As part of the functional-cognitive framework, we define the phenomena that we study in functional-analytic terms (i.e. relations between environment and behavior) and separate these phenomena from explanatory mental mechanisms. For example, we define learning as the the impact of regularities in the environment on behavior (De Houwer et al., 2013a). Learning as a psychological phenomenon can in principle be explained in terms of a variety of mental mechanisms, such as the formation of associations or propositions in memory.
This clear distinction between to-be-explained phenomena and explanatory mechanisms allows for a mutually beneficial collaboration between cognitive and functional-analytic researchers, and may prove useful in promoting a better understanding of both the functional level of analysis (i.e. understanding the conditions under which learning occurs) and the cognitive level (i.e. understanding the mental mechanisms that mediate learning).
During the past decade, there has been an explosion in tasks that measure implicit attitudes and cognitions (see De Houwer et al., 2009; Gawronski & De Houwer, 2014, for a review). The best known example is the Implicit Association Test (see the Project Implicit website).
It has always been assumed that implicit attitudes are learned gradually as the result of many direct experiences. Recent results show, however, that simply giving verbal instructions once is enough to create and change implicit attitudes.
This could either mean that the current measures of implicit attitudes are not valid or that implicit attitudes are more complex than previously thought.
We will try to disentangle these options by (a) comparing the effects of instructions on different implicit measures and (b) by comparing implicit attitudes that are based on instructions with those that are based on experience.
Our research at the functional-analytic level is heavily guided by Relational Frame Theory (RFT; Hayes et al., 2001). Put very briefly, RFT argues that many aspects of human behavior are a type of operant behavior called arbitrarily applicable relational responding. Our cognitive research has a similar relational focus on the role of propositions (representations that specify how events are related) in complex behavior (De Houwer, 2009b; De Houwer, 2014; Mitchell et al., 2009). We examine the idea that propositional knowledge is crucial not only in associative learning (e.g., evaluative conditioning) but also in implicit cognition (e.g., implicit evaluation). In appreciating both levels of analysis, we can explore the links between RFT as a functional-analytic theory and propositional models as a cognitive theory (see De Houwer et al., 2016, for a discussion).