Overcoming duality: the fused bousfieldian function for modeling word production in verbal fluency tasks

Word production is generally assumed to occur as a function of a broadly interconnected language system. In terms of verbal fluency tasks, word production dynamics can be assessed by analyzing respective time courses via curve fitting. Here, a new generalized fitting function is presented by merging the two dichotomous classical Bousfieldian functions into one overarching power function with an adjustable shape parameter. When applied to empirical data from verbal fluency tasks, the error of approximation was significantly reduced while also fulfilling the Bayesian information criterion, suggesting a superior overall application value. Moreover, the approach identified a previously unknown logarithmic time course, providing further evidence of an underlying lexical network structure. In view of theories on lexical access, the corresponding modeling differentiates task-immanent lexical suppression from automatic lexical coactivation. In conclusion, our approach indicates that process dynamics result from an increasing cognitive effort to suppress automatic network functions.