This paper discusses the traceback method, which has been the basis of some influential papers on first language acquisition. The method sets out to demonstrate that many or even all utterances in a test corpus (usually the last two sessions of recording) can be accounted for with the help of recurrent fixed strings (like What’s that?) or frame-and-slot patterns (like [What’s X?]) that can also be identified in the remaining dataset (i.e., the previous sessions of recording). This is taken as evidence that language learning is much more item-based than previously assumed. In the present paper we sketch the development of the method over the last two decades, and discuss its relation to usage-based theory, as well as the cognitive plausibility of its components, and we highlight both its potential and its limitations.

Usage-based approaches assume that children’s early utterances are item-based. This has been demonstrated in a number of studies using the traceback method. In this approach, a small amount of “target utterances” from a child language corpus is “traced back” to earlier utterances. Drawing on a case study of German, this paper provides a critical evaluation of the method from a usage-based perspective. In particular, we check how factors inherent to corpus data as well as methodological choices influence the results of traceback studies. To this end, we present four case studies in which we change thresholds and the composition of the main corpus, use a cross-corpus approach tracing one child’s utterances back to another child’s corpus, and reverse and randomize the target utterances. Overall, the results show that the method can provide interesting insights—particularly regarding different pathways of language acquisition—but they also show the limitations of the method.

Recent years have seen increased interest in code-mixing from a usage-based perspective. In usage-based approaches to monolingual language acquisition, a number of methods have been developed that allow for detecting patterns from usage data. In this paper, we evaluate two of those methods with regard to their performance when applied to code-mixing data: the traceback method, as well as the chunk-based learner model. Both methods make it possible to automatically detect patterns in speech data. In doing so, however, they place different theoretical emphases: while traceback focuses on frame-and-slot patterns, chunk-based learner focuses on chunking processes. Both methods are applied to the code-mixing of a German–English bilingual child between the ages of 2;3 and 3;11. Advantages and disadvantages of both methods will be discussed, and the results will be interpreted against the background of usage-based approaches.

In this paper we use corpora of four monolingual German-speaking children at 2 years of age to analyze the effect of input on the activation of chunks and frame-and-slot patterns. For this purpose, we first investigate to what extent chunks and patterns can be traced back to the direct input compared to input which is not part of the immediate discourse situation. Second, we take mean length of utterance (MLU) into account to see how the level of proficiency influences the amount of priming in each child. Results indicate that children with a lower MLU rely more on priming than children who are more proficient. This conclusion is consistent with the usage-based assumption that children’s linguistic development starts with a strongly item-based reproduction of input patterns that gradually gives rise to increasingly creative and productive uses of constructions.

No abstract