Drawing on our Associative-Propositional Evaluation (APE) Model, we argue for the usefulness of distinguishing between basic operating principles of learning processes (associative linking vs. propositional reasoning) and secondary features pertaining to the conditions of their operation (automatic vs. controlled). We review empirical evidence that supports the joint operation of associative and propositional processes in the formation of new associations.

Recent work in rational probabilistic modeling suggests that a kind of propositional reasoning is ubiquitous in cognition and especially in cognitive development. However, there is no reason to believe that this type of computation is necessarily conscious or resource-intensive.

It is argued that the cognitive revolution provided general support for the view that associative learning requires cognitive processing, but only limited support for the view that it requires conscious processing. The point is illustrated by two studies of associative learning that played an important role in the development of the cognitive revolution, but which are surprisingly neglected by Mitchell et al. in the target article.

Studies of conditioning in simple systems are best interpreted in terms of the formation of excitatory links. The mechanisms responsible for such conditioning contribute to the associative learning effects shown by more complex systems. If a dual-system approach is to be avoided, the best hope lies in developing standard associative theory to deal with phenomena said to show propositional learning.

Can the phenomena of associative learning be replaced wholesale by a propositional reasoning system? Mitchell et al. make a strong case against an automatic, unconscious, and encapsulated associative system. However, their propositional account fails to distinguish inferences based on actions from those based on observation. Causal Bayes networks remedy this shortcoming, and also provide an overarching framework for both learning and reasoning. On this account, causal representations are primary, but associative learning processes are not excluded a priori.

The propositional nature of human associative learning is strongly supported by studies of trace eyeblink and fear conditioning, in which awareness of the contingency of a conditioned stimulus upon an unconditioned stimulus is a prerequisite for successful learning. Studies of animal lesion and human imaging suggest that the hippocampus is critical for establishing functional connections between awareness and trace conditioning.

Against Mitchell et al.'s assertions, we argue that (1) the concordance between learning and awareness does not support any particular learning theory, (2) their propositional approach is at odds with examples of learned behaviours that contradict beliefs about causation, and (3) the relative virtues of the two approaches in terms of parsimony is more ambiguous than Mitchell et al. suggest.

We comment on the similarities and differences of Mitchell et al.'s framework for understanding classical and operant conditioning and the theoretical framework put forth by Rumbaugh et al. (2007). We propose that all nonhuman and human learning may be based on amalgams created by co-occurring stimuli that share their response-eliciting properties and that these amalgams may be propositional in nature.

The notion that human associative learning is a usually conscious, higher-order process is one of the tenets of organization theory, developed over the past century. Propositional/sequential encoding is one of the possible types of organizational structures, but learning may also involve other structures.

Proust's madeleine illustrates the automatic nature of associative learning. Although we agree with Mitchell et al. that no compelling scientific proof for this effect has yet been reported in humans, evolutionary constraints suggest that it should not be discarded: There is no reason by which natural selection should favor individuals who lose a fast and automatic survival tool.

I argue that the dual-process account of human learning rejected by Mitchell et al. in the target article is informative and predictive with respect to human behaviour in a way that the authors' purely propositional account is not. Experiments that reveal different patterns of results under conditions that favour either associative or rule-based performance are the way forward.

We discuss the necessity of conscious thinking in the single-system propositional model of learning. Research from honeybees to humans suggests that associative learning can take place without the need for controlled reasoning or the development of beliefs of relationships between objects or events. We conclude that a single learning system is possible, but not if it depends on complex thinking.

Perhaps the time has come to re-examine the basic notions of cognitive science. Together with previous challenges against associationism, the target article should be viewed as a call to arms to re-evaluate the empirical basis for contemporary conceptualizations of human learning and the notion of “mental representation,” a concept that has become too imprecise for describing the elements of cognition.

Mitchell et al. present a lucid and provocative challenge to the claim that links between mental representations are formed automatically. However, the propositional approach they offer requires clearer specification, especially with regard to how propositions and memories interact. A definition of a system would also clarify the debate, as might an alternative technique for assessing task “dissociations.”

The proposal to recruit available formal structures to build an algorithmic model of all learning falters on close examination of its essential assumption: that the input and output of the model are propositional in structure. After giving three framework considerations, I describe three possibly fatal problems with this assumption, concluding each with a question that needs answering to avoid fatality.

Mitchell et al. propose that associative learning in humans and other animals requires the formation of propositions by means of conscious and controlled reasoning. This approach neglects important aspects of current thinking in evolutionary biology and neuroscience that support the claim that learning, here exemplified by fear learning, neither needs to be conscious nor controlled.

We are big fans of propositions. But we are not big fans of the “propositional approach” proposed by Mitchell et al. The authors ignore the critical role played by implicit, non-inferential processes in biological cognition, overestimate the work that propositions alone can do, and gloss over substantial differences in how different kinds of animals and different kinds of cognitive processes approximate propositional representations.

An attentional-associative model (Schmajuk et al. 1996), previously evaluated against multiple sets of classical conditioning data, is applied to causal learning. In agreement with Mitchell et al.'s suggestion, according to the model associative learning can be a conscious, controlled process. However, whereas our model correctly predicts blocking following or preceding subadditive training, the propositional approach cannot account for those results.

To support their main claim, Mitchell et al. broach the issue of the relationship between the learning performance of human and nonhuman animals. We show that their argumentation is problematic both theoretically and empirically. In fact, results from learning studies with humans and honey-bees strongly suggest that human learning is not entirely propositional.

The extent to which human learning should be thought of in terms of elementary, automatic versus controlled, cognitive processes is unresolved after nearly a century of often fierce debate. Mitchell et al. provide a persuasive review of evidence against automatic, unconscious links. Indeed, unconscious processes seem to play a negligible role in any form of learning, not just in Pavlovian conditioning. But a modern connectionist framework, in which “cognitive” phenomena are emergent properties, is likely to offer a fuller account of human learning than the propositional framework Mitchell et al. propose.