Mitchell et al. describe many fascinating studies, and in the process, propose what they consider to be a unified framework for human learning in which effortful, controlled learning results in propositional knowledge. However, it is unclear how any of their findings privilege a propositional account, and we remain concerned that embedding all knowledge in propositional representations obscures the tight interdependence between learning from experiences and the use of the results of learning as a basis for action.

Contrary to the target article's claims, social cognition research shows considerable learning (about other people) that is relatively automatic. Some of this learning is propositional (spontaneous trait inferences) and some is associative (spontaneous trait transference). Other dichotomies – for example, between learning explicit and implicit attitudes – are also important. However conceived, human conditioning is not synonymous with human learning.

We argue that the propositional and link-based approaches to human contingency learning represent different levels of analysis because propositional reasoning requires a basis, which is plausibly provided by a link-based architecture. Moreover, in their attempt to compare two general classes of models (link-based and propositional), Mitchell et al. refer to only two generic models and ignore the large variety of different models within each class.

In this response, we provide further clarification of the propositional approach to human associative learning. We explain why the empirical evidence favors the propositional approach over a dual-system approach and how the propositional approach is compatible with evolution and neuroscience. Finally, we point out aspects of the propositional approach that need further development and challenge proponents of dual-system models to specify the systems more clearly so that these models can be tested.