Much like current approaches to language processing, contemporary accounts of language acquisition typically assume a sharp distinction between comprehension and production. This assumption is driven, in large part, by evidence for a number of asymmetries between comprehension and production in development. Comprehension is usually taken to precede production (e.g., Fraser et al. Reference Fraser, Bellugi and Brown1963), although there are certain instances in which children exhibit adult-like production of sentence types that they do not appear to comprehend correctly (cf. Grimm et al. Reference Grimm, Muller, Hamann and Ruigendijk2011). Evidence for such asymmetries strongly constrains theories of language acquisition, challenging integrated accounts of development and, by extension, integrated accounts of adult processing. Hence, it is key to determine the plausibility of a unified framework for acquisition that is compatible with evidence for comprehension/production asymmetries.

Although Pickering & Garrod's (P&G's) target article may be construed as a useful point of departure in this respect, P&G pay scant attention to how such a unified system for comprehension and production might develop. As a result, they implicitly subscribe to a different, questionable distinction often made in the language literature: the separation of acquisition from adult processing. In light of this, and given the tendency of developmental psycholinguists to view comprehension and production as separate systems, we briefly sketch a unified developmental framework for understanding comprehension and production as a single system, instantiated by a recent usage-based computational model of acquisition (McCauley & Christiansen Reference McCauley, Christiansen, Carlson, Hölscher and Shipley2011; submitted). Importantly, our approach is consistent with evidence for comprehension/production asymmetries in development, even while uniting comprehension and production within a single framework.

Our computational model, like that of Chang et al. (Reference Chang, Dell and Bock2006), simulates both comprehension and production, but it goes beyond this and previous usage-based models (e.g., Borensztajn et al. Reference Borensztajn, Zuidema and Bod2009; Freudenthal et al. Reference Freudenthal, Pine, Aguado-Orea and Gobet2007) in that (a) it learns to do so incrementally using simple distributional information; (b) it offers broad, cross-linguistic coverage; and (c) it accommodates a range of developmental findings. The model learns from corpora of child and child-directed speech, acquiring item-based knowledge in a purely incremental fashion, through online learning using backward transitional probabilities (which infants track; cf. Pelucchi et al. Reference Pelucchi, Hay and Saffran2009). The model uses peaks and dips in transitional probabilities to chunk words together as they are encountered, incrementally building an item-based “shallow parse” as each incoming utterance unfolds. The model stores the word sequences it groups together, gradually building up an inventory of multiword chunks – a “chunkatory” – which underlies both comprehension and production. When the model encounters a multiword utterance produced by the target child of a corpus, it attempts to generate an identical utterance using only chunks and transitional probabilities learned up to that point. Crucially, the very same chunks and distributional information used during production are used to make predictions about upcoming material during comprehension. This type of prediction-by-association facilitates the model's shallow processing of the input. The model's comprehension abilities are scored against a state-of-the-art shallow parser, and its production abilities are scored against the target child's original utterances (the model's utterances must match the child's).

The model makes close contact with P&G's approach in that it uses information employed during production to make predictions about upcoming linguistic material during comprehension (consistent with recent evidence that children's linguistic predictions are tied to production; cf. Mani & Huettig Reference Mani and Huettig2012). However, our approach extends P&G's account from prediction to the acquisition and use of linguistic knowledge itself; comprehension and production rely upon a single set of statistics and representations, which are reinforced in an identical manner during both processes.

Moreover, our model's design reflects recent psycholinguistic findings that have hitherto remained largely unconnected, but which, when viewed as complementary to one another, strongly support a unified framework for comprehension and production. First, the model is motivated by children's use of multiword units in production (Bannard & Matthews Reference Bannard and Matthews2008), which cautions against models of production in which words are selected independently of one another. The model's primary reliance on the discovery and storage of useful multiword sequences follows this line of evidence. Second, the model is motivated by evidence that children, like adults, can rely on shallow processing and underspecified representations during comprehension (e.g., Gertner & Fisher Reference Gertner and Fisher2012; Sanford & Sturt Reference Sanford and Sturt2002). Shallow processing, supplemented by contextual information (e.g., tied to semantic and pragmatic knowledge) may often give children the appearance of comprehending grammatical constructions they have not yet mastered (and therefore cannot use effectively in production). The model exhibits this in its better comprehension performance; through chunking, the model can arrive at an item-based “shallow parse” of an utterance, which can then be used in conjunction with semantic and pragmatic information to arrive at a “good enough” interpretation of the utterance (Ferreira et al. Reference Ferreira, Ferraro and Bailey2002). On the production side, however, the model – like a child learning to speak – is faced with the task of retrieving and sequencing words and chunks in a particular order. Hence, asymmetries arise from differing task demands, despite the use of the very same statistics and linguistic units during both comprehension and production.

Such an abandonment of the “cognitive sandwich” approach to acquisition clearly has implications for adult processing. If, as we suggest and make explicit in our model, children learn to comprehend and produce speech by using the same distributional information and chunk-based linguistic units for both tasks, we would expect adults to continue to rely on a unified set of representations. This is corroborated by studies showing that, like children, adults not only rely on multiword units in production (Janssen & Barber Reference Janssen and Barber2012), but also use multiword sequences during comprehension (e.g., Arnon & Snider Reference Arnon and Snider2010; Reali & Christiansen Reference Reali and Christiansen2007). This evidence further suggests that prediction-by-association may be more important for language processing than assumed by P&G, not just for children as indicated by our model, but also for adults. It is only by considering how the adult system emerges from the child's attempts to comprehend and produce linguistic utterances that we can hope to reach a complete understanding of the intertwined nature of language comprehension and production.