Dual Process Theory has increasingly gained fame as a framework for explaining evidence in reasoning and decision making tasks. This theory proposes there must be a sharp distinction in thinking to explain two clusters of correlational features. One cluster describes a fast and intuitive process (Type 1), while the other describes a slow andreflective one (Type 2), (see Evans, 2008; Evans & Stanovich, 2013; Kahneman, 2011). However, as Samuels (2009) has noted, there is a problem of determining why these group of features form clusters, more than what the labels Type (or system) 1 and 2 can capture, the unity problem. We understand there might be differences in the processingarchitecture that grounds each type of process, thus requiring distinct cognitive frameworks for each. We argue that the predictive processing approach (as held by Hohwy, 2013 and Clark, 2016) is a more suitable framework for Type 1 processing. Such an approach proposes cognition is in the job of attempting to predict what will perturb sensory inputs next. These are not personal predictions but rather multiple sub-personalpredictions that even the visual system makes at various layers at each millisecond that passes. Rather than being based on a symbolic representation of each aspect of the world, these predictions are made on the basis of statistical information updated moment by moment. This statistical content tracks previous sensory states and the causes of theseprevious sensory states. Kahneman (2011) has been arguing that there is a link between perception and Type 1 processing. What we hold is that such link obtains because Type 1 judgments actually are predictions stemming from higher layers of perceptual systems which work by means of predictive processing. On the other hand, we propose sucharchitecture does not handle Type 2 processes. Rather, these seem to be based on classical symbol systems executing heuristic search as explained by Newell (1980). In conclusion, we propose a dual framework is necessary for explaining why there are two clusters of features. Such a framework would include predictive processing for explaining Type 1processing and computations on symbolic representations for Type 2 processing.