Is research painting a biased picture of implicit learning? The dangers of methodological purity in scientific debate

Explicit feedback maintains implicit knowledge

The role of feedback was investigated with respect to conscious and unconscious knowledge acquired during artificial grammar learning (AGL). After incidental learning of training sequences, participants classified further sequences in terms of grammaticality and reported their decision strategy with or without explicit veridical feedback. Sequences that disobeyed the learning structure conformed to an alternative structure. Feedback led to an increase in the amount of reported conscious knowledge of structure (derived rules and recollections) but did not increase its accuracy. Conversely, feedback maintained the accuracy of unconscious knowledge of structure (intuition or familiarity-based responses) which otherwise degraded. Results support a dual-process account of AGL. They suggest that implicit learning of the to-be-rejected structure at test contaminates familiarity-based classifications whereas feedback allows competing familiarity signals to be contextualised, which is incompatible with theories that consider familiarity context-insensitive.

Facilitative interactions of model- and experience-based processes: implications for type and flexibility of representation

People are often taught using a combination of instruction and practice. In prior research, we have distinguished between model-based knowledge (i.e., acquired from explicit instruction) and experience-based knowledge (i.e., acquired from practice), and have argued that the issue of how these types of knowledge (and associated learning processes) interact has been largely neglected. Two experiments explore this issue using a dynamic control task. Results demonstrate the utility of providing model-based knowledge before practice with the task, but more importantly, suggest how this information improves learning. Results also show that learning in this manner can lead to "costs" such as slowed retrieval, and that this knowledge may not always transfer to new task situations as well as experientially acquired knowledge. Our findings also question the assumption that participants always acquire a highly specific "lookup" table representation while learning this task. We provide an alternate view and discuss the implications for theories of learning.

Measuring unconscious knowledge: distinguishing structural knowledge and judgment knowledge

This paper investigates the dissociation between conscious and unconscious knowledge in an implicit learning paradigm. Two experiments employing the artificial grammar learning task explored the acquisition of unconscious and conscious knowledge of structure (structural knowledge). Structural knowledge was contrasted to knowledge of whether an item has that structure (judgment knowledge). For both structural and judgment knowledge, conscious awareness was assessed using subjective measures. It was found that unconscious structural knowledge could lead to both conscious and unconscious judgment knowledge. When structural knowledge was unconscious, there was no tendency for judgment knowledge to become more conscious over time. Furthermore, conscious rather than unconscious structural knowledge produced more consistent errors in judgments, was facilitated by instructions to search for rules, and after such instructions was harmed by a secondary task. The dissociations validate the use of these subjective measures of conscious awareness.

Effects of model-based and memory-based processing on speed and accuracy of grammar string generation

Learners are able to use 2 different types of knowledge to perform a skill. One type is a conscious mental model, and the other is based on memories of instances. The authors conducted 3 experiments that manipulated training conditions designed to affect the availability of 1 or both types of knowledge about an artificial grammar. Participants were tested for both speed and accuracy of their ability to generate letter sequences. Results indicate that model-based training leads to slow accurate responding. Memory-based training leads to fast, less accurate responding and highest achievement when perfect accuracy was not required. Evidence supports participants' preference for using the memory-based mode when exposed to both types of training. Finally, the accuracy contributed by model-based training declined over a retention interval.