can assume that if you don’t have exposure to language, you won’t acquire it.  In most religions, there appears to be a divine source who provides humans with language. The divine “If human infants were allowed to grow up without hearing any language around them, source then they would spontaneously begin using the original God-given language.” An Egyptian pharaoh named Psammetichus tried experiment with two newborn babies more than 2,500 years ago. After two years in the company of goats and a mute sheperd, the children were reported to have spontaneously uttered, not an Egyptian word, but something that was identified as the Phrygian word bekos, meaning bread. This Photo by Unknown Author is licensed under CC BY-ND  What do you think is the reason behind the ability of these children to produce a word without any contact to human languages? The children may not have picked up this word from any human source, but as several commentators have pointed out, they must have heard what goats were saying. King James the Fourth of Scotland carried out a similar experiment around the year 1,500 and the children were reported to have started speaking Hebrew. Other children, unfortunately, tend not to confirm the results of these types of “divine-source” experiments. Very young children living without access to human language in their early years grow up with no language at all.  So, we can say that language input is necessary. But is it sufficient? This is a very different question. What would it mean for language input to be sufficient? One way to think about this question is to take stock of all that has to be learned when one learns a language: vocabulary, the sounds in the language, how words are ordered in phrases and sentences, how to turn a statement into a question, and so on. These words and rules vary from language to language, and children might be able to learn about them just by listening and “picking it up.” After all, kids are like sponges, right? We hear this all the time. Looking at it this way, it seems like input alone might be sufficient.  But let’s take it a step further. Is the language you know really just a catalog of all the words and sentences you’ve heard before? Sometimes, when you hear a new word, it is impossible to understand until someone gives you more information. But often you’re able to figure out some aspect of the meaning of the new word using clues in the sentence. The point is that learning the meanings of words does not happen simply because you hear the word in your environment. Rather, there’s some mental and linguistic work that happens that helps figure out the meaning. This points to a more complex process than simply being exposed to language: the child is an agent of this process of learning, and not a passive sponge.  Furthermore, if all there was to language acquisition was learning what you hear, then you would not be able to produce and understand brand-new sentences—but surely you do so every day. “The grass screamed danger to the foot soldiers in the Mighty Mouse army.” Have you ever heard that sentence before? Likely not, but you know it is a sentence of English, and you understand what it means (even if that meaning is kind of weird). The ability to understand that sentence could not have come from a catalogue of previously heard sentences. That’s because you know (implicitly) the rules of English syntax, and this allows you to understand the (admittedly bizarre) message in that novel sentence.  Similarly, your knowledge of grammatical structure helps you understand and create new words. Every year dozens, if not hundreds, of words enter the lexicon, and speakers are able to adapt to them quite easily. When new ways of communicating develop through different forms of technology, the language and its speakers adopt new verbs like email, instant-message, or ping, and we use them just like verbs that have been in the language for centuries (“I’m emailing you that file right now,” “She just instant- messaged her friend,” “Ping me if you want to meet up”). You can generalize using the rules of grammar you already know so that you can easily handle these newcomers to the language; typically you can do this the very first time you encounter one of them. You are not dependent on prior exposure through the input. Productivity Humans are continually creating new expressions and novel utterances by manipulating their linguistic resources to describe new objects and situations. This property is described as productivity (or ‘creativity’ or ‘open-endedness’) and it is linked to the fact that the potential number of utterances in any human language is infinite. Another way to think about the sufficiency of language input, specifically with respect to children’s language, is to notice all the neat things they say. Here are a few examples: (1) a. I’m gonna fall this on you! b. Don’t giggle me. c. My legpit hurts. d. I want you pick up me. These are “errors” from the perspective of adult grammar. Children surely have not heard these kinds of sentences from their parents—children invent them on their own. The errors themselves might be funny, but when we look at them carefully we find that they are infrequent (most utterances are totally unremarkable—the cute, deviant ones stand out), highly systematic, and rule driven.  In these examples and the ones we’ve given already, we can see that children’s apparent errors are logical and rule governed. They are also systematic in another sense: children growing up in very different families, different parts of the country, and even across the world are surprisingly uniform in the types of errors they make. Of course there are individual differences among children just as there are among adult speakers, but the uniformity is what stands out. Moreover, the general stages that children go through are quite predictable, both within languages and across languages. For example, we know that children across all languages will typically produce their first word just after the first birthday (with some minor variation, of course); children will go through a one-word stage until just before the second birthday; they go through a stage referred to as telegraphic speech until around age 4; by about age 4 they are fluent speakers of their language; and by around age 5 or 6, children across the globe have acquired the majority of the grammar of their language. This is why a first-grade classroom is one of the noisiest environments on Earth: it is filled with children making the most of their recently acquired grammars.  Not only is acquisition uniform, it is quick. As suggested above, by their sixth birthday children have put together not only the basic rules of their target language but even quite complex rules that allow them to ask questions, embed clauses inside other clauses, create passive sentences and endlessly long descriptions with adjectives, adverbs, and relative clauses. They take a bit longer to learn rules for discourse and pragmatics, like how to tell good jokes and how to know what is appropriate to say, and of course vocabulary words are learned throughout the lifespan. But to become a fluent speaker within three or four years and to acquire a complete grammar within six years is still fast. Compare that to how long it takes you to acquire a second language. With six solid years of learning and speaking a second language, most adults can learn a lot of vocabulary and become quite proficient speakers of a second language. But there will almost inevitably be grammatical structures that they don’t quite master. Even proficient second-language learners of English, for example, commonly make errors with the use of the articles a and the (e.g., It was very interesting journey or working on the similar problem as I; cited in Dušková, 1983) or with the use of -s for verbs with third-person-singular subjects (e.g., He have to finish his work). Native English-speaking children of elementary school age do not make errors like this. Adults also often retain an accent in their second language, so some aspects of the phonology of the learned language are simply beyond their reach.  It is important to remember that motivation cannot fully explain differences in learning outcomes between children and adult language learners. Consider immigrants, for example. They move to a new country and have to learn the language. This is in their economic and social interests —they are highly motivated—and yet very rarely do they fully master the new language. They often acquire some basic skills—enough to get by and function. But typically they do not gain fluency to the degree that they are indistinguishable from native speakers. But the vast majority of children do it. In fact, this accomplishment is so routine that we sometimes struggle to see why it is an impressive feat. Just because all children learn language within six years does not make it any less amazing.  Add to that the fact that learning language seems very easy for children —they just do it. They don’t need to be forced to learn language, and they don’t complain about it either. “Oh Mom, I don’t want to learn new verbs today,” said no child, ever. It is something that they simply seem equipped and ready to do. Again, compare this to how we learn a second language as adults. If you immerse an adult in a second language, they are simply not going to learn that language in the same way, as fast, or as easily as children do.  What’s more, studies show that children very rarely get correction and explicit feedback about the errors they make. As an adult learning a second language, when you make a mistake, your teacher is likely to correct you and tell you what you did wrong. How else are you going to know that what you said was incorrect? And in writing it comes as the dreaded red ink, but such feedback is important for second-language learners. This is called negative evidence: evidence for what is not possible in a language. The opposite (positive evidence) is evidence for what is possible in a language.  Children hear lots of positive evidence: every sentence that they hear in their environment is positive evidence and constitutes one piece of data that they can use to learn a language. But we tend not to give children negative evidence (correction). Parents are busy people: they don’t have the time or energy to give grammar lessons in between getting dinner ready, doing the laundry, cleaning up the bowl of cereal that just spilled, and so on. Children do not get much negative evidence to help them learn the language, but they get plenty of positive evidence. So how are they to know, when they say something ungrammatical, that this is not a possible sentence in their language? This adds to the puzzle of how children learn language.  Making the problem worse, when parents do make a conscious effort to correct children’s errors, they invariably fail. A well-known example, attributed to Braine (1971), demonstrates this failure: (2) Child: Want other one spoon, Daddy. Father: You mean, you want the other spoon. Child: Yes, I want other one spoon, please, Daddy. Father: Can you say, “the other spoon”? Child: Other... one... spoon. Father: Say “other.” Child: Other. Father: “Spoon.” Child: Spoon. Father: Now say “other... spoon.” Child: Other... spoon. Now give me other one spoon? (3) Child: Nobody don’t like me. Mother: No, say “nobody likes me.” Child: Nobody don’t like me. [Eight repetitions of this dialogue follow.] Mother: No, now listen carefully, say “NOBODY LIKES ME.” Child: Oh! Nobody don’t LIKES me. Here, the child produced an error (inserting don’t and saying like instead of likes), and the adult tried to correct it by explicitly modeling the correct form for the child. Not only did the child not pay any attention for the first nine repetitions of this back- and-forth, but when they eventually did, they paid attention to the lesser of the two errors in the original sentence. The more obvious one (insertion of don’t) was simply ignored. Summarizing, children don’t learn through drilling and correction. Their utterances are spontaneous, creative, and systematic, and their ability to spout brand-new sentences is infinite. All children across the globe, irrespective of the language they are born into, acquire language, and they do so quickly, uniformly, and with great ease. How does this happen? The key idea that we focus on in this book is that the task that children have before them in acquiring a language is not to acquire sentences, as people often think. Rather, their job is to acquire a system. What you “know” when you know a language is not a list of sentences, but rather an engine that lets you generate an infinite set of words and sentences. But you, as a child, figured out this engine in only a few short years, and with no correction or direct instruction. How did you do that? Without hearing all the words and sentences of your language, you have, by the time you start kindergarten, acquired the machinery that will allow you to create whatever sentences you want for the rest of your life. What this means is that you know some things that were never explained to you, which is the gap that we referenced earlier. The fact that children routinely overcome this gap presents us with a puzzle that linguists refer to as the Logical Problem of Language Acquisition, or LPLA (Chomsky, 1986; Hyams, 1986). The LPLA basically asks: How do you figure out the underlying machinery of language, without it being explained to you, in such a short amount of time, with such ease, so uniformly, and without negative evidence? This issue is addressed in much more detail in the next chapter, but suffice it to say that this logical problem is the foundation for all of modern linguistics. Different Kinds of Negative Evidence Children rarely hear explicit corrections (referred to as direct negative evidence). However, some researchers have argued that children make use of indirect negative evidence—something that is slightly more common in child-directed speech. This consists of things that indirectly indicate to the child that their utterance was incorrect. For example, if the child says, The ball falled down, the parent might say, Oh, the ball fell down, did it? This way, the parent did not directly correct the child (no direct negative evidence), but the parent did indirectly indicate to the child that the verb should be fell, not falled. This may seem like excellent evidence for the child, but it isn’t. It tells the child something, but not what exactly, is wrong. So the child gets very little useful information from a recast, except that there may be something wrong. Moreover, parents do not always (or even usually) provide recasts, so what happens to all those errors that are not met with a recast? Do they get learned as part of the grammar? All of this means that a child can’t rely on recasts to correct any errors in their grammar, and so it is widely assumed that negative evidence (of any kind) is somewhat ineffectual as a mechanism to explain how children learn language.  We’re now in a position to go back to our question about whether the input is sufficient. If you end up knowing stuff that wasn’t explained to you, and that perhaps wasn’t even there in the input (like knowledge of underlying sentence structure), then the input is not sufficient. If it’s not sufficient, what else helps you acquire language? We’ve already alluded to the answer, or part of it: babies are born with expectations about how human language works. The Developmental Problem of Language Acquisition We have introduced a logical puzzle about how children acquire language: If language input is insufficient, by itself, for children to acquire all the rules of grammar, how is language acquired so quickly, and further, how is it possible at all? The partial answer we just provided is that children are born with certain preconceptions about how grammar can work. These preconceptions restrict children’s hypothesis space and eliminate a lot of the guesswork that would otherwise be needed (e.g., children don’t need to wonder if their language will have hierarchical structure if human language must have hierarchical structure). But this answer raises a new puzzle: If children have preconceptions about how language works, why does language acquisition take so long? Even though we argued above that three or four years is a short period of time for language acquisition to occur, if children are pre-equipped with expectations of how language works, we might ask why the process is not even quicker. This is known as the Developmental Problem of Language Acquisition, or DPLA. More specifically, the DPLA is concerned with how and why children go through the particular stages that they do. Why do they progress the way that they do, and not in the infinite other logically possible ways?  Most of this discussion is devoted to spelling out answers to the DPLA by explaining what children have to learn about language and the developmental stages they go through. We’ll see how humans go from being tiny blobs (or, at least, blobs-with-a-predisposition-for- language) to thinking, speaking, articulating, comprehending chatterboxes. There are many facts and theories, and at times it can get overwhelming. If you find yourself wondering whether there isn’t some simple way to sum up language acquisition, remember this: language acquisition is a process of grammar creation. All children, almost no matter what circumstances they are born into, create a grammar for themselves. That is what language acquisition is. Theoretical Approaches to Studying Language Acquisition We laid out the basic Logical Problem of Language Acquisition (LPLA): children acquire language quickly, universally, with relatively few errors, showing remarkable uniformity, systematicity and adherence to rules, and not needing any kind of correction or grammar lessons. How do children do this? Researchers address this question in numerous ways, but the most basic division in the field centers on the question of whether language learning is primarily driven by the input that children hear or by innate knowledge about human language. We adopt the point of view that emphasizes innate knowledge, and in the first part, we flesh out that perspective more fully. This view is known as the Universal Grammar (UG), or “generative,” approach to language acquisition, and it explains how children acquire language despite the LPLA and why they acquire language in the precise way that they do (the Developmental Problem of Language Acquisition, or DPLA).  But it is important for students of language acquisition to know about other theoretical approaches, so in the second part we address some theories that take a different perspective, referred to as input-based approaches. These approaches emphasize the regular patterns found in language input and explain language acquisition in terms of children’s ability to recognize and make sense of these patterns. We will investigate two such theories of learning that invoke general learning mechanisms— learning mechanisms that apply not only to language but to other domains within human cognition such as vision, number, and spatial knowledge. Although we believe the input-based approaches are less suited to addressing the LPLA and the DPLA, we’d like to emphasize that they are legitimate and worthy of consideration. As we shall see, they provide an intuitive, novel, and extremely clever way in which children learn certain aspects of language.  It is important to recognize that among input-based approaches, there are many different viewpoints, specific claims, and assumptions about language learning, some more extreme than others. As an example of an extreme approach, the school of psychology known as behaviorism held that all language learning (and in fact, all learning) was based on the input. On this view, the learner was simply an empty vessel to be filled with the experience of hearing language in the input. Learning was considered nothing more than forming a stock of habits and drawing associations between a stimulus and a response—much like the stimulus appraisal that all animals can do. Think: Pavlov’s dogs. This view is no longer taken seriously as an account of how language is learned, and many people who study animal cognition do not believe it accounts for animal learning either (see Gallistel, 1990). Ivan Pavlov Theory: Classical Conditioning First discovered by Russian physiologist Ivan Pavlov (1849-1936), classical conditioning is a learning process governed by associations between an environmental stimulus and another stimulus which occurs naturally. All classical conditioned learning involves environmental interaction. For learning to occur, there must also be a ‘neutral stimulus’ which is then followed by a naturally occurring reflex. For instance, Pavlov’s dogs heard a tone (neutral stimulus) followed by salivating (naturally occurring reflex) in response to the arrival of food. Once the sound of the neutral stimulus became linked to the stimulus present in the environment (food arriving), it soon became possible to induce salivating just by sounding the neutral stimulus.  Nowadays all researchers of language agree that there must be some structuring or organizing activity on the part of the child in order for language learning to be successful. But don’t let this fool you: not everyone agrees on everything. There remain deep and important divisions in the field, and this is what makes the field of child language acquisition so interesting and vibrant. The debates and the opportunity for discovery are stimulating and enticing, which is precisely why so many researchers enter the field every year. We begin with a discussion of the UG-based approach to language acquisition. We then introduce the first input-based approach, referred to as Statistical Tracking, and then the second input-based approach, which we label the Constructivist approach. Motivations for Universal Grammar Language input plays a critical role in language acquisition. For one thing, children acquire the language they hear rather than some other language— that’s just common sense. On one level, the common explanation that children are “sponges” is totally true—they soak up the input in their environment, and it seems effortless. But is that really how children acquire language? Do they just memorize their input, simple as that? Well, the answer to that question is categorically no. Children don’t just mimic their input, and their knowledge of language is not just a catalog of all the sentences and words they’ve heard. Children go far beyond what they hear, and this is the key observation that motivates the theory of Universal Grammar (UG). More specifically, the primary observation that drives UG approaches to language acquisition is that there are certain aspects of language that simply cannot be learned from the input. Children acquire language with No correction Although input is impoverished Through biases Uniformly Rapidly Easily To understand why this is the case, we need to first explain two very important concepts that form the basis for all generativist thinking: (i) The Problem of Induction (ii) The Poverty of the Stimulus Once we have laid this foundation, we will go into some of the details of the UG- based approach, explaining the various camps within this approach, what they all have in common, and what some of the differences are. Like within any vibrant intellectual group, there is not complete unanimity in ideas. Rather, the UG-based approach is filled with new ideas and is constantly changing and adapting to new findings. It can be quite frustrating for newcomers and outsiders to get a handle on the nuances of the most modern theory (as it is constantly evolving), but this is also what makes it such a dynamic field. Nonetheless, the foundational ideas remain constant, which we now discuss. The Problem of Induction We’ve just noted that language input is essential for language acquisition, but we don’t mean that children simply mimic their parents. Rather, what we mean is that the input provides words and other language-particular information about the language being acquired, and it serves as the evidence on which children test their innate hypotheses about language. The following analogy from Noam Chomsky may be helpful, where the “key” is language input and the “engine” is knowledge of language (in Piattelli-Palmarini, 1980, p. 172): “It’s a little like turning on the ignition in an automobile: It’s necessary that you turn the key for the car to run, but the structure of the internal combustion engine is not determined by this act.” In other words, you need some input (the key, the gasoline), but the engine, the basic framework for language, is already there. The job of linguists is to make explicit what the engine is like and how exactly the key works to turn it on: How do children make use of the language they hear spoken around them to flesh out their innate biases? It is the job of linguists because these innate biases are considered to be specific to language—they don’t apply to any other domain other than language.  In the next section, we will see some examples of domain-general learning mechanisms (mechanisms that apply across multiple domains of cognition, not just language) that could take input and turn it into knowledge. But domain-general learning mechanisms are typically far too broad for language. Processes like analogy and association formation (usually considered to apply across domains of cognition) are so all-purpose, so overpowering, and so wide in scope that they create more problems than they solve. It’s a little like trying to slice your sandwich in half using a chain saw: the instrument does the job (it cuts), but it does way more than you intend. The problem of induction is one such problem that we get with processes like analogy. Induction and Universal Grammar The problem of induction is a problem only if we assume the child has none of the generative system in place to begin with. If the child already knows some of the system, then the problem of induction is mitigated to some degree. This is why the UG-based approach does not face the same problem of induction that input-based approaches do. The language experience for a child consists of a series of communicative acts. Adults and others in their environment are constantly directing their intentions to the child, and this is accompanied by bits of language. One of the child’s jobs is to pick up on that language and figure out what it means. But the child’s most important job is to go beyond simply figuring out the meaning/intention of the adult’s individual utterances. The child must figure out what the system behind those individual utterances is, so that they can eventually produce utterances like them (and any others that constitute well-formed language). It’s a little like that old saying: Give a man a fish and you feed him for the day; teach a man to fish and you feed him for a lifetime. Likewise, if the child decodes individual utterances, they get immediate but short-term rewards (giving a man a fish, in this metaphor). But if and when the child masters the underlying system of language (learning to fish, in this metaphor), then the child has control over all of language, for life.  Moreover, we know that speakers have a creative, generative capacity for language—that is, after all, one of the hallmarks of human language. We saw in the beginning that children don’t simply memorize all the sentences that they have heard and then reuse them through the rest of their lives. How do we know this? Well, we are able to understand sentences that we have never heard before—we do all the time, in fact. This means that language does not consist of a really long list of sentences that we all know. Rather, language consists of an abstract system that allows us to generate all possible grammatical sentences. So what the child is developing must be this abstract system underlying language. And importantly, the only real external evidence the child has for the nature of this abstract system is the individual examples that constitute the linguistic input. The child never gets to see the underlying system—it is abstract, and no one ever talks about it explicitly—in fact, adult speakers are not even aware of this abstract system. Induction: the process of going from individual examples to a general rule So if we accept that the task facing the child is to acquire an abstract system that can generate all the sentences of their language (and doesn’t incorrectly generate impossible sentences) and that the only evidence before the child are individual examples of language, we need to ask how the child does this. This is precisely the process of induction: going from examples to a general rule. Induction has long been recognized as difficult, perhaps impossible, without the aid of some sophisticated help for the learner. Anticipating our discussion, constructivists argue that domain-general learning mechanisms suffice to overcome this problem of induction, whereas the UG-based approach claims that (i) domain-general mechanisms fail to solve this problem and (ii) children are born with biases that help avoid the very real pitfalls of induction.  So what is so difficult about induction anyway? At first blush, it seems pretty easy to deal with: you get examples, you observe what those examples have in common, and you postulate a generalization that captures your observations. But it is not so simple. Let’s exemplify this with some nonlinguistic cases before we move on to the more pertinent linguistic cases. Consider the series of numbers in 1: (1) 1 … 2 … What is the next number in this series? Let’s assume these two numbers have been put into this series on the basis of a generalization. Which generalization was used? It could be ‘Add one to the previous number’ (in which case the next number is 3), or it could be ‘Double the previous number’ (in which case the next number is 4), or it could even be ‘Add 9 to the previous number, divide by 2, and subtract 3’ (in which case the next number is 2.5). The point is that just by being exposed to individual examples, you can’t immediately tell how those examples were intended to be related to each other. There are too many possibilities.  Here is a slightly more complex example. Imagine we have a deck of cards and we play a game: The dealer chooses five cards (not randomly, but she gets to pick the cards she wants), and she shows you four of them. Your job is to guess what the fifth card is. The only restriction is that she must pick all five cards on the basis of some rule. That is, you should be able to guess her fifth card if you can guess the rule she has in mind. So our dealer thinks for a moment, and then she picks out her five cards. She shows you the first four cards:  Looking at these four cards, the 2 of clubs, the 3 of clubs, the 4 of clubs, and the 5 of clubs, what would you guess is the fifth card? A 6 of clubs? That would be a good guess, but you’d be wrong (an ace of clubs would be an equally good guess, and equally incorrect). Your guess is likely based on the idea that the dealer picked a numerical sequence of cards that were all clubs. A 6 of clubs would be the highest in the sequence, thus completing the run. This makes sense because the numbers are sequentially organized, and the cards are all clubs. But sadly for you, here is the fifth card:  The rule the dealer had in mind when she picked her five cards was “a sequence of cards of any black suit.” Notice that the original four cards that you saw were consistent with that rule, but you did not immediately notice that. (You might have, actually, but for the sake of this explanation, let’s assume you didn’t.) It just so happened, by chance, that the first four black cards the dealer picked were of the same suit, but that was not her intention. So while your guess may have been the most likely, it wasn’t the one the dealer had in mind when she selected the five cards. For poker players, the incorrect rule you had in mind was the straight flush, while the one the dealer had in mind was the more likely straight (with the slight stipulation of the same color). But how were you to know that, you ask? That’s the point: there is no way for you to have known that the dealer had a different rule in her mind. It’s in her mind, after all: you can only go by what’s in front of you. The data you had in front of you (the first four cards) were consistent with at least two hypotheses, and you picked the most obvious one to you. Hypothesis 1: sequential cards, all of one suit (incorrect hypothesis) Hypothesis 2: sequential cards, all of one color (correct hypothesis) This may not seem fair. It may seem like that rule was very arbitrary. And you’d be right—that was a totally rigged game! In language, the rules themselves are not completely arbitrary, but they do vary from language to language, and the child has to figure them out on the basis of what they hear in the input. That is, the child is faced with a series of examples, and they have to figure out how those examples are tied together. The data they get is necessarily ambiguous—it is consistent with multiple hypotheses, just like the cards above are, as we shall see shortly. The child needs to figure out not just any hypothesis that is consistent with the data, but the correct hypothesis—the abstract structure that generated the utterance. What is the underlying system that allowed the adult to use that particular form? Given the complexity of language and the variation that we see in the input to different children, this is (arguably) an impossible task without something else to help the child. Let’s now take a look at a more relevant example of the problem of induction from language. An Example from Language Let’s bring this into focus by looking at a classic linguistic example of the problem of induction, initially put forward by Chomsky (1971) and later taken up by Crain and Nakayama (1987). Chomsky noted that most (syntactic) constructions in human language are compatible with many, many possible analyses (at least to the naïve learner), only one of which is actually correct. In fact, the entire field of syntax is devoted to exactly that: figuring out what the correct underlying structures of various word sequences are. There are so many ways to analyze any given sequence of words that thousands of smart, obsessively hardworking people devote their professional lives to this endeavor. The example Chomsky used is yes-no question formation in English. In order to form a yes-no question, one simply takes the auxiliary verb (is in 2a) and moves it to the front of sentence 2b. Notice that other things like modals (in sentences with no auxiliaries, for example) might move to the front of the sentence, as in 3a–b. (2) a. The man is in trouble. b. Is the man in trouble? (3) a. I can go. b. Can I go?  From these examples, one might hypothesize that the rule in English for yes-no question formation is actually quite simple: move the first verbal element (auxiliary verb is or modal verb can in the examples above) to the front of the sentence and add question intonation. Hypothesis 1: Linear Order Hypothesis Move the first verbal element to the front of the sentence. This hypothesis works very well for the vast majority of yes-no questions in the child’s environment, even sentences like those in 4, in which there are multiple verbal elements. Notice that 4b, in which the first auxiliary is fronted, is acceptable, but 4c, in which the second auxiliary is fronted, is unacceptable. (4) a. The man is in trouble now that he is in custody. b. Is the man in trouble now that he is in custody? c. *Is the man is in trouble now that he in custody?  So Hypothesis 1 works very well. This hypothesis is structure independent in that it does not make reference to any syntactic structure. Rather, it is a hypothesis that makes reference to linear order. It states that in order to form yes-no questions, you need to identify the linearly first verbal element (linearly first = starting from the first word in the sentence, moving to the next) and move it to the front of the sentence. But there is a big problem with this hypothesis: human language syntax is not a linear system but rather a hierarchical structure (see figure 2.1). That is, it is organized into a stratified structure, typically represented as syntax trees. This is simply how syntax works (any introductory course on syntax will teach you this), so a linear hypothesis is bound to be incorrect. Figure 2.1 Linear vs. hierarchical sentence structure.  To see why this linear hypothesis is clearly incorrect, let’s look at another example. Hypothesis 1 (linear order) works for examples 2–4, but it falls apart with examples like 5, which also has two auxiliary verbs but in a slightly different configuration. Applying the above linear principle faithfully (move the first auxiliary to the front of the sentence) to 5, we get the incorrect 5b. Clearly, something is wrong here. What we actually need is a principle that somehow gets us 5c. To account for the sentences in 5, we can’t have a principle that says, ‘Move the linearly second verbal element’ or ‘Move the linearly last verbal element’ since that would directly conflict with Hypothesis 1 and generate the wrong result for sentences like 4. So we are in a bit of a quandary: How do we reconcile example 4 with example 5? Don’t worry—there is a solution. And the solution is a rule that makes reference to the hierarchical structure of syntax; in other words, it is a structure-dependent rule. The first thing to notice is that in sentence 5a, the first auxiliary verb is actually part of the subject of the sentence, which is The man who is in trouble. That means this first auxiliary is not the main auxiliary of the main sentence. The main sentence in 5a is (Subject) is now in custody, where (Subject) = The man who is in trouble. (5) a. [The man who is in trouble] is now in custody.  Looking at this sentence this way, the auxiliary that should be moved to the front of the sentence is the auxiliary verb that belongs to the main sentence. The linearly first auxiliary is utterly irrelevant for this purpose, since it occurs somewhere inside the subject of the sentence and so is invisible to the process of yes-no question formation when the rule makes reference to its structural position. Using linear order blindly (as in Hypothesis 1) does not allow for the observation that in this case, the important auxiliary is the linearly second one, while in example 4, it is the linearly first one. So if we reformulate our hypothesis as below, we get the correct yes-no question in 5c as well as every other yes-no question in the language, including 2–4. Hypothesis 2: Structure Dependent Hypothesis Move the verbal element in the main clause (i.e., after the subject) to the front of the sentence. This is called the structure dependent hypothesis because it makes reference to syntactic structure, not to linear order. It is vital to spell out the rule this way because the majority of yes-no questions that children hear are single-auxiliary questions and thus compatible with both hypotheses (and indeed other hypotheses, if you put your mind to it). So why don’t (at least some) children make errors with questions like 5b, *Is the man who in trouble is now in custody?, in which they front the wrong auxiliary? This kind of error has not been described in the literature, and we must ask why. If children are genuinely learning from the input and not guided by any predispositions, why don’t they, at least some of the time, go down this incorrect path?  Before we go any further, let’s ask if it is true that children do not actually make errors in which they front the wrong auxiliary. We suggested that they don’t, but where’s the evidence? Fortunately, this has been tested empirically. Crain and Nakayama (1987) tested thirty children aged 4–6 years old. They elicited yes-no questions from children using a very clever protocol. They prompted children to ask a Jabba the Hutt action figure questions, and Jabba then answered yes or no. The experimenter provided children with a prompt like ‘Ask Jabba if the dog that is sleeping is on the blue bench.’ This protocol is clever because it provides the child with a full model of a sentence containing multiple auxiliary verbs, but because the question is embedded in a conditional if-clause, the model sentence does not have a fronted auxiliary verb. So all the child has to do is take that model sentence and choose one auxiliary to move to the front of the sentence.  The short version of their results is that children produced many correct yes-no questions (fronting the main-sentence auxiliary). They also made some errors (as children this age typically do), but never once did any of the children produce errors consistent with the linear order hypothesis—that is, ‘*Is the dog that sleeping is on the blue bench?’ This shows that children do not consider the linear order hypothesis, and so they seem to be constrained by structure from very early in development (as young as 3;2 in Crain and Nakayama’s study). The linguistic principle that grammatical processes function primarily on structures in sentences, not on single words or sequences of words is termed structure-dependency. Many linguists view structure-dependency as a principle of universal grammar. The Structure Of Language “The principle of structure-dependency compels all languages to move parts of the sentence around in accordance with its structure rather than just the sheer order of words....” Structure-dependency could not be acquired by children from hearing sentences of the language; rather, it imposes itself on whatever language they encounter, just as in a sense the pitch range of the human ear restricts the sounds we can hear. Children do not have to learn these principles but apply them to any language they hear." (Michael Byram, Routledge Encyclopedia of Language Teaching and Learning. Routledge, 2000)  "All speakers of English know structure-dependency without having given it a moment's thought; they automatically reject *Is Sam is the the cat that black? even if they have never encountered its like before. How do they have this instant response? They would accept many sentences that they have never previously encountered, so it is not just that they have never heard it before. Nor is structure-dependency transparent from the normal language they have encountered--only by concocting sentences that deliberately breach it can linguists show its very existence. Structure-dependency is, then, a principle of language knowledge built-in to the human mind. It becomes part of any language that is learned, not just of English. Principles and parameters theory claims that an important component of the speaker's knowledge of any language such as English is made up of a handful of general language principles such as structure-dependency." (Vivian Cook, "Universal Grammar and the Learning and Teaching of Second Languages." Perspectives On Pedagogical Grammar, ed. by Terence Odlin. Cambridge University Press, 1994)  Interrogative Structures "One example of a universal principle is structure-dependency. When a child learns interrogative sentences, it learns to place the finite verb in sentence initial position: (9a.) The doll is pretty (9b.) Is the doll pretty? (10a.) The doll is gone (10b.) Is the doll gone? If children lacked insight into structure-dependency, it should follow that they make errors such as (11b), since they would not know that the doll is pretty is the sentence to be put in the interrogative form: (11a.) The doll that is gone, is pretty. (11b.) *Is the doll that gone, is pretty? (11c.) Is the doll that is gone pretty? But children do not seem to produce incorrect sentences such as (11b), and nativist linguists therefore conclude that insight into structure-dependency must be innate." (Josine A. Lalleman, "The State of the Art in Second Language Acquisition Research." Investigating Second Language Acquisition, ed. by Peter Jordens and Josine Lalleman. Mouton de Gruyter, 1996)  In sum, the example shows that simple constructions like yes-no questions are logically compatible with several different rules or analyses. There is no principled way to avoid adopting an incorrect hypothesis on the basis of input alone if all you have access to is the input and general learning mechanisms that make no reference to linguistic structure. What is needed is something that guides you to the correct hypothesis; in this case, that something is actually a very broad-level bias toward structure. The bias simply says, “When you hear language, think structurally, not linearly.” That is the essence of UG—a learning bias that guides children in their analysis of the linguistic input. Notice that structure dependence is a linguistic property. It is not a general learning mechanism—nowhere else in cognition does such a mechanism play any role. This is because the “structure” referred to by the principle of structure dependence is uniquely and purely linguistic in nature.  But there remains one other way that children could reach the correct hypothesis for yes-no questions without being guided by UG: perhaps the input provides sufficient evidence to solve this problem after all. As we shall see in the next section, the input is indeed rich in many ways, but it is severely impoverished in other ways, so the input is not going to save this particular situation. Instead, the input is so severely impoverished (in the relevant sense) that it actually forms the second motivating factor for the UG approach: the poverty of the stimulus. The Poverty of the Stimulus The term poverty of the s,mulus refers to the idea that the input to children is missing certain important informa4on. Poverty here means poor or impoverished, and s*mulus refers to what children hear (the input to children). So the expression ‘poverty of the s4mulus’ means that the input is poor or insufficient. Note that the s4mulus being impoverished does not mean that parents fail to speak to their children with enough language. The poverty of the s4mulus makes a claim about certain kinds of evidence being absent from the input, not really about the communica4ve richness of language. Poverty of Stimulus - The Poverty of the Stimulus (PoS) argument holds that children do not receive enough evidence to infer the existence of core aspects of language, such as the dependence of linguistic rules. PoS is considered contrary to empiricism that language is learned solely through experience.  Think back to the little card game we played earlier. The trouble you had with the game was that the four cards the dealer put in front of you were consistent with multiple hypotheses. And the dealer selected the cards such that the most obvious hypothesis was not the correct one. Unfair, true, but it was illustrative of the problem of induction and the process of inductive reasoning: drawing conclusions based on bits of evidence.  One reason this was difficult was that the dealer only gave you four cards from which to build a hypothesis. What if she gave you a dozen cards? Would that make it easier? Probably. So opponents of the UG approach often point out that the problem of induction may be solved by more evidence. The more evidence a child encounters, the easier it is to induce the correct properties of language.  Returning to the yes-no question issue, perhaps more evidence would allow the child to solve this problem without any innate bias toward structure. Possible? Sure, but let’s think about this a little more. What exactly does “more evidence” amount to? Simply hearing more yes-no questions of any type would not help. If a child got a million yes-no questions, all with a single auxiliary, they would be no closer to avoiding the problem of induction than a child that gets a single yes-no question, because all single-auxiliary yes-no questions are compatible with at least two hypotheses. And, in fact, we know that children do hear lots of yes-no questions in the input. The problem is that these are overwhelmingly single-auxiliary questions. The argument from the poverty of the stimulus is not an argument about the amount of input that children get. Rather, it is about the absence of the precise kinds of evidence needed to overcome the problem of induction. As Chomsky (1971) points out, the only evidence that would inform the child that a linear order hypothesis is incorrect is questions of the kind in 5c: (5) c. Is the man who is in trouble now in custody? This sentence type, and this sentence type alone, would tell the child that using Hypothesis 1 (move the first verbal element) is incorrect. This would be akin to our batch of cards (the 2-3-4-5 of clubs sequence) containing one card that was a spade, thereby informing you that we were picking cards by color, not by suit. In the sample of cards below, the four of spades is the crucial evidence that tells you that the dealer did not have suit in mind but just a sequence of black cards.  If the child gets disambiguating evidence of the kind in 5c, then they might be able to tell that the correct rule for yes-no question formation must make reference to syntactic structure. But how often do you think children hear questions like 5c? These are complicated patterns, and most parents don’t talk to their toddlers like this (cf. Pullum and Scholz, 2002). In fact, Legate and Yang (2002) found that in the speech to two children, double-auxiliary yes-no questions in which the second auxiliary had been fronted over the first (i.e., examples like 5c) occurred exactly zero times out of a combined 66,871 utterances, of which there were a combined 29,540 questions. There were some wh-questions in which two verbal elements occurred, with one fronted over the other (e.g., Where’s the part that goes in between), but these were exceedingly rare, with a combined rate of 0.06%.  That’s rare, to be sure, but the argument from the poverty of the stimulus is not really about the precise rate of rare evidence. With yes- no questions, sure, the evidence against Hypothesis 1 is rare, but the important point is that 99.94% of the data is consistent with multiple hypotheses. With the statistics stacked so overwhelmingly toward ambiguous patterns, surely at least some children, at least some of the time, will misanalyze their language. Crain and Nakayama show that this simply is not the case.  So the argument from the poverty of the stimulus says that (i) language is inherently ambiguous in terms of how one could analyze it and (ii) children simply never see this ambiguity because they are predisposed to analyze language in a structural manner. In a sense, children have blinders on that ensure that they only see the analyses that are consistent with how we know human language works. And because of that, they never even consider the hypotheses that are incompatible with UG properties, like a linear rule. What this shows is that the kind of patterns that children consider is restricted, and not simply directed, by the input. In that sense, this is very much like certain other phenomena in animal cognition. Rats can learn to associate a flash of light with an electric shock and a funny taste in their water with an episode of sickness. But it turns out that not all types of associations can be learned. For example, while rats will learn to associate a flash of light with an electric shock and a funny taste in their water with an episode of sickness (and so they will avoid the funny-tasting water), they cannot learn to associate a flash of light with getting sick or funny-tasting water with electric shock, even if the two events are presented with a high degree of regularity. If learning were simply a matter of associating one thing with another, then rats should be able to learn that a flash of light could make them sick, just as it could herald an oncoming electric shock. But this does not seem to occur to the rats. In other words, the rats’ set of hypotheses about what can make them sick is restricted.  We’ve seen that there is both strong empirical evidence and strong logical evidence for the argument from the poverty of the stimulus. But still, we might ask: How plausible is it that children are predisposed to see language in a particular way? Cutting right to the chase, this idea is very plausible. Not only do we have parallels in the cognition of other animals, but we also have countless examples of exactly that kind of thing in other domains of human cognition, as noted by many linguists (e.g., Fodor, 1983). Consider the most obvious one: optical illusions. Optical input to the eye, like linguistic input, is massively ambiguous in terms of how it could be interpreted. In terms of a physical specialization, we see the world in the colors of the rainbow because our eyes have been specialized to see only those wavelengths of the light spectrum. But more relevant to us are optical illusions. Take the famous “horizontal lines” optical illusion (figure 2.2). This figure consists of perfectly horizontal lines with unevenly aligned black and white squares. Our minds find vertical unevenness unnatural to parse, so we perceive the horizontal lines as if they are sloped at the edges. Let’s emphasize this point: the horizontal lines in figure 2.2 are perfectly parallel—your mind is making them look loopy! This shows that the visual domain of our mind has preferences—it doesn’t like vertical unevenness— and it imposes those preferences on what we see. Our minds are predisposed to interpret optical information in one particular way, and that analysis of the optical input just happens to be inaccurate, resulting in an optical illusion. Figure 2.2 A common optical illusion. The horizontal lines don’t look parallel (but they are). The black and white are not aligned, but our eyes naturally follow the vertical lines and compensate for the unevenness by interpreting the horizontal lines as sloping.  Vision, therefore, has a highly articulated internal structure: it is so specialized to see the world in one particular way that we can find hundreds of ways to trick our minds. And judging by the millions of internet hits for the term optical illusion, we get great pleasure out of it. Moreover, these specializations that result in optical illusions are completely unique to vision. They serve no purpose outside of vision and are a clear indicator of domain-specific architecture. This is also true in other domains of cognition —we are biased to interpret data in particular ways, ways that are suited to the needs of that particular domain of cognition. Moreover, none of this is learned behavior. We don’t learn to see optical illusions—we simply do. Such optical illusions are universal, present from the earliest testable ages, and thus are widely assumed to be innate. If it’s the case that other domains of cognition have domain-specific biases innately built into them, then why would this be a strange thing for language to have? The answer is that it isn’t strange. It is perfectly normal and is a reasonable basis on which to pursue a research program, which is what the generative approach to language acquisition does.  In sum, then, the arguments from the poverty of the stimulus and the problem of induction lead us to the conclusion that language acquisition is impossible if it is driven exclusively by what is heard in the input. But language is surely acquired by every typical child in typical circumstances (as well as many atypical circumstances). What’s the solution to this puzzle? Well, the solution is that the mind is biased to interpret data in a manner that is consistent with the structural properties of language. Furthermore, since the biases themselves are not explicitly taught or derivable from experience of the world, and since all children have them, we make the further claim that they are innate—part of what it means to be born a human. This is the essence of the UG approach. As we will see below, the Constructivist approach to language acquisition (a variety of input-based approaches) either downplays the importance of the problems of induction and poverty of the stimulus or addresses them only partially. So far we have described UG in quite broad terms: there are innate biases that prevent children from considering analyses of their language that are incompatible with human grammar. This view of language has as its roots the theory proposed by Noam Chomsky in the 1950s and 1960s, which laid out the architecture of grammar, and the concepts of competence and performance, which play an important role in UG-based approaches to language acquisition. The UG-Based View of Language: A Computational System On the UG-based approach, language consists of two major components: (i) a lexicon and (ii) a computational unit. The lexicon contains all the lexical entries for the language: nouns, verbs, inflectional morphemes, and so on. The computational unit consists of a series of procedures that combine those lexical entries (see figure 2.3). For each sentence, the desired lexical items are extracted from the lexicon and combined to form sentences according to a series of rules that will differ slightly from language to language. These procedures build a structure that adheres to certain fundamental properties that linguists have discovered about human language. For example, we know that human language is organized into hierarchical structures (represented as trees). The procedures within the computational unit will build structures of this sort. We also know that language involves various kinds of dependencies, sometimes local dependencies (such as gender agreement between an adjective and a noun, e.g., una casa bella, ‘a-fem. house-fem. pretty-fem.’) and sometimes lengthy dependencies (such as the one created when a wh- element is moved to the front of the sentence, across multiple clauses, e.g., What did the man say that Mary thinks that John ate [what]?). There are various restrictions on such dependencies, and the procedures of the computational unit of grammar are designed to obey those restrictions. Figure 2.3 Words are drawn from the lexicon and fed into the computational unit, which then applies various procedures to produce the output sentence.  This system is highly appealing since it explains why language is infinite: it takes atomic lexical items and combines them in endless ways. It also allows for the creativity and fluidity of language while at the same time providing a structure that limits the kind of variation language might exhibit. This view of human language, then, is based on a strong computational unit that essentially determines the structure of human language. Because of the very architecture of this computational unit, some things simply never occur in human language. For example, language is not linearly ordered because this computational unit does not work that way. Its architecture is such that all it can do is build hierarchical structures, so syntax is not ever going to make reference to a linear order rule. This is like the rat never considering that funny-tasting water might cause an electric shock or that a flash of light might make it vomit.  What other properties does the computational unit provide? This is an empirical question, and one that linguists are continually working to answer. Whatever those particular properties of the computational unit turn out to be, the UG hypothesis states that children already have a fully formed computational unit. They are born with the procedures that create well-formed linguistic units, and they do so without any difficulty whatsoever. But if that is the case, why don’t children speak perfectly just as soon as they acquire some of the lexicon? There are several reasons for this, but before we can explore these reasons, we need to understand a foundational distinction within the UG-based approach to language: competence versus performance. Competence versus Performance The distinction between competence and performance was established most famously in 1965 by Chomsky, and it is important for students of generative linguistics to understand it. The term linguistic competence refers to the idealized state of one’s linguistic potential: it is the knowledge base that allows you to produce and understand any sentence of your language. A native speaker’s ability reaches a threshold such that they possess the same level of knowledge as others in the language community, so in a sense all native speakers have the same capacity for and knowledge of their language. However, this competence may not be expressed by individuals to the same degree, because the actual use of language in daily life involves something called language performance. Performance can be affected by things like fatigue or competing demands on cognitive processing. For example, you may be a perfectly articulate person, but if you are put on a ledge 100 feet off the ground and blindfolded, you will likely not be able to express yourself all too well. Likewise, if you get drunk, you likely slur your speech and you may even make subject-verb agreement errors because you can’t keep track of who you are talking about. You might also make more slips of the tongue if you are simultaneously performing a cognitively demanding task. So while in principle we may possess the ability to understand and produce any sentence of our language (competence), we may not possess the ability to do that in real time to the same degree in all situations (performance).  Why is this important? Well, it means that we cannot take any piece of language produced by anyone as evidence against their competence. If we judged your language ability only by the time you were on that 100-foot- high ledge, we might think you are not a native speaker of any language! This is especially important when we study the language of children: the fact that they fail to perform language in an adultlike manner (they don’t always produce well-formed sentences) does not necessarily mean that they lack competence in language (in the sense of lacking knowledge of the computational unit). They might be lacking competence, but it does not necessarily mean so.  There are many reasons why children may not perform to adult standards. Children do not possess vocabularies as large as those of adults. This includes nouns and verbs, but importantly it also includes various kinds of morphology, like prepositions and verb endings. Moreover, children have a much more limited working memory capacity than adults do, and they can’t process information as quickly. This impacts how they can express themselves as well as how they comprehend language. And children tend to get distracted very easily, so they may produce ill-formed sentences in part because they change thoughts midsentence. We could go on, but the point is that there are numerous reasons why children might fail to perform like adults, but that’s just what it is: a failure to perform. So it is possible that despite their errors, children actually have full (or fuller) competence in language. Flavors of UG Approaches When linguists say that children have innate knowledge of language, this claim can take different forms. It can mean that children are born expecting grammar to have a hierarchical structure and grammatical categories like verb, noun, and modifier (e.g., adjective) but that they take time to acquire the functional parts of language like tense marking and auxiliary verbs. Or it can mean that children are born with the full architecture of a syntax tree but fail to use it in an adultlike way because of limitations on their ability to process information (and their limited vocabulary). Some researchers take a stance in between these positions. For the sake of simplicity, we present here just the basic concepts. Continuity One approach to UG is to say that the child’s grammar is of the same kind as that of adults, and that children have access to the basics of UG from the very start of life. The procedures, principles, and rules are all in place, as is the overall architecture of language. This approach is called continuity (meaning that the child system is fully continuous with the adult system). One of the most striking advantages of continuity is that it makes it relatively easy to explain how children acquire their grammar. The basics of UG are already there, so all the child has to do is match the input with their internal knowledge of UG. In many ways, this is the classic generativist approach to language acquisition. However, there are some challenges to this position, most notably how to account for the late acquisition of various structures. As we will see later, certain syntactic properties appear to be acquired quite late in development, and this challenges a strong version of continuity. For example, if children have full knowledge of UG, why can’t they comprehend the passive voice in English until after age 5? Why do they sometimes appear to think that John hugged him means the same thing as John hugged himself? Why do children have difficulty comprehending and producing certain complex constructions, such as object relative clauses? These questions pose challenges, and they have led linguists to propose some alternative explanations for why children’s grammars are so systematic and constrained, yet they can differ from the adult grammar in some respects.  There are generally three avenues researchers take in dealing with this issue. The first is to weaken the degree of continuity. On this view, some but not all aspects of UG are present for the child from birth. This means that the task of acquiring a language is still assisted by UG, but not fully, so this gives rise to late acquisition of some structures and various patterns of errors. A second approach is to maintain a strong version of continuity but to explain apparent delays and systematic errors as being due to something outside of grammar, such as memory structures or other cognitive development.  A third approach is referred to as maturation. The idea here is that children are born with UG, but that some aspects of it are not available at first and instead develop at some point after birth. Just like other biological developments, the suggestion is that UG does not endow the child with all the tools of the computational unit until later in life, perhaps age 5. This accounts for the late acquisition of some structures in language, as well as errors in the early stages. The maturation account appeals to some, but not to others. Detractors see maturation as a stipulation. Rather than explaining the developmental patterns, they argue, it simply pushes the unexplained delay in development into something mysterious like biological maturation. But supporters of this view say that maturation is a well-attested fact of human biology. Children begin to lose their baby teeth at around age 6 years—why does it happen at that age and not a few years earlier or a few years later? Puberty occurs between the ages of roughly 10 and 15 years, but the reason for why it happens at those particular ages is somewhat mysterious. Therefore, it is reasonable to assume that some aspects of the biological property of human language are likewise programmed to develop at some point after birth. Principles and Parameters Yet another way to reconcile the concept of UG with the fact that children don’t speak like adults from the outset comes from the hugely influential theory of principles and parameters (Chomsky, 1981). In our discussion so far, we have assumed that UG consists of a series of biases specific to language. But the principles and parameters framework considers a more specific and very intriguing way of seeing UG. We begin with an observation from linguistic typology—a discipline that looks at the similarities and differences across the world’s languages. We know that while languages differ a great deal from each other, they don’t vary in infinitely many ways. Rather, languages come in a limited number of types, while other types seem to not exist or to be vanishingly rare. For example, consider a simple phenomenon: the word order of the subject, verb, and object. English is a subject-verb-object (SVO) language in that the basic word order is such that the subject precedes the verb, and the object follows the verb. Other languages differ from this order. Japanese, for example, prefers the order SOV, while Arabic prefers VSO. But when you consider that there are six possible permutations (SVO, SOV, VSO, VOS, OSV, OVS), we find that the huge majority of known languages prefer one of three word orders: SVO, SOV, and VSO. The other three word orders just seem to be rare. In fact, the OVS order is so rare that it was long thought to be unattested—perhaps impossible in human language. We now know that it does occur (e.g., in the Native American language Hixkaryana, spoken in Brazil), but out of almost seven thousand human languages spoken today, it is found in only a handful. Similar typological facts can be found in numerous other phenomena.  Ideally, UG would provide a way to account for this kind of general property while also accounting for the Logical Problem of Language Acquisition (LPLA). One way that this might be done was proposed by Chomsky (1981) in the Principles and Parameters framework. The idea is that UG consists of a series of principles that are invariant across languages. We could think of these as the biases that we have discussed so far. For example, structure dependence might be a principle of language, in this technical sense. But according to this framework, some principles might contain two or more options (referred to as parameters). Taking the word order example, it may be that there is an invariant principle that states that subjects must precede objects. This accounts for why the OSV, OVS, and VOS word orders are so rare in the world’s languages. Moreover, there are two additional parameters at play here. One parameter says that in any given language, the subject may either precede the verb phrase or follow it, and the second parameter says that within the verb phrase (VP), the object may precede the verb or follow it. (6) a. Principle: Subjects precede Objects b. Subject-VP Parameter: Subjects may precede or follow VPs c. Object-Verb Parameter: Objects may precede or follow verbs  Notice that even though we are using terms like precede and follow, which look like descriptions of linear arrangements, we are talking about structurally defined concepts (subject, object), which can themselves contain (in principle) an infinite number of words; therefore, these definitions are structure dependent. Principle 6a rules out OSV, OVS, and VOS word orders, so children who are acquiring a language know from the outset that such word orders are not likely. This has the desirable effect of reducing the child’s hypothesis space, easing the LPLA. Additionally, children know that they will have to set each of the two parameters (6b–c), so they listen for evidence that will help them get this done. If children set 6b as “subjects precede VPs,” then the language will be either SVO or SOV, and if children set 6b as “subjects follow VPs,” then the language will be VSO. The distinction between SVO and SOV comes from 6c: if children set the parameter as “objects precede verbs,” then the language will be SOV, and if children set 6c as “objects follow verbs,” the language will be SVO.  Another example of a principle and related parameter has to do with wh- movement. All languages have the ability to ask wh-questions: questions that ask who or what did something or was affected by an action and when, where, why, or how something happened. These are different from yes-no questions in that they are answered with a whole phrase rather than a simple yes or no. So, a principle of UG might be that language allows wh- questions. Where languages differ, however, is in the position that the wh- word occupies in the question. In English, Spanish, German, and many other languages, the wh-word moves to the beginning of the question, even if it is interpreted in some other part of the sentence (What did John buy? = ‘John bought [what]?’). In Mandarin, Cantonese, Japanese, Swahili, and many other languages, the wh-word does not move anywhere. It remains, in the question form, in the part of the sentence where it is interpreted, as in the following Japanese example. Note that Japanese word order is SOV, so the statement form in 7b shows the object preceding the verb, just like the wh-word nani ‘what’ does. (7) a. Hanako-ga nani-o tabeta no? Hanako-subject what-object eat-past Q “What did Hanako eat?” b. Hanako-ga sushi-o tabeta Hanako-subject sushi-object eat-past “Hanako ate sushi.”  The wh-movement parameter within UG states that languages come in two kinds: those that move the wh-word in questions and those that do not move it. Importantly, children know this and set about trying to figure out which language they have been born into. In the Principles and Parameters framework, then, UG provides children with a series of these kinds of (ideally binary) parametric choices along the crucial points of difference that describe the languages of the world. It’s almost as if the child has a switchboard of choices (provided by UG) that they must set according to the input they hear, and acquiring language amounts to setting the parameters (switchboard choices). Crucially, this means that the child does not need to learn these choices from induction, since the options are given to the child. However, it also means that there is the potential for children to make the wrong choice. It may be that children select the wrong option at first and later reset it. Child errors, therefore, are seen not as an indication of ignorance of language but rather as evidence of knowledge of the underlying UG system but not yet knowing which language they have been born into.  So here we have a framework that manages to (i) address the LPLA by providing children with the relevant principles through UG, thereby addressing the problem of induction; (ii) account for why children might not speak like adults from the outset (since children must set each parameter on the basis of their input); and (iii) capitalize on typological facts about the world’s languages that otherwise would be unrelated facts about language. Because of this, the Principles and Parameters framework has been hugely influential over the years and remains an important part of our thinking. We turn now to the first of our alternative (non-UG-based) approaches to how children learn language. The following section introduces an approach referred to as statistical tracking. While this is not generally seen as a direct competitor to the UG-based approach, it places greater emphasis on the influence of input on learning and so in that sense is distinct from the UG approach. Statistical Tracking Children have an amazing ability to track patterns in the world around them. They notice cause and effect—the cup falls from the table and Mom picks it up. They notice patterns of all kinds of events and behaviors, and there are many patterns in language that could potentially be tracked. Do children track these patterns in language? And if so, does this help them learn language? Let’s look at some examples of statistical patterns one might encounter in language. One type of pattern relates to something called phonotactics. Phonotactic constraints are constraints on which individual sounds are allowed to occur next to each other within a syllable or word. (A syllable is a unit of sound or sounds, usually consisting of a vowel and one or more consonants adjacent to it; for example, [ba] is a syllable with a consonant and a vowel. Phonotactic constraints can vary by language. For example, in English we can have words that start with [tr] or [st] or [bl] (tree, stick, black)—these are considered to be phonotactically “legal” sequences at word beginning—but not *[tl] or *[ts] or *[rd]—these are phonotactically “illegal” at the start of English words. Because the first three clusters are phonotactically legal sequences at the beginning of a word or syllable, we can make up new words that have those patterns: troob, stame, blorg. But we cannot make up new words of English like *tloob, *tsame or *rdorg. Some of these phonotactically illegal sequences are actually possible in other languages. For example, Cherokee has [tl], as in tlvdatsi [tləd̃ atsi], ‘lion’; German has [ts], as in Zeit [tsaɪt],‘time’; and Russian has [rd], as in рдеть [rdetj], ‘to glow’. Phonotactically legal sequences are more likely to occur next to each other within a syllable or word than phonotactically illegal sequences are, even though those “illegal” sequences will be encountered by learners across syllable and word boundaries (Saffran et al., 1996; Storkel, 2001). For example, English learners will hear sequences such as that log, but soon, her dog.  Since the speech stream is continuous, there is an interesting question about how children begin to segment that stream of sound into smaller units like words. One idea about how children might begin to identify word boundaries is by noticing the relative probabilities with which a given sound follows another sound. If [t] is followed by [r] with a high probability, for example, then there’s a good chance they form part of the same syllable, and so no word boundary occurs between [t] and [r] in a [tr] sequence. This is referred to as a transitional probability: the probability of one segment following another. Similarly, if [t] is followed by [l] with a low probability, then it is more likely that a word boundary occurs between [t] and [l] in a [tl] sequence. In this way, a learner might be able to predict word boundaries simply by tracking the probability of such combinations. The learner simply postulates word boundaries where low transitional probability is detected and ignores those places where high transitional probability is detected. A fascinating demonstration of infants’ ability to track transitional probabilities comes from a study by Saffran, Aslin, and Newport (1996). The researchers played for 8-month-old babies a two-minute stream of synthesized speech containing unbroken sequences of syllables. The syllables were arranged so that they formed four distinct groupings that could be called “words.” Syllables were consonant-vowel (CV) sequences such as [pa], [ti], [go], [la], and [do], and the four “words” were (8) a.) pabiku b.) tibudo c.) golatu d.) daropi Babies heard unbroken sequences of these “words,” so that there were no pauses between them or other prosodic information. For example: (9) pabikutibudogolatudaropitibudopabiku … After the two-minute training phase (in which the babies simply listened to this stream of auditory input), the babies were presented with three-syllable test stimuli, some of which were the “words” presented in the training phase and some of which were “part-words,” sequences of three of the syllables heard in training but not necessarily all together in one sequence. (10) a. word: golatu b. part-word: tudaro The sequence in 10b contains the final syllable of one of the “words” from the training set (final syllable of golatu), together with the first two syllables of another “word” from the training set (daropi). Crucially, the babies had heard all of these syllables adjacent to one another in the training phase. However, because the “words” always cohered as units, the transitional probability between syllables within a “word” was very high: in fact, the syllable [pa] was always followed by the syllable [bi], so the transitional probability between [pa] and [bi] was 1. But a “word” in this set could have been followed by any other “word,” so the syllable [tu] could have been followed by [pa], [ti], [go], or [da] (the first syllable of any of the “words”). Therefore, the transitional probability between [tu] (the final syllable of one word) and any of these syllables was only 1 out of 4, or.25. This is illustrated in figure 2.4. Figure 2.4 Illustration of transitional probabilities within vs. between words in an artificial “language.”  The result of the experiment was that the babies indicated surprise (as measured by visual fixation) when presented with the part-word test stimuli—the infants didn’t expect to hear these sequences. The researchers concluded that 8-month-olds were able to identify plausible word units within which transitional probability between syllables was high, as distinct from syllable sequences with lower transitional probabilities. This result raises the question of whether statistical tracking could be used for learning more abstract and complex aspects of grammar, such as sentence structure. Marcus et al. (1999) conducted an experiment similar to that of Saffran et al. (1996), but it differed in an important respect: the actual syllables used for training and for testing were different, which means that the transitional probabilities for all of the syllables in the testing phase were 0 (because they had not been encountered before). Marcus and his colleagues exposed 7-month-olds to sequences of syllables with either an ABA pattern or an ABB pattern. For example: (8) a. ga ti ga (ABA) b. ga ti ti (ABB) Following a 2-minute training phase (exposure to a stream of these syllables in artificial speech), the babies were presented with sequences of new syllables that either matched or did not match the pattern they had heard during training (see table 2.1). The babies listened significantly longer to the mismatched patterns, indicating they were surprised by these sequences (see appendix B). So even though all of the syllables in the testing phase were brand-new and the babies couldn’t use transitional probabilities to judge which test sequence was familiar, they could still tell the difference between the familiar and unfamiliar patterns. This result suggests that even 7-month-olds are capable of establishing an abstract representation of a pattern based on minimal exposure. This is an important result because it shows that children’s ability to track statistics in their input is not limited to the transitional probabilities between syllables. Rather, this shows that children (and adults, actually) can also track the frequencies of abstract symbolic structures like ABA and ABB. This is very important because grammar involves abstract symbolic structures. The fact that babies are attuned to this kind of abstract symbolic pattern shows us that children have a bias to look for such phenomena in their input.  This research program has been extended to investigate a variety of interesting questions about whether statistical tracking might be useful in acquiring syntactic patterns (Takahashi and Lidz, 2008). In fact, statistical tracking has proven to be a powerful tool in linguists’ experimental arsenal for discovering what and how babies learn. Moreover, although statistical learning places an emphasis on how much babies can learn from input patterns, the existence of statistical learning procedures is not at odds with theoretical approaches that advocate innate linguistic knowledge. Together, statistical information and innate grammatical knowledge are used to acquire the target grammar. For example, Yang (2002) has proposed a learning algorithm by which learners use statistical properties of language input to decide between competing grammars that are specified by UG. Researchers disagree about how much knowledge about language needs to be innate in order to use statistical tracking to learn the specifics of one’s target language (e.g., whether babies can learn that sentences are hierarchical structures simply by tracking statistical patterns of language, or whether statistical patterns are useful only if you start out expecting language to consist of such structures). In this discussion we adopt the latter approach, but we recognize that many important questions remain open about the interplay between innate knowledge and statistical learning. We turn now to the constructivist approach. This approach provides an intriguing way to think about language acquisition, one that does not rely on any innate knowledge of language whatsoever. For researchers who favor a smaller role for innate knowledge, or only domain-general innate knowledge, this is an appealing approach indeed. However, as we will see at the end of the next section, this approach faces serious challenges in addressing the key issues that UG does: the problem of induction and the poverty of the stimulus. Modern Constructivist Approaches Put simply, constructivists adopt the view that a combination of (i) rich input and (ii) domain- general learning mechanisms suffice to account for both the LPLA and the DPLA. Several questions might arise from this statement. For example, what exactly is a domain-general learning mechanism? How does constructivism work? After answering these questions we’ll flesh out the difference between constructivism and UG. What is a Domain-General Mechanism? The term domain refers to a cognitive function that is operationally distinct from other functions and depends on principles and procedures that are specific to that domain. For example, vision is a domain of cognition and can be considered separately from other domains of cognition, like number (the ability to count and represent numeracy) or memory. At least some of the principles that govern vision seem to apply only to vision and no other function in cognition. Vision scientists must master a particular set of facts that are unique to vision as well as understand the internal architecture of the function of vision. Because vision works in a way that is unlike any other function of the mind, it is considered a domain of cognition. Other domains of cognition might include music, social cognition, mathematics, and object perception. Importantly for our purposes, language is considered a domain of cognition by most researchers.  Think of a domain (such as vision) like a component, or a module, in a large machine. In this metaphor, the mind is the whole machine, and each domain of cognition is a smaller module of the machine. Each module has its own internal architecture and a particular function for which it is specialized. The different modules can interact with one another, but each module does its own work: the vision module only concerns visual perception, and the language module only concerns language, but the two can share information so that we can, for example, talk about what we see. Or imagine a car engine, which has several smaller modules, like a carburetor, ignition system, cooling system, and braking system. Each has its own job, and some modules feed into other modules, forming a large, complicated machine.  This view of the mind is often referred to as the modular view of mental architecture (Fodor, 1983). On this modular view, each domain (or module) has learning mechanisms that are unique to that domain. Such learning mechanisms are considered domain-specific learning mechanisms. Thus, a language-specific learning mechanism is one that applies within the domain of language only. It takes as its input some linguistic material (e.g., a sentence), and its output is knowledge of some aspect of that linguistic material (e.g., its structure and meaning). Crucially, this learning mechanism has no application outside of that domain, or else it would be considered a domain-general learning mechanism. A domain-general learning mechanism (for our purposes) is one that helps learn language as well as some other function(s).  A classic example of a domain-general learning mechanism is analogy. A