The authors identify 2 major types of statistical data from which semantic representations can be learned. These are denoted as experiential data and distributional data. Experiential data are derived by way of experience with the physical world and comprise the sensory-motor data obtained through sense receptors. Distributional data, by contrast, describe the statistical distribution of words across spoken and written language. The authors claim that experiential and distributional data represent distinct data types and that each is a nontrivial source of semantic information. Their theoretical proposal is that human semantic representations are derived from an optimal statistical combination of these 2 data types. Using a Bayesian probabilistic model, they demonstrate how word meanings can be learned by treating experiential and distributional data as a single joint distribution and learning the statistical structure that underlies it. The semantic representations that are learned in this manner are measurably more realistic—as verified by comparison to a set of human-based measures of semantic representation—than those available from either data type individually or from both sources independently. This is not a result of merely using quantitatively more data, but rather it is because experiential and distributional data are qualitatively distinct, yet intercorrelated, types of data. The semantic representations that are learned are based on statistical structures that exist both within and between the experiential and distributional data types.

A series of three experiments investigated semantic and syntactic effects in the production of Adjective+Noun phrases in Dutch. Bilinguals (Dutch native speakers) were presented with English nouns and were asked to produce an Adjective+Noun phrase in Dutch which included the translation of the noun. In two experiments, we blocked items by either semantic category or grammatical gender.We found that participants performed the task slower when the target nouns were of the same semantic category than when they were from different categories; and faster when they were of the same grammatical gender than when they were of different gender. In a final experiment, both manipulations were crossed in order to both replicate the previous experiments and to test for interactions between the two effects. The results of the first two experiments were replicated, and crucially no interaction was found. These findings are compatible with models of lexical retrieval in production in which, first lexico-semantic and lexico-syntactic information are separable; second the flow of activation between the two is feedforward.

A discussion of modularity in language production processes, with special emphasis on processes for retrieving words and building syntactic structures for a to-be-uttered sentence, is presented. The authors’ 1st goal was to assess the extent to which information processing is encapsulated between different processing stages. In particular, they assessed whether the input from one processing stage to the next is minimal and whether the flow of information in the system is strictly unidirectional. On the basis of the reviewed evidence, they conclude that both assumptions have to be revised. Their 2nd goal was to propose an alternative framework that does not assume strict encapsulation but that maintains multiple levels of integration for production. During the past 20 years a “great divide ” (as Boland & Cutler, 1996, p. 309, labeled it) has characterized the psycholinguistic world: Can the processes engaged during comprehension and production of language be conceived of as modular or not? Whereas a number of influential theories that do not assume a modular system have been put forward in the comprehension domain (e.g., MacDonald, Pearlmutter, & Seidenberg, 1994; Tabor