Grammatical gender agreement has been well addressed in language comprehension but less so in language production. The present article discusses the arguments derived from the most prominent language production models on the representation and processing of the grammatical gender of nouns in language production and then reviews recent empirical studies that provide some answers to these arguments.

Preparing words in speech production is normally a fast and accurate process. We generate them two or three per second in fluent conversation; and overtly naming a clear picture of an object can easily be initiated within 600 msec after picture onset. The underlying process, however, is exceedingly complex. The theory reviewed in this target article analyzes this process as staged and feedforward. After a first stage of conceptual preparation, word generation proceeds through lexical selection, morphological and phonological encoding, phonetic (...) encoding, and articulation itself. In addition, the speaker exerts some degree of output control, by monitoring of self-produced internal and overt speech. The core of the theory, ranging from lexical selection to the initiation of phonetic encoding, is captured in a computational model, called WEAVER++. Both the theory and the computational model have been developed in interaction with reaction time experiments, particularly in picture naming or related word production paradigms, with the aim of accounting for the real-time processing in normal word production. A comprehensive review of theory, model, and experiments is presented. The model can handle some of the main observations in the domain of speech errors (the major empirical domain for most other theories of lexical access), and the theory opens new ways of approaching the cerebral organization of speech production by way of high-temporal-resolution imaging. (shrink)

The lexical–semantic organization of the mental lexicon is bound to change across the lifespan. Nevertheless, the effects of lexical–semantic factors on word processing are usually based on studies enrolling young adult cohorts. The current study aims to investigate to what extent age‐specific semantic organization predicts performance in referential word production over the lifespan, from school‐age children to older adults. In Study 1, we conducted a free semantic association task with participants from six age‐groups (ranging from 10 to 80 years old) (...) to compute measures that capture age‐specific properties of the mental lexicon across the lifespan. These measures relate to lifespan changes in the Available Richness of the mental lexicon and in the lexical–semantic Network Prototypicality of concrete words. In Study 2, we used the collected data to predict performance in a picture‐naming task on a new group of participants within the same age‐groups as for Study 1. The results show that age‐specific semantic Available Richness and Network Prototypicality affect word production speed while the semantic variables collected only in young adults do not. A richer and more prototypical semantic network across subjects from a given age‐group is associated with faster word production speed. The current results indicate that age‐specific semantic organization is crucial to predict lexical–semantic behaviors across the lifespan. Similarly, these results also provide cues to the understanding of the lexical–semantic properties of the mental lexicon and to lexical selection in referential tasks. (shrink)

Investigating instances where lexical selection fails can lead to deeper insights into the cognitive machinery and architecture supporting successful word retrieval and speech production. In this paper, we used a multiplex lexical network approach that combines semantic and phonological similarities among words to model the structure of the mental lexicon. Network measures at different levels of analysis (degree, network distance, and closeness centrality) were used to investigate the influence of network structure on picture naming accuracy and errors by people with (...) Anomic, Broca's, Conduction, and Wernicke's aphasia. Our results reveal that word retrieval is influenced by the multiplex lexical network structure in at least two ways—(a) the accuracy of production and error type on incorrect productions were influenced by the degree and closeness centrality of the target word, and (b) error type also varied in terms of network distance between the target word and produced error word. Taken together, the analyses demonstrate that network science techniques, particularly the use of the multiplex lexical network to simultaneously represent semantic and phonological relationships among words, reveal how the structure of the mental lexicon influences language processes beyond traditionally examined psycholinguistic variables. We propose a framework for how the multiplex lexical network approach allows for understanding the influence of mental lexicon structure on word retrieval processes, with an eye toward a better understanding of the nature of clinical impairments, like aphasia. (shrink)