This paper presents a connectionist network simulation of Livesey and McLaren’s (2009) results. In that paper they showed that participants with post-discrimination gradients that were initially peak shifted became monotonic as they were exposed to the full range of test stimuli. While the authors suggest that this is the result of rule-based processes ‘taking over’ responding, we show how a connectionist network with an attentional parameter and realistic activation functions for the input can simulate both the peak shifted and monotonic gradients. Although we do not infer that the monotonic gradient obtained in peak shift paradigms is entirely the result of associative, rather than propositional processes, we suggest that perhaps it is a change in the allocation of attention, in conjunction with the underlying representational structures used for the stimuli that facilitates rule induction in this case.

Dual-process accounts posit that human learning can occur as a consequence of both associative and propositional processes. This can be contrasted with single process accounts that suggest learning is entirely propositional. In this paper, we offer evidence for both associative and propositional processes using a within-subjects two alternative forced choice discrimination paradigm. Stimuli that varied concurrently along two dimensions were created and each participant’s awareness was directed toward one, facilitating rule induction (i.e., propositional processing) on that dimension. Performance on the other dimension was then used to assess associatively-based performance. We report results that are initially inconsistent with both single process and dual-process accounts of discrimination learning. However, we then show how an associative network, that represents stimuli integrally, can predict the performance shown by participants in the experiment, providing evidence for a dual-process account.