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关键概要critical summary

时间:2016-02-25 09:28来源:www.ukassignment.org 编辑:anne 点击:
写作课老师给了篇文章,根据文章内容写critical summary。在以下内容中,我将对实验1进行具体的浏览,对方法和主要结果进行总结,并尽可能详细地先容这些结果。
至于语法部分,人的良好的外观显示不一致的限定词无论数量,对于他们的学习影响不大。接下来判测试,大家不难发现,频繁模具的帮助下,在嘈杂的环境下的人更有可能比正规化从控制那些输入。那么对于判断部分,其中有确定错位置的句子被所有恨,而那些从来没有听说过的参与者被评为比通常少的人低。在一般情况下,一个当时的现象是,随着越来越多的嘈杂限定词引起的,参加者将更有可能享受主要限定词份比小于通常的。
As for grammar parts, persons’ good appearance demonstrate that no matter quantity of inconsistent determiners , there is little influence for their learning. Next to sentence tests, we can easily find that with the help of frequent molds, persons from noisy circumstances are more likely to regularize the input than that from control ones. Then for judgments parts, sentences which have wrong positions for determiner are hated by all, and those participants never  heard are rated lower than the less usually ones. In general, a prevailing phenomenon is that with more and more noisy determiners arising, participants will be more likely to enjoy the main determiners parts than the less usually ones.
 
总而言之,试验清楚地表明,正规化,可以引导和成年人实现。然而,显著结果仅发生在那里的情况很多不安嘈杂的限定词,它激励着大家要强调另外两个因素显著。All in all, the experiment clearly indicated that regularization could be guided and achieved by adults. However ,  significant results only occurred in the circumstance where many noisy determiners disturbed, it inspired us to emphasize another two significant factors.
 
首先,显著正规化可以是较不频繁的嘈杂限定词不为特色的自然语言现象不一致的人的结果。要解决这个问题,大家设计了实验2.其次,同样的现象是否也会发生儿童。更准确的说,什么孩子的程度和趋势,以规范不同于成人的投入。所有上述问题将在实验3进行调查。Firstly, the significant regularization may be the result of less frequent noisy determiners not for  inconsistent ones featured natural language phenomenon. To resolve this ,we designed experiment 2. Secondly , whether the same phenomenon will also happened for children. More accurately, what the extent and trend for children to regularize the input different from adults. All the questions above will be investigated in experiment 3.    

Body:主体

In order to get the explanation and conclusion about this kind of regularization, three experiments were carried out. Some clearly, defined targets are put forward before experiments, including how learners learn languages under disturbance of inconsistency or probabilistic trend, at what time and for what reasons learners get the abnormal language as regular and natural as their learned ones, in the process of reproducing, adults are tendency to realize the complexity and characters of inconsistence before accomplishing regularizations, contrary, children are not.
In the followings, I will take a specific glance on experiment 1, to summarize the methodology and main findings in detail and try to introduce the findings as clearly as possible.
In experiment 1, the focus is that if input filled with complexity is regularized more than the simple ones also described as absence input by adults. For the sake of investigation, an artificial language paradigm is applied, and then participants are exposed to abnormal input with inconsistency cannot be predicted, provided with much more alternatives, this kind of input is identified as more complex than former ones. 
 
The methodology is designed aimed for five different parts: participants, the language, presentation, experimental manipulation, tests. In the first place, participants are all adults and attendants from the same university, of course, they are paied for their devotion to the experiment. Secondly, what presented before the participants was languages formed by small basic words, combined with videotapes describing things and performence. Under the premise that, the sentences are able to meet the basic application for the realistic world, although  the combination will be limited to the group of small words. Thirdly, there are 9 videotapes (including 1 for testing) provides languages that showed for participants. They are required to report again the sentence which are showed on the videotape. In order to ensure they accomplish the sentences on their own, they are informed it is only a test for pronunciation. Subsequently, there are many measures applied to make sure the equality of the experiment, such as match quantity of subjects, the same conditions designed for persons. Under these circumstances, the only difference is the appearance of determiners. Besides, there are 4 various tests for evaluation their actions. 2 vocabulary tests were given but few persons realized the second performance and record are equivalent to the first. One of the most interesting portions is completing the sentence. By the way, we could estimate the inconsistent portions like determiners. There is another assistant test to evaluate what a person knows about the use of determiners, called judgment task. Be careful to notice the difference between judgment and production. When it comes to the remains of the input, there is also a judgment test for the sake of general grammar.
 
After the scientific methods, we reached the primary results. According to vocabulary results, it is a little disappointed that we do not find difference significantly. As for grammar parts, persons’ good appearance demonstrate that no matter quantity of inconsistent determiners , there is little influence for their learning. Next to sentence tests, we can easily find that with the help of frequent molds, persons from noisy circumstances are more likely to regularize the input than that from control ones. Then for judgments parts, sentences which have wrong positions for determiner are hated by all, and those participants never  heard are rated lower than the less usually ones. In general, a prevailing phenomenon is that with more and more noisy determiners arising, participants will be more likely to enjoy the main determiners parts than the less usually ones.

Discussion:讨论

All in all, the experiment clearly indicated that regularization could be guided and achieved by adults. However,significant results only occurred in the circumstance where many noisy determiners disturbed, it inspired us to emphasize another two significant factors.
Firstly, the significant regularization may be the result of less frequent noisy determiners not for  inconsistent ones featured natural language phenomenon. To resolve this ,we designed experiment 2. Secondly , whether the same phenomenon will also happened for children. More accurately, what the extent and trend for children to regularize the input different from adults. All the questions above will be investigated in experiment 3.

Despite all the scientific and systematic experiment methods , there is one thing in experiment 1 that is sort of weak. From my perspective, the foundation theory of the experiment is Universal Grammar. There are still remain lots of controversy for the theory ,whether human ability of language is born,or acquired; the evolution of language is the result of human genes, or historical and cultural influence. Recursion plays an crucial role in acquiring language, if there is no similar expression in a nation's existing language system, then regularization will absolutely not happen .These factors were missed when choosing the participants, the balance of gender, the background of culture and other things varies in nations. Therefore, the controversial and debatable theory of Universal Grammar lead to lack of consideration and rigorous application.

阅读的原文:When natural language input contains grammatical forms that areused probabilistically and inconsistently, learners will sometimesreproduce the inconsistencies; but sometimes they will insteadregularize the use of these forms, introducing consistency in thelanguage that was not present in the input. In this paper we askwhat produces such regularization. We conducted three artificiallanguage experiments, varying the use of determiners in the typesof inconsistency with which they are used, and also comparingadult and child learners. In Experiment 1 we presented adult learnerswith scattered inconsistency – the use of multiple determinersvarying in frequency in the same context – and found that adultswill reproduce these inconsistencies at low levels of scatter, butat very high levels of scatter will regularize the determiner system,producing the most frequent determiner form almost all the time.In Experiment 2 we showed that this is not merely the result of frequency:when determiners are used with low frequencies but inconsistent contexts, adults will learn all of the determiners veridically.In Experiment 3 we compared adult and child learners, findingthat children will almost always regularize inconsistent forms,whereas adult learners will only regularize the most complexinconsistencies. Taken together, these results suggest that regularizationprocesses in natural language learning, such as those seenin the acquisition of language from non-native speakers or in theformation of young languages, may depend crucially on the natureof language learning by young children.[1]2009 Elsevier Inc. All rights reserved.0010-0285/$ - see front matter [1]2009 Elsevier Inc. All rights reserved.doi:10.1016/j.cogpsych.2009.01.001* Corresponding author.E-mail address: clhudson@berkeley.edu (C.L. Hudson Kam).Cognitive Psychology xxx (2009) xxx–xxxContents lists available at ScienceDirectCognitive Psychologyjournal homepage: www.elsevier.com/locate/cogpsychARTICLE IN PRESSPlease cite this article in press as: Hudson Kam, C. L., & Newport, E. L. Getting it right by getting it wrong: Whenlearners change languages. Cognitive Psychology (2009), doi:10.1016/j.cogpsych.2009.01.0011. IntroductionIn the past half-century, great strides have been made in documenting the linguistic accomplishmentsof children as they learn their native languages. Despite this increased base of knowledgeregarding what learners know as their language abilities develop, we are only beginning to understandhow this learning takes place. This paper addresses questions about the learning mechanisms themselves,focusing on the limits of the system by examining cases where learners acquire something differentthan the patterns in their input.We examine what human language learners can (and cannot) acquire when the input is abnormalin a particular way. Occasionally, language learners are exposed to input that contains grammaticalinconsistency, what we call probabilistic grammatical tendencies: a form is used some percentage ofthe time in a particular context, with its occurrence not predictable on the basis of any features ofthe context. This kind of input is unusual in that probabilistic grammatical tendencies of this sortare not typically found in human languages. However, they do occasionally occur, for instance whenlearners are acquiring their language from non-native speakers. Evidence suggests that this unpredictablevariation disappears as the language is learned; it is regularized (Newport, 1999; Ross & Newport,in prep; Singleton & Newport, 2004). The result of this change is a language that is no longer abnormal;the language as spoken by the learner is like other natural human languages. Learners, it seems,are able to ‘fix’ or repair this kind of abnormal input.Here we examine how this change is accomplished, by examining the kinds of information that humanlearners extract from inconsistent linguistic input. Our interests are broader, however. We are notsimply interested in characterizing the learning mechanisms involved in abnormal situations; we areinterested in gaining a greater understanding of the mechanisms involved in language acquisitionmore generally. We submit that understanding the performance of learning mechanisms when the inputis atypical can contribute to our understanding of the learning mechanisms involved in typicallanguage acquisition as well; the input may be unusual, but the learners are not.We present results from three experiments investigating how humans learn from languages containinginconsistent or probabilistic grammatical tendencies, asking about the circumstances underwhich they succeed at learning the variation veridically, and when and why they make the languagemore regular and more like other natural languages as it is learned. In previous research we haveshown that, at least in one circumstance, adults reproduce the inconsistencies they are exposedto, while children do not. In the present work we investigate this question more comprehensively,examining both the age of learners and also the nature and complexity of the inconsistencies towhich they are exposed, to see how these variables affect what is learned. In Experiment 1 we presentadult learners with inconsistency that is either relatively simple or more complex, to investigatewhether they might be less likely to learn veridically, and more likely to regularize, when the inconsistenciesare complex. In Experiment 2 we present adult learners with a complex but consistentlanguage, to see whether complexity in and of itself is enough to induce regularization in adultlearners. In Experiment 3 we vary the age of the learner, testing both adults and children to investigatewhether children are more likely to regularize a range of types of inconsistencies than areadults. To foreshadow, our results suggest that humans can learn from inconsistent linguistic input,but also that they do indeed make it more consistent under certain circumstances. Importantly, thedegree to which they regularize depends on both the age of the learner and the presence and natureof the inconsistency. In the discussion we will return to the question of how our findings fit intobroader issues, and particularly what we think our findings say about the mechanisms involvedin language learning and the circumstances under which natural languages become less inconsistentand more regular.1.1. Linguistic variation and accompanying change by learnersTypically, language learners are exposed to input that contains very consistent grammatical patterns.Sometimes these patterns are deterministic: a particular grammatical form is used every timea particular meaning is expressed. The regular plural form in English is an example of a deterministic2 C.L. Hudson Kam, E.L. Newport / Cognitive Psychology xxx (2009) xxx–xxxARTICLE IN PRESSPlease cite this article in press as: Hudson Kam, C. L., & Newport, E. L. Getting it right by getting it wrong: Whenlearners change languages. Cognitive Psychology (2009), doi:10.1016/j.cogpsych.2009.01.001pattern (though even such patterns can have lexical exceptions).1 Nouns take –s, -z, or –iz in the plural,depending on the phonological form of the noun (and those that are exceptions are always exceptional,in every context in which they occur). Other patterns are variable but are nonetheless grammatically predictable:the same form does not always occur with a particular meaning, but the variation among formsis contextually dependent and consistent across speakers (Labov, 1969, and papers in Chambers, Trudgill,& Schilling-Estes, 2003). As Smith, Durham, and Fortune (2007) put it, the alternation between forms is‘variable but highly structured’. For example, the pronunciation of –ing in English varies between ‘–ing’and ‘–in,’ with ‘–in’ being the casual form, used in faster speech, less formal contexts, and more often byyounger rather than older speakers. However, sometimes learners are exposed to linguistic input thatcontains grammatical patterns that are truly inconsistent tendencies. These patterns are unpredictableand probabilistic in nature; a form is used some percentage of the time in a particular context, withits occurrence or non-occurrence not predictable on the basis of any features of the context (see e.g.Newport, 1999; Singleton & Newport, 2004). We call these inconsistent or probabilistic grammaticaltendencies.Probabilistic grammatical tendencies are very common in the speech of late learners of a secondlanguage (Adamson, 1988; Goldowsky & Newport, 1993; Johnson, Shenkman, Newport, & Medin,1996; Newport, 1990, 1999). Late learners are generally not as proficient as early learners. The mostnoticeable difference is often their accent, but late learners also have problems with grammatical deviceslike tense and aspect, agreement marking, and case marking (Birdsong, 1999; Johnson & Newport,1989). Speakers who have learned a language as adults may simply omit the grammaticalmarking altogether, but often they will use a grammatical device inconsistently and probabilistically.This probabilistic usage can take different forms: the probabilistic usage of a single form; an unpredictablealternation between several forms (only one of which would be considered correct in the nativeform of the language); or a combination of the two, with the speaker sometimes using the correctform, sometimes an incorrect form, and sometimes using no form at all. Probabilistic usage is seen insecond language interlanguage (during learning, Kanno, 1998), as well as in fossilized asymptotic secondlanguage grammars (Sorace, 2000). Thus, although probabilistic usage is atypical of mature nativespeakers, it is not simply a characteristic of language learning in progress. Furthermore, the specificprobabilities and patterns of usage differ between individual second language speakers, even whenthey share the same native language, with the result that there may be no consistency across speakerswithin the same community (see, for example, Meisel, Clahsen, & Pienemann, 1981; Wolfram, 1985).A question of interest is therefore how children might behave if they had to acquire their own nativelanguage from parents (or other adults) whose usage contained such inconsistencies. Children areknown to have difficulty acquiring lexical exceptions to grammatical rules (Marcus et al., 1992). Linguisticinput of the kind described above, with true inconsistencies, seems as if it should be particularlydifficult to learn from. There are a few recorded instances of learners facing just this kind of input(see e.g. Aitchison, 1996; Kotsinas, 1988; Newport, 1999; Sankoff, 1994; Sankoff & Laberge, 1973; Singleton& Newport, 2004). The outcomes suggest that, while learners can acquire language from thiskind of input, they do not acquire such inconsistent variation veridically. In contrast with the moretypical situation of structured variation in the input, which is learned correctly (Kovac, 1981; Labov,1989; Roberts, 1997; Smith et al., 2007), learners exposed to inconsistent input appear to change thelanguage as they learn it, making it more regular.1.1.1. Children learning from non-native inputSingleton and Newport have conducted very detailed studies of the acquisition of American SignLanguage (ASL) by a deaf child they called Simon, whose only input source was his deaf parentswho were late learners of the language (Newport, 1999; Ross & Newport, 1996; Singleton, 1989; Singleton& Newport, 2004). Like other late learners of ASL, the parents’ signing contained many errorsand was governed by probabilistic, rather than deterministic, rules. That is, they would use complexmorphemes each some percentage of the time in the obligatory context, with its occurrence or non-1 Although there is variation in the pronunciation of the plural form (cats versus dogs versus canvases), it is completely consistentand predictable, and depends on phonological form of the noun.C.L. Hudson Kam, E.L. Newport / Cognitive Psychology xxx (2009) xxx–xxx 3ARTICLE IN PRESSPlease cite this article in press as: Hudson Kam, C. L., & Newport, E. L. Getting it right by getting it wrong: Whenlearners change languages. Cognitive Psychology (2009), doi:10.1016/j.cogpsych.2009.01.001occurrence inconsistent and not predictable on the basis of features of the context. Simon did notreproduce the unpredictable inconsistency present in his input, however; he changed the system,using the forms most frequently present in his input almost categorically. This is most compellinglydemonstrated by one particular regularization that Simon made. His parents most frequently useda handshape for vehicles that is not typical in ASL; Simon used their incorrect handshape consistently.As Singleton and Newport suggested, this result indicates that he is regularizing his parents’ inconsistentsystem, and not somehow secretly learning ASL from another source (Singleton & Newport, 2004).He received variable input and made it far more consistent and predictable. Ross and Newport (1996and in preparation; Ross, 2001) verified this claim in longitudinal analyses of Simon’s ASL usage, andalso demonstrated a similar outcome in three other deaf children learning ASL from their late-learninghearing parents.Kotsinas (1988) reports on a similar case involving children who were immigrants to Sweden.These children lived in immigrant communities in Stokholm and learned their Swedish primarily fromtheir parents and other immigrants to the community, all of whom were late learners of Swedish.According to Kotsinas, the parents’ speech contained ‘‘considerable variation among the speakers’varieties” (p. 133). The children’s productions, however, displayed more consistency, indicating theemergent grammaticalization of some of the forms present probabilistically in the parent’s speech.Importantly, the non-standard Swedish the children spoke was not the same as the vernacular Swedishthat ethnic Swedes speak – it was a modified version of the pidgin-like variety spoken by their parents.Notably, although some of the children also spoke Standard Swedish learned through exposure atschool, their vernacular was well stabilized prior to exposure to the standard variety.1.1.2. The language of the first generation of native speakers of a new languageSimilar phenomena also occur during the emergence of a new language. A number of researchershave documented the changes that occurred in two related pidgin languages as they were acquired bythe first generation of native speakers. The first, Tok Pisin, a contact language spoken in Papua NewGuinea, has been extensively studied by Sankoff and her colleagues, among others. They were particularlyinterested in the presence and form of grammatical devices (such as tense and aspect marking)and the clause and sentence structure in the speech of those who learned the language as adults, ascompared with the speech of those who learned the language as a native language.Though the language was in the early formational stages, they did find grammatical structures inthe speech of the adults; but they also found some variability in the use of those structures. This variabilitytook several forms, with two of these most relevant to the present work. First, as would be expectedin a late-learned language, there was variation in occurrence: any particular form occurred inits appropriate context probabilistically (Aitchison, 1996; Sankoff, 1994).2 Second, there were meaningsthat could be expressed in any of several ways (Sankoff, 1979).3Importantly, the speech of the children learning Tok Pisin as their native language from these latelearningmodels contained less unpredictability (Romaine & Wright, 1987; Sankoff, 1979, 1994; Sankoff& Laberge, 1973). For instance, although the native speakers produced the preverbal form i in thesame locations as did their non-native-speaker elders, the frequencies of use in the various syntacticenvironments differed between the children and adults (Aitchison, 1996; Sankoff, 1994).Similar results have been found in Solomon Islands Pijin (Jourdan & Keesing, 1997), anotherdescendant of Melanesian Pidgin (Keesing, 1988), and these kinds of changes have also been proposed,although not directly witnessed, in French-based creoles (Becker & Veenstra, 2003).2 It should be noted that there was also variation between speakers that was semi-predictable (Sankoff, 1994). Some of theinterspeaker variation was conditional on age, such that speakers who learned the language at different times spoke a littledifferently from each other. Some of it was correlated with location of origin, such that speakers from different regions of thecountry had different typical speech patterns. There were also sex differences. However, even beyond this, there was a great deal ofunpredictability in the speech of any one individual.3 Non-creole languages of course also often possess multiple ways of expressing the same meaning. For instance, the Englishfuture tense can be expressed using ‘ to be going to’ or ‘will’, or in short colloquial forms ‘be gonna’ or ‘’ll’ (e.g., I am going to go toSouth Carolina, I will go to South Carolina, I’m gonna go to South Carolina, I’ll go to South Carolina). These are not truly in freevariation with each other, however; they differ in certainty and formality, and possibly in focus. The Tok Pisin forms are not in freevariation with each other either, but they differ more according to context. See Sankoff (1979) for a more complete discussion.4 C.L. Hudson Kam, E.L. Newport / Cognitive Psychology xxx (2009) xxx–xxxARTICLE IN PRESSPlease cite this article in press as: Hudson Kam, C. L., & Newport, E. L. Getting it right by getting it wrong: Whenlearners change languages. Cognitive Psychology (2009), doi:10.1016/j.cogpsych.2009.01.0011.1.3. Regularization by adult learnersThus far, all the examples we have given involve regularization by children. However, there are reasonsto believe that adults may also regularize variability in languages. Aitchison (1996; Aitchison &Agnihotri, 1985) points out the tendency of adult language learners to overregularize morphological aswell as syntactic patterns. For example, one adult learner of German described by Klein and Perdue(1993) always used eine as the indefinite article (when he used an article with indefinites), despitethe fact that German has multiple indefinite article forms that vary by gender of the noun (as doesItalian, his native language). This can be seen as a reduction or regularization of variation (even thoughthe variation in German is predictable). Unlike overregularization in children’s first language acquisitionfrom native input (e.g. the overregularization of –ed to irregular verbs), much of the adult learner’soverregularization remains in their system as it fossilizes; they do not outgrow it (Adamson,1988; Sorace, 2000).1.1.4. What kind of mechanism is likely to produce regularization?As presented, the evidence suggests that both adults and children can, at least in principle, introducegreater regularity into languages. Are the learning mechanisms that produce regularization specificto language learning, or might they be more general in scope? Some results from studies ofprobability learning suggest that it might be the latter. The general aim of probability learning studieswas to describe what participants learn when they are provided with information that is probabilisticin nature. For instance, participants are asked to watch two lights that flash, one at a time. The participant’stask is to make a prediction about which of the two lights will flash just before each flash event.Which light actually flashes is probabilistically determined so that the overall probability is within apre-determined range. For instance, in a 70/30 experiment, light A flashes 70% of the time, and light Bflashes 30% of the time, entirely probabilistically. (The particular ratio can, of course, differ byexperiment.)Most experiments in this literature show that after very little exposure, adults’ predictions begin tomatch the exposure probabilities. For example, in the 70/30 example, participants predict that light Awill flash next on 70% of the trials and that light B will flash next on 30% of the trials (Estes, 1964,1976). This kind of response pattern is called probability matching. (Note that probability matchingis not the optimal response for success in prediction or in securing reinforcement, as the paradigmis run in animals, since predicting A on 70% of the trials, when it does in fact flash unpredictably witha .70 probability, will lead the participant to be correct only 58% of the time. Nonetheless, probabilitymatching is the most common response pattern seen in these experiments.) However, under certaincircumstances another type of response appears. For instance, when participants are asked to attendto their level of correctness on blocks of trials rather than for each individual trial, they tend to overmatch,selecting the more frequent alternative more often than it actually occurs. Of particular interestto us, some experiments suggest that one can induce overmatching by changing features of the presentation.Gardner (1957) and Weir (1972), for example, found that adults overmatched when presentedwith more than two alternatives. For example, if light A flashes 70% of the time, and lights Band C each flash 15% of the time, participants guess light A more than 70% of the time. This literaturethus suggests that adults will regularize non-deterministic information under some conditions.Similar experiments conducted with young children show that they are less likely than adults toprobability match and more likely to regularize. (This is our term, which we use to suggest similarityto regularization in language acquisition. In the terminology of this literature, children are more likelyto overmatch or even to pick the more frequent item all the time, called maximizing.) However, thedegree to which they regularize, and the age at which they stop regularizing and begin to probabilitymatch, varies across studies. The general trend in the child literature is that younger children are morelikely than older children to overmatch (see discussion in Hudson, 2002). The upper limit of regularizingbehavior is dependent on the task, with more complex tasks producing regularizing at older agesthan easier tasks. Bever (1982) found high degrees of overmatching in 2- and 3-year old children in atwo-choice task, and very little maximizing in 4-year olds in the same experiment. Kessen and Kessen(1961) found probability matching by age 3;7. However, overmatching has been found in children asold as 5 and 7 using a slightly different task that included three choices (Stevenson & Weir, 1959; Stevenson& Zigler, 1958; Weir, 1964). The findings, then, are similar to those in the adult studies, whereC.L. Hudson Kam, E.L. Newport / Cognitive Psychology xxx (2009) xxx–xxx 5ARTICLE IN PRESSPlease cite this article in press as: Hudson Kam, C. L., & Newport, E. L. Getting it right by getting it wrong: Whenlearners change languages. Cognitive Psychology (2009), doi:10.1016/j.cogpsych.2009.01.001increased complexity produces less probability matching and increased regularizing behavior. However,the literature also shows quite a bit of variation in the tasks (especially with respect to theamount of information available to the learner) and in the details of their outcomes.1.1.5. Predictions for language learningTaken together, the findings from studies of language acquisition and probability learning reviewedabove provide some suggestions about variables that might lead to the regularization that occurs inthe acquisition of non-native language input or in the emergence of a new language. These findingssuggest that the nature of the inconsistencies themselves may play a role in regularization, and alsothat children may be more likely than adults to regularize inconsistencies; but neither of these variableshas previously been investigated systematically. Indeed, as discussed above, typical human languagesdo not contain much unpredictable inconsistency, so it is difficult to answer this question byexamining normal language acquisition. There are, as discussed above, natural situations where linguisticinput is provided by non-native speakers. However, in such cases many variables are confounded,making it difficult to determine which of them lead learners to change the language. Wehave therefore developed a miniature artificial language paradigm for use in investigating this question.In previous work using this paradigm, we presented learners with simple linguistic input thatcontained one inconsistent part of the grammar. We found that adult learners did tend to reproduce(or probability match) the inconsistencies in their input; but, in contrast, children turned inconsistentforms into rules (Hudson Kam & Newport, 2005).In that study we exposed learners to an artificial language with probabilistically occurring determiners(articles, like ‘the’ and ‘a’ in English). Nouns were accompanied by determiners some percentageof the time; the rest of the time determiners were absent, and the nouns were alone in the nounphrase. When nouns had determiners and when they did not was determined randomly: there wereno differences in meaning or other aspects of context when the nouns appeared with versus withoutdeterminers. In this way the variation was unlike that typically present in natural human languages,though much like that in the speech of late learners, adult speakers of emerging contact languages,and the parents of children like Simon. At testing, adult participants produced about as many determinersin their own productions as they had heard in the exposure (that is, they probability-matchedthe use of determiners). In contrast, children’s productions were more systematic than their input.However, the type of inconsistency investigated in that study – variation in the presence versus absenceof a form – is not the only type of inconsistency that occurs in the natural language phenomenawe are interested in understanding. In the present series of studies, we examine a different type ofinconsistency, more like that found in the natural input received by Simon and by those exposed toan emerging language, to see how this type of inconsistency affects adult learning; and we also observechild learners exposed to the same type of inconsistency, to see how age differences interactwith this. In Experiment 1 we ask whether adult language learners will regularize more when the languagethey are exposed to contains more complex variation than when it contains simple two-alternativevariation. In Experiment 2 we consider in greater detail the character of the complexity andinconsistency required to produce regularization. In Experiment 3 we compare the performance ofadult learners with that of children exposed to these types of inconsistencies, to see if they regularizein the same or different ways.2. Experiment 1In this experiment we examine whether adult learners regularize complex variable input morethan the simple presence/absence input. To investigate this we used an artificial language paradigmto expose participants to miniature languages containing unpredictable inconsistency that was morecomplex than the inconsistency we had previously found to produce probability matching in adultlearners (Hudson Kam & Newport, 2005). We did this by increasing the number of options in a particulargrammatical category. (Exactly what this means is described below.) This is much like increasingthe number of lights over which participants make predictions, a manipulation that produces overmatchingin probability learning studies, and thus should produce overmatching in our language-6 C.L. Hudson Kam, E.L. Newport / Cognitive Psychology xxx (2009) xxx–xxxARTICLE IN PRESSPlease cite this article in press as: Hudson Kam, C. L., & Newport, E. L. Getting it right by getting it wrong: Whenlearners change languages. Cognitive Psychology (2009), doi:10.1016/j.cogpsych.2009.01.001learning task if the two result from the same mechanisms. It is also very much like the kind of inconsistencyto which Simon was exposed and which he regularized. We exposed participants in differentconditions to increasing numbers of options, reasoning that increased options equals increased complexityin the nature of the probabilistic variation and therefore perhaps increased regularization.As in our earlier studies, we exposed participants to a language in which all the elements displayedregular properties except the determiners. Determiners were selected because we wanted to study thelearning of an inconsistent functional category, and in a short period of time. This makes most otherfunctional categories unsuitable, given the complexities of the meanings they encode. As in HudsonKam and Newport (2005), we examined what participants had learned about the language using severaldifferent measures, including production and grammaticality judgment tasks.2.1. Method2.1.1. ParticipantsParticipants were students at the University of Rochester at the time of the study. Average age was19.8 years. Forty-one women and 19 men participated. They were paid daily for their participation andreceived a bonus after the final session for completing the entire experiment. They were recruited primarilyfrom the department’s subject pool list via an email describing the study and inviting them toparticipate.2.1.2. The LanguageThe basic language to which we exposed participants was small, consisting of 51 words: 36 nouns,7 intransitive verbs, 5 transitive verbs, 1 negative, and 2 main determiners, 1 for each of 2 nounclasses. These words were in the input of participants in all conditions. In addition, there were another16 noise determiners used in the experimental manipulation. These did not vary by noun class, but theexact number of noise determiners varied across the experimental conditions. (This is described ingreater detail below.)The language was presented in conjunction with a small world of videotaped events showing objectsand actions, whose permissible combinations restricted the number of possible sentences. Evenwith these semantic restrictions there were over 13,200 possible sentences in the language. Thegrammatical structure of the language is shown in Fig. 1. The basic word order is (NEG) V-S-O. Asis typical for a real VSO language (Greenberg, 1963), the determiner follows the noun within theNP. This word order was selected to be quite different from that of English, and also to permitthe use of a sentence completion task (see below) that would readily elicit NPs (the crucial portionof the artificial language sentences) from participants during testing. This basic grammatical structurepermits four possible sentence types: intransitive, transitive, negative intransitive, and negativetransitive.Although the vocabulary is relatively small in comparison to full natural languages, we took greatcare to make the language as realistic as possible. (See MacWhinney, 1983, for a discussion of thispoint with respect to research using miniature artificial languages). Complete sentences can be producedin the language, and there are different kinds of sentences (e.g. negative and positive, transitiveand intransitive). The word order and functional category properties were modeled after those of naturallanguages (though unlike English, in order to avoid simple transfer), and the sentences expressedmeanings. In addition, while some of the natural language cases we were modeling did not containtheir inconsistencies within the determiner system, the details of their probabilistic variations arevery similar to the inconsistencies of our artificial languages.Fig. 1. Grammatical structure of the language.C.L. Hudson Kam, E.L. Newport / Cognitive Psychology xxx (2009) xxx–xxx 7ARTICLE IN PRESSPlease cite this article in press as: Hudson Kam, C. L., & Newport, E. L. Getting it right by getting it wrong: Whenlearners change languages. Cognitive Psychology (2009), doi:10.1016/j.cogpsych.2009.01.001The nouns were divided into two classes. Nouns were assigned to classes on a completely arbitrarybasis, with 20 nouns in class 1 and the remaining 16 nouns in class 2. This was done to keep the methodsas similar as possible to those in earlier studies to permit comparisons of previous and current results.The only grammatical consequence of noun class membership in the language is determinerselection: each class of nouns takes a different main determiner. (A word list and gloss for each wordcan be found in Hudson Kam & Newport, 2005.) This division into two noun classes, with a differentmain determiner for each, is similar to the division of nouns into two gender or declension classes inmany natural languages. The exact nature of the linguistic input received by a participant variedaccording to consistency condition assignment and is described below.2.1.3. PresentationParticipants were exposed to the language by videotape for eight sessions, each lasting 25–29 min.Participants were seated in front of a video monitor, on which they watched a scene or event. Theythen heard a sentence in the miniature language that described the scene. Sentences were spokenat a normal rate with English prosody and phonology and sounded very natural and fluent. Therewas no explicit instruction in grammar or vocabulary, and they never saw anything written. Participantslearned the language solely from the auditory exposure to the sentences. For example, a participantsaw a toy boat hitting a girl-figurine and heard:(1) /flIm m[1]wzner kA ferlukV po/hit boat DET1 girl DET2‘The boat hits the girl.’The exposure set contained 230 sentences and their corresponding videotaped scenes. Half the exposureset sentences were intransitive and the other half were transitive. Negative sentences were includedto help the participants learn the meaning of the verbs, especially the intransitives, as wellas to expand the number of possible sentences in the language. Overall, however, there were relativelyfew negative sentences in the presentation set (seven transitive sentences and 43 intransitivesentences).Verb frequencies varied due to the importance of keeping noun occurrences balanced, along withthe constraints arising from the meanings and associated selectional restrictions of the verbs. Eachintransitive verb occurred 15–18 times in the 115 intransitive sentences. Each transitive verb occurred14–27 times in the 115 transitive sentences. Individual verbs were presented either in both negativeand positive sentences or in only positive sentences; no verb was presented in only negative sentences.Each noun in the language occurred 3–4 times in the intransitive sentences, and 3–4 timesin each syntactic position (subject and object) in the transitive sentences. Like the verbs, each nouncould appear in both positive and negative sentences, or only in positive sentences; no noun appearedin only negative sentences. (The exact number of times any particular word occurred in the exposureset is listed in Appendix B of Hudson Kam & Newport, 2005).Each exposure session contained a different set of approximately 115 sentences drawn from the230 sentence exposure set. Each sentence (and scene) was presented four times over the course ofthe eight exposure sessions. Participants were asked to repeat each sentence after hearing it. Theywere told that this was pronunciation practice which would be helpful since they would have to producetheir own sentences at the end of the experiment. The entire experiment took nine sessions tocomplete (the eight exposure sessions and one test session). Participants completed the experimentin 9–12 days. All exposure and testing was done individually.2.1.4. Experimental manipulationIn this experiment as well as those that follow, equal numbers of subjects were assigned to each ofthe five conditions (input groups) described below. Participants in all five conditions were exposed tothe same basic sentences, and therefore their exposure was equivalent in almost all aspects of sentencestructure. Input sentences differed between conditions only in the occurrence of determiners8 C.L. Hudson Kam, E.L. Newport / Cognitive Psychology xxx (2009) xxx–xxxARTICLE IN PRESSPlease cite this article in press as: Hudson Kam, C. L., & Newport, E. L. Getting it right by getting it wrong: Whenlearners change languages. Cognitive Psychology (2009), doi:10.1016/j.cogpsych.2009.01.001in the noun phrases. All participants were exposed to sentences containing the main determiner forms(those agreeing with the class of the noun) 60% of the time. Although the sentences containing themain determiner forms were selected randomly from the exposure set, they were the same for all participants,regardless of complexity condition.The experimental manipulation occurred in the remaining 40% of noun phrases. Participants in thecontrol condition heard no determiner form in those noun phrases. These participants were exposedto the same input as in our previous study (Hudson Kam & Newport, 2005), a kind of inconsistency wecall presence/absence inconsistency. This inconsistency exhibited the least complex variation, and onthe basis of previous results participants were expected to probability match the occurrence of thedeterminers in their own usage. All other participants received more complex determiner variation,of a type we call scattered inconsistency. In scattered inconsistency there is one main form that occursa majority of the time, but a number of other forms may occur instead of this main form, inconsistentlyand at a lower frequency. The degree of scatter differed across conditions. Like the control participants,those in the 2 ND group (ND = noise determiner) heard the main determiner forms 60% of thetime, but in the remaining 40% of noun occurrences, one of two other determiner forms (hereaftercalled ‘noise’ forms) occurred, each in 20% of the noun occurrences of each noun class. For noun class1, 60% of the noun phrases occurred with the main determiner form, /kA/, 20% occurred with the noisedeterminer form /te/, and the remaining 20% with the noise form /meg/. The same was true for nounclass 2: 60% of the noun phrases had the main form /po/, 20% had /te/, and 20% had /meg/. Note thatthe noise forms occurred with both noun classes, unlike the main forms, which were restricted in theirdistribution to the main noun class. Noise forms were thus both lower in frequency and more unpredictablein the context in which they occurred.The input for the three other complexity groups was similar, but contained more noise determinersthat each occurred with lower frequency. The 4 ND group heard the main determiner form 60% of thetime and 4 noise determiner forms, /te/, /meg/, /li/, and /kum/, that each occurred with 10% of thenoun phrases within a noun class. Again, the noise forms occurred with nouns in both classes. The8 ND condition heard the main determiner 60% of the time and 8 noise forms that each occurred 5%of the time. In addition to the 4 noise forms listed above, they heard /su/, /gI/, /ler/, and /bAn/. The finalcondition was the 16 ND condition. They heard the main determiner form 60% of the time and 16 noiseforms that each occurred 2.5% of the time. The additional noise forms they heard (over and abovethose present in 8 ND condition) were /bIp/, /fu/, /z[1]l/, /zo/, /sep/, /mIb/, /lfm/, and /d[1]f/. This inputis represented graphically in Fig. 2, which shows the percentage of occurrence of each determinerform within each noun class across the different conditions. Each fill pattern represents one determinerform.As noted, the noise forms are less predictable than the main forms in two ways. First, they are lessfrequent. Second, they occur with nouns in both classes. This is not true of the main determiner forms.Note that the percentages given above are true within each noun class, but change when consideringFig. 2. Occurrence of determiner forms within each noun class in the input languages, across the five different conditions.C.L. Hudson Kam, E.L. Newport / Cognitive Psychology xxx (2009) xxx–xxx 9ARTICLE IN PRESSPlease cite this article in press as: Hudson Kam, C. L., & Newport, E. L. Getting it right by getting it wrong: Whenlearners change languages. Cognitive Psychology (2009), doi:10.1016/j.cogpsych.2009.01.001nouns overall. In the 2 ND condition, for example, the percentage of all nouns occurring with each maindeterminer form is 30%, whereas the percentage of all nouns occurring with each noise form is 20%.While these overall percentages are much closer together than 60% and 20% (the percent occurrencesfor the main and noun determiners within each noun class), the main forms are still more frequentthan the noise forms even when considering nouns overall.In all other ways the input languages across the groups were as similar as possible. As mentioned,the noun phrases containing the main determiner forms were the same for all participants. Similarly,all noun phrases occurring with /te/ and /meg/ in the input of the 16 ND participants also occurred with/te/ and /meg/ in the input of the 8,4, and 2 ND groups. Similarly, a noun phrase containing /kum/ in the16 ND input also contained /kum/ in the 8 and 4 ND input sets. All other parts of the grammar were thesame, and completely consistent, in all four input groups.In order to be sure that the various determiner forms were actually inconsistent and probabilisticin their appearance (and not accidentally associated with a syntactic function or with a particular lexicalitem), the occurrence percentages for each determiner for noun phrases in general were alsomaintained for each syntactic position and for each noun. For example, in the presentation set ofthe 2 ND group, 60% of intransitive subjects, transitive subjects, and transitive objects occurred withmain determiner forms, and the 2 noise forms were similarly evenly distributed across each syntacticposition. While it was not possible to maintain precisely the same occurrence percentages for eachnoun, individual nouns occurred with particular determiner forms within a range centered aroundthe condition percentage. For example, in the 2 ND condition, the main forms occurred 41–74% ofthe time with individual nouns, with an average of 60% across nouns, and the noise determiners occurredwith particular nouns 9–33% of the time, with an average of 20%. In addition, each presentationof a particular sentence within and across sessions could be different from the other three. This lastpoint is particularly important, because it ensures that there were no consistent conditioning contexts.Thus there was no pattern of determiner use available to be learned from the input data, other thanthe percentages of use of the various forms.2.1.5. TestsParticipants were given four different types of tests to evaluate their performance. Tests were givenin the order in which they are described below.2.1.5.1. Vocabulary. A vocabulary test was given twice. The first was administered after participantswatched the videotape in the fourth session. In this task, participants were tested on their knowledgeof twelve vocabulary items. They were told that this test was designed to give them some idea of howthey were doing up to this point – that it was for their own benefit and would not be analyzed. Participantswere asked to provide a name for each object as it appeared on a video monitor and weregiven as much time as they needed to respond. All responses were videotaped, but (in accord withthe instructions) the results were not analyzed.A second vocabulary test was used to evaluate whether participants had learned enough vocabularyto be tested on more complex aspects of the language and was administered with the other testsin the final session. Participants were tested on the same 12 items as in the first vocabulary test, butthe order in which the items appeared was different. We tested the same nouns twice for one principalreason: these are the nouns required to complete the sentences in the production task, and we thereforewanted to direct attention to them in an implicit way. Post-test debriefing indicated that very fewparticipants had noticed this. Presentation and recording were the same as in the first vocabulary test.2.1.5.2. Sentence completion task. The test of primary interest was a sentence completion task. Thistask was designed to evaluate the participants’ production of determiners – the inconsistent part ofthe language. Participants saw a novel scene on the video monitor and heard the first word of the correspondingsentence. They were then asked to produce the complete sentence and were given asmuch time as they needed to provide an answer. For example, a participant sees a toy bird jumparound and hears the word /mErt/ ‘move’. She should then say /mErt ffmpogV po/ ‘move bird det’. Becausethe language is V-S-O, participants were always given the verb and had to produce the wholesentence, thus generating the NP(s) themselves.10 C.L. Hudson Kam, E.L. Newport / Cognitive Psychology xxx (2009) xxx–xxxARTICLE IN PRESSPlease cite this article in press as: Hudson Kam, C. L., & Newport, E. L. Getting it right by getting it wrong: Whenlearners change languages. Cognitive Psychology (2009), doi:10.1016/j.cogpsych.2009.01.001There were 24 test sentences (12 transitive and 12 intransitive), resulting in 36 possible NPs andtherefore 36 possible determiners. Participants were first tested on the transitive sentences and thenon the intransitives. The test set was designed so that 12 nouns each appeared once in each possiblesyntactic position (intransitive subject, transitive subject, and transitive object). The first use of theindividual noun varied between subject and object position in the transitive sentences; some nounswere first used as subjects and others as objects. Participants were asked to indicate where a nounthey could not recall should go in the sentence (for instance, by saying X instead of the noun). Thisallowed us to include the data from incomplete responses. Responses were videotaped and later transcribedfor analysis. All sentences used in this and other tests were novel to the participants and werenot part of the exposure set.2.1.5.3. Determiner judgment task. The third test was a grammaticality judgment task that also examinedparticipants’ knowledge of determiner usage, but through judgment rather than production. Participantswere asked to listen to 48 sentences one at a time and judge each of them on a four-pointscale according to how much they ‘liked’ or ‘disliked’ the sentence. Participants were instructed to respondthat they really liked a sentence when it sounded like a sentence from the language that theyhad been learning, and to respond that they really disliked a sentence when it sounded completelyunlike a sentence from the language. They were also told that if they thought a sentence was mostly,but not completely, like or unlike sentences from the language, they should use the middle of thescale. Participants responded by pointing to one of four different ‘happy’ or ‘sad’ faces. The experimentwas designed in this fashion so that it also could be done with children without changing the tasks.The 48 test sentences consisted of four variations of 12 base sentences: one form contained themain determiner form appropriate to the noun, one contained a noise determiner (one of the formsto which all subjects other than the control subjects had been exposed), one had the determiner inthe wrong location (preceding the noun), and one had no determiner at all. The sentences were randomlyordered, with the constraint that two versions of the same base sentence could not follow eachother. The four variations of one base sentence are shown in (2):(2) a. /gern ferluka po/ (correct: det follows noun)‘fall girl det’b. /gern ferluka meg/ (infrequent/incorrect: noise det)a‘fall girl det’c. /gern po ferluka/ (incorrect: main det precedes noun)‘fall det girl’d. /gern ferluka/ (incorrect/infrequent: no det)b‘fall girl’a This is a lower frequency form for all participants exposed to noise determiners, although the frequency varies by condition.For the control participants, however, it is an incorrect form which they have never heard.b As with example (b), the type of this example varies in its correctness by input group. For the control participants it is thelower frequency form in their exposure. For all other participants this type of sentence is an incorrect form to which they werenot exposed.Four of the 12 base sentences varied the determiner occurring with the transitive subject, four variedthe determiner occurring with the transitive object, and four were intransitive (and therefore variedthe determiner occurring with the subject). Sentences were presented on a 索尼TM minidisk deckMDS-S38 through headphones, preventing the experimenter from hearing the sentence to whichthe participant was responding. This prevented the experimenter from being able to inadvertentlycue the participant to any particular response. Responses were recorded on an answer sheet by theexperimenter. Participants had 3 s to respond to each test item. Again, all sentences were novel.2.1.5.4. General Grammar Test. The fourth test, also a grammaticality judgment task, examined whatparticipants had learned about the rest of the language. Participants listened to 16 pairs of sentencesand were asked to select the sentence from each pair that sounded most like a sentence from theC.L. Hudson Kam, E.L. Newport / Cognitive Psychology xxx (2009) xxx–xxx 11ARTICLE IN PRESSPlease cite this article in press as: Hudson Kam, C. L., & Newport, E. L. Getting it right by getting it wrong: Whenlearners change languages. Cognitive Psychology (2009), doi:10.1016/j.cogpsych.2009.01.001language that they had been learning. The two sentences in each pair were versions of the same sentence,one grammatical, the other ungrammatical. Test sentences were presented using the 索尼TMminidisk deck MDS-S38 over headphones. Participants listened to both versions of the sentence andcircled 1 or 2 on an answer sheet, indicating whether they preferred the first or second sentence inthe pair. Half of the sentence pairs tested participants’ knowledge of verb subcategorization, that is,whether they knew that transitive verbs required two nouns and intransitives only one. The remainingsentence pairs tested whether participants knew that a verb was required in every sentence. Theserules of the grammar were tested for both transitive and intransitive sentences. For the transitive sentenceswith missing arguments, either the subject or the object could be the missing argument. Whichsentence (first or second) in the pair was grammatical was randomized, as was the ordering of sentencepairs in the test, with the constraint that no more than two sentences could occur in a row thattested the same rule and were of the same valence. There was a 1-s pause between the two sentencesthat formed a pair and a 5-s pause between pairs. Pairs were not identified as such, except by theoccurrence of the longer pause. Again, all test sentences were novel; none appeared in the exposureset.2.2. ResultsWe begin by reporting results for tests that demonstrate more general knowledge of the language,and then move on to describe the results on determiners that are of primary interest.2.2.1. Vocabulary testsIn accord with the instructions given to participants, the data from the first vocabulary test werenot tabulated. On the second vocabulary test, all participants scored at least 5 out of a possible 12(the criterion we have previously used for deciding whether participants would be given certain ofthe remaining tests), with a mean of 8.88 items (SD = 2.39) for participants overall. Means variedslightly across the input groups, ranging from 7.83 (16 ND) to 10 (2 ND). A one-way ANOVA with inputcondition as a between-subjects factor indicated that the differences in vocabulary scores were notsignificant.2.2.2. General Grammar TestThis test examined participants’ knowledge of parts of the grammar other than determiners. Weconducted this test to ensure that learners in all conditions successfully acquired those parts of thegrammar represented consistently in the input. The test examined participants’ knowledge of sentenceconstruction (did they know that a verb is required in every sentence) and verb subcategorization(did they know that one set of verbs is transitive, requiring two nouns, and another intransitive,requiring only one noun).Fig. 3 shows the mean score by noise condition. The overall mean was 14.5 out of a possible 16,SD = 1.66. This was significantly and substantially above chance (t(59) = 30.29, p < .001). We conductedFig. 3. Mean score on general grammar test by input group. (In this and all other figures, error bars represent standard error.)12 C.L. Hudson Kam, E.L. Newport / Cognitive Psychology xxx (2009) xxx–xxxARTICLE IN PRESSPlease cite this article in press as: Hudson Kam, C. L., & Newport, E. L. Getting it right by getting it wrong: Whenlearners change languages. Cognitive Psychology (2009), doi:10.1016/j.cogpsych.2009.01.001a repeated measures ANOVA with rule type (two levels) and transitivity (two levels) as within-subjectfactors and noise condition (five levels) as a between-subjects factor. The main effect for noise conditionwas not significant, indicating that the manipulation of determiners had no effect on participants’learning of the other, consistent parts of the grammar. There was a significant effect of rule type(F(1,55) = 9.2, p = .004), with participants scoring slightly higher on knowledge of basic sentencestructure (every sentence must have a verb) than on verb subcategorization (7.55 vs. 6.95 out of apossible 8) – not surprising since the former requires very general knowledge of the language, whilethe latter depends on knowledge of particular verbs. There was also a significant main effect of transitivity(F(1,55) = 16.55, p < .001), with participants performing slightly better on transitive test itemsthan intransitive ones (7.63 vs. 6.87 out of 8). There were no significant interactions.Overall, participants performed very well on this test, indicating that they had learned these consistentfacets of the grammar. Moreover, their performance was not affected by the amount of inconsistencyof the determiners: participants performed equally well in all input conditions.2.2.3. Sentence completion taskThe results of this test were of primary interest. It permitted us to observe the effect of the presenceand amount of scatter in the linguistic input on the production of determiners. In particular, wewanted to know whether, when exposed to scattered inconsistency in determiner usage, participantswould reproduce the inconsistency present in their input or would regularize the more frequent formsto which they were exposed. For each participant we computed the percentage of main determinerproduction (the number of correct main determiners used by the participant, divided by the numberof possible determiner usages, multiplied by 100). The number of possible determiner usages was simplythe number of correct nouns produced by the participant in this task. Fig. 4 shows the mean percentageof main determiner production for the five input groups. Recall that the percentage of mainforms in the input was the same – 60% – for all input groups.As can be seen in Fig. 4, participants exposed to scattered inconsistency (input containing noisedeterminers – all of the ND conditions) produced more main determiner forms than those exposedto presence/absence inconsistency (main determiner forms alternating with determiner omission –the Control condition). A one-way ANOVA confirms that there is a significant effect of input conditionon the production of determiners (F(4,55) = 8.27, p < .001), and a t-test comparing the control groupwith the four scatter groups is significant (t(55) = [1]4.889, p < .001, with pooled variance estimate).4Fig. 4. Mean production of main determiner forms by input group.4 Tests for homogeneity of variance were not significant, indicating that it is licit to pool the variances. The contrast is alsosignificant using a separate variance estimate (p<.001).C.L. Hudson Kam, E.L. Newport / Cognitive Psychology xxx (2009) xxx–xxx 13ARTICLE IN PRESSPlease cite this article in press as: Hudson Kam, C. L., & Newport, E. L. Getting it right by getting it wrong: Whenlearners change languages. Cognitive Psychology (2009), doi:10.1016/j.cogpsych.2009.01.001We were particularly interested in whether increasing the noise, or scatter, would induce increasedregularization behavior in adult learners. The data indicate that this is indeed the case: As participantsare exposed to increasing numbers of noise determiners, they produce increasing percentages of maindeterminer forms (Flinear(1,57) = 25.92, p < .001).5 This effect shows that extensive scattered inconsistencydoes produce regularization, with participants in the 16 ND producing almost 90% main determinerforms, even though their input contained these forms only 60% of the time.The design of the test allowed us to examine whether participants were using determiner formsdifferently according to the syntactic position of the noun (subject of an intransitive verb, subject ofa transitive verb, or object of a transitive verb). A repeated-measures ANOVA with input conditionas a between-subjects factor and syntactic position as a within-subject factor showed no effect of syntacticposition and no significant interaction between input and syntactic position. This indicates thatparticipants were not imposing on the input a more deterministic, linguistically-based rule, such asusing the main determiners in association with subjects versus objects or with transitives versusintransitives.What were participants doing when not using the main determiner forms? Control participantsused no determiner form at all. Thus they were replicating their input, as we found in our earlierstudy. Participants in the noise determiner input groups primarily used noise forms, with many participantsusing 1 or 2 noise forms to the exclusion of the others. (This is the only possibility in the 2 NDcondition. However, this was common among participants in the 8 and 16 ND groups as well.) This useof a few noise determiners did not reduce the amount of inconsistency present in the language, however,as different speakers preferred different noise forms. Most participants preferred the phonologicallysimpler CV forms, but this trend did not hold for all participants; some used a CVC form as theirpreferred noise form. There were also some who used the incorrect main form (the main determinerform for the other noun class), and some who created a novel determiner form (usually blends of oneor more existing forms). Occasionally, a participant in a scatter condition used a bare noun, somethingnot present in their input. Thus, participants were not regularizing the noise or scatter in their input;their non-main form productions remained inconsistent and noisy. Regularizations, when they occurred,involved more frequent use of the main determiner forms.In sum, speakers in the control group basically reproduced their input. In contrast, participants innoise groups showed a tendency to regularize their input, using the most frequent forms more oftenthan they had heard them. Moreover, increased complexity of variation in the input resulted in increasedregularization, with participants producing increasingly more main forms in their speech asthe number of noise forms in their input increased. Participants exposed to a few noise forms producedthe main determiner forms only slightly more often than they had heard them, though muchmore often than participants who heard no noise forms at all (presence/absence inconsistency); participantsexposed to 16 noise determiners produced the main determiner forms almost 90% of thetime, a full 30% more often than they heard them.2.2.4. Determiner judgment taskThis task was designed to assess participants’ knowledge of determiners in a different way –through grammaticality judgments. As described above, participants were asked to rate 48 novel sentences,one at a time. Twelve of the test sentences were correct, 12 contained a noise determiner (towhich all subjects other than the control subjects had been exposed), 12 had the determiner in thewrong location (preceding the noun), and 12 had no determiner at all.Fig. 5 shows the mean ratings given by participants to each kind of sentence for the five inputgroups. A MANOVA with sentence type and syntactic position as within-subject repeated-measuresvariables and input groups as a between-subjects variable was conducted on the data.5 This F was computed adjusting for unequal spacing between the categories of the factor (input condition). Because it is notclear whether the control condition falls along a continuum with the scatter conditions, we also conducted the linear trend analysiswith only the 4 noise conditions included, and it too is significant (F(1,45) = 11.21, p = .002).14 C.L. Hudson Kam, E.L. Newport / Cognitive Psychology xxx (2009) xxx–xxxARTICLE IN PRESSPlease cite this article in press as: Hudson Kam, C. L., & Newport, E. L. Getting it right by getting it wrong: Whenlearners change languages. Cognitive Psychology (2009), doi:10.1016/j.cogpsych.2009.01.001The primary variables of interest are the effects of input group and determiner manipulation. Themain effect of input group is not significant. The main effect of determiner manipulation is significant(F(2.4, 130.9) = 368.31, p < .001).6 This is modulated by a significant interaction between the two variables(F(9.5, 130.9) = 35.29, p < .001), reflecting the fact that all participants liked sentences with main determinersand disliked sentences with the determiner in the wrong location, but the groups differed in theirratings of sentences without determiners and with noise determiners. We were particularly interestedin the ratings given by participants to sentences which they had encountered in their input. For the controlgroup these were sentences with main determiners and those without determiners. For all othergroups these were sentences with main determiners and those with noise determiners. For each inputgroup, we compared the ratings given to these two sentence types. The results show that all participantsreliably rated the more frequent sentences higher than the less frequent sentences. Repeated-measuresANOVAs with sentence type as a within-subjects repeated-measure were significant for all five inputgroups: control group (F(1,11) = 12.19, p = .005); 2-noise (F(1,11) = 7.79, p = .018); 4-noise (F(1,11) = 20.51,p = .001); 8-noise (F(1,11) = 12.20, p = .005); 16-noise (F(1,11) = 60.71, p < .001).We also examined the data to see whether the degree of difference between the ratings given to thetwo types of sentences increased as the number of noise determiners increased. That is, did the participantsexposed to 16 noise determiners distinguish between sentences with main determiner formsand those with noise determiner forms to a greater degree than participants exposed to fewer (or no)noise forms? To examine this, we computed a difference score for each participant between the meanrating given to sentences with main determiners and the mean rating given to whichever kind of sentencewas the other one in her input (sentences with no determiners for control participants and sentenceswith noise determiners for all others). We then performed a trend analysis on the differencescores. This analysis is significant (F(1,56) = 22.89, p < .001), indicating that the difference in ratingsdoes indeed increase as the number of noise determiners increases, mirroring the trend we foundin the production data.Also as in the production task, we asked whether participants would judge determiner forms differentlyaccording to the syntactic position of the noun, indicating that they might be imposing on theinput a more deterministic linguistic rule, such as using the determiner forms in association with subjectsversus objects or with transitive but not intransitive sentences. Such a tendency would be reflectedin a significant interaction between syntactic position and sentence type. However, this wasnot the case. Although the main effect of syntactic position is significant (F(2,110) = 4.59, p = .012), there6 The degrees of freedom used in the sentence/determiner manipulation analyses are adjusted using the Huynh-Feldt Epsilondue to a significant test for heterogeneity of variance.Fig. 5. Mean ratings by input group.C.L. Hudson Kam, E.L. Newport / Cognitive Psychology xxx (2009) xxx–xxx 15ARTICLE IN PRESSPlease cite this article in press as: Hudson Kam, C. L., & Newport, E. L. Getting it right by getting it wrong: Whenlearners change languages. Cognitive Psychology (2009), doi:10.1016/j.cogpsych.2009.01.001is not a significant interaction between sentence type and syntactic position, nor is there a significantthree-way interaction between sentence type, syntactic position, and input group.In sum, to some degree participants’ judgments reflected their input. Sentences with the determinerin the wrong location were universally disliked by all groups. None of these sentences occurredin the input of any grou


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