Cens de la població nidificant del gavià de potes grogues (Larus Cachinnans) a l'arxipèlag de les Illes Medes durant l'any 2001

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Conceptual combination with PUNC

Noun-noun compounds play a key role in the growth of language. In this article we present a system for producing and understanding noun-noun compounds (PUNC). PUNC is based on the Constraint theory of conceptual combination and the C-3 model. The new model incorporates the primary constraints of the Constraint theory in an integrated fashion, creating a cognitively plausible mechanism of interpreting noun-noun phrases. It also tries to overcome algorithmic limitations of the C-3 model in being more efficient in its computational complexity, and deal with a wider span of empirical phenomena, such as dimensions of word familiarity. We detail the model, including knowledge representation and interpretation production mechanisms. We show that by integrating the constraints of the Constraint theory of conceptual combination and prioritizing the knowledge available within a concept's representation, PUNC can not only generate interpretations that reflect those produced by people, but also mirror the differences in processing times for understanding familiar, similar and novel word combinations

Sources of individual differences in the speed of naming objects and actions: The contribution of executive control

Item does not contain fulltextWe examined the contribution of executive control to individual differences in response time (RT) for naming objects and actions. Following Miyake et al., executive control was assumed to include updating, shifting, and inhibiting abilities, which were assessed using operation span, task-switching, and stop-signal tasks, respectively. Experiment 1 showed that updating ability was significantly correlated with the mean RT of action naming, but not of object naming. This finding was replicated in Experiment 2 using a larger stimulus set. Inhibiting ability was significantly correlated with the mean RT of both action and object naming, whereas shifting ability was not correlated with the mean naming RTs. Ex-Gaussian analyses of the RT distributions revealed that updating ability was correlated with the distribution tail of both action and object naming, whereas inhibiting ability was correlated with the leading edge of the distribution for action naming and the tail for object naming. Shifting ability provided no independent contribution. These results indicate that the executive control abilities of updating and inhibiting contribute to the speed of naming objects and actions, although there are differences in the way and extent these abilities are involved