In this commentary, we discuss an important pattern of results in the literature on the neural basis of expertise: (a) decrease of cerebral activation at the beginning of acquisition of expertise and (b) functional cerebral reorganization as a consequence of years of practice. We show how these two results can be integrated with the neural reuse framework.

In this study, we use corpus analysis and computational modelling techniques to compare two recent accounts of the OI stage: Legate & Yang's (2007) Variational Learning Model and Freudenthal, Pine & Gobet's (2006) Model of Syntax Acquisition in Children. We first assess the extent to which each of these accounts can explain the level of OI errors across five different languages (English, Dutch, German, French and Spanish). We then differentiate between the two accounts by testing their predictions about the relation between children's OI errors and the distribution of infinitival verb forms in the input language. We conclude that, although both accounts fit the cross-linguistic patterning of OI errors reasonably well, only MOSAIC is able to explain why verbs that occur more frequently as infinitives than as finite verb forms in the input also occur more frequently as OI errors than as correct finite verb forms in the children's output.

The origin of talent and expertise is currently the subject of intense debate, with explanations ranging from purely biological to purely environmental. This report shows that the population of expert chess players in the northern hemisphere shows a seasonal pattern, with an excess of births in late winter and early spring. This effect remains when taking into account the distribution of births in the population at large, using statistics from the European Union member countries. A similar pattern has been found with schizophrenia, and the possible link between these two phenomena is discussed.

P. Bloom's (1990) data on subject omission are often taken as strong support for the view that child language can be explained in terms of full competence coupled with processing limitations in production. This paper examines whether processing limitations in learning may provide a more parsimonious explanation of the data without the need to assume full competence. We extended P. Bloom’s study by using a larger sample (12 children) and measuring subject omission phenomena in three developmental phases. The results revealed a Verb Phrase-length effect consistent with that reported by P. Bloom. However, contrary to the predictions of the processing limitations account, the proportion of overt subjects that were pronominal increased with developmental phase. The data were simulated with MOSAIC, a computational model that learns to produce progressively longer utterances as a function of training. MOSAIC was able to capture all of the effects reported by P. Bloom through a resource-limited distributional analysis of child-directed speech. Since MOSAIC does not have any built-in linguistic knowledge, these results show that the phenomena identified by P. Bloom do not constitute evidence for underlying competence on the part of the child. They also underline the need to develop more empirically grounded models of the way that processing limitations in learning might influence the language acquisition process.

A grandmaster and an international chess master were compared with a group of novices in a memory task with chess and non-chess stimuli, varying the structure and familiarity of the stimuli, while functional magnetic resonance images were acquired. The pattern of brain activity in the masters was different from that of the novices. Masters showed no differences in brain activity when different degrees of structure and familiarity where compared; however, novices did show differences in brain activity in such contrasts. The most important differences were found in the contrast of stimulus familiarity with chess positions. In this contrast, there was an extended brain activity in bilateral frontal areas such as the anterior cingulate and the superior, middle, and inferior frontal gyri; furthermore, posterior areas, such as posterior cingulate and cerebellum, showed great bilateral activation. These results strengthen the hypothesis that when performing a domain-specific task, experts activate different brain systems from that of novices. The use of the experts-versus-novices paradigm in brain imaging contributes towards the search for brain systems involved in cognitive processes.

We discuss the relation of the Theory of Event Coding (TEC) to a computational model of expert perception, CHREST, based on the chunking theory. TEC's status as a verbal theory leaves several questions unanswerable, such as the precise nature of internal representations used, or the degree of learning required to obtain a particular level of competence: CHREST may help answer such questions.

Computational models of learning provide an alternative technique for identifying the number and type of chunks used by a subject in a specific task. Results from applying CHREST to chess expertise support the theoretical framework of Cowan and a limit in visual short-term memory capacity of 3–4 looms. An application to learning from diagrams illustrates different identifiable forms of chunk.

In his review, Rispoli's main concern is that Elman et al.'s book will aggravate the degree of polarization in developmental psycholinguistics. I cannot really comment on this worry, as developmental psycholinguistics is not my field. Instead, I will discuss some questions more related to my background – the role of computational modelling in Elman et al.'s approach.

Elman et al.'s ambitious goal is to propose theories of cognitive development that are grounded in our knowledge of biology. This is of course what the great Jean Piaget tried to achieve during his lifetime – unsuccessfully, as we know. Elman et al.'s advantage over Piaget is that they have a set of computational tools, connectionism, which both allows them to specify theories precisely and to study complex behaviours (such as epigenesis, where innate and environmental factors interact to create new levels of complexity) that are just beyond the (unaided) human mind. Even though I will highlight some of the weaknesses of their approach below, I should emphasize that reading their book was an exciting and enjoyable experience.

As noted by Rispoli, there are important problems with the simulations reported by Elman et al. Rispoli focuses on simulations of past tense acquisition and syntax acquisition, but the problems are by no means limited to these areas. I will briefly consider two recent developments in neural net research, one taken from the field of language acquisition, and one from elsewhere, which underscore some of the difficulties of the simulations discussed in the book.