Pavlovian conditioning plays a fundamental role in our cog-nitive architecture, by its capacity to bind values to stimuli. Due to the multifarious characteristics of this learning mode, many approaches in machine learning have been proposed to implement it in artificial intelligence models. Considering the complementary properties of these models and inspired by biological evidences, we propose not to select the best model but rather to combine them, thereby forming a cognitive architecture. From a functional point of view, we report the good properties and performances of this architecture. From a methodological point of view, this work highlights the interest of defining a cognitive function at the algorithmic level, binding its general properties to its implementation details. It is also proposed that it is a fruitful approach to decipher and organize many information extracted by modern approaches in neu-roscience, towards the definition of a global cognitive architecture.