Abstract

The Optimal Innovation Hypothesis, following from the Graded Salience Hypothesis, is being reviewed and revisited. The attempt is to expand the notion of Optimal Innovation to allow it to apply to both stimuli’s coded meanings as well as their noncoded, constructed interpretations. According to the Optimal Innovation Hypothesis, Optimal Innovations, when devised, will be more pleasing than nonoptimally innovative counterparts. Unlike such competitors, Optimal Innovations deautomatize familiar coded alternatives, which invoke unconditional responses alongside novel but distinct ones, allowing both responses to interact. Conversely, the Revised Optimal Innovation Hypothesis, introduced and tested here, follows from the Defaultness Hypothesis. It posits that both default lexicalized meanings and default constructed interpretations might be qualifiable for Optimal Innovation once they are deautomatized by nondefault, context-dependent counterparts. Such nondefault Optimal Innovations will be pleasing, more pleasing than default and nondefault counterparts not qualifiable for Optimal Innovation. Results of two experiments support the Revised Optimal Innovation Hypothesis, while further corroborating the Defaultness Hypothesis.